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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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What is Research Design? Characteristics, Types, Process, & Examples

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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design? Research design is the structure of research methods and techniques selected to conduct a study. It refines the methods suited to the subject and ensures a successful setup. Defining a research topic clarifies the type of research (experimental, survey research, correlational, semi-experimental, review) and its sub-type (experimental design, research problem, descriptive case-study).

There are three main types of designs for research:

1. Data Collection

2. Measurement

3. Data Analysis

Elements of Research Design 

Now that you know what is research design, it is important to know the elements and components of research design. Impactful research minimises bias and enhances data accuracy. Designs with minimal error margins are ideal. Key elements include:

1. Accurate purpose statement

2. Techniques for data collection and analysis

3. Methods for data analysis

4. Type of research methodology

5. Probable objections to research

6. Research settings

7. Timeline

8. Measurement of analysis

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Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability

A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity

A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality

A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability

A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

Research Design Process

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

1. Consider Aims and Approaches

Define the research questions and objectives, and establish the theoretical framework and methodology.

2. Choose a Type of Research Design

Select the suitable research design, such as experimental, correlational, survey, case study, or ethnographic, according to the research questions and objectives.

3. Identify Population and Sampling Method

Determine the target population and sample size, and select the sampling method, like random, stratified random sampling, or convenience sampling.

4. Choose Data Collection Methods

Decide on the data collection methods, such as surveys, interviews, observations, or experiments, and choose the appropriate instruments for data collection.

5. Plan Data Collection Procedures

Create a plan for data collection, detailing the timeframe, location, and personnel involved, while ensuring ethical considerations are met.

6. Decide on Data Analysis Strategies

Select the appropriate data analysis techniques, like statistical analysis, content analysis, or discourse analysis, and plan the interpretation of the results.

What are the Types of Research Design?

A researcher must grasp various types to decide which model to use for a study. There are different research designs that can be broadly classified into quantitative and qualitative.

Qualitative Research

Qualitative research identifies relationships between collected data and observations through mathematical calculations. Statistical methods validate or refute theories about natural phenomena. This research method answers "why" a theory exists and explores respondents' perspectives.

Quantitative Research

Quantitative research is essential when statistical conclusions are needed to gather actionable insights. Numbers provide clarity for critical business decisions. This method is crucial for organizational growth, with insights from complex numerical data guiding future business decisions.

Qualitative Research vs Quantitative Research

While researching, it is important to know the difference between qualitative and quantitative research. Here's a quick difference between the two:

The research types can be further divided into 5 categories:

1. Descriptive Research

Descriptive research design focuses on detailing a situation or case. It's a theory-driven method that involves gathering, analysing, and presenting data. This approach offers insights into the reasons and mechanisms behind a research subject, enhancing understanding of the research's importance. When the problem statement is unclear, exploratory research can be conducted.

2. Experimental Research

Experimental research design investigates cause-and-effect relationships. It’s a causal design where the impact of an independent variable on a dependent variable is observed. For example, the effect of price on customer satisfaction. This method efficiently addresses problems by manipulating independent variables to see their effect on dependent variables. Often used in social sciences, it involves analysing human behaviour by studying changes in one group's actions and their impact on another group.

3. Correlational Research

Correlational research design is a non-experimental technique that identifies relationships between closely linked variables. It uses statistical analysis to determine these relationships without assumptions. This method requires two different groups. A correlation coefficient between -1 and +1 indicates the strength and direction of the relationship, with +1 showing a positive correlation and -1 a negative correlation.

4. Diagnostic Research

Diagnostic research design aims to identify the underlying causes of specific issues. This method delves into factors creating problematic situations and has three phases: 

• Issue inception
• Issue diagnosis
• Issue resolution

5. Explanatory Research

Explanatory research design builds on a researcher’s ideas to explore theories further. It seeks to explain the unexplored aspects of a subject, addressing the what, how, and why of research questions.

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the key components of a research design, how can i choose the best research design for my study, what are some common pitfalls in research design, and how can they be avoided, how does research design impact the validity and reliability of a study, what ethical considerations should be taken into account in research design.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

14 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

Wow! This post has an awful explanation. Appreciated.

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Design Research

What is design research.

Design research is the practice of gaining insights by observing users and understanding industry and market shifts. For example, in service design it involves designers’ using ethnography—an area of anthropology—to access study participants, to gain the best insights and so be able to start to design popular services.

“We think we listen, but very rarely do we listen with real understanding, true empathy. Yet listening, of this very special kind, is one of the most potent forces for change that I know.” — Carl Rogers, Psychologist and founding father of the humanistic approach & psychotherapy research

Service design expert and Senior Director of User Research at Twitch Kendra Shimmell explains what goes into good design research in this video.

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Get Powerful Insights with Proper Design Research

When you do user research well, you can fuel your design process with rich insights into how your target users interact—or might interact—in contexts to do the things they must do to achieve their goals using whatever they need on the way. That’s why it’s essential to choose the right research methods and execute them properly. Then, you’ll be able to reach those participants who agree to be test users/customers, so they’ll be comfortable enough to give you accurate, truthful insights about their needs, desires, pain points and much more. As service design can involve highly intricate user journeys , things can be far more complex than in “regular” user experience (UX) design . That’s where design research comes in, with its two main ingredients:

Qualitative research – to understand core human behaviors, habits and tasks/goals

Industry and Market research – to understand shifts in technology and in business models and design-relevant signs

An ideal situation—where you have enough resources and input from experts—is to combine the above to obtain the clearest view of the target customers of your proposed—or improved—service and get the most accurate barometer reading of what your market wants and why. In any case, ethnography is essential. It’s your key to decoding this very human economy of habits, motivations, pain points, values and other hard-to-spot factors that influence what people think, feel, say and do on their user journeys. It’s your pathway to creating personas —fictitious distillations that prove you empathize with your target users as customers—and to gain the best insights means you carefully consider how to access these people on their level. When you do ethnographic field studies, you strive for accurate observations of your users/customers in the context of using a service .

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How to Leverage Ethnography to Do Proper Design Research

Whatever your method or combination of methods (e.g., semi-structured interviews and video ethnography), the “golden rules” are:

Build rapport – Your “test users” will only open up in trusting, relaxed, informal, natural settings. Simple courtesies such as thanking them and not pressuring them to answer will go a long way. Remember, human users want a human touch, and as customers they will have the final say on a design’s success.

Hide/Forget your own bias – This is a skill that will show in how you ask questions, which can subtly tell users what you might want to hear. Instead of asking (e.g.) “The last time you used a pay app on your phone, what was your worst security concern?”, try “Can you tell me about the last time you used an app on your phone to pay for something?”. Questions that betray how you might view things can make people distort their answers.

Embrace the not-knowing mindset and a blank-slate approach – to help you find users’ deep motivations and why they’ve created workarounds. Trying to forget—temporarily—everything you’ve learned about one or more things can be challenging. However, it can pay big dividends if you can ignore the assumptions that naturally creep into our understanding of our world.

Accept ambiguity – Try to avoid imposing a rigid binary (black-and-white/“yes”-or-“no”) scientific framework over your users’ human world.

Don’t jump to conclusions – Try to stay objective. The patterns we tend to establish to help us make sense of our world more easily can work against you as an observer if you let them. It’s perfectly human to rely on these patterns so we can think on our feet. But your users/customers already will be doing this with what they encounter. If you add your own subjectivity, you’ll distort things.

Keep an open mind to absorb the users’ world as present it – hence why it’s vital to get some proper grounding in user research. It takes a skilled eye, ear and mouth to zero in on everything there is to observe, without losing sight of anything by catering to your own agendas, etc.

Gentle encouragement helps; Silence is golden – a big part of keeping a naturalistic setting means letting your users stay comfortable at their own pace (within reason). Your “Mm-mmhs” of encouragement and appropriate silent stretches can keep your research safe from users’ suddenly putting politeness ahead of honesty if they feel (or feel that you’re) uncomfortable.

Overall, remember that two people can see the same thing very differently, and it takes an open-minded, inquisitive, informal approach to find truly valuable insights to understand users’ real problems.

Learn More about Design Research

Take our Service Design course, featuring many helpful templates: Service Design: How to Design Integrated Service Experiences

This Smashing Magazine piece nicely explores the human dimensions of design research: How To Get To Know Your Users

Let Invision expand your understanding of design research’s value, here: 4 types of research methods all designers should know .

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What is the main goal of design research?

• To increase the speed of the design process
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Why are ethnographic studies important in design research?

• They focus on quantitative data collection.
• They help understand user behavior in natural contexts.
• They prioritize technological advancements.

What are the two main types of research methods used in design research?

• Qualitative and market research
• Qualitative and quantitative research
• Quantitative and user experience design

What is a key aspect of empathy in design research?

• Focus on aesthetic design
• Prioritize designers' ideas and needs
• Understand users' perspectives and needs

Why is it important to avoid bias in design research?

• To decrease the overall research costs
• To make sure insights are objective and accurate
• To speed up the research process

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Literature on Design Research

Here’s the entire UX literature on Design Research by the Interaction Design Foundation, collated in one place:

Learn more about Design Research

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Services are everywhere! When you get a new passport, order a pizza or make a reservation on AirBnB, you're engaging with services. How those services are designed is crucial to whether they provide a pleasant experience or an exasperating one. The experience of a service is essential to its success or failure no matter if your goal is to gain and retain customers for your app or to design an efficient waiting system for a doctor’s office.

In a service design process, you use an in-depth understanding of the business and its customers to ensure that all the touchpoints of your service are perfect and, just as importantly, that your organization can deliver a great service experience every time . It’s not just about designing the customer interactions; you also need to design the entire ecosystem surrounding those interactions.

In this course, you’ll learn how to go through a robust service design process and which methods to use at each step along the way. You’ll also learn how to create a service design culture in your organization and set up a service design team . We’ll provide you with lots of case studies to learn from as well as interviews with top designers in the field. For each practical method, you’ll get downloadable templates that guide you on how to use the methods in your own work.

This course contains a series of practical exercises that build on one another to create a complete service design project . The exercises are optional, but you’ll get invaluable hands-on experience with the methods you encounter in this course if you complete them, because they will teach you to take your first steps as a service designer. What’s equally important is that you can use your work as a case study for your portfolio to showcase your abilities to future employers! A portfolio is essential if you want to step into or move ahead in a career in service design.

Your primary instructor in the course is Frank Spillers . Frank is CXO of award-winning design agency Experience Dynamics and a service design expert who has consulted with companies all over the world. Much of the written learning material also comes from John Zimmerman and Jodi Forlizzi , both Professors in Human-Computer Interaction at Carnegie Mellon University and highly influential in establishing design research as we know it today.

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What is design research methodology and why is it important?

What is design research.

Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It’s a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions.

Not to be confused with user experience research – focused on the usability of primarily digital products and experiences – design research is a broader discipline that informs the entire design process across various design fields. Beyond focusing solely on researching with users, design research can also explore aesthetics, cultural trends, historical context and more.

Design research has become more important in business, as brands place greater emphasis on building high-quality customer experiences as a point of differentiation.

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Design research vs. market research

The two may seem like the same thing at face value, but really they use different methods, serve different purposes and produce different insights.

Design research focuses on understanding user needs, behaviors and experiences to inform and improve product or service design.  Market research , on the other hand, is more concerned with the broader market dynamics, identifying opportunities, and maximizing sales and profitability.

Both are essential for the success of a product or service, but cater to different aspects of its lifecycle.

Design research in action: A mini mock case study

A popular furniture brand, known for its sleek and simple designs, faced an unexpected challenge: dropping sales in some overseas markets. To address this, they turned to design research – using quantitative and qualitative methods – to build a holistic view of the issue.

Company researchers visited homes in these areas to interview members of their target audience and understand local living spaces and preferences. Through these visits, they realized that while the local customers appreciated quality, their choices in furniture were heavily influenced by traditions and regional aesthetics, which the company's portfolio wasn’t addressing.

To further their understanding, the company rolled out surveys, asking people about their favorite materials, colors and furniture functionalities. They discovered a consistent desire for versatile furniture pieces that could serve multiple purposes. Additionally, the preference leaned towards certain regional colors and patterns that echoed local culture.

Armed with these insights, the company took to the drawing board. They worked on combining their minimalist style with the elements people in those markets valued. The result was a refreshed furniture line that seamlessly blended the brand's signature simplicity with local tastes. As this new line hit the market, it resonated deeply with customers in the markets, leading to a notable recovery in sales and even attracting new buyers.

When to use design research

Like most forms of research, design research should be used whenever there are gaps in your understanding of your audience’s needs, behaviors or preferences. It’s most valuable when used throughout the product development and design process.

When differing opinions within a team can derail a design process, design research provides concrete data and evidence-based insights, preventing decisions based on assumptions.

Design research brings value to any product development and design process, but it’s especially important in larger, resource intensive projects to minimize risk and create better outcomes for all.

The benefits of design research

Design research may be perceived as time-consuming, but in reality it’s often a time – and money – saver that can. easily prove to be the difference between strong product-market fit and a product with no real audience.

Deeper customer knowledge

Understanding your audience on a granular level is paramount – without tapping into the nuances of their desires, preferences and pain points, you run the risk of misalignment.

Design research dives deep into these intricacies, ensuring that products and services don't just meet surface level demands. Instead, they can resonate and foster a bond between the user and the brand, building foundations for lasting loyalty.

Efficiency and cost savings

More often than not, designing products or services based on assumptions or gut feelings leads to costly revisions, underwhelming market reception and wasted resources.

Design research offers a safeguard against these pitfalls by grounding decisions in real, tangible insights directly from the target market – streamlining the development process and ensuring that every dollar spent yields maximum value.

New opportunities

Design research often brings to light overlooked customer needs and emerging trends. The insights generated can shift the trajectory of product development, open doors to new and novel solutions, and carve out fresh market niches.

Sometimes it's not just about avoiding mistakes – it can be about illuminating new paths of innovation.

Enhanced competitive edge

In today’s world, one of the most powerful ways to stand out as a business is to be relentlessly user focused. By ensuring that products and services are continuously refined based on user feedback, businesses can maintain a step ahead of competitors.

Whether it’s addressing pain points competitors might overlook, or creating user experiences that are not just satisfactory but delightful, design research can be the foundations for a sharpened competitive edge.

Design research methods

The broad scope of design research means it demands a variety of research tools, with both numbers-driven and people-driven methods coming into play. There are many methods to choose from, so we’ve outlined those that are most common and can have the biggest impact.

This stage is about gathering initial insights to set a clear direction.

Literature review

Simply put, this research method involves investigating existing secondary research, like studies and articles, in your design area. It's a foundational method that helps you understand current knowledge and identify any gaps – think of it like surveying the landscape before navigating through it.

Field observations

By observing people's interactions in real-world settings, we gather genuine insights. Field observations are about connecting the dots between observed behaviors and your design's intended purpose. This method proves invaluable as it can reveal how design choices can impact everyday experiences.

Stakeholder interviews

Talking to those invested in the design's outcome, be it users or experts, is key. These discussions provide first-hand feedback that can clarify user expectations and illuminate the path towards a design that resonates with its audience.

This stage is about delving deeper and starting to shape your design concepts based on what you’ve already discovered.

Design review

This is a closer look at existing designs in the market or other related areas. Design reviews are very valuable because they can provide an understanding of current design trends and standards – helping you see where there's room for innovation or improvement.

Without a design review, you could be at risk of reinventing the wheel.

Persona building

This involves creating detailed profiles representing different groups in your target audience using real data and insights.

Personas help bring to life potential users, ensuring your designs address actual needs and scenarios. By having these "stand-in" users, you can make more informed design choices tailored to specific user experiences.

Putting your evolving design ideas to the test and gauging their effectiveness in the real world.

Usability testing

This is about seeing how real users interact with a design.

In usability testing you observe this process, note where they face difficulties and moments of satisfaction. It's a hands-on way to ensure that the design is intuitive and meets user needs.

Benchmark testing

Benchmark testing is about comparing your design's performance against set standards or competitor products.

Doing this gives a clearer idea of where your design stands in the broader context and highlights areas for improvement or differentiation. With these insights you can make informed decisions to either meet or exceed those benchmarks.

This final stage is about gathering feedback once your design is out in the world, ensuring it stays relevant and effective.

Feedback surveys

After users have interacted with the design for some time, use feedback surveys to gather their thoughts. The results of these surveys will help to ensure that you have your finger on the pulse of user sentiment – enabling iterative improvements.

Remember, simple questions can reveal a lot about what's working and where improvements might be needed.

Focus groups

These are structured, moderator-led discussions with a small group of users . The aim is for the conversation to dive deep into their experiences with the design and extract rich insights – not only capturing what users think but also why.

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• Cost-effective research: Cut back on outsourced studies and get more bang for your buck, all while ensuring top-notch quality
• Premium quality: Stand shoulder to shoulder with leading brands, using best-in-class research methods

Karen Goldstein

Karen brings over 25 years of experience in B2C and B2B research, cultivating deep experience in Innovation research methods and tools.

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What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

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Designing and Proposing Your Research Project

Available formats, also available from.

• Table of contents
• Contributor bios
• Reviews and awards
• Book details
• Supplemental Resources

Designing your own study and writing your research proposal takes time, often more so than conducting the study. This practical, accessible guide walks you through the entire process.

You will learn to identify and narrow your research topic, develop your research question, design your study, and choose appropriate sampling and measurement strategies.

The figures, tables, and exhibits offer a wealth of relatable examples, and students can use the many activities and worksheets to explore and apply concepts, as individuals or in groups.

This book is part of APA's Concise Guides to Conducting Behavioral, Health, and Social Science Research series. Aimed at undergraduate students in research methods courses or others with a lab or research project, each book describes a key stage in the research process. Collectively, these books provide a solid grounding in research from start to finish.

Series Foreword

• Introduction
• Choosing Your Research Question and Hypotheses
• Choosing Your Study's Purpose
• Choosing Whether to Use a Qualitative, Quantitative, or Mixed-Methods Approach
• Understanding Terms for Quantitative Studies: Concepts, Constructs, and Variables
• Choosing Your Design
• Choosing Your Sample
• Planning Your Measurement Strategy for Collecting Data
• Establishing Validity for Quantitative Studies
• Establishing Validity for Qualitative Studies

About the Authors

About the Series Editor

Jennifer Brown Urban, PhD, is a professor in the Department of Family Science and Human Development at Montclair State University, where she also directs the Research on Evaluation and Developmental Systems Science lab.

She is trained as a developmental scientist with specific expertise in youth development and program evaluation. Her scholarship is encapsulated under the umbrella of systems science, including both theoretical approaches and methodologies.

Dr. Urban's most recent research focuses on character development and innovative approaches to program evaluation and planning. She is currently principal investigator on several grant-funded projects. The goals of this work are to build the capacity of youth program practitioners and evaluators to engage in high-quality evaluation of character development programs, to determine the key features of character development programs that promote positive youth development, and to advance the application of character science in multiple contexts to enhance human flourishing across the lifespan.

She uses mixed-method approaches in her own research and has mentored many undergraduate and graduate students in designing and executing applied research projects.

Bradley Matheus van Eeden-Moorefield, PhD, is an associate professor in the Department of Family Science and Human Development at Montclair State University and director of the PhD program.

His research includes a strong social justice commitment to understanding and strengthening marginalized families, with his most recent work focused on stepfamilies headed by same-sex couples. Much of this research focuses on identifying how factors in the social world (e.g., stigma, stereotypes, policy) influence everyday family life and how each influence various indicators of individual (e.g., depression, happiness) and family well-being (stability).

Dr. van Eeden-Moorefield uses various qualitative and quantitative methodologies and has particular expertise in Internet-based methodologies.

He has provided training to various family and childcare practitioners and uses his previous clinical experiences to translate research into practice and practice into research.

The chapters are organized around the choices students need to make, rather than the types of research and issues specific to each type — an important distinguishing feature that sets this book apart from other research methods text…. In the current environment of increasing interdisciplinarity, this text is very useful to students who find themselves coming to social science research from other disciplines, or to students in need of clear guidelines who do not have the time to complete another entire research methods course. — Choice

Urban and van Eeden-Moorefield take the often daunting topic of research methods and make it — dare I say — fun and engaging. Through personal stories and good humor, they demystify the research process and find ways to connect research to everyday life and experiences. This book should be a required supplementary text for every introductory research methods course. —William M. Trochim, PhD Professor, Cornell University, Ithaca, NY

The authors use vivid and engaging examples and masterfully crafted exhibits to create an irresistible proposition to students: "You can do excellent research and enjoy doing it!" They creatively help readers understand and make the choices involved in exemplary research. This book is an invaluable asset for students in psychology and in the social and behavioral sciences more generally. —Richard M. Lerner, PhD Bergstrom Chair in Applied Developmental Science and Director, Institute for Applied Research in Youth Development, Tufts University, Medford, MA

This book will help beginning researchers identify a meaningful and testable research question as well as deal with basic choices in designing their study. The accessible text and a host of tables guide readers through key issues in designing and proposing a research project. —Melvin M. Mark, PhD Professor and Head of Psychology, The Pennsylvania State University, University Park

Welcome to the supplemental resources for Designing and Proposing Your Research Project . The links below provide copies of many of the worksheets seen throughout the text for ease of use.

We also have included several features referred to, but not discussed at length, in the text. These include handouts on ethics, mixed-methods designs, writing integrated literature reviews, and an example research proposal format.

We hope you find these extra features useful. Good luck!

• Anatomy of a Research Article and Comparison of Qualitative and Quantitative Approaches to Research (PDF: 35KB)
• Research Design and Ethics (PDF: 18KB)
• Integrated Literature Review, Research Question-Hypothesis (PDF: 9KB)
• Mixed Methods Approaches (PDF: 32KB)
• Research Proposal Format Example (PDF: 9KB)
• Worksheet: Planning for Trustworthiness in My Study (PDF: 7KB)
• Worksheet: Planning and Tracking Validity in My Study (PDF: 28KB)

You may also like

• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

Find how to write research questions with the mentioned steps required for a perfect research question. Choose an interesting topic and begin your research.

Let’s briefly examine the concept of research paradigms, their pillars, purposes, types, examples, and how they can be combined.

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Selecting a Design Research Topic

Selecting a topic to research can feel like a daunting task. One strength that’s consistent with great designers is their curiosity. Designers tend to have a wide range of interests that take them far afield when exploring the world. Oftentimes, designers are generalists—able to address contexts from many different points of view. Unfortunately, this also means that selecting a topic can be tough for designers because they often have many diverse interests. The world is so big and so curious… how could we ever choose just one part of it to study?

For experience-centered design approaches like experience, service, and interaction design, a research project should focus on the content the design approaches address. In other words, a design research project in experience design should:

• involve human actors in some way
• include the design of outcomes/an intervention
• inspect the effects of interactions between human actors and design outcomes

When selecting topics for your research, be sure they are related to experience design.

Collecting Topics

List as many topics and topic groups as you can and continue to compile this list over months and years. Below each section of listed statements, write questions that address the content in each of these areas. If you can write a compelling and answerable question, then you have the start of a research topic. As you get closer to developing and producing your project, this list of topics will change and evolve over time. Like a photographer, the more topics you list (photographs you take), the more likely you’ll be to get a great topic.

Don’ts: How to Avoid Picking a “Bad” Topic

What to avoid when creating a design research project. Don’t select a topic…

• That involves people you cannot access. You must involve these people in the research, and you cannot do that if they won’t with you.
• That you think your instructor will like. Do something that matters to you.
• That’s too big. You cannot save the world in one project.
• That seems too small. There’s no such thing as too small. Every topic has tons of layers.
• That is in a location you cannot access. Field research requires using a design and or visiting a place firsthand.
• That can be answered with a simple yes or no.

Grouping Topics and Questions

Building a list of topics will produce a list of topical interests and specific questions sparked by your list. For example, exploring the domain “Attitudes and Mental Health” could produce the list below:

• Self-esteem and encouraging self-worth
• Stress management
• Life goals and determination
• Men’s resistance to counseling
• Combatting Apathy

This list then can lead to questions like:

• How can counseling and mentoring become more culturally acceptable for men to bring healthier mental states?
• Are there ways we can educate stress management more effectively?
• Can intrinsic motivation be imparted culturally?

Another example of a topic list that focuses on “Design Aesthetic and Function” could be summed as:

• Value of grunge design in interaction design
• How flat interaction design is hard to use
• Color theory and usability
• Out of place, attention-getting visuals

This list then could lead to questions like:

Design Aesthetic and Function Questions

• How has a move to flat design on the web affected print design?
• In what ways could grunge design increase the way people value physical versus screen-based media?
• How does the hamburger menu in web design relate to other iconographic elements in the past?

Developing topics is an iterative process that’s best started early. Again, the longer you have to develop topics, the better the topics will be. Think of it as a sketchbook-type approach to developing research—the result being a range of topics that can fuel many research projects to come.

Start that list of topics now and visit it often. Begin by identifying things that interest you. List these topics in any way you see fit. Concept mapping , brainstorming, affinity mapping, and other means of generating ideas can be effective ways of driving this process. You’ll be glad you did when you’re looking for a research topic.

Make a big, wide list. Don’t limit yourself by thinking something is not worth researching. If you are interested in it, write it down!

Dennis Cheatham

Associate Professor, Communication Design

Miami University

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User Interface Design and UX Design: 80+ Important Research Papers Covering Peer-Reviewed and Informal Studies

Charles Mauro CHFP

Important peer-reviewed and informally published recent research on user interface design and user experience (UX) design.

For the benefit of clients and colleagues we have culled a list of approximately 70 curated recent research publications dealing with user interface design, UX design and e-commerce optimization.

In our opinion these publications represent some of the best formal research thinking on UI and UX design. These papers are also among the most widely downloaded and cited formal research on UI / UX design. We have referenced many of these studies in our work at MauroNewMedia.

Pay walls: As you will note in reviewing the following links and abstracts, most of the serious research on UI / UX design and optimization is located behind pay walls controlled by major publishers. However, in the end, good data is well worth the investment. Many links and other cited references are, of course, free.

Important disclaimer: We do not receive any form of compensation for citing any of the following content. Either Charles L Mauro CHFP or Paul Thurman MBA has personally reviewed all papers and links in this list. Some of these references were utilized in the recent NYTECH UX talk given by Paul Thurman MBA titled: Critical New UX Design Optimization Research

In addition to historical research papers, we frequently receive requests from colleagues, clients and journalists for recommended reading lists on topics covering our expertise in UX design, usability research and human factors engineering. These requests prompted us to pull from our research library (yes, we still have real books) 30+ books which our professional staff felt should be considered primary conceptual literature for anyone well-read in the theory and practice of UX design and research. Please follow the for PulseUX’s compilation of the 30+ Best UX Design and Research Books of All Time

Title: The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences

Abstract User experience seeks to promote rich, engaging interactions between users and systems. In order for this experience to unfold, the user must be motivated to initiate an interaction with the technology. This study explored hedonic and utilitarian motivations in the context of user engagement with online shopping. Factor analysis was performed to identify a parsimonious set of factors from the Hedonic and Utilitarian Shopping Motivation Scale and the User Engagement Scale based on responses from 802 shoppers. Multiple linear regression was used to test hypotheses with hedonic and utilitarian motivations (Idea, Social, Adventure/Gratification, Value and Achievement Shopping) and attributes of user engagement (Aesthetics, Focused Attention, Perceived Usability, and Endurability). Results demonstrate the salience of Adventure/Gratification Shopping and Achievement Shopping Motivations to specific variables of user engagement in the e-commerce environment and provide considerations for the inclusion of different types of motivation into models of engaging user experiences. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: New Support for Marketing Analytics

Abstract Consumer surveys and myriad other forms of research have long been the grist for marketing decisions at large companies. But many firms have been reluctant to embrace the high-tech approach to data gathering and number crunching that falls under the rubric of marketing analytics, which uses advanced techniques to transform the tracking of promotional efforts, customer preferences, and industry developments into sophisticated branding and advertising campaigns. Fueled in part by Tom Peters and Robert Waterman’s seminal 1982 book In Search of Excellence , which coined the phrase “paralysis through analysis,” skepticism about the approach remains widespread, even in the face of a number of positive research results over the years. This new study, involving Fortune 1000 companies, offers yet more ammunition for supporters of marketing analytics. Abstract Copyright © 2013 Booz & Company Inc. All rights reserved.

Title: Video game values: Human-computer interaction and games

Abstract Current human–computer interaction (HCI) research into video games rarely considers how they are different from other forms of software. This leads to research that, while useful concerning standard issues of interface design, does not address the nature of video games as games specifically. Unlike most software, video games are not made to support external, user-defined tasks, but instead define their own activities for players to engage in. We argue that video games contain systems of values which players perceive and adopt, and which shape the play of the game. A focus on video game values promotes a holistic view of video games as software, media, and as games specifically, which leads to a genuine video game HCI. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: When fingers do the talking: a study of text messaging

Abstract SMS or text messaging is an area of growth in the communications field. The studies described below consisted of a questionnaire and a diary study. The questionnaire was designed to examine texting activities in 565 users of the mobile phone. The diary study was carried out by 24 subjects over a period of 2 weeks. The findings suggest that text messaging is being used by a wide range of people for all kinds of activities and that for some people it is the preferred means of communication. These studies should prove interesting for those examining the use and impact of SMS. Abstract Copyright © 2004 Elsevier B.V. All rights reserved.

Title: Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective

Abstract As mobile technology has developed, mobile banking has become accepted as part of daily life. Although many studies have been conducted to assess users’ satisfaction with mobile applications, none has focused on the ways in which the three quality factors associated with mobile banking – system quality, information quality and interface design quality – affect consumers’ trust and satisfaction. Our proposed research model, based on DeLone and McLean’s model, assesses how these three external quality factors can impact satisfaction and trust. We collected 276 valid questionnaires from mobile banking customers, then analyzed them using structural equation modeling. Our results show that system quality and information quality significantly influence customers’ trust and satisfaction, and that interface design quality does not. We present herein implications and suggestions for further research. Abstract Copyright © 2009 Elsevier B.V. All rights reserved.

Title: What is beautiful is usable

Abstract An experiment was conducted to test the relationships between users’ perceptions of a computerized system’s beauty and usability. The experiment used a computerized application as a surrogate for an Automated Teller Machine (ATM). Perceptions were elicited before and after the participants used the system. Pre-experimental measures indicate strong correlations between system’s perceived aesthetics and perceived usability. Post-experimental measures indicated that the strong correlation remained intact. A multivariate analysis of covariance revealed that the degree of system’s aesthetics affected the post-use perceptions of both aesthetics and usability, whereas the degree of actual usability had no such effect. The results resemble those found by social psychologists regarding the effect of physical attractiveness on the valuation of other personality attributes. The findings stress the importance of studying the aesthetic aspect of human–computer interaction (HCI) design and its relationships to other design dimensions. Abstract Copyright © 2000 Elsevier Science B.V. All rights reserved.

Title: UX Curve: A method for evaluating long-term user experience

Abstract The goal of user experience design in industry is to improve customer satisfaction and loyalty through the utility, ease of use, and pleasure provided in the interaction with a product. So far, user experience studies have mostly focused on short-term evaluations and consequently on aspects relating to the initial adoption of new product designs. Nevertheless, the relationship between the user and the product evolves over long periods of time and the relevance of prolonged use for market success has been recently highlighted. In this paper, we argue for the cost-effective elicitation of longitudinal user experience data. We propose a method called the “UX Curve” which aims at assisting users in retrospectively reporting how and why their experience with a product has changed over time. The usefulness of the UX Curve method was assessed in a qualitative study with 20 mobile phone users. In particular, we investigated how users’ specific memories of their experiences with their mobile phones guide their behavior and their willingness to recommend the product to others. The results suggest that the UX Curve method enables users and researchers to determine the quality of long-term user experience and the influences that improve user experience over time or cause it to deteriorate. The method provided rich qualitative data and we found that an improving trend of perceived attractiveness of mobile phones was related to user satisfaction and willingness to recommend their phone to friends. This highlights that sustaining perceived attractiveness can be a differentiating factor in the user acceptance of personal interactive products such as mobile phones. The study suggests that the proposed method can be used as a straightforward tool for understanding the reasons why user experience improves or worsens in long-term product use and how these reasons relate to customer loyalty. Abstract Copyright 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Heuristic evaluation: Comparing ways of finding and reporting usability problems

Abstract Research on heuristic evaluation in recent years has focused on improving its effectiveness and efficiency with respect to user testing. The aim of this paper is to refine a research agenda for comparing and contrasting evaluation methods. To reach this goal, a framework is presented to evaluate the effectiveness of different types of support for structured usability problem reporting. This paper reports on an empirical study of this framework that compares two sets of heuristics, Nielsen’s heuristics and the cognitive principles of Gerhardt-Powals, and two media of reporting a usability problem, i.e. either using a web tool or paper. The study found that there were no significant differences between any of the four groups in effectiveness, efficiency and inter-evaluator reliability. A more significant contribution of this research is that the framework used for the experiments proved successful and should be reusable by other researchers because of its thorough structure. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: Socio-technical systems: From design methods to systems engineering

Abstract It is widely acknowledged that adopting a socio-technical approach to system development leads to systems that are more acceptable to end users and deliver better value to stakeholders. Despite this, such approaches are not widely practised. We analyse the reasons for this, highlighting some of the problems with the better known socio-technical design methods. Based on this analysis we propose a new pragmatic framework for socio-technical systems engineering (STSE) which builds on the (largely independent) research of groups investigating work design, information systems, computer-supported cooperative work, and cognitive systems engineering. STSE bridges the traditional gap between organisational change and system development using two main types of activity: sensitisation and awareness; and constructive engagement. From the framework, we identify an initial set of interdisciplinary research problems that address how to apply socio-technical approaches in a cost-effective way, and how to facilitate the integration of STSE with existing systems and software engineering approaches. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: Five reasons for scenario-based design

Abstract Scenarios of human–computer interaction help us to understand and to create computer systems and applications as artifacts of human activity—as things to learn from, as tools to use in one’s work, as media for interacting with other people. Scenario-based design of information technology addresses five technical challenges: scenarios evoke reflection in the content of design work, helping developers coordinate design action and reflection. Scenarios are at once concrete and flexible, helping developers manage the fluidity of design situations. Scenarios afford multiple views of an interaction, diverse kinds and amounts of detailing, helping developers manage the many consequences entailed by any given design move. Scenarios can also be abstracted and categorized, helping designers to recognize, capture and reuse generalizations and to address the challenge that technical knowledge often lags the needs of technical design. Finally, scenarios promote work-oriented communication among stakeholders, helping to make design activities more accessible to the great variety of expertise that can contribute to design, and addressing the challenge that external constraints designers and clients face often distract attention from the needs and concerns of the people who will use the technology. Abstract Copyright © 2000 Elsevier Science B.V. All rights reserved.

Title: Needs, affect, and interactive products – Facets of user experience

Abstract Subsumed under the umbrella of User Experience (UX), practitioners and academics of Human–Computer Interaction look for ways to broaden their understanding of what constitutes “pleasurable experiences” with technology. The present study considered the fulfilment of universal psychological needs, such as competence, relatedness, popularity, stimulation, meaning, security, or autonomy, to be the major source of positive experience with interactive technologies. To explore this, we collected over 500 positive experiences with interactive products (e.g., mobile phones, computers). As expected, we found a clear relationship between need fulfilment and positive affect, with stimulation, relatedness, competence and popularity being especially salient needs. Experiences could be further categorized by the primary need they fulfil, with apparent qualitative differences among some of the categories in terms of the emotions involved. Need fulfilment was clearly linked to hedonic quality perceptions, but not as strongly to pragmatic quality (i.e., perceived usability), which supports the notion of hedonic quality as “motivator” and pragmatic quality as “hygiene factor.” Whether hedonic quality ratings reflected need fulfilment depended on the belief that the product was responsible for the experience (i.e., attribution). Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: The role of social presence in establishing loyalty in e-Service environments

Abstract Compared to offline shopping, the online shopping experience may be viewed as lacking human warmth and sociability as it is more impersonal, anonymous, automated and generally devoid of face-to-face interactions. Thus, understanding how to create customer loyalty in online environments (e-Loyalty) is a complex process. In this paper a model for e-Loyalty is proposed and used to examine how varied conditions of social presence in a B2C e-Services context influence e-Loyalty and its antecedents of perceived usefulness, trust and enjoyment. This model is examined through an empirical study involving 185 subjects using structural equation modeling techniques. Further analysis is conducted to reveal gender differences concerning hedonic elements in the model on e-Loyalty. Abstract Copyright © 2006 Elsevier B.V. All rights reserved.

Title: A framework for evaluating the usability of mobile phones based on multi-level, hierarchical model of usability factors

Abstract As a mobile phone has various advanced functionalities or features, usability issues are increasingly challenging. Due to the particular characteristics of a mobile phone, typical usability evaluation methods and heuristics, most of which are relevant to a software system, might not effectively be applied to a mobile phone. Another point to consider is that usability evaluation activities should help designers find usability problems easily and produce better design solutions. To support usability practitioners of the mobile phone industry, we propose a framework for evaluating the usability of a mobile phone, based on a multi-level, hierarchical model of usability factors, in an analytic way. The model was developed on the basis of a set of collected usability problems and our previous study on a conceptual framework for identifying usability impact factors. It has multi-abstraction levels, each of which considers the usability of a mobile phone from a particular perspective. As there are goal-means relationships between adjacent levels, a range of usability issues can be interpreted in a holistic as well as diagnostic way. Another advantage is that it supports two different types of evaluation approaches: task-based and interface-based. To support both evaluation approaches, we developed four sets of checklists, each of which is concerned, respectively, with task-based evaluation and three different interface types: Logical User Interface (LUI), Physical User Interface (PUI) and Graphical User Interface (GUI). The proposed framework specifies an approach to quantifying usability so that several usability aspects are collectively measured to give a single score with the use of the checklists. A small case study was conducted in order to examine the applicability of the framework and to identify the aspects of the framework to be improved. It showed that it could be a useful tool for evaluating the usability of a mobile phone. Based on the case study, we improved the framework in order that usability practitioners can use it more easily and consistently. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context

Abstract The aim of this research was to study the structure of the most satisfying and unsatisfying user experiences in terms of experienced emotions, psychological needs, and contextual factors. 45 university students wrote descriptions of their most satisfying and unsatisfying recent user experiences and analyzed those experiences using the Positive and Negative Affect Schedule (PANAS) method for experienced emotions, a questionnaire probing the salience of 10 psychological needs, and a self-made set of rating scales for analyzing context. The results suggested that it was possible to capture variations in user experiences in terms of experienced emotions, fulfillment of psychological needs, and context effectively by using psychometric rating scales. The results for emotional experiences showed significant differences in 16 out of 20 PANAS emotions between the most satisfying and unsatisfying experiences. The results for psychological needs indicated that feelings of autonomy and competence emerged as highly salient in the most satisfying experiences and missing in the unsatisfying experiences. High self-esteem was also notably salient in the most satisfying experiences. The qualitative results indicated that most of the participants’ free-form qualitative descriptions, especially for the most unsatisfying user experiences, gave important information about the pragmatic aspects of the interaction, but often omitted information about hedonic and social aspects of user experience. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: The Usability Metric for User Experience

Abstract The Usability Metric for User Experience (UMUX) is a four-item Likert scale used for the subjective assessment of an application’s perceived usability. It is designed to provide results similar to those obtained with the 10-item System Usability Scale, and is organized around the ISO 9241-11 definition of usability. A pilot version was assembled from candidate items, which was then tested alongside the System Usability Scale during usability testing. It was shown that the two scales correlate well, are reliable, and both align on one underlying usability factor. In addition, the Usability Metric for User Experience is compact enough to serve as a usability module in a broader user experience metric. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: User acceptance of mobile Internet: Implication for convergence technologies

Abstract Using the Technology Acceptance Model as a conceptual framework and a method of structural equation modeling, this study analyzes the consumer attitude toward Wi-Bro drawing data from 515 consumers. Individuals’ responses to questions about whether they use/accept Wi-Bro were collected and combined with various factors modified from the Technology Acceptance Model.

The result of this study show that users’ perceptions are significantly associated with their motivation to use Wi-Bro. Specifically, perceived quality and perceived availability are found to have significant effect on users’ extrinsic and intrinsic motivation. These new factors are found to be Wi-Bro-specific factors, playing as enhancing factors to attitudes and intention. Abstract Copyright © 2007 Elsevier B.V. All rights reserved.

Title: Understanding purchasing behaviors in a virtual economy: Consumer behavior involving virtual currency in Web 2.0 communities

Abstract This study analyzes consumer purchasing behavior in Web 2.0, expanding the technology acceptance model (TAM), focusing on which variables influence the intention to transact with virtual currency. Individuals’ responses to questions about attitude and intention to transact in Web 2.0 were collected and analyzed with various factors modified from the TAM. The results of the proposed model show that subjective norm is a key behavioral antecedent to using virtual currency. In the extended model, the moderating effects of subjective norm on the relations among the variables were found to be significant. The new set of variables is virtual environment-specific, acting as factors enhancing attitudes and behavioral intentions in Web 2.0 transactions. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Fundamentals of physiological computing

Abstract This review paper is concerned with the development of physiological computing systems that employ real-time measures of psychophysiology to communicate the psychological state of the user to an adaptive system. It is argued that physiological computing has enormous potential to innovate human–computer interaction by extending the communication bandwidth to enable the development of ‘smart’ technology. This paper focuses on six fundamental issues for physiological computing systems through a review and synthesis of existing literature, these are (1) the complexity of the psychophysiological inference, (2) validating the psychophysiological inference, (3) representing the psychological state of the user, (4) designing explicit and implicit system interventions, (5) defining the biocybernetic loop that controls system adaptation, and (6) ethical implications. The paper concludes that physiological computing provides opportunities to innovate HCI but complex methodological/conceptual issues must be fully tackled during the research and development phase if this nascent technology is to achieve its potential. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Modelling user experience with web sites: Usability, hedonic value, beauty and goodness

Abstract Recent research into user experience has identified the need for a theoretical model to build cumulative knowledge in research addressing how the overall quality or ‘goodness’ of an interactive product is formed. An experiment tested and extended Hassenzahl’s model of aesthetic experience. The study used a 2 × 2 × (2) experimental design with three factors: principles of screen design, principles for organizing information on a web page and experience of using a web site. Dependent variables included hedonic perceptions and evaluations of a web site as well as measures of task performance, navigation behaviour and mental effort. Measures, except Beauty, were sensitive to manipulation of web design. Beauty was influenced by hedonic attributes (identification and stimulation), but Goodness by both hedonic and pragmatic (user-perceived usability) attributes as well as task performance and mental effort. Hedonic quality was more stable with experience of web-site use than pragmatic quality and Beauty was more stable than Goodness. Abstract Copyright © 2008 Elsevier B.V. All rights reserved.

Title: Sample Size In Usability Studies

Abstract Usability studies are a cornerstone activity for developing usable products. Their effectiveness depends on sample size, and determining sample sizehas been a research issue in usability engineering for the past 30 years. In 2010, Hwang and Salvendy reported a meta study on the effectiveness of usability evaluation, concluding that a sample size of 10±2 is sufficient for discovering 80% of usability problems (not five, as suggested earlier by Nielsen in 2000). Here, I show the Hwang and Salvendy study ignored fundamental mathematical properties of the problem, severely limiting the validity of the 10±2 rule, then look to reframe the issue of effectiveness and sample-size estimation to the practices and requirements commonly encountered in industrial-scale usability studies. Abstract Copyright © 2013 ACM, Inc. Title: An experimental study of learner perceptions of the interactivity of web-based instruction

Abstract An effectively designed interaction mechanism creates a shortcut for human–computer interaction. Most studies in this area have concluded that the higher the level of interactivity, the better, especially regarding interactive websites applied in the fields of business and education. Previous studies have also suggested that designs with a higher level of interactivity result in higher learner evaluations of websites. However, little research has examined learner perceptions as they interact with web-based instruction (WBI) systems in a situation with limited time. To assist learners in acquiring knowledge quickly, the interactivity design must make the web learning environment easier to use by reducing the complexity of the interface. The aim of the present study is to explore learner perceptions of three WBI systems with different interaction levels under time limitations. This study was therefore designed to provide a new framework to design systems with different degrees of interactivity, and to examine learners’ perceptions of these interaction elements. Three WBI systems were developed with different degrees of interactivity from high to low, and a between-subject experiment was conducted with 45 subjects. The results of the experiment indicate that a higher level of interactivity does not necessarily guarantee a higher perception of interactivity in a short-term learning situation. Therefore, the instructors must pay attention to modifying or selecting appropriate interactive elements that are more suitable for various learning stages. The findings provide insights for designers to adopt different degrees of interactivity in their designs that will best fulfill various learners’ needs. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Age differences in the perception of social presence in the use of 3D virtual world for social interaction

Abstract 3D virtual worlds are becoming increasingly popular as tool for social interaction, with the potential of augmenting the user’s perception of physical and social presence. Thus, this technology could be of great benefit to older people, providing home-bound older users with access to social, educational and recreational resources. However, so far there have been few studies looking into how older people engage with virtual worlds, as most research in this area focuses on younger users. In this study, an online experiment was conducted with 30 older and 30 younger users to investigate age differences in the perception of presence in the use of virtual worlds for social interaction. Overall, we found that factors such as navigation and prior experience with text messaging tools played a key role in older people’s perception of presence. Both physical and social presence was found to be linked to the quality of social interaction for users of both age groups. In addition, older people displayed proxemic behavior which was more similar to proxemic behavior in the physical world when compared to younger users. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Human error and information systems failure: the case of the London ambulance service computer-aided despatch system project

Abstract Human error and systems failure have been two constructs that have become linked in many contexts. In this paper we particularly focus on the issue of failure in relation to that group of software systems known as information systems. We first review the extant theoretical and empirical work on this topic. Then we discuss one particular well-known case — that of the London ambulance service computer-aided despatch system (Lascad) project — and use it as a particularly cogent example of the features of information systems failure. We maintain that the tendency to analyse information systems failure solely from a technological standpoint is limiting, that the nature of information systems failure is multi-faceted, and hence cannot be adequately understood purely in terms of the immediate problems of systems construction. Our purpose is also to use the generic material on IS failure and the specific details of this particular case study to critique the issues of safety, criticality, human error and risk in relation to systems not currently well considered in relation to these areas. Abstract Copyright © 1999 Elsevier B.V. All rights reserved.

Title: Feminist HCI meets facebook: Performativity and social networking sites

Abstract In this paper, I reflect on a specific product of interaction design, social networking sites. The goals of this paper are twofold. One is to bring a feminist reflexivity, to HCI, drawing on the work of Judith Butler and her concepts of peformativity, citationality, and interpellation. Her approach is, I argue, highly relevant to issues of identity and self-representation on social networking sites; and to the co-constitution of the subject and technology. A critical, feminist HCI must ask how social media and other HCI institutions, practices, and discourses are part of the processes by which sociotechnical configurations are constructed. My second goal is to examine the implications of such an approach by applying it to social networking sites (SNSs) drawing the empirical research literature on SNSs, to show how SNS structures and policies help shape the subject and hide the contingency of subject categories. Abstract Copyright © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing

Abstract Physiological computing represents a mode of human–computer interaction where the computer monitors, analyzes and responds to the user’s psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subject’s psychological state. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: Positive mood induction procedures for virtual environments designed for elderly people

Abstract Positive emotions have a significant influence on mental and physical health. Their role in the elderly’s wellbeing has been established in numerous studies. It is therefore worthwhile to explore ways in which elderly people can increase the number of positive experiences in their daily lives. This paper describes two Virtual Environments (VEs) that were used as mood induction procedures (MIPs) for this population. In addition, the VEs’ efficacy at increasing joy and relaxation in elderly users is analyzed. The VEs contain exercises for generating positive-autobiographic memories, mindfulness and slow breathing rhythms. The total sample comprised 18 participants over 55 years old who used the VEs on two occasions. Twelve of them used the joy environment, while 16 used the relaxation environment. Moods before and after each session were assessed using Visual Analogical Scales. After using both VEs, results indicated significant increases in joy and relaxation and significant decreases in sadness and anxiety. The participants also indicated low levels of difficulty of use and high levels of satisfaction and sense of presence. Hence, the VEs demonstrate their usefulness at promoting positive affects and enhancing the wellbeing of elderly people. Abstract Copyright © 2012 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Title: The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption

Abstract Social network services (SNS) focus on building online communities of people who share interests and/or activities, or who are interested in exploring the interests and activities of others. This study examines security, trust, and privacy concerns with regard to social networking Websites among consumers using both reliable scales and measures. It proposes an SNS acceptance model by integrating cognitive as well as affective attitudes as primary influencing factors, which are driven by underlying beliefs, perceived security, perceived privacy, trust, attitude, and intention. Results from a survey of SNS users validate that the proposed theoretical model explains and predicts user acceptance of SNS substantially well. The model shows excellent measurement properties and establishes perceived privacy and perceived security of SNS as distinct constructs. The finding also reveals that perceived security moderates the effect of perceived privacy on trust. Based on the results of this study, practical implications for marketing strategies in SNS markets and theoretical implications are recommended accordingly. Abstract Copyright © 2010 Elsevier B.V. All rights reserved.

Title: Usability testing: what have we overlooked?

Abstract For more than a decade, the number of usability test participants has been a major theme of debate among usability practitioners and researchers keen to improve usability test performance. This paper provides evidence suggesting that the focus be shifted to task coverage instead. Our data analysis of nine commercial usability test teams participating in the CUE-4 study revealed no significant correlation between the percentage of problems found or of new problems and number of test users, but correlations of both variables and number of user tasks used by each usability team were significant. The role of participant recruitment on usability test performance and future research directions are discussed. Abstract Copyright © 2013 ACM, Inc.

Title: Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior

Abstract This paper tests the ability of two consumer theories—the theory of reasoned action and the theory of planned behavior—in predicting consumer online grocery buying intention. In addition, a comparison of the two theories is conducted. Data were collected from two web-based surveys of Danish ( n =1222) and Swedish ( n =1038) consumers using self-administered questionnaires. These results suggest that the theory of planned behavior (with the inclusion of a path from subjective norm to attitude) provides the best fit to the data and explains the highest proportion of variation in online grocery buying intention. Abstract Copyright © 2013 Elsevier B.V. All rights reserved.

Title: Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions

Abstract The Theory of Planned Behavior, an extension of the well-known Theory of Reasoned Action, is proposed as a model to predict consumer adoption intention. Three variations of the Theory of Planned Behavior are examined and compared to the Theory of Reasoned Action. The appropriateness of each model is assessed with data from a consumer setting. Structural equation modelling using maximum likelihood estimation for the four models revealed that the traditional forms of the Theory of Reasoned Action and the Theory of Planned Behavior fit the data adequately. Decomposing the belief structures and allowing for crossover effects in the Theory of Planned Behavior resulted in improvements in model prediction. The application of each model to theory development and management intervention is explored. Abstract Copyright © 1995 Elsevier B.V. All rights reserved.

Title: Knowledge and the Prediction of Behavior: The Role of Information Accuracy in the Theory of Planned Behavior

Abstract The results of the present research question the common assumption that being well informed is a prerequisite for effective action to produce desired outcomes. In Study 1 ( N = 79), environmental knowledge had no effect on energy conservation, and in Study 2 ( N = 79), alcohol knowledge was unrelated to drinking behavior. Such disappointing correlations may result from an inappropriate focus on accuracy of information at the expense of its relevance to and support for the behavior. Study 3 ( N = 85) obtained a positive correlation between knowledge and pro-Muslim behavior, but Study 4 ( N = 89) confirmed the proposition that this correlation arose because responses on the knowledge test reflected underlying attitudes. Study 4 also showed that the correlation could become positive or negative by appropriate selection of questions for the knowledge test. The theory of planned behavior (Ajzen, 1991 ), with its focus on specific actions, predicted intentions and behavior in all four studies. Abstract Copyright © 2013 Informa plc

Link: h ttp://www.businessinsider.com/ron-johnson-apple-store-j-c-penney-2011-11

People come to the Apple Store for the experience — and they’re willing to pay a premium for that. There are lots of components to that experience, but maybe the most important — and this is something that can translate to any retailer — is that the staff isn’t focused on selling stuff, it’s focused on building relationships and trying to make people’s lives better. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Title : Naturalizing aesthetics: Brain areas for aesthetic appraisal across sensory modalities

Abstract We present here the most comprehensive analysis to date of neuroaesthetic processing by reporting the results of voxel-based meta-analyses of 93 neuroimaging studies of positive-valence aesthetic appraisal across four sensory modalities. The results demonstrate that the most concordant area of activation across all four modalities is the right anterior insula, an area typically associated with visceral perception, especially of negative valence (disgust, pain, etc.). We argue that aesthetic processing is, at its core, the appraisal of the valence of perceived objects. This appraisal is in no way limited to artworks but is instead applicable to all types of perceived objects. Therefore, one way to naturalize aesthetics is to argue that such a system evolved first for the appraisal of objects of survival advantage, such as food sources, and was later co-opted in humans for the experience of artworks for the satisfaction of social needs. Abstract Copyright © 2011 Elsevier Inc. All rights reserved.

Link: http://www.scientificamerican.com/article.cfm?id=the-neuroscience-of-beauty

Studies from neuroscience and evolutionary biology challenge this separation of art from non-art. Human neuroimaging studies have convincingly shown that the brain areas involved in aesthetic responses to artworks overlap with those that mediate the appraisal of objects of evolutionary importance, such as the desirability of foods or the attractiveness of potential mates. Hence, it is unlikely that there are brain systems specific to the appreciation of artworks; instead there are general aesthetic systems that determine how appealing an object is, be that a piece of cake or a piece of music. Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://blogs.scientificamerican.com/symbiartic/2011/10/03/need-proof-that-were-visual-beings/

This video offers proof that humans are visual beings. Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://hbr.org/web/slideshows/five-charts-that-changed-business/1-slide

Once in a while, a chart so deftly captures an important strategic insight that it becomes an iconic part of management thinking and a tool that shows up in MBA classrooms and corporate boardrooms for years to come. As HBR prepares for its 90th anniversary, in 2012, their editors have combed the magazine archives and other sources to select five charts that changed the shape of strategy. Abstract Copyright © 2013 Harvard Business School Publishing. All rights reserved.

Link: http://www.strategy-business.com/article/04412

It is a widely accepted and rarely challenged tenet of marketing that companies can sustain competitive advantage only through “new and improved” product differentiation based on unique features and benefits. What a mistake. By paying attention to what consumers really want, companies can attract new customers and create a distinctive brand. Abstract © 2013 Booz & Company Inc. All rights reserved.

Link: http://www.economist.com/node/17723028

If you can have everything in 57 varieties, making decisions becomes hard work. Many of these options have improved life immeasurably in the rich world, and to a lesser extent in poorer parts. They are testimony to human ingenuity and innovation. Free choice is the basis on which markets work, driving competition and generating economic growth. It is the cornerstone of liberal democracy. The 20th century bears the scars of too many failed experiments in which people had no choice. But amid all the dizzying possibilities, a nagging question lurks: is so much extra choice unambiguously a good thing? Abstract Copyright © The Economist Newspaper Limited 2013. All rights reserved.

Link: http://e.businessinsider.com/public/1099804

Mobile apps are becoming more important to people, not less important, according to this chart plucked from a big presentation on the internet. It’s an interesting trend because it shows how mobile behavior is different than traditional desktop computing behavior when it comes to the web. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://blogs.scientificamerican.com/scicurious-brain/2012/07/30/you-want-that-well-i-want-it-too-the-neuroscience-of-mimetic-desire/

Mimetic desire is more than jealously wanting something because someone else has it. Rather, it’s about valuing something because someone else values it . And it’s pretty easy to transmit the value. Just writing about Person A’s activities and habits and showing it to Person B will make Person B start to think Person A must have seen something good about the Toyota Camry…maybe his next car…

But what is behind this contagion of desires? Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://www.united-academics.org/magazine/27212/visual-memory-blindness/

A well-known pheonomenon in psychology has been the ‘inattentional blindness’ principle. In fact, you might know it from experience: it means that people tend to fail seeing things in their visible fields when they have to focus on a task. Until now, it was thought that in order to cause the effect, a cluttered visual field is required. Recent research shows that the effect is present though in many more situations. Abstract Copyright United Academics 2012 Coypright – All rights Reserved

Link: http://www.businessinsider.com/18-24-texting-2011-9

Chart of the Day: According to the Pew Internet project , people in the 18-24 year-old range are sending and receiving 110 texts per day on average. The median number of texts sent/received by that group is 50 per day. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://www.businessinsider.com/chart-of-the-day-facebook-time-2011-9

Chart of the Day: A new report on social media from Nielsen shows U.S. users spent 53.5 billion minutes on Facebook in May, which is more time than was spent on the next four biggest sites. Abstract Copyright © 2013 Business Insider, Inc. All rights reserved.

Link: http://www.scientificamerican.com/article.cfm?id=your-brain-on-facebook

A recent study showed that certain brain areas expand in people who have greater numbers of friends on Facebook . There was a problem, though. The study, in Proceedings of the Royal Society B , was unable to resolve the question of whether “friending” plumps up the brain areas or whether people with a type of robustness in brain physiology are just natural social butterflies. But with the help of a few monkeys in England, teenagers everywhere may now have more ammunition to use against parents. Abstract © 2013 Scientific American, a Division of Nature America, Inc.

Link: http://iwc.oxfordjournals.org/content/26/3/196.abstract.html?etoc

Although advances in technology now enable people to communicate ‘anytime, anyplace’, it is not clear how citizens can be motivated to actually do so. This paper evaluates the impact of three principles of psychological empowerment, namely perceived self-efficacy, sense of community and causal importance, on public transport passengers’ motivation to report issues and complaints while on the move. A week-long study with 65 participants revealed that self-efficacy and causal importance increased participation in short bursts and increased perceptions of service quality over longer periods. Finally, we discuss the implications of these findings for citizen participation projects and reflect on design opportunities for mobile technologies that motivate citizen participation. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/208.abstract.html?etoc

This review paper argues that users of personal information management systems have three particularly pressing requirements, for which current systems do not fully cater: (i) To combat information overload, as the volume of information increases. (ii) To ease context switching, in particular, for users who face frequent interrupts in their work. (iii) To be supported in information integration, across a variety of applications. To meet these requirements, four broad technological approaches should be adopted in an incremental fashion: (i) The deployment of a unified file system to manage all information objects, including files, emails and webpage URLs. (ii) The use of tags to categorize information; implemented in a way which is backward-compatible with existing hierarchical file systems. (iii) The use of context to aid information retrieval; built upon existing file and tagging systems rather than creating a parallel context management system. (iv) The deployment of semantic technologies, coupled with the harvesting of all useful metadata. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/238.abstract.html?etoc

Projective techniques are used in psychology and consumer research to provide information about individuals’ motivations, thoughts and feelings. This paper reviews the use of projective techniques in marketing research and user experience (UX) research and discusses their potential role in understanding users, their needs and values, and evaluating UX in practical product development contexts. A projective technique called sentence completion is evaluated through three case studies. Sentence completion produces qualitative data about users’ views in a structured form. The results are less time-consuming to analyze than interview results. Compared with quantitative methods such as AttrakDiff, the results are more time consuming to analyze, but more information is retrieved on negative feelings. The results show that sentence completion is useful in understanding users’ perceptions and that the technique can be used to complement other methods. Sentence completion can also be used online to reach wider user groups. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/256.abstract.html?etoc

Cognitive load (CL) is experienced during critical tasks and also while engaged emotional states are induced either by the task itself or by extraneous experiences. Emotions irrelevant to the working memory representation may interfere with the processing of relevant tasks and can influence task performance and behavior, making the accurate detection of CL from nonverbal information challenging. This paper investigates automatic CL detection from facial features, physiology and task performance under affective interference. Data were collected from participants (n=20) solving mental arithmetic tasks with emotional stimuli in the background, and a combined classifier was used for detecting CL levels. Results indicate that the face modality for CL detection was more accurate under affective interference, whereas physiology and task performance were more accurate without the affective interference. Multimodal fusion improved detection accuracies, but it was less accurate under affective interferences. More specifically, the accuracy decreased with an increasing intensity of emotional arousal. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/269.abstract.html?etoc

In the field of virtual reality (VR), many efforts have been made to analyze presence, the sense of being in the virtual world. However, it is only recently that functional magnetic resonance imaging (fMRI) has been used to study presence during an automatic navigation through a virtual environment. In the present work, our aim was to use fMRI to study the sense of presence during a VR-free navigation task, in comparison with visualization of photographs and videos (automatic navigations through the same environment). The main goal was to analyze the usefulness of fMRI for this purpose, evaluating whether, in this context, the interaction between the subject and the environment is performed naturally, hiding the role of technology in the experience. We monitored 14 right-handed healthy females aged between 19 and 25 years. Frontal, parietal and occipital regions showed their involvement during free virtual navigation. Moreover, activation in the dorsolateral prefrontal cortex was also shown to be negatively correlated to sense of presence and the postcentral parietal cortex and insula showed a parametric increased activation according to the condition-related sense of presence, which suggests that stimulus attention and self-awareness processes related to the insula may be linked to the sense of presence. Abstract 2013 Oxford University Press.

Link: http://iwc.oxfordjournals.org/content/26/3/285.abstract.html?etoc

Unlike visual stimuli, little attention has been paid to auditory stimuli in terms of emotion prediction with physiological signals. This paper aimed to investigate whether auditory stimuli can be used as an effective elicitor as visual stimuli for emotion prediction using physiological channels. For this purpose, a well-controlled experiment was designed, in which standardized visual and auditory stimuli were systematically selected and presented to participants to induce various emotions spontaneously in a laboratory setting. Numerous physiological signals, including facial electromyogram, electroencephalography, skin conductivity and respiration data, were recorded when participants were exposed to the stimulus presentation. Two data mining methods, namely decision rules and k-nearest neighbor based on the rough set technique, were applied to construct emotion prediction models based on the features extracted from the physiological data. Experimental results demonstrated that auditory stimuli were as effective as visual stimuli in eliciting emotions in terms of systematic physiological reactivity. This was evidenced by the best prediction accuracy quantified by the F1 measure (visual: 76.2% vs. auditory: 76.1%) among six emotion categories (excited, happy, neutral, sad, fearful and disgusted). Furthermore, we also constructed culture-specific (Chinese vs. Indian) prediction models. The results showed that model prediction accuracy was not significantly different between culture-specific models. Finally, the implications of affective auditory stimuli in human–computer interaction, limitations of the study and suggestions for further research are discussed. Abstract 2013 Oxford University Press.

Link: http://www.sciencedirect.com/science/article/pii/S0160289614000087

The deliberate practice view has generated a great deal of scientific and popular interest in expert performance. At the same time, empirical evidence now indicates that deliberate practice, while certainly important, is not as important as Ericsson and colleagues have argued it is. In particular, we (Hambrick, Oswald, Altmann, Meinz, Gobet, & Campitelli, 2014) found that individual differences in accumulated amount of deliberate practice accounted for about one-third of the reliable variance in performance in chess and music, leaving the majority of the reliable variance unexplained and potentially explainable by other factors. Ericsson’s (2014) defense of the deliberate practice view, though vigorous, is undercut by contradictions, oversights, and errors in his arguments and criticisms, several of which we describe here. We reiterate that the task now is to develop and rigorously test falsifiable theories of expert performance that take into account as many potentially relevant constructs as possible. Abstract © 2014 Elsevier Inc.

Link: http://techcrunch.com/2013/02/05/amazon-to-launch-virtual-currency-amazon-coins-in-its-appstore-in-may/

Amazon has just announced a new virtual currency for Kindle Fire owners to use on in-app purchases, app purchases, etc. in the Amazon Appstore. Abstract © 2013 AOL Inc. All rights reserved.

Link: http://onlinelibrary.wiley.com/doi/10.1002/smj.2284/abstract

Link: http://iwc.oxfordjournals.org/content/early/2014/05/09/iwc.iwu016.abstract.html?papetoc

Wizard of Oz (WOZ) is a well-established method for simulating the functionality and user experience of future systems. Using a human wizard to mimic certain operations of a potential system is particularly useful in situations where extensive engineering effort would otherwise be needed to explore the design possibilities offered by such operations. The WOZ method has been widely used in connection with speech and language technologies, but advances in sensor technology and pattern recognition as well as new application areas such as human–robot interaction have made it increasingly relevant to the design of a wider range of interactive systems. In such cases, achieving acceptable performance at the user interface level often hinges on resource-intensive improvements such as domain tuning, which are better done once the overall design is relatively stable. Although WOZ is recognized as a valuable prototyping technique, surprisingly little effort has been put into exploring it from a methodological point of view. Starting from a survey of the literature, this paper presents a systematic investigation and analysis of the design space for WOZ for language technology applications, and proposes a generic architecture for tool support that supports the integration of components for speech recognition and synthesis as well as for machine translation. This architecture is instantiated in WebWOZ—a new web-based open-source WOZ prototyping platform. The viability of generic support is explored empirically through a series of evaluations. Researchers from a variety of backgrounds were able to create experiments, independent of their previous experience with WOZ. The approach was further validated through a number of real experiments, which also helped to identify a number of possibilities for additional support, and flagged potential issues relating to consistency in wizard performance. Abstract 2014 Oxford University Press

Link: http://www.thinkwithgoogle.com/insights/library/studies/the-new-multi-screen-world-study/

This paper studies how business models can be designed to tap effectively into open innovation labor markets with heterogeneously motivated workers. Using data on open source software, we show that motivations are diverse, and demonstrate how managers can strategically influence the flow of code contributions and their impact on project performance. Unlike previous literature using survey data, we exploit the observed pattern of project membership and code contributions—the “revealed preference” of developers—to infer the motivations driving their decision to contribute. Developers strongly sort along key dimensions of the business model chosen by project managers, especially the degree of openness of the project license. The results indicate an important role for intrinsic motivation, reputation, and labor market signaling, and a more limited role for reciprocity. Abstract 2014 John Wiley & Sons, Ltd.

updated on 5/13

Title: Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches

Abstract The term user experience (UX) encompasses the concepts of usability and affective engineering. However, UX has not been defined clearly. In this study, a literature survey, user interview and indirect observation were conducted to develop definitions of UX and its elements. A literature survey investigated 127 articles that were considered to be helpful to define the concept of UX. An in-depth interview targeted 14 hands-on workers in the Korean mobile phone industry. An indirect observation captured daily experiences of eight end-users with mobile phones. This study collected various views on UX from academia, industry, and end-users using these three approaches. As a result, this article proposes definitions of UX and its elements: usability, affect, and user value. These results are expected to help design products or services with greater levels of UX. Abstract Copyright 2011 Wiley Periodicals, Inc.

Title: Why different people prefer different systems for different tasks: An activity perspective on technology adoption in a dynamic user environment

Abstract In a contemporary user environment, there are often multiple information systems available for a certain type of task. Based on the premises of Activity Theory, this study examines how user characteristics, system experiences, and task situations influence an individual’s preferences among different systems in terms of user readiness to interact with each. It hypothesizes that system experiences directly shape specific user readiness at the within-subject level, user characteristics and task situations make differences in general user readiness at the between-subject level, and task situations also affect specific user readiness through the mediation of system experiences. An empirical study was conducted, and the results supported the hypothesized relationships. The findings provide insights on how to enhance technology adoption by tailoring system development and management to various task contexts and different user groups. Abstract Copyright 2011 ASIS&T

Title: A review of factors influencing user satisfaction in information retrieval

Abstract The authors investigate factors influencing user satisfaction in information retrieval. It is evident from this study that user satisfaction is a subjective variable, which can be influenced by several factors such as system effectiveness, user effectiveness, user effort, and user characteristics and expectations. Therefore, information retrieval evaluators should consider all these factors in obtaining user satisfaction and in using it as a criterion of system effectiveness. Previous studies have conflicting conclusions on the relationship between user satisfaction and system effectiveness; this study has substantiated these findings and supports using user satisfaction as a criterion of system effectiveness. Abstract Copyright 2010 ASIS&T

Title: The development and evaluation of a survey to measure user engagement

Abstract Facilitating engaging user experiences is essential in the design of interactive systems. To accomplish this, it is necessary to understand the composition of this construct and how to evaluate it. Building on previous work that posited a theory of engagement and identified a core set of attributes that operationalized this construct, we constructed and evaluated a multidimensional scale to measure user engagement. In this paper we describe the development of the scale, as well as two large-scale studies (N=440 and N=802) that were undertaken to assess its reliability and validity in online shopping environments. In the first we used Reliability Analysis and Exploratory Factor Analysis to identify six attributes of engagement: Perceived Usability, Aesthetics, Focused Attention, Felt Involvement, Novelty, and Endurability. In the second we tested the validity of and relationships among those attributes using Structural Equation Modeling. The result of this research is a multidimensional scale that may be used to test the engagement of software applications. In addition, findings indicate that attributes of engagement are highly intertwined, a complex interplay of user-system interaction variables. Notably, Perceived Usability played a mediating role in the relationship between Endurability and Novelty, Aesthetics, Felt Involvement, and Focused Attention. Abstract Copyright 2009 ASIS&T

Title: Exploring user engagement in online news interactions

Abstract This paper describes a qualitative study of online news reading and browsing. Thirty people participated in a quasi-experimental study in which they were asked to browse a news website and select three stories to discuss at a social gathering. Semi-structured interviews were conducted post-task to understand participants’ perceptions of what makes online news reading and browsing engaging or non-engaging. Findings as presented within the experience-based framework of user engagement and demonstrate the complexity of users’ interactions with information content and systems in online news environments. This study extends the model of user engagement and contributes new insights into user’s experience in casual-leisure settings, such as online news, which has implications for other information domains. Abstract Copyright 2011 by American Society for Information Science and Technology

Abstract This chapter of The Fabric of Mobile Services: Software Paradigms and Business Demands contains sections titled: New Services and User Experience, User-Centered Simplicity and Experience, Methodologies for Simplicity and User Experience, and Case Studies: Simplifying Paradigms Abstract Copyright 2009 John Wiley & Sons, Inc.

Title: The Right Angle: Visual Portrayal of Products Affects Observers’ Impressions of Owners

Abstract Consumer products have long been known to influence observers’ impressions of product owners. The angle at which products are visually portrayed in advertisements, however, may be an overlooked factor in these effects. We hypothesize and find that portrayals of the same product from different viewpoints can prime different associations that color impressions of product and owner in parallel ways. In Study 1, automobiles were rated higher on status- and power-related traits (e.g., dominant , powerful ) when portrayed head-on versus in side profile, an effect found for sport utility vehicles (SUVs)—a category with a reputation for dominance—but not sedans. In Study 2, these portrayal-based associations influenced the impressions formed about the product’s owner: a target person was rated higher on status- and power-related traits when his SUV was portrayed head-on versus in side profile. These results suggest that the influence of visual portrayal extends beyond general evaluations of products to affect more specific impressions of products and owners alike, and highlight that primed traits are likely to influence impressions when compatible with other knowledge about the target. Abstract Copyright 2012 Wiley Periodicals, Inc

Title: The Counterfeit Self: The Deceptive Costs of Faking It

Abstract Although people buy counterfeit products to signal positive traits, we show that wearing counterfeit products makes individuals feel less authentic and increases their likelihood of both behaving dishonestly and judging others as unethical. In four experiments, participants wore purportedly fake or authentically branded sunglasses. Those wearing fake sunglasses cheated more across multiple tasks than did participants wearing authentic sunglasses, both when they believed they had a preference for counterfeits (Experiment 1a) and when they were randomly assigned to wear them (Experiment 1b). Experiment 2 shows that the effects of wearing counterfeit sunglasses extend beyond the self, influencing judgments of other people’s unethical behavior. Experiment 3 demonstrates that the feelings of inauthenticity that wearing fake products engenders—what we term the counterfeit selfmediate the impact of counterfeits on unethical behavior. Finally, we show that people do not predict the impact of counterfeits on ethicality; thus, the costs of counterfeits are deceptive. Abstract Copyright 2010 Francesca Gino, Michael I. Norton, and Dan Ariely3

Link: http://iwc.oxfordjournals.org/content/26/5/389.full.html?etoc

Menus are a key mechanism for organizing different commands in graphical user interfaces. Nowadays low-cost devices that allow using different interaction techniques in remote interfaces have become widespread. Nevertheless, their corresponding menus are direct adaptations from traditional ones. As a consequence, they are inaccurate and slow, and also produce tiredness. In this paper, we design, implement and evaluate a menu selection technique for remote interfaces, the Body Menu. This technique permits whole-body interaction and is specifically designed to take advantage of the proprioception sense. The Body Menu attaches virtual menu items to different parts of the body and selects them when the users reach these zones with their hands. We use the Microsoft Kinect to implement this system. Additionally, we compared it with the most representative menus, studied the best number of body parts to be used and analyzed how children interact with it. Abstract © 2013 Oxford University Publishing.

Link: http://iwc.oxfordjournals.org/content/26/5/403.full.html?etoc

We present the evaluation of an interactive audio map system that enables blind and partially sighted users to explore and navigate city maps from the safety of their home using simulated 3D audio and synthetic speech alone. We begin with a review of existing literature in the areas of spatial knowledge and wayfinding, auditory displays and auditory map systems, before describing how this research builds on and differentiates itself from this body of work. One key requirement was the ability to quantify the effectiveness of the audio map, so we describe the design and implementation of the evaluation, which took the form of a game downloaded by participants to their own computers. The results demonstrate that participants (blind, partially sighted and sighted) have acquired detailed spatial knowledge and also that the availability of positional audio cues significantly improves wayfinding performance. Abstract © 2013 Oxford University Publishing.

Link: http://iwc.oxfordjournals.org/content/26/5/417.full.html?etoc

Delegation is the practice of sharing authority with another individual to enable them to complete a specific task as a proxy. Practices to permit delegation can range from formal to informal arrangements and can involve spontaneous yet finely balanced notions of trust between people. This paper argues that delegation is a ubiquitous yet an unsupported feature of socio-technical computer systems and that this lack of support illustrates a particular neglect to the everyday financial practices of the more vulnerable people in society. Our contribution is to provide a first exploration of the domain of person-to-person delegation in digital payments, a particularly pressing context. We first report qualitative data collected across several studies concerning banking practices of individuals over 80 years of age. We then use analytical techniques centred upon identification of stakeholders, their concerns and interactions, to characterize the delegation practices we observed. We propose a Concerns Matrix as a suitable representation to capture conflicts in the needs of individuals in such complex socio-technical systems, and finally propose a putative design response in the form of a Helper Card. Abstract © 2013 Oxford University Publishing..

Link: Why We Love Beautiful Things

Great design, the management expert Gary Hamel once said, is like Justice Potter Stewart’s famous definition of pornography — you know it when you see it. You want it, too: brain scan studies reveal that the sight of an attractive product can trigger the part of the motor cerebellum that governs hand movement. Instinctively, we reach out for attractive things; beauty literally moves us. © 2013 New York Times

Link: http://www.bris.ac.uk/news/2013/9478.html

A new study has analysed tens of thousands of articles available to readers of online news and created a model to find out ‘what makes people click’. The aim of the study was to model the reading preferences for the audiences of 14 online news outlets using machine learning techniques. The models, describing the appeal of an article to each audience, were developed by linear functions of word frequencies. The models compared articles that became “most popular” on a given day in a given outlet with articles that did not. The research dentified the most attractive keywords, as well as the least attractive ones, and explained the choices readers made. Abstract © 2013 University of Bristol.

Title: Pointing and Selecting with Facial Activity

Abstract The aim of this paper was to evaluate the use of three facial actions (i.e. frowning, raising the eyebrows, and smiling) in selecting objects on a computer screen when gaze was used for pointing. Dwell time is the most commonly used selection technique in gaze-based interaction, and thus, a dwell time of 400 ms was used as a reference selection technique. A wireless, head-mounted prototype device that carried out eye tracking and contactless, capacitive measurement of facial actions was used for the interaction task. Participants (N=16) performed point-and-select tasks with three pointing distances (i.e. 60, 120 and 240 mm) and three target sizes (i.e. 25, 30 and 40 mm). Task completion times, pointing errors and throughput values based on Fitts’ law were used to compare the selection techniques. The participants also rated the techniques with subjective ratings scales. The results showed that the different techniques performed equally well in many respects. However, throughput values varied from 8.38 bits/s (raising the eyebrows) to 15.33 bits/s (smiling) and were comparable to or, in the case of smiling, better than in earlier research with similar interaction techniques. The dwell time was found to be the least accurate selection technique in terms of the magnitudes of point-and-select errors. Smiling technique was rated as more accurate to use than the frowning or the raising techniques. The results give further support for methods that combine facial behavior to eye tracking when interacting with technology.

Abstract Copyright 2014 Outi Tuisku1, Ville Rantanen, Oleg Špakov, Veikko Surakka and Jukka Lekkala

Title: Modeling Traditional Literacy, Internet Skills and Internet Usage: An Empirical Study

Abstract This paper focuses on the relationships among traditional literacy (reading, writing and understanding text), medium-related Internet skills (consisting of operational and formal skills), content-related Internet skills (consisting of information and strategic skills) and Internet usage types (information- and career-directed Internet use and entertainment use). We conducted a large-scale survey that resulted in a dataset of 1008 respondents. The results reveal the following: (i) traditional literacy has a direct effect on formal and information Internet skills and an indirect effect on strategic Internet skills and (ii) differences in types of Internet usage are indirectly determined by traditional literacy and directly affected by Internet skills, such that higher levels of strategic Internet skills result in more information- and career-directed Internet use. Traditional literacy is a pre-condition for the employment of Internet skills, and Internet skills should not be considered an easy means of disrupting historically grounded inequalities caused by differences in traditional literacy.

Abstract Copyright 2014 A.J.A.M. van Deursen and J.A.G.M. van Dijk

Title: Life Is Too Short to RTFM: How Users Relate to Documentation and Excess Features in Consumer Products

Abstract This paper addresses two common problems that users of various products and interfaces encounter—over-featured interfaces and product documentation. Over-featured interfaces are seen as a problem as they can confuse and over-complicate everyday interactions. Researchers also often claim that users do not read product documentation, although they are often exhorted to ‘RTFM’ (read the field manual). We conducted two sets of studies with users which looked at the issues of both manuals and excess features with common domestic and personal products. The quantitative set was a series of questionnaires administered to 170 people over 7 years. The qualitative set consisted of two 6-month longitudinal studies based on diaries and interviews with a total of 15 participants. We found that manuals are not read by the majority of people, and most do not use all the features of the products that they own and use regularly. Men are more likely to do both than women, and younger people are less likely to use manuals than middle-aged and older ones. More educated people are also less likely to read manuals. Over-featuring and being forced to consult manuals also appears to cause negative emotional experiences. Implications of these findings are discussed.

Abstract Copyright 2014 Alethea L. Blackler, Rafael Gomez, Vesna Popovic and M. Helen Thompson

Title: Effect of Age on Human–Computer Interface Control Via Neck Electromyography

Abstract The purpose of this study was to determine the effect of age on visuomotor tracking using submental and anterior neck surface electromyography (sEMG) to assess feasibility of computer control via neck musculature, which allows people with little remaining motor function to interact with computers. Thirty-two healthy adults participated: 16 younger adults aged 18–29 years and 16 older adults aged 69–85 years. Participants modulated sEMG to achieve targets presented at different amplitudes using real-time visual feedback. Root mean squared (RMS) error was used to quantify tracking performance. RMS error was increased for older adults relative to younger adults. Older adults demonstrated more RMS error than younger adults as a function of increasing target amplitude. The differential effects of age found on static tracking performance in anterior neck musculature suggest more difficult translation of human–computer interfaces controlled using anterior neck musculature for static tasks to older populations.

Abstract Copyright 2014 Gabrielle L. Hands and Cara E. Stepp

Title: Should I Stay or Should I Go? Improving Event Recommendation in the Social Web

Abstract This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users’ interests and should thereby be taken into account in recommendation. We consider, in particular, three ‘additional’ features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community and the participation of the user’s friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user’s interest. We approached the problem by carrying out two surveys with users, who were asked to express their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of ∼10:4.

Abstract Copyright 2014 Federica Cena, Silvia Likavec, Ilaria Lombardi and Claudia Picardi

Title: “I Need to Be Explicit: You’re Wrong”: Impact of Face Threats on Social Evaluations in Online Instructional Communication

Abstract Online instructional communication, as found in ask-an-expert forums, e-learning discussion boards or online help desks, creates situations that threaten the recipient’s face. This study analyzed the evaluation of face-threatening acts with a 1×3 design. An online forum thread confronted a layperson with an expert who either (a) addressed the layperson’s misconceptions directly and frankly, (b) mitigated face threats through explicit hints about the need to be direct or (c) communicated politely and indirectly. College students read these dialogues and assessed the expert communicator’s facework, recipient orientation, credibility and likability. Results showed that polite experts were evaluated most positively; explicit hints did not improve perceptions of face-threatening acts. This implies that users of instructional forums prefer communicators to be polite even when face threats are necessary. We discuss practical implications for different online instruction contexts and make suggestions for further research.

Abstract Copyright 2014 Regina Jucks, Lena Päuler and Benjamin Brummernhenrich

Title: The Potential of a Text-Based Interface as a Design Medium: An Experiment in a Computer Animation Environment

Abstract Since the birth of the concept of direct manipulation, the graphical user interface has been the dominant means of controlling digital objects. In this research, we hypothesize that the benefits of a text-based interface involve multiple tradeoffs, and we explore the potential of text as a medium of design from three perspectives: (i) the perceived level of control of the designed object, (ii) a tool for realizing creative ideas and (iii) an effective form for a highly learnable user interface. Our experiment in a computer animation environment shows that (i) participants did feel a high level of control of characters, (ii) creativity was both restricted and facilitated depending on the task and (iii) natural language expedited the learning of a new interface language. Our research provides experimental proof of the effect of a text-based interface and offers guidelines for the design of future computer-aided design applications.

Abstract Copyright 2014 Sangwon Lee and Jin Yan

Title: Framing a Set: Understanding the Curatorial Character of Personal Digital Bibliographies

Abstract We articulate a model of curatorship that emphasizes framing the character of the curated set as the focus of curatorial activity. This curatorial character is structured through the articulation, via mechanisms of selection, description and arrangement, of coherent classificatory principles. We describe the latest stage of a continuing project to examine the curatorial character of personal digital bibliographies, such as Pinterest boards, Flickr galleries and GoodReads shelves, and to support the design of such curatorially expressive personal collections. In the study reported here, 24 participants created personal bibliographies using either a structured design process, with explicit tasks for selecting, describing and arranging collection items, or an unstructured process that did not separate these activities. Our findings lead to a more complex understanding of personal collections as curatorial, expressive artifacts. We explore the role of cohesion as a quality that facilitates expression of the curatorial frame, and we find that when designers read source materials as a part of a set, they are more likely to write cohesive collections. Our findings also suggest that the curatorial act involves both the definition of abstract classificatory principles and their instantiation in a specific material environment. We describe various framing devices that facilitate these reading and writing activities, and we suggest design directions for supporting curatorial reading and writing tasks.

Abstract Copyright 2014 Melanie Feinberg, Ramona Broussard and Eryn Whitworth

Title: Identifying Problems Associated with Focus and Context Awareness in 3D Modelling Tasks

Abstract Creating complex 3D models is a challenging process. One of the main reasons for this is that 3D models are usually created using software developed for conventional 2D displays which lack true depth perspective, and therefore do not support correct perception of spatial placement and depth-ordering of displayed content. As a result, modellers often have to deal with many overlapping components of 3D models (e.g. vertices, edges, faces, etc.) on a 2D display surface. This in turn causes them to have difficulties in distinguishing distances, maintaining position and orientation awareness, etc. To better understand the nature of these problems, which can collectively be defined as ‘focus and context awareness’ problems, we have conducted a pilot study with a group of novice 3D modellers, and a series of interviews with a group of professional 3D modellers. This article presents these two studies, and their findings, which have resulted in identifying a set of focus and context awareness problems that modellers face in creating 3D models using conventional modelling software. The article also provides a review of potential solutions to these problems in the related literature.

Abstract Copyright 2014 Masood Masoodian, Azmi bin Mohd Yusof and Bill Rogers

Abstract The goal of user experience design in industry is to improve customer satisfaction and loyalty through the utility, ease of use, and pleasure provided in the interaction with a product. So far, user experience studies have mostly focused on short-term evaluations and consequently on aspects relating to the initial adoption of new product designs. Nevertheless, the relationship between the user and the product evolves over long periods of time and the relevance of prolonged use for market success has been recently highlighted. In this paper, we argue for the cost-effective elicitation of longitudinal user experience data. We propose a method called the “UX Curve” which aims at assisting users in retrospectively reporting how and why their experience with a product has changed over time. The usefulness of the UX Curve method was assessed in a qualitative study with 20 mobile phone users. In particular, we investigated how users’ specific memories of their experiences with their mobile phones guide their behavior and their willingness to recommend the product to others. The results suggest that the UX Curve method enables users and researchers to determine the quality of long-term user experience and the influences that improve user experience over time or cause it to deteriorate. The method provided rich qualitative data and we found that an improving trend of perceived attractiveness of mobile phones was related to user satisfaction and willingness to recommend their phone to friends. This highlights that sustaining perceived attractiveness can be a differentiating factor in the user acceptance of personal interactive products such as mobile phones. The study suggests that the proposed method can be used as a straightforward tool for understanding the reasons why user experience improves or worsens in long-term product use and how these reasons relate to customer loyalty.

Abstract Copyright 2011 Sari Kujalaa, Virpi Rotob, Kaisa Väänänen-Vainio-Mattilaa, Evangelos Karapanosc and Arto Sinneläa

Title: Researching Young Children’s Everyday Uses of Technology in the Family Home

Abstract Studies of the everyday uses of technology in family homes have tended to overlook the role of children and, in particular, young children. A study that was framed by an ecocultural approach focusing on children’s play and learning with toys and technologies is used to illustrate some of the methodological challenges of conducting research with young children in the home. This theoretical framework enabled us to identify and develop a range of methods that illuminated the home’s unique mix of inhabitants, learning opportunities and resources and to investigate parents’ ethnotheories, or cultural beliefs, that gave rise to the complex of practices, values and attitudes and their intersections with technology and support for learning in the home. This resulted in a better understanding of the role of technology in the lives of these 3- and 4-year-old children.

Abstract Copyright 2014 Lydia Plowman

Title: Measuring web usability using item response theory: Principles, features and opportunities

Abstract Usability is considered a critical issue on the web that determines either the success or the failure of a company. Thus, the evaluation of usability has gained substantial attention. However, most current tools for usability evaluation have some limitations, such as excessive generality and a lack of reliability and validity. The present work proposes the construction of a tool to measure usability in e-commerce websites using item response theory (IRT). While usability issues have only been considered in theoretical or empirical contexts, in this study, we discuss them from a mathematical point of view using IRT. In particular, we develop a standardised scale to measure usability in e-commerce websites. This study opens a new field of research in the ergonomics of interfaces with respect to the development of scales using IRT.

Abstract Copyright 2011 Rafael Tezzaa, Antonio Cezar Borniaa and Dalton Francisco de Andrade

Title: Everything Science Knows Right Now About Standing Desks

Abstract If it wasn’t already clear through common sense, it’s become painfully clear through science that sitting all day is terrible for your health. What’s especially alarming about this evidence is that extra physical activity doesn’t seem to offset the costs of what researchers call “prolonged sedentary time.” Just as jogging and tomato juice don’t make up for a night of smoking and drinking, a little evening exercise doesn’t erase the physical damage done by a full work day at your desk.

In response some people have turned to active desks—be it a standing workspace or even a treadmill desk—but the research on this recent trend has been too scattered to draw clear conclusions on its benefits (and potential drawbacks). At least until now. A trio of Canada-based researchers has analyzed the strongest 23 active desk studies to draw some conclusions on how standing and treadmill desks impact both physiological health and psychological performance. Abstract Copyright 2015 Eric Jaffe

Send Us Your Research References: If you have interesting and relevant research references post, post content as comment below for possible inclusion in next year’s updated list.

Other Content from PulseUX: Here are 2 other references from widely read and quoted long-form posts you may find interesting.

Angry Birds UX: Why Angry Birds is so successful and popular: a cognitive teardown of the user experience (1.5 million page views). https://live-mauro-usability-science.pantheonsite.io/blog/why-angry-birds-is-so-successful-a-cognitive-teardown-of-the-user-experience/

Apple v. Samsung: Impact and Implications for Product Design, User Interface Design (UX), Software Development and the Future of High-Technology Consumer Products https://live-mauro-usability-science.pantheonsite.io/blog/apple-v-samsung-implications-for-product-design-user-interface-ux-design-software-development-and-the-future-of-high-technology-consumer-products/

Charles L Mauro CHFP President / Founder MauroNewMedia

Find out more about Charles L Mauro Find out more about MauroNewMedia Follow Pulse>UX on Twitter @PulseUX

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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What is Research Design? Elements, Types, Examples

Appinio Research · 06.09.2023 · 24min read

Ever wondered what lays the foundation for successful research studies? It all starts with a well-crafted research design. In the world of inquiry, research design is the guiding compass that shapes the entire process, helping you navigate complexities and unlock the doors to meaningful insights. Whether you're embarking on your first research journey or seeking to refine your skills, understanding the art and science of research design is the key to unlocking the true potential of your investigations.

What is Research Design?

Research design is like the roadmap for your research journey. Imagine planning a cross-country trip: you wouldn't hit the road without a clear route, right? Similarly, research design provides the structure and strategy you need to navigate your way through the complexities of a study.

It's the blueprint that outlines the steps you'll take, the methods you'll use, and the goals you aim to achieve.

At its core, research design is all about making smart decisions. It's about choosing the best tools to answer your questions and gather information. Whether you're exploring the effects of a new drug, understanding the habits of a specific demographic, or investigating the behaviors of animals, a well-designed research plan sets the stage for success.

In a nutshell, research design is your guide, helping you collect data, draw conclusions, and make meaningful contributions to your field.

Why is Research Design Important in the Research Process?

Research design plays a crucial role in ensuring the success of your research study. A well-designed research plan:

• Provides structure and direction to your study.
• Helps in clearly defining research objectives and questions.
• Guides the choice of appropriate methodologies and data collection methods .
• Ensures that ethical considerations are addressed.
• Enhances the validity and reliability of your findings.

How Research Design Affects Study Outcomes

Your research design has a direct impact on the outcomes of your study. A well-crafted research plan:

• Increases the likelihood of obtaining accurate and reliable results.
• Enables you to draw valid conclusions and make meaningful interpretations.
• Enhances the credibility and generalizability of your findings.
• Guides the implementation of research procedures in a consistent and organized manner.

Key Elements of a Research Study

A well-designed research study is like a puzzle where every piece fits perfectly to reveal a clear picture. These fundamental elements ensure that your research is structured, meaningful, and capable of generating credible insights.

Clear Research Objectives

Think of research objectives as your guiding stars. They define what you aim to achieve with your study. Clear goals keep you on track, guiding your research questions, methods, and analysis.

Precise Research Questions and Hypotheses

Research questions and hypotheses are the compass that points you in the right direction. They provide focus by outlining what you want to explore and predict. Well-crafted questions and hypotheses make your study purposeful and relevant.

Appropriate Methodology Selection

Choosing a suitable methodology is like selecting the best tool for the job. Quantitative methods are your go-to for measurable data, while qualitative methods help you dive deep into complex human experiences. Mixed methods offer the best of both worlds.

Thoughtful Participant Selection

Selecting the right participants is like assembling a diverse team for a project. Your sample should represent the population you're studying. Choose appropriate sampling techniques and determine the sample size that strikes the right balance between accuracy and feasibility.

Effective Data Collection Strategies

Data collection is like gathering puzzle pieces. Choose methods that align with your research goals. Surveys, interviews, observations, and experiments are just a few of the tools at your disposal.

Reliable Research Instrument Development

Research instruments are your tools for collecting data. Whether it's a questionnaire or an interview guide, they need to be well-constructed, unbiased, and capable of capturing the information you need.

Thoughtful Research Procedure Design

Your research procedure is the timeline that ensures everything happens in the proper order. From recruiting participants to data analysis, a well-structured procedure keeps your study organized and efficient.

Rigorous Data Analysis and Interpretation

Data analysis is where you piece the puzzle together. Applying the right techniques to your data—whether quantitative or qualitative —reveals patterns, relationships, and insights that answer your research questions.

Validity and Reliability Considerations

Validity and reliability are the quality checks of your study. Validity ensures that your measurements are accurate, while reliability guarantees consistency. Addressing these ensures your findings hold true and can be trusted.

Ethical Considerations

Ethical considerations are the foundation of responsible research. Protect participants' rights, ensure their consent, and follow ethical guidelines to conduct your study with integrity.

A well-designed research study brings all these elements together harmoniously, resulting in a comprehensive, credible, and impactful exploration of your chosen research topic.

Types of Research Design

Research design comes in various flavors, each tailored to answer different types of questions and explore diverse aspects of your research topic. Let's dive into the main types of research designs to help you choose the one that aligns with your objectives.

Quantitative Research Designs

Quantitative research is all about numbers and measurements. If you're interested in uncovering patterns, relationships, and trends through numerical data, these designs are your go-to options:

• Experimental Design: This design allows you to manipulate variables to establish cause-and-effect relationships. Think of it as a controlled experiment where you change one thing to see how it impacts another.
• Survey Research: Surveys are your ticket to collecting a lot of data from a wide range of people. Structured questionnaires help gather standardized responses, making it easy to analyze patterns.
• Longitudinal Studies : Imagine tracking a group of people over years to see how they change. Longitudinal studies dive deep into understanding development, behaviors, or changes within a specific group.

Qualitative Research Designs

Qualitative research focuses on understanding the complexities of human experiences, behaviors, and contexts. If you're intrigued by narratives and in-depth insights, consider these designs:

• Case Study: Dive deep into a single subject, exploring it from every angle. It's like zooming in on a single puzzle piece to understand its intricate details.
• Ethnographic Study : If you want to immerse yourself in a culture or community, ethnography is your tool. Live among the people you're studying to grasp their worldviews and practices.
• Grounded Theory: This design is all about building theories from scratch based on the data you collect. It's like letting the information guide you toward new insights and concepts.

Mixed Methods Research

Sometimes, one approach just isn't enough. Mixed methods research combines both quantitative and qualitative methods to give you a comprehensive view of your research topic. It's like using wide-angle and macro lenses together to capture the big picture and the tiny details.

Each research design has its strengths and shines in different situations. The type you choose will depend on your research questions, goals, and the kind of insights you aim to uncover.

How to Define Research Objectives and Questions?

At the heart of every research study are clear and focused objectives, along with well-crafted research questions or hypotheses. We'll dive into the process of formulating these crucial components, ensuring that your study remains on track and purposeful.

1. Formulate Clear Research Objectives

Research objectives outline the specific goals you aim to achieve through your study. Clear and concise (SMART) objectives provide direction and purpose to your research. Here's how to formulate well-crafted research objectives:

• Be Specific: Clearly state what you intend to accomplish.
• Be Measurable: Define outcomes that can be quantified or observed.
• Be Achievable: Set realistic goals within the scope of your study.
• Be Relevant: Ensure that your objectives align with the research problem.
• Be Time-Bound: Specify a timeframe for achieving your objectives.

2. Develop Research Questions and Hypotheses

Research questions and hypotheses guide your study and direct your research efforts. They should be focused, relevant, and provide a clear framework for investigation.

• Research Questions: These are open-ended queries that help you explore a particular topic. They often start with words like "what," "how," or "why." For example: "What are the factors that influence consumer purchasing decisions?"
• Hypotheses: Hypotheses are statements that propose a specific relationship between variables. They are testable predictions about the outcomes of your study. For example: "Increasing the price of a product will result in decreased sales."

3. Ensure Alignment Between Objectives and Questions

It's essential to ensure that your research objectives and questions are well-aligned. Your research questions should directly address your objectives, helping you fulfill the purpose of your study.

By formulating clear research objectives and crafting well-structured questions or hypotheses, you'll establish a strong foundation for your research study.

How to Select Research Participants?

The participants in your research study form the foundation upon which your findings rest. Proper participant selection is crucial for obtaining relevant and reliable data.

Sampling Techniques

Sampling involves selecting a subset of individuals from a larger pool to represent the whole. The choice of sampling technique depends on the research goals and the nature of the population.

• Probability Sampling: Probability sampling ensures that each member of the population has an equal chance of being selected. Common methods include simple random sampling, stratified sampling, and cluster sampling .
• Non-Probability Sampling: Non-probability sampling methods do not guarantee equal representation. These methods include convenience sampling, purposive sampling, and snowball sampling.

Sample Size Determination

Determining the appropriate sample size  is essential to ensure the reliability of your findings. An inadequate sample size might lead to biased results, while an excessively large sample might be wasteful.

Ethical Considerations in Participant Selection

Respecting the rights and well-being of your participants is paramount. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and minimizing potential harm.

By selecting the right participants and adhering to ethical guidelines, you'll lay the groundwork for collecting meaningful and trustworthy data.

Research Data Collection Strategies

Collecting data is a fundamental step in the research process. The strategies you choose for data collection directly influence the quality and validity of your findings.

Quantitative Data Collection

Quantitative data collection involves gathering numerical information that can be analyzed statistically. Here are some common strategies:

• Surveys and Questionnaires: Surveys and questionnaires allow you to collect standardized responses from a large number of participants. They are useful for obtaining quantitative data on attitudes, preferences, and behaviors.
• Experiments: Experimental design involves manipulating variables to observe their effects. Controlled experiments provide insights into causal relationships, and random assignment helps minimize bias.
• Observations and Secondary Data Analysis: Direct observations of subjects or behaviors can provide valuable data. Additionally, analyzing existing datasets (secondary data) can save time and resources.

Qualitative Data Collection

Qualitative data collection focuses on capturing rich, context-specific information. Here are some effective methods:

• Interviews: Interviews involve direct interaction with participants to gather in-depth insights. Types include structured, semi-structured, and unstructured interviews, each offering a different level of flexibility.
• Focus Groups : Focus groups bring together a small group of participants to discuss a specific topic. This method encourages open discussions and the exploration of diverse perspectives.
• Participant Observation: Participant observation involves immersing yourself in the research setting to understand behaviors, interactions, and dynamics. It's particularly beneficial in ethnographic studies.

Data Validity and Reliability Across Methods

Ensuring the validity and reliability of collected data is crucial for drawing accurate conclusions. Validity refers to the accuracy of measurements, while reliability is the consistency of results. Across quantitative and qualitative methods, these principles apply:

• Quantitative: Ensure survey questions are straightforward, and measures are accurate and consistent.
• Qualitative: Maintain consistency in data collection procedures, and use techniques like member checking and triangulation to enhance validity.

Enhance your data collection strategies with the power of modern research technology. Appinio offers a comprehensive platform designed to streamline both quantitative and qualitative data collection. With user-friendly survey and questionnaire tools and in-depth interview capabilities, Appinio empowers researchers to gather high-quality data effortlessly.

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How to Develop Research Instruments?

Research instruments, such as surveys, interview protocols, and observation guides, are tools that help you collect data from participants. Developing effective instruments requires careful planning and attention to detail.

How to Construct Survey Instruments?

Surveys are a standard method for collecting data from many participants. To construct an effective survey instrument:

• Define Your Variables: Clearly define the variables you're measuring and ensure they align with your research questions.
• Use Clear Language: Write clear and concise questions using simple language to avoid confusion.
• Avoid Bias: Avoid leading or biased questions that could influence participant responses.
• Include Validity Checks: Incorporate validation questions to ensure respondents are providing accurate information.

How to Create Interview Protocols?

Interviews offer an opportunity to gather in-depth insights directly from participants. To create effective interview protocols:

• Structure Questions: Organize questions logically and flow from general to specific topics.
• Open-Ended Questions: Include open-ended questions encouraging participants to share their thoughts and experiences.
• Probing Questions: Develop probing questions to dig deeper into participant responses and gain deeper insights.

Pre-testing and Piloting Research Instruments

Before launching your research, pre-test or pilot your instruments with a small group of participants. This helps identify issues with clarity, wording, or question order and allows you to refine the instruments for maximum effectiveness.

By investing time in constructing well-designed research instruments, you'll collect accurate and relevant data that contribute to the success of your study.

How to Design the Research Procedure?

The research procedure outlines the step-by-step plan for conducting your study. A well-designed procedure ensures consistency, reliability, and efficiency in data collection.

To design an effective research procedure:

1. Sequence Research Activities

Sequencing research activities involves arranging the order in which different tasks will be carried out. Consider the following when creating your sequence:

• Logical Flow: Ensure that activities are organized in a logical order, from participant recruitment to data analysis.
• Dependencies: Identify tasks that depend on the completion of others and plan accordingly.
• Flexibility: Allow for some flexibility to accommodate unexpected challenges or opportunities.

2. Establish a Data Collection Timeline

Creating a timeline for your research helps you stay on track and manage your resources efficiently. Consider the following when establishing your timeline:

• Breakdown of Tasks: Divide the research process into manageable tasks and allocate time for each.
• Realistic Deadlines: Set realistic deadlines that consider the complexity of each task and potential delays.
• Buffer Periods: Include buffer periods to account for unforeseen delays or revisions.

3. Ensure Consistency in Data Collection Procedures

Consistency is crucial in obtaining reliable and valid data. Establish standardized procedures for data collection:

• Training: Train researchers involved in data collection to follow consistent procedures and protocols.
• Detailed Instructions: Provide clear and detailed instructions for each data collection method.
• Monitoring: Regularly monitor data collection to ensure adherence to procedures and address any issues.

By designing a well-structured research procedure, you'll ensure that your study progresses smoothly, data is collected consistently, and timelines are met. The next step is moving on to the crucial phase of data analysis and interpretation.

Research Data Analysis and Interpretation

Data analysis is the process of transforming raw data into meaningful insights. It's where you draw conclusions and make sense of the information you collected.

Quantitative Data Analysis Techniques

Quantitative data analysis involves processing numerical data to identify patterns and relationships. Here are some common techniques:

• Descriptive Statistics : Descriptive statistics, such as mean, median, and standard deviation, summarize and describe the main features of a dataset.
• Inferential Statistics : Inferential statistics help you draw conclusions about a population based on a sample. Techniques include t-tests , ANOVA , and regression analysis.
• Regression Analysis : Regression analysis helps you understand the relationships between variables and predict outcomes. Linear and logistic regressions are widely used.

Chi-Square Calculator :

t-Test Calculator :

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Qualitative Data Analysis Approaches

Qualitative data analysis involves interpreting non-numerical data to uncover themes and patterns. Here are some common approaches:

• Thematic Analysis : Thematic analysis involves identifying recurring themes or patterns in qualitative data. It helps you discover meaningful insights and concepts.
• Content Analysis: Content analysis is used to systematically analyze textual or visual content to identify specific patterns, themes, or trends.
• Constant Comparative Method: The constant comparative method involves comparing data points throughout the analysis to uncover patterns and relationships.

Validity and Reliability in Data Analysis

Ensuring the validity and reliability of your data analysis is essential for producing accurate findings:

• Triangulation: Use multiple data sources, methods, or analysts to validate your findings.
• Member Checking: Share your findings with participants to confirm that your interpretations align with their experiences.

By carefully analyzing and interpreting your data, you'll uncover insights that address your research questions and contribute to the overall understanding of your topic.

Validity and Reliability in Research Design

Validity and reliability are essential concepts in research design that ensure the credibility and trustworthiness of your study. In this section, we'll delve into these concepts and explore how they impact the quality of your research.

Internal Validity: Controlling for Confounding Variables

Internal validity refers to the degree to which your study accurately measures the cause-and-effect relationship you intend to study without interference from extraneous variables. To enhance internal validity:

• Control Groups : Use control groups in experimental designs to compare the effects of variables.
• Randomization: Randomly assign participants to groups to ensure unbiased distribution of characteristics.
• Eliminate Confounding Variables: Identify and control for factors that could influence your results but are not part of your research question.

External Validity: Generalizability of Findings

External validity refers to the extent to which your findings can be generalized to a broader population or real-world settings. To enhance external validity:

• Random Sampling: Use random sampling to ensure that your sample is representative of the larger population.
• Ecological Validity: Design your study to mirror real-world situations as closely as possible.
• Replication: Replicate your study with different populations or settings to validate your findings.

How to Ensure Research Reliability and Reproducibility?

Reliability refers to the consistency and stability of your measurements over time and across different conditions. To ensure research reliability:

• Consistent Procedures: Use standardized procedures for data collection and analysis.
• Inter-Rater Reliability: Have multiple researchers analyze data independently to assess agreement.
• Test-Retest Reliability: Repeat measurements on the same subjects to evaluate consistency.

Ethical Considerations in Research Design

Ethical guidelines are a fundamental aspect of research design. Respecting the rights and well-being of participants is paramount. These include:

• Informed Consent: Obtain informed consent from participants, ensuring they understand the study's purpose, procedures, and risks.
• Confidentiality: Protect participant privacy by safeguarding their personal information.
• Institutional Review Board (IRB): Obtain ethical approval from an IRB before conducting research involving human participants.
• Minimizing Harm: Ensure participants are not subjected to unnecessary physical, emotional, or psychological harm.

By addressing these validity, reliability, and ethical considerations, you'll ensure that your research study is rigorous, credible, and contributes meaningfully to the field.

As you progress, it's crucial to communicate your findings effectively. Let's explore how to do that next.

How to Report and Present Research Findings?

Effectively reporting and presenting your research findings is essential for sharing your insights with the academic community and beyond.

1. Structure the Research Report

A well-structured research report communicates your study clearly and concisely. The typical structure includes:

• Title: A clear and informative title that captures the essence of your study.
• Abstract: A brief summary of your research question, methods, findings, and conclusions.
• Introduction: Introduce the research problem, objectives, and significance of the study.
• Literature Review: Review existing research and theories relevant to your topic.
• Methodology: Describe your research design, participants, data collection, and analysis methods.
• Results: Present your findings using tables, charts, and statistical analysis .
• Discussion: Interpret your results, relate them to existing literature, and address implications.
• Conclusion: Summarize your study, restate findings, and suggest future research directions.
• References: Cite sources you've referenced throughout the report.

2. Create Visual Representations of Data

Visual representations, such as graphs, charts, and tables, help convey complex information more easily. Use appropriate visuals to illustrate trends, patterns, and relationships in your data.

3. Write Clear and Compelling Research Summaries

In addition to your full research report, consider creating concise and engaging summaries that capture the essence of your study. These summaries help share findings with a broader audience, such as policymakers or the general public.

By effectively reporting and presenting your research findings, you contribute to disseminating knowledge and ensuring that your study's insights are accessible and impactful.

In conclusion, research design is like the blueprint of your investigation. It's the plan that makes sure everything fits together just right. By choosing the proper methods, asking the right questions, and following ethical guidelines, you're setting yourself up for success. Remember, research design isn't just for the experts—it's a powerful tool anyone can use to uncover knowledge and make informed decisions. So, whether you're analyzing economic trends or trying to understand your customers' preferences, a solid research design will guide you on your path to discovery.

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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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Researcher, Academic Writer, Web developer

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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Research Methodologies

Research design, external validity, internal validity, threats to validity.

• What are research methodologies?
• What are research methods?
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According to Jenkins-Smith, et al. (2017), a research design is the set of steps you take to collect and analyze your research data.  In other words, it is the general plan to answer your research topic or question.  You can also think of it as a combination of your research methodology and your research method.  Your research design should include the following: 

• A clear research question
• Theoretical frameworks you will use to analyze your data
• Key concepts
• Your hypothesis/hypotheses
• Independent and dependent variables (if applicable)
• Strengths and weaknesses of your chosen design

There are two types of research designs:

• Experimental design: This design is like a standard science lab experiment because the researcher controls as many variables as they can and assigns research subjects to groups.  The researcher manipulates the experimental treatment and gives it to one group.  The other group receives the unmanipulated treatment (or not treatment) and the researcher examines affect of the treatment in each group (dependent variable).  This design can have more than two groups depending on your study requirements.
• Observational design: This is when the researcher has no control over the independent variable and which research participants get exposed to it.  Depending on your research topic, this is the only design you can use.  This is a more natural approach to a study because you are not controlling the experimental treatment.  You are allowing the variable to occur on its own without your interference.  Weather experiments are a great example of observational design because the researcher has no control over the weather and how it changes.

When considering your research design, you will also need to consider your study's validity and any potential threats to its validity.  There are two types of validity: external and internal validity.  Each type demonstrates a degree of accuracy and thoughtfulness in a study and they contribute to a study's reliability.  Information about external and internal validity is included below.

External validity is the degree to which you can generalize the findings of your research study.  It is determining whether or not the findings are applicable to other settings (Jenkins-Smith, 2017).  In many cases, the external validity of a study is strongly linked to the sample population.  For example, if you studied a group of twenty-five year old male Americans, you could potentially generalize your findings to all twenty-five year old American males.  External validity is also the ability for someone else to replicate your study and achieve the same results (Jenkins-Smith, 2017).  If someone replicates your exact study and gets different results, then your study may have weak external validity.

Questions to ask when assessing external validity:

• Do my conclusions apply to other studies?
• If someone were to replicate my study, would they get the same results?
• Are my findings generalizable to a certain population?

Internal validity is when a researcher can conclude a causal relationship between their independent variable and their dependent variable.  It is a way to verify the study's findings because it draws a relationship between the variables (Jenkins-Smith, 2017).  In other words, it is the actual factors that result in the study's outcome (Singh, 2007).  According to Singh (2007), internal validity can be placed into 4 subcategories:

• Face validity: This confirms the fact that the measure accurately reflects the research question.
• Content validity: This assesses the measurement technique's compatibility with other literature on the topic.  It determines how well the tool used to gather data measures the item or concept that the researcher is interested in.
• Criterion validity: This demonstrates the accuracy of a study by comparing it to a similar study.
• Construct validity: This measures the appropriateness of the conclusions drawn from a study.

According to Jenkins-Smith (2017), there are several threats that may impact the internal and external validity of a study:

Threats to External Validity

• Interaction with testing: Any testing done before the actual experiment may decrease participants' sensitivity to the actual treatment.
• Sample misrepresentation: A population sample that is unrepresentative of the entire population.
• Selection bias: Researchers may have bias towards selecting certain subjects to participate in the study who may be more or less sensitive to the experimental treatment.
• Environment: If the study was conducted in a lab setting, the findings may not be able to transfer to a more natural setting.

Threats to Internal Validity

• Unplanned events that occur during the experiment that effect the results.
• Changes to the participants during the experiment, such as fatigue, aging, etc.
• Selection bias: When research subjects are not selected randomly.
• If participants drop out of the study without completing it.
• Changing the way the data is collected or measured during the study.
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117 Awesome Fashion Research Topics: Inspirational Ideas List

Finding some decent fashion research topics that you can use for your next papers is not easy nowadays. You want something new, something original. Your classmates are probably scouring the Internet as we speak, so why are you still wasting time? Take a look at our long list of 117 exceptional fashion research topics and choose the best one right now.

What makes our topics different, you ask? Our experts are constantly updating the list and adding new ideas. This means you will always be able to find an original idea here on this page. We will soon be adding new topics for 2023, so stay tuned!

What Is The Fashion Research Paper?

Keep in mind that finding some great fashion topics to write about is not enough. You need to be able to create a well-organized, concise research paper. To help you do just that, we will show you the 8 main parts of a research paper:

Title page (or cover page) Start with a hook to catch the attention of your readers, then talk a bit about the background of the problem and present your thesis. Literature review. Here, you will need to demonstrate that you have analyzed the literature related to the topic and that there is a gap in knowledge that needs to be addressed. Research In this section, you will explain in great detail all the methods you have used to gather the data. Be as specific as possible. Data analysis. This is the section where you present and analyze the data. Be objective and avoid discussing the results. This is the section where you can discuss your findings and prove how your research results back your thesis. Don’t forget to acknowledge the limitations of your research. Restate your thesis and summarize your research and findings. Show your readers how your findings answer the research questions. References page. This is where you list all the resources you have used to write your research Make sure you don’t miss any.

Now that you know the overall structure of a research paper, it’s time to give you some excellent topics to write about:

Brand New Fashion Research Paper Topics

We will start our list with the brand new fashion research paper topics. These have been added to the list recently, so you can pick one right now knowing that it’s original:

• Fashion in Ancient Rome
• The impact of Jane Austen on the world of fashion
• Swimwear in the 1980s
• Using bizarre colors in fashion
• The rise and fall of the jeans
• Peer pressure related to fashion trends
• Social networking and fashion
• The life and work of Giorgio Armani
• Talk about hippie fashion
• Fashion in Islamic religions

Interesting Fashion Topics To Write About

If you are looking for something out of the ordinary, we have a long list of interesting fashion topics to write about. Take a look at the following ideas:

• The rise of the Chanel brand
• Does price reflect quality?
• Fashion in Ancient Egypt
• The sense of fashion in women
• The link between art and fashion
• Discuss ethics in fashion
• The relationship between style and money
• The role of clothes in your culture
• Interesting fashion hacks

Fashion Research Topics 2023

In the fashion research topics 2023, you can find topics that were greatly appreciated in 2023. These may or may not be as appreciated in 2024 though:

• Fashion in developing countries
• Research smart casual fashion
• Compare Asian fashion with American fashion
• Fashion and aesthetics
• Marketing a new brand of clothes
• Fashion in vlogging
• What are cycles in fashion?
• The rise of the Versace empire
• Fashion in Paris

Advanced Fashion Topics To Discuss

We also have a list of more advanced fashion topics to discuss. Just keep in mind that the following topics are not easy to write about. But as an option, you can buy a dissertation on any topic.

• Negative effects of fashion on the environment
• Forecasting new trends in 2023
• Celebrities and fashion
• Negative effects of fashion on the human psychology
• Influencer marketing of fashion products
• Fashion from a religious standpoint
• The place of leather in fashion in 2023
• Largest fashion shows in the world
• The importance of Fashion Weeks in Eastern Europe

Fun Research Topics On Fashion

Who said a research paper can’t be fun? Choose one of these fun research topics on fashion and start writing the perfect paper today:

• Fashion in 1990s media
• Funny fashion mishaps
• Men in fashion advertisements/commercials
• Fashion in medieval times
• Crossover fashion in 2023
• Can you start a fashion business?
• Fashion in the royal family (the UK)
• Fashion and school uniforms

Important People In Fashion

One of the easiest ways to write a research paper in the field of fashion is to research an icon. Here are some important people in a fashion that you can talk about:

• Karl Lagerfeld
• Stella McCartney
• Audrey Hepburn
• David Bowie
• Princess Diana
• Charles Frederick Worth
• Harry Styles
• Kim Taehyung
• Coco Chanel
• Designer Paul Poiret

Fashion Research Paper Topics For High School

If you are a high school student, you need some easier topics to write on. Check out these fashion research paper topics for high school and pick the one you like:

• Fashion in Ancient Egyptian times
• Michael Jackson’s fashion
• Fashion in Western Europe
• Fashion at the workplace
• Fashion in schools in the UK
• Discuss fashion in North Korea
• Luxury products and the human brain
• Fashion trends and the science that explains them

Captivating Fashion Design Research Paper Topics

In case you want to discuss fashion design, we have a nice list of captivating fashion design research paper topics right here. All these topics are, of course, 100% free to use:

• Fashion in the LGBTQ community
• Fashion in Nazi Germany
• Fun facts about beachwear
• The role of Versace in fashion
• New York as a fashion center
• Effects of Tik-Tok on fashion
• The origins of ethnic clothing
• Mixing 3 styles the right way
• Fashion and sexism in 2023

Fast Fashion Research Paper Topics

Don’t want to spend a lot of time working on that research paper? No problem! Simply choose one of these fast fashion research paper topics:

• The role of politics in fashion in the United States
• Talk about wedding ceremony fashion
• Talk about trends in baby clothing in the United Kingdom
• The role celebrities play in fashion marketing
• Talk about 3 iconic fashion characters
• An in-depth look at fashion in the punk world

Fashion Topics To Research In 2023

It’s time to think about the topics that should work great in 2023. In fact, our experts have already compiled a list of fashion topics to research in 2023:

• Talk about the notion of “invisible branding” in fashion
• Research women’s fashion in the 1980s
• The role played by art in fashion trends
• Research 3 major fashion companies
• Talk about the low rise fashion trend
• Discuss the women’s oversized bomber jackets trend

Fashion And Marketing Research Topics

As you probably know, fashion and marketing go hand in hand. Take a look at our latest and most interesting fashion and marketing research topics right here:

• Fashion marketing on social media
• Fashion marketing in the 1960s
• Effective marketing strategies for luxury products
• Style vs. functionality in marketing
• Marketing and fashion cycles
• The role of fashion in TV commercials

Fashion Ideas For College Students

College students should research topics that are more complex in nature. Don’t worry though; we have more than enough fashion ideas for college students:

• Research the hoodies under blazers fashion trend
• Compare Asian and European fashion
• Research Jane Austen’s style
• A closer look at minimalist fashion
• The beginning of the Haute Couture
• Fashion and the Internet

Unique Ideas Related To Fashion

This list of topics has been revised recently to make sure all ideas are unique. So, if you’re looking for unique ideas related to fashion, you have definitely arrived at the right place:

• Analyze the cropped cardigans trend
• Research the plus-size fashion industry in Indonesia
• The impact of feminism on fashion
• Social issues caused by fashion
• Fashion and cheap labor
• Effects of religion on fashion

Easy Fashion Essay Topics

If you want to make sure you ace that research paper, you should find an easy topic to talk about. Take a look at these easy fashion essay topics and pick one today:

• Discuss the notion of “color blocking”
• Fashion trends during World War II
• The evolution of men’s suits over the last 100 years
• Fashion and child labor
• What is organic clothing?
• Talk about the rise of wig fashion

Creative Fashion Research Questions

Professors really appreciate creativity, so you should definitely go through this list of creative fashion research questions:

• A closer look at the puff sleeves trend
• The Kardashian family’s impact on fashion
• How did Chanel rise to fame?
• Sustainability in the fashion industry
• Fashion and body types
• Interesting fashion trends in Dubai
• Talk about fashion in the armed forces

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QEP Research and Design

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During the 2023-2024 academic year, the QEP Topic Selection Task Force reviewed input from the university community through the strategic planning process and institutional data to identify the topic for our next QEP. The next phase of the QEP is developing the topic into an action plan based on a review of research-based best practices and continued dialogue with the university community. The QEP Research and Design (R&D) Team, representing a wide range of stakeholders across the university, including faculty, staff, administrators, and students, will lead this phase. The QEP research and design process will take place primarily during the 2024-2025 academic year leading up to the on-site visit in March 2026.

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Explore NIAID Topics for Small Business Innovation Research Contract Solicitation

Funding News Edition: September 4, 2024 See more articles in this edition

NIH's SBIR program accepts Phase I, Phase II, Fast Track, and Direct-to-Phase II research proposals.

Each year, NIH solicits research proposals from small businesses through A Solicitation of the National Institutes of Health (NIH) and the Centers for Disease Control and Prevention (CDC) for Small Business Innovation Research (SBIR) Contract Proposals . The latest version was published on August 2, 2024. The solicitation serves as a vehicle for offerors to propose research projects on a multitude of scientific topics from across NIH.

Proposals are due by October 18, 2024, at 5 p.m. Eastern Time.  

NIH’s Small Business Education and Entrepreneurial Development (SEED) program will host an HHS SBIR Contract RFP Pre-proposal Conference Webinar (PHS-2025-1) to discuss the mechanics of the contract opportunity on September 23, 2024, from 2 to 4 p.m. Eastern Time. The presentation materials will be posted on that same event page following the session. 

Note : This SBIR contract solicitation is distinct from the 2024 SBIR and STTR Omnibus/Parent Grant Solicitations for the NIH, CDC, and FDA released in July, which are notices of funding opportunities for grant awards (despite the word “solicitation” appearing in their titles). Learn about those grant opportunities in our August 7, 2024 article “ Small Business Research: Priority Funding Topics for 2025 .” 

To differentiate among the proposal types: 

• Phase I—research to determine the scientific or technical feasibility and commercial merit of the proposed research or research and development (R&D) efforts. 
• Phase II—continuance of Phase I research efforts, dependent on successful Phase I results as well as scientific and technical merit and commercial potential of further work. 
• Fast Track—simultaneous submission of Phase I and Phase II proposals, to facilitate a streamlined transition from Phase I to Phase II if merited by research outcomes. 
• Direct-to-Phase II—allows a small business concern to commence with Phase II research if Phase I stage-type research funded through other, non-SBIR/STTR sources is already complete. 

The table below summarizes NIAID’s research topics of interest for contract proposals. Refer to the attachment posted within the solicitation linked above for full details, including the number of anticipated awards and descriptions of required activities and deliverables. 

Your contract proposal should address only one topic; if you wish to pursue multiple topics, submit a separate proposal for each topic. Submit your proposal(s) through the electronic Contract Proposal Submission . Direct any technical questions about the solicitation and NIAID’s topics to Jonathan Bryan in NIAID’s Office of Acquisitions at [email protected] or 240-669-5180.  

Find general information and advice on our Small Business Programs page and contact NIAID SBIR/STTR Program Coordinator Natalia Kruchinin, Ph.D., at [email protected] for funding questions specific to small businesses.

Email us at [email protected] for help navigating NIAID’s grant and contract policies and procedures.

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Peer Reviewed

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.

Swedish School of Library and Information Science, University of Borås, Sweden

Department of Arts and Cultural Sciences, Lund University, Sweden

Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden

Research Questions

• Where are questionable publications produced with generative pre-trained transformers (GPTs) that can be found via Google Scholar published or deposited?
• What are the main characteristics of these publications in relation to predominant subject categories?
• How are these publications spread in the research infrastructure for scholarly communication?
• How is the role of the scholarly communication infrastructure challenged in maintaining public trust in science and evidence through inappropriate use of generative AI?

research note Summary

• A sample of scientific papers with signs of GPT-use found on Google Scholar was retrieved, downloaded, and analyzed using a combination of qualitative coding and descriptive statistics. All papers contained at least one of two common phrases returned by conversational agents that use large language models (LLM) like OpenAI’s ChatGPT. Google Search was then used to determine the extent to which copies of questionable, GPT-fabricated papers were available in various repositories, archives, citation databases, and social media platforms.
• Roughly two-thirds of the retrieved papers were found to have been produced, at least in part, through undisclosed, potentially deceptive use of GPT. The majority (57%) of these questionable papers dealt with policy-relevant subjects (i.e., environment, health, computing), susceptible to influence operations. Most were available in several copies on different domains (e.g., social media, archives, and repositories).
• Two main risks arise from the increasingly common use of GPT to (mass-)produce fake, scientific publications. First, the abundance of fabricated “studies” seeping into all areas of the research infrastructure threatens to overwhelm the scholarly communication system and jeopardize the integrity of the scientific record. A second risk lies in the increased possibility that convincingly scientific-looking content was in fact deceitfully created with AI tools and is also optimized to be retrieved by publicly available academic search engines, particularly Google Scholar. However small, this possibility and awareness of it risks undermining the basis for trust in scientific knowledge and poses serious societal risks.

Implications

The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated  (Simon et al., 2023).

Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.

To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.

The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few.  While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.

Evidence hacking and backfiring effects

Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.

The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.

However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.

Recommendations

Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of  science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.

Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.

Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.

Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.

Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.

The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs.  Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.

As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.

A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster.  Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”

The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).

Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.

Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.

We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .

We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.

The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.

To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.

We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”

• Artificial Intelligence
• / Search engines

Cite this Essay

Haider, J., Söderström, K. R., Ekström, B., & Rödl, M. (2024). GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-156

• / Appendix B

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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).

Competing Interests

The authors declare no competing interests.

The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).

This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.

Data Availability

All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X

Acknowledgements

The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.