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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• 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.

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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
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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.

• 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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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RESEARCH DESIGN.

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Presentation on theme: "RESEARCH DESIGN."— Presentation transcript:

Research Methodology For reader assistance, have an introductory paragraph in which attention is given to the organization of the section in relation to.

Stratification (Blocking) Grouping similar experimental units together and assigning different treatments within such groups of experimental units A technique.

Experimental Design  The term experimental design refers to a plan for assigning subjects to experimental conditions and the statistical analysis associated.

RESEARCH PROBLEM.

Understanding the Research Process

Chapter 28 Design of Experiments (DOE). Objectives Define basic design of experiments (DOE) terminology. Apply DOE principles. Plan, organize, and evaluate.

Chapter 2 Understanding the Research Process

Data and Data Collection Quantitative – Numbers, tests, counting, measuring Fundamentally--2 types of data Qualitative – Words, images, observations, conversations,

Fig Theory construction. A good theory will generate a host of testable hypotheses. In a typical study, only one or a few of these hypotheses can.

Magister of Electrical Engineering Udayana University September 2011

Chapter 1: Introduction to Statistics

Research Problem.

RESEARCH A systematic quest for undiscovered truth A way of thinking

The Research Enterprise in Psychology. The Scientific Method: Terminology Operational definitions are used to clarify precisely what is meant by each.

Understanding Variability Unraveling the Mystery of the Data’s Message Becoming a “Data Whisperer”

1 Research Methodology Model. 2 Hypothesis a prediction of what is the case (fact) based on theory Conclusions Observation (s): Phenomena; Problem (Tree)

The Scientific Method Formulation of an H ypothesis P lanning an experiment to objectively test the hypothesis Careful observation and collection of D.

Understanding Hypothesis- your prediction Experimental Hypothesis- there will be a difference and here is what I think it will be and why (based on previous.

Psychology 290 Lab #2 Sept. 26 – 28 Types & Parts of Articles Operational Definition Variables Reliability & Validity.

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Thoughtful Engagement with Disability Through Inclusive Design Principles

by Sydney Dolan

In both conference submissions and grant writing, writers are often asked to describe the broader impact of their research. In some cases, writers may be asked to address how their work will contribute to diversity and equity in their field. Disability is often appealed to as a justification for technological development, and as a moral imperative toward investment in technological research. While there is significant interest in developing assistive technologies for disabled people, this work is often done by non-disabled academics who make problematic assumptions. The purpose of this blogpost is to:

• discuss the medical vs social models of disability. 
• highlight the use of inclusive design principles to improve accessibility of your research.
• demonstrate the importance of communicating often with your desired target audience  (‘nothing about us, without us’) for a technology throughout the research, design, and testing stages.

The medical model of disability defines disability as a defect within the individual. Under the medical model, disabilities should be ‘fixed’ or changed by medical and other treatments, even when the impairment or difference does not cause pain or illness. This model looks at what is “wrong” with the person, instead of looking at what the person needs. 

Alternatively, the disabled community generally prefers an alternative framework to engage with disability: the social model of disability . The social model of disability is context-dependent, and views disability as a form of exclusion propagated by a society that discriminates against people who are seen as being impaired. In this framework, disability is not seen as a set of mental or physical differences, but as a complex set of interactions across physical, cultural, and political environments that shape perception and human experience. The social model of disability makes the distinction that disability is only disabling when it prevents someone from doing what they want or need to do.

Inclusive design is a process based on the social model of disability that seeks to alleviate barriers to access, thereby increasing the number of audience members who can access and accomplish the main goals of the interaction. This means considering questions like who is served and supported by current processes, and how we might adapt designs to be inclusive for more students. Inclusive design is an iterative process that requires constant re-evaluation of design choices to recognize how choices can open up forms of exclusion and barriers for learners. In this blog, we will highlight three case studies from research projects that approach disability in different ways, and describe how inclusive design principles could have been applied.

Case Study 1: Relying on the Medical Model of Disability Creates Designs that Fail to Meet User Needs 

A key tenet of inclusive design is intentionally centering the voices and experiences of people for whom you’re designing, especially those who are typically marginalized by design.  Otherwise, we can unintentionally reinforce barriers to access for disabled users. Consider the development of a robotic cane from Stanford’s Intelligent Systems Laboratory . In an interview with the graduate student and first author of the project, the student shared that he wanted to create “something more user-friendly.” But this attitude ultimately demonstrates a poor understanding of what blind people enjoy about canes. Current canes are lightweight (0.5 lbs), easy to carry, effective, and cheap, whereas the smart cane weighs in at 3 lbs and costs $400. The first author of the project goes on to state that the smart cane can improve walking speed among the test users, and that “increased walking speed is related to better quality of life.”  This is an incredibly misguided notion about the lives of cane users, as Cricket Bidleman writes in the Stanford Daily, 

“ The “smart cane” assumes misguided notions of quality of life. The developers cite improvements in walking speed for both sighted and blind users while using this cane, and the [researchers] claim that “this can provide a significant improvement in terms of their quality of life due to improvement in mobility. This kind of assumption is deeply troubling and offensive, because a person or group of people is projecting their image of quality of life onto the disabled.”  

The problem with the smart cane project, and the issue with work like this more broadly, is that it is assuming that disabled people have incomplete lives compared to abled people. This type of research is aligned with the medical model of disability, where disabled people are seen broken and in need of repair. In this specific case, the researchers assumed that disabled people were using canes as an aberration from the norm, and thus in need of fixing. 

The rhetoric surrounding the development of the smart cane exemplifies technoableism; the harmful belief that technology is a solution for disability.  This term was coined by Ashely Shew , and refers to the phenomena common in the engineering and tech industry that narratively frames technology as a cure for disability.  Through the rhetorical framing of their work, these researchers ultimately influence how broader society imagines disabled people. These framings ultimately create a shallow and misleading portrayal of disabled people, and neglect to acknowledge a rich culture of disability pride.

If inclusive design principles were used in this research project, the smart cane would be significantly overhauled to be cheaper, and lighter. Cane users could have been involved from the start of the problem solving process to help the researchers learn about what works well and the needs of the user. Alternatively, inclusive design principles and the social model of disability could also be applied to help the researchers involved develop a more nuanced understanding of cane-users themselves. Under the social model of disability, researchers would have recognized that using a cane itself is not a problem, and that canes have many desirable qualities that cane-users enjoy. Canes are lightweight, cost-effective, and provide sound feedback that people can use to further characterize their environment. Canes are also simple, trustworthy, and reliable, and don’t require software updates or repeated fixes to work. If something were to go wrong with a normal cane, the user can easily troubleshoot because they have a complete understanding of the cane as an instrument. A sensing augmented cane can give strange information, ruining the trust the user has in the device, and thus defeating its purpose.  The research project demonstrates how research can be misdirected when the voices of those who could benefit are not included from the onset.  

Case Study 2: The Medical Model of Disability Removes User-Autonomy and Historical Context

             Inclusive design seeks to understand the context by which technologies will be applied, to better understand what barriers are implicitly being created through technology. One contentious example of technology development for a disabled community is sign-language gloves, which aim to translate sign language in real time to text or speech as the wearer gestures. One of the first sign-language gloves was developed by a high school student from Colorado who fitted a leather golf glove with 10 sensors that monitored finger position, and then relayed finger spellings to a computer with text on screen. Despite the fact that the glove couldn’t translate anything beyond individual letters, and only worked with the American Manual Alphabet, the glove went on to win the 2001 Intel International Science and Engineering Fair and a $100,000 scholarship. Since 2001, numerous research groups and companies have sought to develop sign language gloves, and received attention and thousands of dollars despite the fact sign language gloves are only able to translate a small set of words, and not complete sentences. These automated gloves require Deaf users to use a butchered variant of sign language that requires the signer to sign in an English sentence structure. American Sign Language is not a linear translation of English via hand signals, it has distinct sentence structures, vocabulary sets, and grammatical elements. While these gloves seek to translate finger signs, many of the grammatical elements of ASL are conveyed using facial expressions and body language, something that cannot be captured through these gloves.

Implicitly, sign-language gloves are relying on the medical model of disability by treating sign language as something that needs to be ‘corrected’ so that non-Deaf people can understand what is being said. Sign-language gloves put the burden on the Deaf person to translate themselves for others. This is troubling, as these efforts to automate sign language translation strip the Deaf community of the language they communicate in, and ultimately mandate an ‘easier’ ‘universal’ communication variant that the non-Deaf use.  Sign language holds tremendous cultural and developmental importance to the Deaf community, and has been shown to have numerous benefits including enhanced cognitive development, improved social skills, and increased access to information and communication [1]. Despite these benefits, it was only in the recent past that sign language was formally recognized as a distinct language with its own grammar and phonologies. Prior to that, there was a concentrated movement in deaf education to ban sign language and force all Deaf people to speak instead [2]. When sign language gloves are being developed, they are not only alienating signers, but echoing historical context that sought to eliminate sign language in its entirety. 

If inclusive design principles were used in this case, researchers could brainstorm methodologies to better teach sign language that do not place the burden of responsibility on the sign language user.  While there are genuine desires from non-signers to communicate with a sign language speaker, using a pen and paper is a sufficient alternative, and there are serious concerns around resource allocation for these research projects.  Due to historical persecution against sign language, many Deaf people have had issues learning the language due to underfunding and sparse availability of Deaf language schools. Rather than creating a glove that would translate sign-language, people should sincerely reflect on if the research could be better spent making sign language education accessible to more people. This research project demonstrates the importance of identifying the broader context of the work that you are doing, to be respectful of the history of the population that you are working with. 

Case Study 3: Collaborating with Disabled Users Leads to Better Designs 

In the MIT Assistive Technologies Hackathon (ATHack), teams work directly with a community member who lives with a disability, to develop solutions to the barriers the clients face. In the weeks prior to the hackathon, the teams meet with a community member to get a better sense of the kinds of assistive technologies that could help them live more independently. Then, during the hackathon, the students work with the community members to build hardware and software for their use.  

For one project, a team of students collaborated with Adriana Mallozi to design a new device for her to use to operate her smart devices.  Adriana has cerebral palsy and limited upper body dexterity, which makes using her hands to operate a phone difficult. Together with Adriana, the team fabricated a custom bluetooth joystick, named Puffin, that was controlled via inhaling (“sipping”)  and exhaling (“puffing”). During the hackathon, Adriana Mallozi successfully used Puffin to take pictures with her smartphone. This hackathon project was later spun into a company – Puffininno , where Mallozi is the founder and CEO. The initial Puffin prototype has been adapted to include a customizable mounting system and a bluetooth capability so users can independently control their smart home devices like lights, doors, thermostats, and speakers. As Adriana Mallozi writes about the power of the product, 

“When you have to rely on others for every aspect of your life, which basically I have to, the small things really matter. Any type of control that you can have over your life is a really big deal, for instance having full control of my mobile phone and access to apps such as Lyft and Uber. People with disabilities who rely on others for their daily living activities have very scheduled lives and it’s little things like that, that make a difference.”

This project is a good example of using inclusive design principles early in the design process to make the technologies involved useful and relevant. Rather than assuming what Adriana would need, teams spent extensive time getting to know her and understanding the challenges she faces due to inaccessible designs. In this case, the team came to find that inhaling and exhaling was an easier control mechanism than using her hands. This design selection recognizes the woman’s personal autonomy in the design process, and works with her to come up with a solution rather than engineering a solution for her . 

In the 2019 MIT Assistive Technology Hackathon , one team worked with a client who needed a portable bidet so that they could safely and efficiently use the bathroom outside of their home. A lack of bidet in public spaces meant the client would need a personal care attendant with them at all times in case they needed to have a bowel movement outside of their home. Personal care attendants (PCAs) are incredibly expensive, and the client shared they would not leave the home on days when their stomach was upset, and even shifted their meal patterns to avoid eating at work for fear of aggravating their stomach. The client disclosed that the stress of finding an accessible bathroom has negatively impacted their self esteem, as well as their personal and professional relationships. To address the client’s concerns, one hackathon team built the Bom-Bidet, a portable bidet for users who have limited range of motion in their arms. 

As with the Puffin design, the students applied inclusive design principles to empathize and understand the user of their intended project. In doing so, they worked to address some  of the key limitations of the PCA option: the cost and lack of autonomy for the client. The students focused on using low cost items like water bottles, food safe tubing, and aquarium motors. The solution they developed costs roughly $48 for materials, and involves items that can all be bought from Amazon. The students also developed an in-depth instruction manual for others who may want to replicate the design. Through inclusive design principles and the social model of disability, the students were able to engineer a solution that provided real benefit to this specific client. As demonstrated here, the best ideas always start with a real problem that a disabled person has, and finding ways to help that specific problem. 

In this blogpost, we first introduced the medical and social models of disability, and discussed the importance of relying on the social model of disability in the design process. Inclusive design is an integral part of the design process, as it seeks to constantly re-evaluate how design choices can open up forms of exclusion and barriers for users. We emphasize the importance of designing collaboratively the intended users by engaging in a meaningful dialogue with disabled users and, when possible, including disabled engineers in the process. This collaboration will require engineers to understand the needs of the user they are designing for, rather than assuming them. As our case studies demonstrated, inclusive design principles led to more useful, cost-effective, relevant, and efficient designs. 

Others Resources and Recommended Readings:

• Accessible Communication CommKit
• Inclusive Language Guide
• Social vs Medical Model of Disability
• The Design of Everyday Things by Don Norman
• “Against Technoableism” By Ashley Shew  

Acknowledgements

Thank you to Sophia Vlahakis and Brian Mernoff for reviewing this article. 

[1] Bennedit, B. S., and Legg, J.  “ Deaf Culture & Community ”. Hands & Voices . 

[2] Marks, M.  “ Linguistic Neglect of Deaf Children in the United States ”. Spring 2020

Research Design

Mar 21, 2019

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Research Design. Chapter 3. Research Design: Definition. A framework or blueprint for conducting the marketing research project. It Specifies the details of the procedures necessary for obtaining the information needed to structure and/or solve marketing research problems.

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Research Design Chapter 3

Research Design: Definition • A framework or blueprint for conducting the marketing research project. It Specifies the details of the procedures necessary for obtaining the information needed to structure and/or solve marketing research problems

Research Design: Classification • Research design is broadly classified as exploratory or conclusive • The differences between exploratory and conclusive research are summarized in the following table

A Comparison of Basic Research Designs

Exploratory Research Design • Exploratory research could be used for any of the following purposes: • Formulate a problem or define a problem more precisely • Identify alternative courses of action • Develop hypotheses • Isolate key variables and relationships for further examination • Gain insights for developing an approach to the problem • Establish priorities for further research

Methods useful for Exploratory Research • Survey of Experts • Pilot Surveys • Secondary Data Analysis • Qualitative Research

Exploratory Research • Exploratory research, as the name suggests, leads to insights into research problem • Exploratory research is, by nature inconclusive and is often followed by conclusive research • However, when exploratory research is the only research to be conducted, we should exercise caution in sample selecting and interpretation of results

Descriptive Research • A type of research that has as its major objective the description of something – usually market characteristics of functions

Descriptive Research • Descriptive research is conducted for following reasons: • To describe the characteristics of relevant groups, such as consumers, salespeople, organizations, or market areas • For example we could develop a profile of heavy users of Ufone prepaid cards • To estimate the percentage of units in a specified population exhibiting a certain behavior • For example we might be interested in the proportion of Ufone heavy users in the population

Descriptive Research • Descriptive research is conducted for following reasons: • To determine perceptions of product characteristics • For example, how do customers perceive various cell phone operators in terms of hidden charges criteria • To determine the degree to which marketing variables are associated • For example, to what extent visit to a children playland is related to shopping • To make specific predictions. For example, what will be the retail sales of LG KG 275 in Feb in the N. Nazimabad area?

Descriptive Research • The descriptive research assumes that researcher has much prior knowledge about the problem situation • Descriptive research is characterized by the prior formulation of research hypotheses • Information needed is clearly defined

Descriptive Research • The research is planned and structured • It is based on large representative samples • A formal research design specifies methods specifies the methods for selecting the sources of information and for collecting data from those sources

Examples of descriptive research • Market studies, which describe the size of the market, buying power of the consumers, availability of distributors, and consumer profiles • Market share studies, which determine proportion of total sales received by a company and its competitors

Examples of descriptive research • Sales analysis studies, which describe sales by geographic region, product line, type and size of the account • Image studies, which determine consumer perceptions of the firm and its products • Product usage studies, which describe consumption patterns

Examples of descriptive research • Distribution studies, which determine traffic flow patterns and the number and location of distributors • Pricing studies, which the range and frequency of price changes and probable consumer response to proposed price changes • Advertising studies, which describe media consumption habits and audience profiles for specific television programs and magazines

Descriptive Research Design • A descriptive research design requires a clear specification of the who, what, when, where, why and way (the six Ws) of the research

The six Ws of a research design • Who – who should be considered a respondent • What – what information should be obtained from the respondents • When - when should the information be obtained from the respondents • Where – where should the respondents be contacted to obtain the requried information

The six Ws of a research design • Why – why are we obtaining information from the respondents. Why is the marketing research project being conducted • Way – In what way are we going to obtain information from the respondents

Major methods for descriptive research • Secondary data analysis in a quantitative way • Surveys • Panels • Observational and other data

Cross-Sectional designs • A type of research design involving the collection of information from any given sample of population elements only once. These are also called surveys • Single cross-sectional design • Only one sample of respondents is drawn from the target population, and information is obtained from this ample only once

Cross-Sectional designs • Multiple cross-sectional design • A cross-sectional design in which there are two or more samples of respondents and information is obtained only once • Cohort analysis • A multiple cross-sectional design consisting of a series of surveys conducted at appropriate time intervals. The cohort refers to the group of respondents who experience the same event within the same time interval

Longitudinal Designs • A type of research design involving a fixed sample of population elements that is measured repeatedly. The sample remains the same over time, thus providing a series of pictures, which when viewed together, portray a vivid illustration of the situation and the changes that are taking place over time

Longitudinal Design • In longitudinal design, the same people are studied over and time and the same variables are measured • Longitudinal designs are also called panels • Panel is a sample of respondents who have agreed to provide information at specified time intervals over an extended period

Relative Advantages and Disadvantages of surveys and panels A + indicates a relative advantage over other design, whereas a – indicates a relative disadvantage

Causal Research • A type of conclusive research where the major objective is to obtain evidence regarding cause-and-effect (causal) relationship • Causal research is appropriate for following purposes: • To understand which variables are the cause (independent variables) and which variables are the effect (dependent variables) • To determine the nature of relationship between the causal variables and the effect to be predicted

Relationship among Exploratory, Descriptive and Causal Research • When little is known about the problem situation, it is desirable to begin with exploratory research • Exploratory research is the initial step in the overall research design framework. It should, in most instances, be followed by descriptive or causal research

Relationship among Exploratory, Descriptive and Causal Research • It is not necessary to begin every research design with exploratory research. It depends upon the precision with which the problem is defined • Although exploratory research is generally the initial step, it need not be. Exploratory research may follow descriptive or causal research

Potential sources of error • Several potential sources of error can affect a research design

The difference between an Error and a Bias • Bias is systematic favoring of some outcomes of over others • Bias is “intentional” • The major difference between bias and error is that an error tends to diminish in the long run whereas bias keeps increasing in the long run

Potential Sources of Errors in Research Design

Total Error • The variation between the true mean value of the population parameter and observed value of the sample statistic obtained in the market research project

Random Sampling Error • Random sampling error occurs due to the particular sample selected is an imperfect representation of the population of interest • Random sampling error is the difference between the population parameter and the observed sample statistic for the original sample

Nonsampling Error • Nonsampling errors are errors that can e attributed to sources other than sampling, and they can be random or nonrandom • Nonsampling errors consists of nonresponse and response errors

Nonresponse Error • A type of nonsampling error that occurs when some of the respondents included in the sample do not respond. This error may be defined as the variation between the true mean value of variable in the original sample and the true mean value in the net sample

Response Error • A type of nonsampling error arising from respondents who do respond but given inaccurate answers, or their answers are misrecorded or misnalayzed. It may defined as the variation between the true mean value of the variable in the net sample and the observed mean value in the marketing research project

Response Errors • Response errors can e made by researchers, interviewers, or respondents

Response Errors by Researchers • Errors made by researcher include surrogate information, measurement, population definition, sampling frame, and data analysis errors

Surrogate Information Error • This error is the variation between the information needed for marketing research problem and the information sought by the researcher • For example, instead of obtaining information on consumer choice of a new brand (needed for the marketing research problem), the researcher obtains information on consumer preferences because the choice process cannot e easily observed

Measurement Error • This error may be defined as the variation between the information sought and the information generated by the measurement process • For example, seeking to measure consumer preferences, the consumer employs a scale that measures perceptions rather than preferences

Population Definition Error • The variation between the actual population relevant to the problem at hand and the population as defined by the researcher

Data Analysis Error • Data analysis error encompasses errors that occur when raw data from questionnaires are transformed into research findings • For example, an inappropriate statistical procedure is used resulting in incorrect interpretation and findings

Response Errors by Interviewer • Response errors made by interviewers include • Respondent selection error • Selection of a respondent other than specified by the sampling design or in a manner inconsistent with the sampling design • Questioning error • Errors made in asking questions of the respondents or in not probing where more information is required

Response Errors by Interviewer • Recording error • Arises due to errors in hearing, interpreting, and recording the answers given by the respondents • Cheating error • Arises when interviewer fabricates answers to a part or all of the interview

Response Errors by Respondents • Inability error • Results from the inability of respondent to provide accurate answers • Reasons for inability may be unfamiliarity, fatigue, boredom, faulty recall, question format, question content, and other factors • Unwillingness error • Arises from the respondent’s unwillingness to provide accurate information • Reasons could be to provide socially desirable answers, avoid embarrassment, or to please the interviewer

Controlling total error • In formulating research design, researcher should try to minimize total error not just any one component of the total error

Components of a research design • Research design may also include a Research Brief from the client and a Research Proposal or Return on Brief from the researcher

Research Brief: Contents • Why do this market research? – what action will be taken when the research is completed? • What has caused this problem or led to this opportunity? • What is known about the area of research already?

Research Brief: Contents • Target groups for the research • What specific information is needed from the research (for example, market size, trends, buying behavior, customer needs, segmentation) What is known about the area of research already?

Research Brief: Contents • What is the proposed budget? • Are there any initial ideas for the research method? • Are there any reporting requirements? • When are the findings required?

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