survey research design example

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Survey Research: Definition, Examples and Methods

Survey Research is a quantitative research method used for collecting data from a set of respondents. It has been perhaps one of the most used methodologies in the industry for several years due to the multiple benefits and advantages that it has when collecting and analyzing data.

LEARN ABOUT: Behavioral Research

In this article, you will learn everything about survey research, such as types, methods, and examples.

Survey Research Definition

Survey Research is defined as the process of conducting research using surveys that researchers send to survey respondents. The data collected from surveys is then statistically analyzed to draw meaningful research conclusions. In the 21st century, every organization’s eager to understand what their customers think about their products or services and make better business decisions. Researchers can conduct research in multiple ways, but surveys are proven to be one of the most effective and trustworthy research methods. An online survey is a method for extracting information about a significant business matter from an individual or a group of individuals. It consists of structured survey questions that motivate the participants to respond. Creditable survey research can give these businesses access to a vast information bank. Organizations in media, other companies, and even governments rely on survey research to obtain accurate data.

The traditional definition of survey research is a quantitative method for collecting information from a pool of respondents by asking multiple survey questions. This research type includes the recruitment of individuals collection, and analysis of data. It’s useful for researchers who aim to communicate new features or trends to their respondents.

LEARN ABOUT: Level of Analysis Generally, it’s the primary step towards obtaining quick information about mainstream topics and conducting more rigorous and detailed quantitative research methods like surveys/polls or qualitative research methods like focus groups/on-call interviews can follow. There are many situations where researchers can conduct research using a blend of both qualitative and quantitative strategies.

LEARN ABOUT: Survey Sampling

Survey Research Methods

Survey research methods can be derived based on two critical factors: Survey research tool and time involved in conducting research. There are three main survey research methods, divided based on the medium of conducting survey research:

• Online/ Email:   Online survey research is one of the most popular survey research methods today. The survey cost involved in online survey research is extremely minimal, and the responses gathered are highly accurate.
• Phone:  Survey research conducted over the telephone ( CATI survey ) can be useful in collecting data from a more extensive section of the target population. There are chances that the money invested in phone surveys will be higher than other mediums, and the time required will be higher.
• Face-to-face:  Researchers conduct face-to-face in-depth interviews in situations where there is a complicated problem to solve. The response rate for this method is the highest, but it can be costly.

Further, based on the time taken, survey research can be classified into two methods:

• Longitudinal survey research:  Longitudinal survey research involves conducting survey research over a continuum of time and spread across years and decades. The data collected using this survey research method from one time period to another is qualitative or quantitative. Respondent behavior, preferences, and attitudes are continuously observed over time to analyze reasons for a change in behavior or preferences. For example, suppose a researcher intends to learn about the eating habits of teenagers. In that case, he/she will follow a sample of teenagers over a considerable period to ensure that the collected information is reliable. Often, cross-sectional survey research follows a longitudinal study .
• Cross-sectional survey research:  Researchers conduct a cross-sectional survey to collect insights from a target audience at a particular time interval. This survey research method is implemented in various sectors such as retail, education, healthcare, SME businesses, etc. Cross-sectional studies can either be descriptive or analytical. It is quick and helps researchers collect information in a brief period. Researchers rely on the cross-sectional survey research method in situations where descriptive analysis of a subject is required.

Survey research also is bifurcated according to the sampling methods used to form samples for research: Probability and Non-probability sampling. Every individual in a population should be considered equally to be a part of the survey research sample. Probability sampling is a sampling method in which the researcher chooses the elements based on probability theory. The are various probability research methods, such as simple random sampling , systematic sampling, cluster sampling, stratified random sampling, etc. Non-probability sampling is a sampling method where the researcher uses his/her knowledge and experience to form samples.

LEARN ABOUT: Survey Sample Sizes

The various non-probability sampling techniques are :

• Convenience sampling
• Snowball sampling
• Consecutive sampling
• Judgemental sampling
• Quota sampling

Process of implementing survey research methods:

• Decide survey questions:  Brainstorm and put together valid survey questions that are grammatically and logically appropriate. Understanding the objective and expected outcomes of the survey helps a lot. There are many surveys where details of responses are not as important as gaining insights about what customers prefer from the provided options. In such situations, a researcher can include multiple-choice questions or closed-ended questions . Whereas, if researchers need to obtain details about specific issues, they can consist of open-ended questions in the questionnaire. Ideally, the surveys should include a smart balance of open-ended and closed-ended questions. Use survey questions like Likert Scale , Semantic Scale, Net Promoter Score question, etc., to avoid fence-sitting.

LEARN ABOUT: System Usability Scale

• Finalize a target audience:  Send out relevant surveys as per the target audience and filter out irrelevant questions as per the requirement. The survey research will be instrumental in case the target population decides on a sample. This way, results can be according to the desired market and be generalized to the entire population.

LEARN ABOUT:  Testimonial Questions

• Send out surveys via decided mediums:  Distribute the surveys to the target audience and patiently wait for the feedback and comments- this is the most crucial step of the survey research. The survey needs to be scheduled, keeping in mind the nature of the target audience and its regions. Surveys can be conducted via email, embedded in a website, shared via social media, etc., to gain maximum responses.
• Analyze survey results:  Analyze the feedback in real-time and identify patterns in the responses which might lead to a much-needed breakthrough for your organization. GAP, TURF Analysis , Conjoint analysis, Cross tabulation, and many such survey feedback analysis methods can be used to spot and shed light on respondent behavior. Researchers can use the results to implement corrective measures to improve customer/employee satisfaction.

Reasons to conduct survey research

The most crucial and integral reason for conducting market research using surveys is that you can collect answers regarding specific, essential questions. You can ask these questions in multiple survey formats as per the target audience and the intent of the survey. Before designing a study, every organization must figure out the objective of carrying this out so that the study can be structured, planned, and executed to perfection.

LEARN ABOUT: Research Process Steps

Questions that need to be on your mind while designing a survey are:

• What is the primary aim of conducting the survey?
• How do you plan to utilize the collected survey data?
• What type of decisions do you plan to take based on the points mentioned above?

There are three critical reasons why an organization must conduct survey research.

• Understand respondent behavior to get solutions to your queries:  If you’ve carefully curated a survey, the respondents will provide insights about what they like about your organization as well as suggestions for improvement. To motivate them to respond, you must be very vocal about how secure their responses will be and how you will utilize the answers. This will push them to be 100% honest about their feedback, opinions, and comments. Online surveys or mobile surveys have proved their privacy, and due to this, more and more respondents feel free to put forth their feedback through these mediums.
• Present a medium for discussion:  A survey can be the perfect platform for respondents to provide criticism or applause for an organization. Important topics like product quality or quality of customer service etc., can be put on the table for discussion. A way you can do it is by including open-ended questions where the respondents can write their thoughts. This will make it easy for you to correlate your survey to what you intend to do with your product or service.
• Strategy for never-ending improvements:  An organization can establish the target audience’s attributes from the pilot phase of survey research . Researchers can use the criticism and feedback received from this survey to improve the product/services. Once the company successfully makes the improvements, it can send out another survey to measure the change in feedback keeping the pilot phase the benchmark. By doing this activity, the organization can track what was effectively improved and what still needs improvement.

Survey Research Scales

There are four main scales for the measurement of variables:

• Nominal Scale:  A nominal scale associates numbers with variables for mere naming or labeling, and the numbers usually have no other relevance. It is the most basic of the four levels of measurement.
• Ordinal Scale:  The ordinal scale has an innate order within the variables along with labels. It establishes the rank between the variables of a scale but not the difference value between the variables.
• Interval Scale:  The interval scale is a step ahead in comparison to the other two scales. Along with establishing a rank and name of variables, the scale also makes known the difference between the two variables. The only drawback is that there is no fixed start point of the scale, i.e., the actual zero value is absent.
• Ratio Scale:  The ratio scale is the most advanced measurement scale, which has variables that are labeled in order and have a calculated difference between variables. In addition to what interval scale orders, this scale has a fixed starting point, i.e., the actual zero value is present.

Benefits of survey research

In case survey research is used for all the right purposes and is implemented properly, marketers can benefit by gaining useful, trustworthy data that they can use to better the ROI of the organization.

Other benefits of survey research are:

• Minimum investment:  Mobile surveys and online surveys have minimal finance invested per respondent. Even with the gifts and other incentives provided to the people who participate in the study, online surveys are extremely economical compared to paper-based surveys.
• Versatile sources for response collection:  You can conduct surveys via various mediums like online and mobile surveys. You can further classify them into qualitative mediums like focus groups , and interviews and quantitative mediums like customer-centric surveys. Due to the offline survey response collection option, researchers can conduct surveys in remote areas with limited internet connectivity. This can make data collection and analysis more convenient and extensive.
• Reliable for respondents:  Surveys are extremely secure as the respondent details and responses are kept safeguarded. This anonymity makes respondents answer the survey questions candidly and with absolute honesty. An organization seeking to receive explicit responses for its survey research must mention that it will be confidential.

Survey research design

Researchers implement a survey research design in cases where there is a limited cost involved and there is a need to access details easily. This method is often used by small and large organizations to understand and analyze new trends, market demands, and opinions. Collecting information through tactfully designed survey research can be much more effective and productive than a casually conducted survey.

There are five stages of survey research design:

• Decide an aim of the research:  There can be multiple reasons for a researcher to conduct a survey, but they need to decide a purpose for the research. This is the primary stage of survey research as it can mold the entire path of a survey, impacting its results.
• Filter the sample from target population:  Who to target? is an essential question that a researcher should answer and keep in mind while conducting research. The precision of the results is driven by who the members of a sample are and how useful their opinions are. The quality of respondents in a sample is essential for the results received for research and not the quantity. If a researcher seeks to understand whether a product feature will work well with their target market, he/she can conduct survey research with a group of market experts for that product or technology.
• Zero-in on a survey method:  Many qualitative and quantitative research methods can be discussed and decided. Focus groups, online interviews, surveys, polls, questionnaires, etc. can be carried out with a pre-decided sample of individuals.
• Design the questionnaire:  What will the content of the survey be? A researcher is required to answer this question to be able to design it effectively. What will the content of the cover letter be? Or what are the survey questions of this questionnaire? Understand the target market thoroughly to create a questionnaire that targets a sample to gain insights about a survey research topic.
• Send out surveys and analyze results:  Once the researcher decides on which questions to include in a study, they can send it across to the selected sample . Answers obtained from this survey can be analyzed to make product-related or marketing-related decisions.

Survey examples: 10 tips to design the perfect research survey

Picking the right survey design can be the key to gaining the information you need to make crucial decisions for all your research. It is essential to choose the right topic, choose the right question types, and pick a corresponding design. If this is your first time creating a survey, it can seem like an intimidating task. But with QuestionPro, each step of the process is made simple and easy.

Below are 10 Tips To Design The Perfect Research Survey:

• Set your SMART goals:  Before conducting any market research or creating a particular plan, set your SMART Goals . What is that you want to achieve with the survey? How will you measure it promptly, and what are the results you are expecting?
• Choose the right questions:  Designing a survey can be a tricky task. Asking the right questions may help you get the answers you are looking for and ease the task of analyzing. So, always choose those specific questions – relevant to your research.
• Begin your survey with a generalized question:  Preferably, start your survey with a general question to understand whether the respondent uses the product or not. That also provides an excellent base and intro for your survey.
• Enhance your survey:  Choose the best, most relevant, 15-20 questions. Frame each question as a different question type based on the kind of answer you would like to gather from each. Create a survey using different types of questions such as multiple-choice, rating scale, open-ended, etc. Look at more survey examples and four measurement scales every researcher should remember.
• Prepare yes/no questions:  You may also want to use yes/no questions to separate people or branch them into groups of those who “have purchased” and those who “have not yet purchased” your products or services. Once you separate them, you can ask them different questions.
• Test all electronic devices:  It becomes effortless to distribute your surveys if respondents can answer them on different electronic devices like mobiles, tablets, etc. Once you have created your survey, it’s time to TEST. You can also make any corrections if needed at this stage.
• Distribute your survey:  Once your survey is ready, it is time to share and distribute it to the right audience. You can share handouts and share them via email, social media, and other industry-related offline/online communities.
• Collect and analyze responses:  After distributing your survey, it is time to gather all responses. Make sure you store your results in a particular document or an Excel sheet with all the necessary categories mentioned so that you don’t lose your data. Remember, this is the most crucial stage. Segregate your responses based on demographics, psychographics, and behavior. This is because, as a researcher, you must know where your responses are coming from. It will help you to analyze, predict decisions, and help write the summary report.
• Prepare your summary report:  Now is the time to share your analysis. At this stage, you should mention all the responses gathered from a survey in a fixed format. Also, the reader/customer must get clarity about your goal, which you were trying to gain from the study. Questions such as – whether the product or service has been used/preferred or not. Do respondents prefer some other product to another? Any recommendations?

Having a tool that helps you carry out all the necessary steps to carry out this type of study is a vital part of any project. At QuestionPro, we have helped more than 10,000 clients around the world to carry out data collection in a simple and effective way, in addition to offering a wide range of solutions to take advantage of this data in the best possible way.

From dashboards, advanced analysis tools, automation, and dedicated functions, in QuestionPro, you will find everything you need to execute your research projects effectively. Uncover insights that matter the most!

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What is survey research.

15 min read Find out everything you need to know about survey research, from what it is and how it works to the different methods and tools you can use to ensure you’re successful.

Survey research is the process of collecting data from a predefined group (e.g. customers or potential customers) with the ultimate goal of uncovering insights about your products, services, or brand overall .

As a quantitative data collection method, survey research can provide you with a goldmine of information that can inform crucial business and product decisions. But survey research needs careful planning and execution to get the results you want.

So if you’re thinking about using surveys to carry out research, read on.

Get started with our free survey maker tool

Types of survey research

Calling these methods ‘survey research’ slightly underplays the complexity of this type of information gathering. From the expertise required to carry out each activity to the analysis of the data and its eventual application, a considerable amount of effort is required.

As for how you can carry out your research, there are several options to choose from — face-to-face interviews, telephone surveys, focus groups (though more interviews than surveys), online surveys , and panel surveys.

Typically, the survey method you choose will largely be guided by who you want to survey, the size of your sample , your budget, and the type of information you’re hoping to gather.

Here are a few of the most-used survey types:

Face-to-face interviews

Before technology made it possible to conduct research using online surveys, telephone, and mail were the most popular methods for survey research. However face-to-face interviews were considered the gold standard — the only reason they weren’t as popular was due to their highly prohibitive costs.

When it came to face-to-face interviews, organizations would use highly trained researchers who knew when to probe or follow up on vague or problematic answers. They also knew when to offer assistance to respondents when they seemed to be struggling. The result was that these interviewers could get sample members to participate and engage in surveys in the most effective way possible, leading to higher response rates and better quality data.

Telephone surveys

While phone surveys have been popular in the past, particularly for measuring general consumer behavior or beliefs, response rates have been declining since the 1990s .

Phone surveys are usually conducted using a random dialing system and software that a researcher can use to record responses.

This method is beneficial when you want to survey a large population but don’t have the resources to conduct face-to-face research surveys or run focus groups, or want to ask multiple-choice and open-ended questions .

The downsides are they can: take a long time to complete depending on the response rate, and you may have to do a lot of cold-calling to get the information you need.

You also run the risk of respondents not being completely honest . Instead, they’ll answer your survey questions quickly just to get off the phone.

Focus groups (interviews — not surveys)

Focus groups are a separate qualitative methodology rather than surveys — even though they’re often bunched together. They’re normally used for survey pretesting and designing , but they’re also a great way to generate opinions and data from a diverse range of people.

Focus groups involve putting a cohort of demographically or socially diverse people in a room with a moderator and engaging them in a discussion on a particular topic, such as your product, brand, or service.

They remain a highly popular method for market research , but they’re expensive and require a lot of administration to conduct and analyze the data properly.

You also run the risk of more dominant members of the group taking over the discussion and swaying the opinions of other people — potentially providing you with unreliable data.

Online surveys

Online surveys have become one of the most popular survey methods due to being cost-effective, enabling researchers to accurately survey a large population quickly.

Online surveys can essentially be used by anyone for any research purpose – we’ve all seen the increasing popularity of polls on social media (although these are not scientific).

Using an online survey allows you to ask a series of different question types and collect data instantly that’s easy to analyze with the right software.

There are also several methods for running and distributing online surveys that allow you to get your questionnaire in front of a large population at a fraction of the cost of face-to-face interviews or focus groups.

This is particularly true when it comes to mobile surveys as most people with a smartphone can access them online.

However, you have to be aware of the potential dangers of using online surveys, particularly when it comes to the survey respondents. The biggest risk is because online surveys require access to a computer or mobile device to complete, they could exclude elderly members of the population who don’t have access to the technology — or don’t know how to use it.

It could also exclude those from poorer socio-economic backgrounds who can’t afford a computer or consistent internet access. This could mean the data collected is more biased towards a certain group and can lead to less accurate data when you’re looking for a representative population sample.

When it comes to surveys, every voice matters.

Find out how to create more inclusive and representative surveys for your research.

Panel surveys

A panel survey involves recruiting respondents who have specifically signed up to answer questionnaires and who are put on a list by a research company. This could be a workforce of a small company or a major subset of a national population. Usually, these groups are carefully selected so that they represent a sample of your target population — giving you balance across criteria such as age, gender, background, and so on.

Panel surveys give you access to the respondents you need and are usually provided by the research company in question. As a result, it’s much easier to get access to the right audiences as you just need to tell the research company your criteria. They’ll then determine the right panels to use to answer your questionnaire.

However, there are downsides. The main one being that if the research company offers its panels incentives, e.g. discounts, coupons, money — respondents may answer a lot of questionnaires just for the benefits.

This might mean they rush through your survey without providing considered and truthful answers. As a consequence, this can damage the credibility of your data and potentially ruin your analyses.

What are the benefits of using survey research?

Depending on the research method you use, there are lots of benefits to conducting survey research for data collection. Here, we cover a few:

1.   They’re relatively easy to do

Most research surveys are easy to set up, administer and analyze. As long as the planning and survey design is thorough and you target the right audience , the data collection is usually straightforward regardless of which survey type you use.

2.   They can be cost effective

Survey research can be relatively cheap depending on the type of survey you use.

Generally, qualitative research methods that require access to people in person or over the phone are more expensive and require more administration.

Online surveys or mobile surveys are often more cost-effective for market research and can give you access to the global population for a fraction of the cost.

3.   You can collect data from a large sample

Again, depending on the type of survey, you can obtain survey results from an entire population at a relatively low price. You can also administer a large variety of survey types to fit the project you’re running.

4.   You can use survey software to analyze results immediately

Using survey software, you can use advanced statistical analysis techniques to gain insights into your responses immediately.

Analysis can be conducted using a variety of parameters to determine the validity and reliability of your survey data at scale.

5.   Surveys can collect any type of data

While most people view surveys as a quantitative research method, they can just as easily be adapted to gain qualitative information by simply including open-ended questions or conducting interviews face to face.

How to measure concepts with survey questions

While surveys are a great way to obtain data, that data on its own is useless unless it can be analyzed and developed into actionable insights.

The easiest, and most effective way to measure survey results, is to use a dedicated research tool that puts all of your survey results into one place.

When it comes to survey measurement, there are four measurement types to be aware of that will determine how you treat your different survey results:

Nominal scale

With a nominal scale , you can only keep track of how many respondents chose each option from a question, and which response generated the most selections.

An example of this would be simply asking a responder to choose a product or brand from a list.

You could find out which brand was chosen the most but have no insight as to why.

Ordinal scale

Ordinal scales are used to judge an order of preference. They do provide some level of quantitative value because you’re asking responders to choose a preference of one option over another.

Ratio scale

Ratio scales can be used to judge the order and difference between responses. For example, asking respondents how much they spend on their weekly shopping on average.

Interval scale

In an interval scale, values are lined up in order with a meaningful difference between the two values — for example, measuring temperature or measuring a credit score between one value and another.

Step by step: How to conduct surveys and collect data

Conducting a survey and collecting data is relatively straightforward, but it does require some careful planning and design to ensure it results in reliable data.

Step 1 – Define your objectives

What do you want to learn from the survey? How is the data going to help you? Having a hypothesis or series of assumptions about survey responses will allow you to create the right questions to test them.

Step 2 – Create your survey questions

Once you’ve got your hypotheses or assumptions, write out the questions you need answering to test your theories or beliefs. Be wary about framing questions that could lead respondents or inadvertently create biased responses .

Step 3 – Choose your question types

Your survey should include a variety of question types and should aim to obtain quantitative data with some qualitative responses from open-ended questions. Using a mix of questions (simple Yes/ No, multiple-choice, rank in order, etc) not only increases the reliability of your data but also reduces survey fatigue and respondents simply answering questions quickly without thinking.

Find out how to create a survey that’s easy to engage with

Step 4 – Test your questions

Before sending your questionnaire out, you should test it (e.g. have a random internal group do the survey) and carry out A/B tests to ensure you’ll gain accurate responses.

Step 5 – Choose your target and send out the survey

Depending on your objectives, you might want to target the general population with your survey or a specific segment of the population. Once you’ve narrowed down who you want to target, it’s time to send out the survey.

After you’ve deployed the survey, keep an eye on the response rate to ensure you’re getting the number you expected. If your response rate is low, you might need to send the survey out to a second group to obtain a large enough sample — or do some troubleshooting to work out why your response rates are so low. This could be down to your questions, delivery method, selected sample, or otherwise.

Step 6 – Analyze results and draw conclusions

Once you’ve got your results back, it’s time for the fun part.

Break down your survey responses using the parameters you’ve set in your objectives and analyze the data to compare to your original assumptions. At this stage, a research tool or software can make the analysis a lot easier — and that’s somewhere Qualtrics can help.

Get reliable insights with survey software from Qualtrics

Gaining feedback from customers and leads is critical for any business, data gathered from surveys can prove invaluable for understanding your products and your market position, and with survey software from Qualtrics, it couldn’t be easier.

Used by more than 13,000 brands and supporting more than 1 billion surveys a year, Qualtrics empowers everyone in your organization to gather insights and take action. No coding required — and your data is housed in one system.

Get feedback from more than 125 sources on a single platform and view and measure your data in one place to create actionable insights and gain a deeper understanding of your target customers .

Automatically run complex text and statistical analysis to uncover exactly what your survey data is telling you, so you can react in real-time and make smarter decisions.

We can help you with survey management, too. From designing your survey and finding your target respondents to getting your survey in the field and reporting back on the results, we can help you every step of the way.

And for expert market researchers and survey designers, Qualtrics features custom programming to give you total flexibility over question types, survey design, embedded data, and other variables.

No matter what type of survey you want to run, what target audience you want to reach, or what assumptions you want to test or answers you want to uncover, we’ll help you design, deploy and analyze your survey with our team of experts.

Ready to find out more about Qualtrics CoreXM?

Get started with our free survey maker tool today

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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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• Survey Research: Types, Examples & Methods

Surveys have been proven to be one of the most effective methods of conducting research. They help you to gather relevant data from a large audience, which helps you to arrive at a valid and objective conclusion. 

Just like other research methods, survey research had to be conducted the right way to be effective. In this article, we’ll dive into the nitty-gritty of survey research and show you how to get the most out of it. 

What is Survey Research? 

Survey research is simply a systematic investigation conducted via a survey. In other words, it is a type of research carried out by administering surveys to respondents. 

Surveys already serve as a great method of opinion sampling and finding out what people think about different contexts and situations. Applying this to research means you can gather first-hand information from persons affected by specific contexts. 

Survey research proves useful in numerous primary research scenarios. Consider the case whereby a restaurant wants to gather feedback from its customers on its new signatory dish. A good way to do this is to conduct survey research on a defined customer demographic. 

By doing this, the restaurant is better able to gather primary data from the customers (respondents) with regards to what they think and feel about the new dish across multiple facets. This means they’d have more valid and objective information to work with. 

Why Conduct Survey Research?  

One of the strongest arguments for survey research is that it helps you gather the most authentic data sets in the systematic investigation. Survey research is a gateway to collecting specific information from defined respondents, first-hand.  

Surveys combine different question types that make it easy for you to collect numerous information from respondents. When you come across a questionnaire for survey research, you’re likely to see a neat blend of close-ended and open-ended questions, together with other survey response scale questions. 

Apart from what we’ve discussed so far, here are some other reasons why survey research is important: 

• It gives you insights into respondents’ behaviors and preferences which is valid in any systematic investigation.
• Many times, survey research is structured in an interactive manner which makes it easier for respondents to communicate their thoughts and experiences. 
• It allows you to gather important data that proves useful for product improvement; especially in market research. 

Characteristics of Survey Research

• Usage : Survey research is mostly deployed in the field of social science; especially to gather information about human behavior in different social contexts. 
• Systematic : Like other research methods, survey research is systematic. This means that it is usually conducted in line with empirical methods and follows specific processes.
• Replicable : In survey research, applying the same methods often translates to achieving similar results. 
• Types : Survey research can be conducted using forms (offline and online) or via structured, semi-structured, and unstructured interviews . 
• Data : The data gathered from survey research is mostly quantitative; although it can be qualitative. 
• Impartial Sampling : The data sample in survey research is random and not subject to unavoidable biases.
• Ecological Validity : Survey research often makes use of data samples obtained from real-world occurrences. 

Types of Survey Research

Survey research can be subdivided into different types based on its objectives, data source, and methodology. 

Types of Survey Research Based on Objective

• Exploratory Survey Research

Exploratory survey research is aimed at finding out more about the research context. Here, the survey research pays attention to discovering new ideas and insights about the research subject(s) or contexts. 

Exploratory survey research is usually made up of open-ended questions that allow respondents to fully communicate their thoughts and varying perspectives on the subject matter. In many cases, systematic investigation kicks off with an exploratory research survey. 

• Predictive Survey Research

This type of research is also referred to as causal survey research because it pays attention to the causative relationship between the variables in the survey research. In other words, predictive survey research pays attention to existing patterns to explain the relationship between two variables. 

It can also be referred to as conclusive research because it allows you to identify causal variables and resultant variables; that is cause and effect. Predictive variables allow you to determine the nature of the relationship between the causal variables and the effect to be predicted. 

• Descriptive Survey Research

Unlike predictive research, descriptive survey research is largely observational. It is ideal for quantitative research because it helps you to gather numeric data. 

The questions listed in descriptive survey research help you to uncover new insights into the actions, thoughts, and feelings of survey respondents. With this data, you can know the extent to which different conditions can be obtained among these subjects. 

Types of Survey Research Based on Data Source

• Secondary Data

Survey research can be designed to collect and process secondary data. Secondary data is a type of data that has been collected from primary sources in the past and is readily available for use. It is the type of data that is already existing.

Since secondary data is gathered from third-party sources, it is mostly generic, unlike primary data that is specific to the research context. Common sources of secondary data in survey research include books, data collected through other surveys, online data, data from government archives, and libraries. 

• Primary Data

This is the type of research data that is collected directly; that is, data collected from first-hand sources. Primary data is usually tailored to a specific research context so that reflects the aims and objectives of the systematic investigation.

One of the strongest points of primary data over its secondary counterpart is validity. Because it is collected directly from first-hand sources, primary data typically results in objective research findings. 

You can collect primary data via interviews, surveys, and questionnaires, and observation methods. 

Types of Survey Research Based on Methodology

• Quantitative Research

Quantitative research is a common research method that is used to gather numerical data in a systematic investigation. It is often deployed in research contexts that require statistical information to arrive at valid results such as in social science or science. 

For instance, as an organization looking to find out how many persons are using your product in a particular location, you can administer survey research to collect useful quantitative data. Other quantitative research methods include polls, face-to-face interviews, and systematic observation. 

• Qualitative Research

This is a method of systematic investigation that is used to collect non-numerical data from research participants. In other words, it is a research method that allows you to gather open-ended information from your target audience. 

Typically, organizations deploy qualitative research methods when they need to gather descriptive data from their customers; for example, when they need to collect customer feedback in product evaluation. Qualitative research methods include one-on-one interviews, observation, case studies, and focus groups. 

Survey Research Scales

• Nominal Scale

This is a type of survey research scale that uses numbers to label the different answer options in a survey. On a nominal scale , the numbers have no value in themselves; they simply serve as labels for qualitative variables in the survey. 

In cases where a nominal scale is used for identification, there is typically a specific one-on-one relationship between the numeric value and the variable it represents. On the other hand, when the variable is used for classification, then each number on the scale serves as a label or a tag. 

Examples of Nominal Scale in Survey Research 

1. How would you describe your complexion? 

2. Have you used this product?

• Ordinal Scale

This is a type of variable measurement scale that arranges answer options in a specific ranking order without necessarily indicating the degree of variation between these options. Ordinal data is qualitative and can be named, ranked, or grouped. 

In an ordinal scale , the different properties of the variables are relatively unknown, and it also identifies, describes, and shows the rank of the different variables. With an ordered scale, it is easier for researchers to measure the degree of agreement and/or disagreement with different variables. 

With ordinal scales, you can measure non-numerical attributes such as the degree of happiness, agreement, or opposition of respondents in specific contexts. Using an ordinal scale makes it easy for you to compare variables and process survey responses accordingly. 

Examples of Ordinal Scale in Survey Research

1. How often do you use this product?

• Prefer not to say

2. How much do you agree with our new policies? 

• Totally agree
• Somewhat agree
• Totally disagree
• Interval Scale

This is a type of survey scale that is used to measure variables existing at equal intervals along a common scale. In some way, it combines the attributes of nominal and ordinal scales since it is used where there is order and there is a meaningful difference between 2 variables. 

With an interval scale, you can quantify the difference in value between two variables in survey research. In addition to this, you can carry out other mathematical processes like calculating the mean and median of research variables. 

Examples of Interval Scale in Survey Research

1. Our customer support team was very effective. 

• Completely agree
• Neither agree nor disagree
• Somewhat disagree
• Completely disagree 

2. I enjoyed using this product.

Another example of an interval scale can be seen in the Net Promoter Score.

• Ratio Scale

Just like the interval scale, the ratio scale is quantitative and it is used when you need to compare intervals or differences in survey research. It is the highest level of measurement and it is made up of bits and pieces of the other survey scales. 

One of the unique features of the ratio scale is it has a true zero and equal intervals between the variables on the scale. This zero indicates an absence of the variable being measured by the scale. Common occurrences of ratio scales can be seen with distance (length), area, and population measurement. 

Examples of Ratio Scale in Survey Research

1. How old are you?

• Below 18 years
• 41 and above

2. How many times do you shop in a week?

• Less than twice
• Three times
• More than four times

Uses of Survey Research

• Health Surveys

Survey research is used by health practitioners to gather useful data from patients in different medical and safety contexts. It helps you to gather primary and secondary data about medical conditions and risk factors of multiple diseases and infections. 

In addition to this, administering health surveys regularly helps you to monitor the overall health status of your population; whether in the workplace, school, or community. This kind of data can be used to help prevent outbreaks and minimize medical emergencies in these contexts. 

Survey research is also useful when conducting polls; whether online or offline. A poll is a data collection tool that helps you to gather public opinion about a particular subject from a well-defined research sample.

By administering survey research, you can gather valid data from a well-defined research sample, and utilize research findings for decision making. For example, during elections, individuals can be asked to choose their preferred leader via questionnaires administered as part of survey research.

• Customer Satisfaction

Customer satisfaction is one of the cores of every organization as it is directly concerned with how well your product or service meets the needs of your clients. Survey research is an effective way to measure customer satisfaction at different intervals. 

As a restaurant, for example, you can send out online surveys to customers immediately when they patronize your business. In these surveys, encourage them to provide feedback on their experience and to provide information on how your service delivery can be improved. 

Survey research makes data collection and analysis easy during a census. With an online survey tool like Formplus , you can seamlessly gather data during a census without moving from a spot. Formplus has multiple sharing options that help you collect information without stress. 

Survey Research Methods

Survey research can be done using different online and offline methods. Let’s examine a few of them here.

• Telephone Surveys

This is a means of conducting survey research via phone calls. In a telephone survey, the researcher places a call to the survey respondents and gathers information from them by asking questions about the research context under consideration. 

A telephone survey is a kind of simulation of the face-to-face survey experience since it involves discussing with respondents to gather and process valid data. However, major challenges with this method include the fact that it is expensive and time-consuming. 

• Online Surveys

An online survey is a data collection tool used to create and administer surveys and questionnaires using data tools like Formplus. Online surveys work better than paper forms and other offline survey methods because you can easily gather and process data from a large sample size with them. 

• Face-to-Face Interviews

Face-to-face interviews for survey research can be structured, semi-structured, or unstructured depending on the research context and the type of data you want to collect. If you want to gather qualitative data , then unstructured and semi-structured interviews are the way to go. 

On the other hand, if you want to collect quantifiable information from your research sample, conducting a structured interview is the best way to go. Face-to-face interviews can also be time-consuming and cost-intensive. Let’s mention here that face-to-face surveys are one of the most widely used methods of survey data collection. 

How to Conduct Research Surveys on Formplus 

With Formplus, you can create forms for survey research without any hassles. Follow this step-by-step guide to create and administer online surveys for research via Formplus. 

1. Sign up at www.formpl.us to create your Formplus account. If you already have a Formplus account, click here to log in.

5. Use the form customization options to change the appearance of your survey. You can add your organization’s logo to the survey, change the form font and layout, and insert preferred background images.

Advantages of Survey Research

• It is inexpensive – with survey research, you can avoid the cost of in-person interviews. It’s also easy to receive data as you can share your surveys online and get responses from a large demographic
• It is the fastest way to get a large amount of first-hand data
• Surveys allow you to compare the results you get through charts and graphs
• It is versatile as it can be used for any research topic
• Surveys are perfect for anonymous respondents in the research 

Disadvantages of Survey Research

• Some questions may not get answers
• People may understand survey questions differently
• It may not be the best option for respondents with visual or hearing impairments as well as a demographic with no literacy levels
• People can provide dishonest answers in a survey research

Conclusion 

In this article, we’ve discussed survey research extensively; touching on different important aspects of this concept. As a researcher, organization, individual, or student, it is important to understand how survey research works to utilize it effectively and get the most from this method of systematic investigation. 

As we’ve already stated, conducting survey research online is one of the most effective methods of data collection as it allows you to gather valid data from a large group of respondents. If you’re looking to kick off your survey research, you can start by signing up for a Formplus account here. 

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Survey Research — Types, Methods and Example Questions

Survey research The world of research is vast and complex, but with the right tools and understanding, it's an open field of discovery. Welcome to a journey into the heart of survey research. What is survey research? Survey research is the lens through which we view the opinions, behaviors, and experiences of a population. Think of it as the research world's detective, cleverly sleuthing out the truths hidden beneath layers of human complexity. Why is survey research important? Survey research is a Swiss Army Knife in a researcher's toolbox. It’s adaptable, reliable, and incredibly versatile, but its real power? It gives voice to the silent majority. Whether it's understanding customer preferences or assessing the impact of a social policy, survey research is the bridge between unanswered questions and insightful data. Let's embark on this exploration, armed with the spirit of openness, a sprinkle of curiosity, and the thirst for making knowledge accessible. As we journey further into the realm of survey research, we'll delve deeper into the diverse types of surveys, innovative data collection methods, and the rewards and challenges that come with them. Types of survey research Survey research is like an artist's palette, offering a variety of types to suit your unique research needs. Each type paints a different picture, giving us fascinating insights into the world around us. Cross-Sectional Surveys: Capture a snapshot of a population at a specific moment in time. They're your trusty Polaroid camera, freezing a moment for analysis and understanding. Longitudinal Surveys: Track changes over time, much like a time-lapse video. They help to identify trends and patterns, offering a dynamic perspective of your subject. Descriptive Surveys: Draw a detailed picture of the current state of affairs. They're your magnifying glass, examining the prevalence of a phenomenon or attitudes within a group. Analytical Surveys: Deep dive into the reasons behind certain outcomes. They're the research world's version of Sherlock Holmes, unraveling the complex web of cause and effect. But, what method should you choose for data collection? The plot thickens, doesn't it? Let's unravel this mystery in our next section. Survey research and data collection methods Data collection in survey research is an art form, and there's no one-size-fits-all method. Think of it as your paintbrush, each stroke represents a different way of capturing data. Online Surveys: In the digital age, online surveys have surged in popularity. They're fast, cost-effective, and can reach a global audience. But like a mysterious online acquaintance, respondents may not always be who they say they are. Mail Surveys: Like a postcard from a distant friend, mail surveys have a certain charm. They're great for reaching respondents without internet access. However, they’re slower and have lower response rates. They’re a test of patience and persistence. Telephone Surveys: With the sound of a ringing phone, the human element enters the picture. Great for reaching a diverse audience, they bring a touch of personal connection. But, remember, not all are fans of unsolicited calls. Face-to-Face Surveys: These are the heart-to-heart conversations of the survey world. While they require more resources, they're the gold standard for in-depth, high-quality data. As we journey further, let’s weigh the pros and cons of survey research. Advantages and disadvantages of survey research Every hero has its strengths and weaknesses, and survey research is no exception. Let's unwrap the gift box of survey research to see what lies inside. Advantages: Versatility: Like a superhero with multiple powers, surveys can be adapted to different topics, audiences, and research needs. Accessibility: With online surveys, geographical boundaries dissolve. We can reach out to the world from our living room. Anonymity: Like a confessional booth, surveys allow respondents to share their views without fear of judgment. Disadvantages: Response Bias: Ever met someone who says what you want to hear? Survey respondents can be like that too. Limited Depth: Like a puddle after a rainstorm, some surveys only skim the surface of complex issues. Nonresponse: Sometimes, potential respondents play hard to get, skewing the data. Survey research may have its challenges, but it also presents opportunities to learn and grow. As we forge ahead on our journey, we dive into the design process of survey research. Limitations of survey research Every research method has its limitations, like bumps on the road to discovery. But don't worry, with the right approach, these challenges become opportunities for growth. Misinterpretation: Sometimes, respondents might misunderstand your questions, like a badly translated novel. To overcome this, keep your questions simple and clear. Social Desirability Bias: People often want to present themselves in the best light. They might answer questions in a way that portrays them positively, even if it's not entirely accurate. Overcome this by ensuring anonymity and emphasizing honesty. Sample Representation: If your survey sample isn't representative of the population you're studying, it can skew your results. Aiming for a diverse sample can mitigate this. Now that we're aware of the limitations let's delve into the world of survey design. {loadmoduleid 430} Survey research design Designing a survey is like crafting a roadmap to discovery. It's an intricate process that involves careful planning, innovative strategies, and a deep understanding of your research goals. Let's get started. Approach and Strategy Your approach and strategy are the compasses guiding your survey research. Clear objectives, defined research questions, and an understanding of your target audience lay the foundation for a successful survey. Panel The panel is the heartbeat of your survey, the respondents who breathe life into your research. Selecting a representative panel ensures your research is accurate and inclusive. 9 Tips on Building the Perfect Survey Research Questionnaire Keep It Simple: Clear and straightforward questions lead to accurate responses. Make It Relevant: Ensure every question ties back to your research objectives. Order Matters: Start with easy questions to build rapport and save sensitive ones for later. Avoid Double-Barreled Questions: Stick to one idea per question. Offer a Balanced Scale: For rating scales, provide an equal number of positive and negative options. Provide a ‘Don't Know’ Option: This prevents guessing and keeps your data accurate. Pretest Your Survey: A pilot run helps you spot any issues before the final launch. Keep It Short: Respect your respondents' time. Make It Engaging: Keep your respondents interested with a mix of question types. Survey research examples and questions Examples serve as a bridge connecting theoretical concepts to real-world scenarios. Let's consider a few practical examples of survey research across various domains. User Experience (UX) Imagine being a UX designer at a budding tech start-up. Your app is gaining traction, but to keep your user base growing and engaged, you must ensure that your app's UX is top-notch. In this case, a well-designed survey could be a beacon, guiding you toward understanding user behavior, preferences, and pain points. Here's an example of how such a survey could look: "On a scale of 1 to 10, how would you rate the ease of navigating our app?" "How often do you encounter difficulties while using our app?" "What features do you use most frequently in our app?" "What improvements would you suggest for our app?" "What features would you like to see in future updates?" This line of questioning, while straightforward, provides invaluable insights. It enables the UX designer to identify strengths to capitalize on and weaknesses to improve, ultimately leading to a product that resonates with users. Psychology and Ethics in survey research The realm of survey research is not just about data and numbers, but it's also about understanding human behavior and treating respondents ethically. Psychology: In-depth understanding of cognitive biases and social dynamics can profoundly influence survey design. Let's take the 'Recency Effect,' a psychological principle stating that people tend to remember recent events more vividly than those in the past. While framing questions about user experiences, this insight could be invaluable. For example, a question like "Can you recall an instance in the past week when our customer service exceeded your expectations?" is likely to fetch more accurate responses than asking about an event several months ago. Ethics: On the other hand, maintaining privacy, confidentiality, and informed consent is more than ethical - it's fundamental to the integrity of the research process. Imagine conducting a sensitive survey about workplace culture. Ensuring respondents that their responses will remain confidential and anonymous can encourage more honest responses. An introductory note stating these assurances, along with a clear outline of the survey's purpose, can help build trust with your respondents. Survey research software In the age of digital information, survey research software has become a trusted ally for researchers. It simplifies complex processes like data collection, analysis, and visualization, democratizing research and making it more accessible to a broad audience. LimeSurvey, our innovative, user-friendly tool, brings this vision to life. It stands at the crossroads of simplicity and power, embodying the essence of accessible survey research. Whether you're a freelancer exploring new market trends, a psychology student curious about human behavior, or an HR officer aiming to improve company culture, LimeSurvey empowers you to conduct efficient, effective research. Its suite of features and intuitive design matches your research pace, allowing your curiosity to take the front seat. For instance, consider you're a researcher studying consumer behavior across different demographics. With LimeSurvey, you can easily design demographic-specific questions, distribute your survey across various channels, collect responses in real-time, and visualize your data through intuitive dashboards. This synergy of tools and functionalities makes LimeSurvey a perfect ally in your quest for knowledge. Conclusion If you've come this far, we can sense your spark of curiosity. Are you eager to take the reins and conduct your own survey research? Are you ready to embrace the simple yet powerful tool that LimeSurvey offers? If so, we can't wait to see where your journey takes you next! In the world of survey research, there's always more to explore, more to learn and more to discover. So, keep your curiosity alive, stay open to new ideas, and remember, your exploration is just beginning! We hope that our exploration has been as enlightening for you as it was exciting for us. Remember, the journey doesn't end here. With the power of knowledge and the right tools in your hands, there's no limit to what you can achieve. So, let your curiosity be your guide and dive into the fascinating world of survey research with LimeSurvey! Try it out for free now! Happy surveying! {loadmoduleid 429}

Table Content

Survey research.

The world of research is vast and complex, but with the right tools and understanding, it's an open field of discovery. Welcome to a journey into the heart of survey research.

What is survey research?

Survey research is the lens through which we view the opinions, behaviors, and experiences of a population. Think of it as the research world's detective, cleverly sleuthing out the truths hidden beneath layers of human complexity.

Why is survey research important?

Survey research is a Swiss Army Knife in a researcher's toolbox. It’s adaptable, reliable, and incredibly versatile, but its real power? It gives voice to the silent majority. Whether it's understanding customer preferences or assessing the impact of a social policy, survey research is the bridge between unanswered questions and insightful data.

Let's embark on this exploration, armed with the spirit of openness, a sprinkle of curiosity, and the thirst for making knowledge accessible. As we journey further into the realm of survey research, we'll delve deeper into the diverse types of surveys, innovative data collection methods, and the rewards and challenges that come with them.

Types of survey research

Survey research is like an artist's palette, offering a variety of types to suit your unique research needs. Each type paints a different picture, giving us fascinating insights into the world around us.

• Cross-Sectional Surveys: Capture a snapshot of a population at a specific moment in time. They're your trusty Polaroid camera, freezing a moment for analysis and understanding.
• Longitudinal Surveys: Track changes over time, much like a time-lapse video. They help to identify trends and patterns, offering a dynamic perspective of your subject.
• Descriptive Surveys: Draw a detailed picture of the current state of affairs. They're your magnifying glass, examining the prevalence of a phenomenon or attitudes within a group.
• Analytical Surveys: Deep dive into the reasons behind certain outcomes. They're the research world's version of Sherlock Holmes, unraveling the complex web of cause and effect.

But, what method should you choose for data collection? The plot thickens, doesn't it? Let's unravel this mystery in our next section.

Survey research and data collection methods

Data collection in survey research is an art form, and there's no one-size-fits-all method. Think of it as your paintbrush, each stroke represents a different way of capturing data.

• Online Surveys: In the digital age, online surveys have surged in popularity. They're fast, cost-effective, and can reach a global audience. But like a mysterious online acquaintance, respondents may not always be who they say they are.
• Mail Surveys: Like a postcard from a distant friend, mail surveys have a certain charm. They're great for reaching respondents without internet access. However, they’re slower and have lower response rates. They’re a test of patience and persistence.
• Telephone Surveys: With the sound of a ringing phone, the human element enters the picture. Great for reaching a diverse audience, they bring a touch of personal connection. But, remember, not all are fans of unsolicited calls.
• Face-to-Face Surveys: These are the heart-to-heart conversations of the survey world. While they require more resources, they're the gold standard for in-depth, high-quality data.

As we journey further, let’s weigh the pros and cons of survey research.

Advantages and disadvantages of survey research

Every hero has its strengths and weaknesses, and survey research is no exception. Let's unwrap the gift box of survey research to see what lies inside.

Advantages:

• Versatility: Like a superhero with multiple powers, surveys can be adapted to different topics, audiences, and research needs.
• Accessibility: With online surveys, geographical boundaries dissolve. We can reach out to the world from our living room.
• Anonymity: Like a confessional booth, surveys allow respondents to share their views without fear of judgment.

Disadvantages:

• Response Bias: Ever met someone who says what you want to hear? Survey respondents can be like that too.
• Limited Depth: Like a puddle after a rainstorm, some surveys only skim the surface of complex issues.
• Nonresponse: Sometimes, potential respondents play hard to get, skewing the data.

Survey research may have its challenges, but it also presents opportunities to learn and grow. As we forge ahead on our journey, we dive into the design process of survey research.

Limitations of survey research

Every research method has its limitations, like bumps on the road to discovery. But don't worry, with the right approach, these challenges become opportunities for growth.

Misinterpretation: Sometimes, respondents might misunderstand your questions, like a badly translated novel. To overcome this, keep your questions simple and clear.

Social Desirability Bias: People often want to present themselves in the best light. They might answer questions in a way that portrays them positively, even if it's not entirely accurate. Overcome this by ensuring anonymity and emphasizing honesty.

Sample Representation: If your survey sample isn't representative of the population you're studying, it can skew your results. Aiming for a diverse sample can mitigate this.

Now that we're aware of the limitations let's delve into the world of survey design.

•   Create surveys in 40+ languages
•   Unlimited number of users
•   Ready-to-go survey templates
•   So much more...

Survey research design

Designing a survey is like crafting a roadmap to discovery. It's an intricate process that involves careful planning, innovative strategies, and a deep understanding of your research goals. Let's get started.

Approach and Strategy

Your approach and strategy are the compasses guiding your survey research. Clear objectives, defined research questions, and an understanding of your target audience lay the foundation for a successful survey.

The panel is the heartbeat of your survey, the respondents who breathe life into your research. Selecting a representative panel ensures your research is accurate and inclusive.

9 Tips on Building the Perfect Survey Research Questionnaire

• Keep It Simple: Clear and straightforward questions lead to accurate responses.
• Make It Relevant: Ensure every question ties back to your research objectives.
• Order Matters: Start with easy questions to build rapport and save sensitive ones for later.
• Avoid Double-Barreled Questions: Stick to one idea per question.
• Offer a Balanced Scale: For rating scales, provide an equal number of positive and negative options.
• Provide a ‘Don't Know’ Option: This prevents guessing and keeps your data accurate.
• Pretest Your Survey: A pilot run helps you spot any issues before the final launch.
• Keep It Short: Respect your respondents' time.
• Make It Engaging: Keep your respondents interested with a mix of question types.

Survey research examples and questions

Examples serve as a bridge connecting theoretical concepts to real-world scenarios. Let's consider a few practical examples of survey research across various domains.

User Experience (UX)

Imagine being a UX designer at a budding tech start-up. Your app is gaining traction, but to keep your user base growing and engaged, you must ensure that your app's UX is top-notch. In this case, a well-designed survey could be a beacon, guiding you toward understanding user behavior, preferences, and pain points.

Here's an example of how such a survey could look:

• "On a scale of 1 to 10, how would you rate the ease of navigating our app?"
• "How often do you encounter difficulties while using our app?"
• "What features do you use most frequently in our app?"
• "What improvements would you suggest for our app?"
• "What features would you like to see in future updates?"

This line of questioning, while straightforward, provides invaluable insights. It enables the UX designer to identify strengths to capitalize on and weaknesses to improve, ultimately leading to a product that resonates with users.

Psychology and Ethics in survey research

The realm of survey research is not just about data and numbers, but it's also about understanding human behavior and treating respondents ethically.

Psychology: In-depth understanding of cognitive biases and social dynamics can profoundly influence survey design. Let's take the 'Recency Effect,' a psychological principle stating that people tend to remember recent events more vividly than those in the past. While framing questions about user experiences, this insight could be invaluable.

For example, a question like "Can you recall an instance in the past week when our customer service exceeded your expectations?" is likely to fetch more accurate responses than asking about an event several months ago.

Ethics: On the other hand, maintaining privacy, confidentiality, and informed consent is more than ethical - it's fundamental to the integrity of the research process.

Imagine conducting a sensitive survey about workplace culture. Ensuring respondents that their responses will remain confidential and anonymous can encourage more honest responses. An introductory note stating these assurances, along with a clear outline of the survey's purpose, can help build trust with your respondents.

Survey research software

In the age of digital information, survey research software has become a trusted ally for researchers. It simplifies complex processes like data collection, analysis, and visualization, democratizing research and making it more accessible to a broad audience.

LimeSurvey, our innovative, user-friendly tool, brings this vision to life. It stands at the crossroads of simplicity and power, embodying the essence of accessible survey research.

Whether you're a freelancer exploring new market trends, a psychology student curious about human behavior, or an HR officer aiming to improve company culture, LimeSurvey empowers you to conduct efficient, effective research. Its suite of features and intuitive design matches your research pace, allowing your curiosity to take the front seat.

For instance, consider you're a researcher studying consumer behavior across different demographics. With LimeSurvey, you can easily design demographic-specific questions, distribute your survey across various channels, collect responses in real-time, and visualize your data through intuitive dashboards. This synergy of tools and functionalities makes LimeSurvey a perfect ally in your quest for knowledge.

If you've come this far, we can sense your spark of curiosity. Are you eager to take the reins and conduct your own survey research? Are you ready to embrace the simple yet powerful tool that LimeSurvey offers? If so, we can't wait to see where your journey takes you next!

In the world of survey research, there's always more to explore, more to learn and more to discover. So, keep your curiosity alive, stay open to new ideas, and remember, your exploration is just beginning!

We hope that our exploration has been as enlightening for you as it was exciting for us. Remember, the journey doesn't end here. With the power of knowledge and the right tools in your hands, there's no limit to what you can achieve. So, let your curiosity be your guide and dive into the fascinating world of survey research with LimeSurvey! Try it out for free now!

Happy surveying!

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Survey descriptive research: Method, design, and examples

• November 2, 2022

What is survey descriptive research?

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Survey descriptive research is a quantitative method that focuses on describing the characteristics of a phenomenon rather than asking why it occurs. Doing this provides a better understanding of the nature of the subject at hand and creates a good foundation for further research.

Descriptive market research is one of the most commonly used ways of examining trends and changes in the market. It is easy, low-cost, and provides valuable in-depth information on a chosen subject.

This article will examine the basic principles of the descriptive survey study and show how to make the best descriptive survey questionnaire and how to conduct effective research.

It is often said to be quantitative research that focuses more on the what, how, when, and where instead of the why. But what does that actually mean?

The answer is simple. By conducting descriptive survey research, the nature of a phenomenon is focused upon without asking about what causes it.

The main goal of survey descriptive research is to shed light on the heart of the research problem and better understand it. The technique provides in-depth knowledge of what the research problem is before investigating why it exists.

Survey descriptive research and data collection methods

Descriptive research methods can differ based on data collection. We distinguish three main data collection methods: case study, observational method, and descriptive survey method.

Of these, the descriptive survey research method is most commonly used in fields such as market research, social research, psychology, politics, etc.

Sometimes also called the observational descriptive method, this is simply monitoring people while they engage with a particular subject. The aim is to examine people’s real-life behavior by maintaining a natural environment that does not change the respondents’ behavior—because they do not know they are being observed.

It is often used in fields such as market research, psychology, or social research. For example, customers can be monitored while dining at a restaurant or browsing through the products in a shop.

When doing case studies, researchers conduct thorough examinations of individuals or groups. The case study method is not used to collect general information on a particular subject. Instead, it provides an in-depth understanding of a particular subject and can give rise to interesting conclusions and new hypotheses.

The term case study can also refer to a sample group, which is a specific group of people that are examined and, afterward, findings are generalized to a larger group of people. However, this kind of generalization is rather risky because it is not always accurate.

Additionally, case studies cannot be used to determine cause and effect because of potential bias on the researcher’s part.

The survey descriptive research method consists of creating questionnaires or polls and distributing them to respondents, who then answer the questions (usually a mix of open-ended and closed-ended).

Surveys are the easiest and most cost-efficient way to gain feedback on a particular topic. They can be conducted online or offline, the size of the sample is highly flexible, and they can be distributed through many different channels.

When doing market research , use such surveys to understand the demographic of a certain market or population, better determine the target audience, keep track of the changes in the market, and learn about customer experience and satisfaction with products and services.

Several types of survey descriptive research are classified based on the approach used:

• Descriptive surveys gather information about a certain subject.
• Descriptive-normative surveys gather information just like a descriptive survey, after which results are compared with a norm.
• Correlative surveys explore the relationship between two variables and conclude if it is positive, neutral, or negative.

A descriptive survey research design is a methodology used in social science and other fields to gather information and describe the characteristics, behaviors, or attitudes of a particular population or group of interest. While there may not be a single definition provided by specific authors, the concept is widely understood and defined similarly across the literature.

Here’s a general definition that captures the essence of a descriptive survey research design definition by authors:

A descriptive survey research design is a systematic and structured approach to collecting data from a sample of individuals or entities within a larger population, with the primary aim of providing a detailed and accurate description of the characteristics, behaviors, opinions, or attitudes that exist within the target group. This method involves the use of surveys, questionnaires, interviews, or observations to collect data, which is then analyzed and summarized to draw conclusions about the population of interest.

It’s important to note that descriptive survey research is often used when researchers want to gain insights into a population or phenomenon, but without manipulating variables or testing hypotheses, as is common in experimental research. Instead, it focuses on providing a comprehensive overview of the subject under investigation. Researchers often use various statistical and analytical techniques to summarize and interpret the collected data in descriptive survey research.

The characteristics and advantages of a descriptive survey questionnaire

There are numerous advantages to using a descriptive survey design. First of all, it is cheap and easy to conduct. A large sample can be surveyed and extensive data gathered quickly and inexpensively.

The data collected provides both quantitative and qualitative information , which provides a holistic understanding of the topic. Moreover, it can be used in further research on this or related topics.

Here are some of the most important advantages of conducting a survey descriptive research:

The descriptive survey research design uses both quantitative and qualitative research methods. It is used primarily to conduct quantitative research and gather data that is statistically easy to analyze. However, it can also provide qualitative data that helps describe and understand the research subject.

Descriptive research explores more than one variable. However, unlike experimental research, descriptive survey research design doesn’t allow control of variables. Instead, observational methods are used during research. Even though these variables can change and have an unexpected impact on an inquiry, they will give access to honest responses.

The descriptive research is conducted in a natural environment. This way, answers gathered from responses are more honest because the nature of the research does not influence them.

The data collected through descriptive research can be used to further explore the same or related subjects. Additionally, it can help develop the next line of research and the best method to use moving forward.

Descriptive survey example: When to use a descriptive research questionnaire?

Descriptive research design can be used for many purposes. It is mainly utilized to test a hypothesis, define the characteristics of a certain phenomenon, and examine the correlations between them.

Market research is one of the main fields in which descriptive methods are used to conduct studies. Here’s what can be done using this method:

Understanding the needs of customers and their desires is the key to a business’s success. By truly understanding these, it will be possible to offer exactly what customers need and prevent them from turning to competitors.

By using a descriptive survey, different customer characteristics—such as traits, opinions, or behavior patterns—can be determined. With this data, different customer types can be defined and profiles developed that focus on their interests and the behavior they exhibit. This information can be used to develop new products and services that will be successful.

Measuring data trends is extremely important. Explore the market and get valuable insights into how consumers’ interests change over time—as well as how the competition is performing in the marketplace.

Over time, the data gathered from a descriptive questionnaire can be subjected to statistical analysis. This will deliver valuable insights.

Another important aspect to consider is brand awareness. People need to know about your brand, and they need to have a positive opinion of it. The best way to discover their perception is to conduct a brand survey , which gives deeper insight into brand awareness, perception, identity, and customer loyalty .

When conducting survey descriptive research, there are a few basic steps that are needed for a survey to be successful:

• Define the research goals.
• Decide on the research method.
• Define the sample population.
• Design the questionnaire.
• Write specific questions.
• Distribute the questionnaire.
• Analyze the data .
• Make a survey report.

First of all, define the research goals. By setting up clear objectives, every other step can be worked through. This will result in the perfect descriptive questionnaire example and collect only valuable data.

Next, decide on the research method to use—in this case, the descriptive survey method. Then, define the sample population for (that is, the target audience). After that, think about the design itself and the questions that will be asked in the survey .

If you’re not sure where to start, we’ve got you covered. As free survey software, SurveyPlanet offers pre-made themes that are clean and eye-catching, as well as pre-made questions that will save you the trouble of making new ones.

Simply scroll through our library and choose a descriptive survey questionnaire sample that best suits your needs, though our user-friendly interface can help you create bespoke questions in a process that is easy and efficient.

With a survey in hand, it will then need to be delivered to the target audience. This is easy with our survey embedding feature, which allows for the linking of surveys on a website, via emails, or by sharing on social media.

When all the responses are gathered, it’s time to analyze them. Use SurveyPlanet to easily filter data and do cross-sectional analysis. Finally, just export the results and make a survey report.

Conducting descriptive survey research is the best way to gain a deeper knowledge of a topic of interest and develop a sound basis for further research. Sign up for a free SurveyPlanet account to start improving your business today!

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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9 Survey research

Survey research is a research method involving the use of standardised questionnaires or interviews to collect data about people and their preferences, thoughts, and behaviours in a systematic manner. Although census surveys were conducted as early as Ancient Egypt, survey as a formal research method was pioneered in the 1930–40s by sociologist Paul Lazarsfeld to examine the effects of the radio on political opinion formation of the United States. This method has since become a very popular method for quantitative research in the social sciences.

The survey method can be used for descriptive, exploratory, or explanatory research. This method is best suited for studies that have individual people as the unit of analysis. Although other units of analysis, such as groups, organisations or dyads—pairs of organisations, such as buyers and sellers—are also studied using surveys, such studies often use a specific person from each unit as a ‘key informant’ or a ‘proxy’ for that unit. Consequently, such surveys may be subject to respondent bias if the chosen informant does not have adequate knowledge or has a biased opinion about the phenomenon of interest. For instance, Chief Executive Officers may not adequately know employees’ perceptions or teamwork in their own companies, and may therefore be the wrong informant for studies of team dynamics or employee self-esteem.

Survey research has several inherent strengths compared to other research methods. First, surveys are an excellent vehicle for measuring a wide variety of unobservable data, such as people’s preferences (e.g., political orientation), traits (e.g., self-esteem), attitudes (e.g., toward immigrants), beliefs (e.g., about a new law), behaviours (e.g., smoking or drinking habits), or factual information (e.g., income). Second, survey research is also ideally suited for remotely collecting data about a population that is too large to observe directly. A large area—such as an entire country—can be covered by postal, email, or telephone surveys using meticulous sampling to ensure that the population is adequately represented in a small sample. Third, due to their unobtrusive nature and the ability to respond at one’s convenience, questionnaire surveys are preferred by some respondents. Fourth, interviews may be the only way of reaching certain population groups such as the homeless or illegal immigrants for which there is no sampling frame available. Fifth, large sample surveys may allow detection of small effects even while analysing multiple variables, and depending on the survey design, may also allow comparative analysis of population subgroups (i.e., within-group and between-group analysis). Sixth, survey research is more economical in terms of researcher time, effort and cost than other methods such as experimental research and case research. At the same time, survey research also has some unique disadvantages. It is subject to a large number of biases such as non-response bias, sampling bias, social desirability bias, and recall bias, as discussed at the end of this chapter.

Depending on how the data is collected, survey research can be divided into two broad categories: questionnaire surveys (which may be postal, group-administered, or online surveys), and interview surveys (which may be personal, telephone, or focus group interviews). Questionnaires are instruments that are completed in writing by respondents, while interviews are completed by the interviewer based on verbal responses provided by respondents. As discussed below, each type has its own strengths and weaknesses in terms of their costs, coverage of the target population, and researcher’s flexibility in asking questions.

Questionnaire surveys

Invented by Sir Francis Galton, a questionnaire is a research instrument consisting of a set of questions (items) intended to capture responses from respondents in a standardised manner. Questions may be unstructured or structured. Unstructured questions ask respondents to provide a response in their own words, while structured questions ask respondents to select an answer from a given set of choices. Subjects’ responses to individual questions (items) on a structured questionnaire may be aggregated into a composite scale or index for statistical analysis. Questions should be designed in such a way that respondents are able to read, understand, and respond to them in a meaningful way, and hence the survey method may not be appropriate or practical for certain demographic groups such as children or the illiterate.

Most questionnaire surveys tend to be self-administered postal surveys , where the same questionnaire is posted to a large number of people, and willing respondents can complete the survey at their convenience and return it in prepaid envelopes. Postal surveys are advantageous in that they are unobtrusive and inexpensive to administer, since bulk postage is cheap in most countries. However, response rates from postal surveys tend to be quite low since most people ignore survey requests. There may also be long delays (several months) in respondents’ completing and returning the survey, or they may even simply lose it. Hence, the researcher must continuously monitor responses as they are being returned, track and send non-respondents repeated reminders (two or three reminders at intervals of one to one and a half months is ideal). Questionnaire surveys are also not well-suited for issues that require clarification on the part of the respondent or those that require detailed written responses. Longitudinal designs can be used to survey the same set of respondents at different times, but response rates tend to fall precipitously from one survey to the next.

A second type of survey is a group-administered questionnaire . A sample of respondents is brought together at a common place and time, and each respondent is asked to complete the survey questionnaire while in that room. Respondents enter their responses independently without interacting with one another. This format is convenient for the researcher, and a high response rate is assured. If respondents do not understand any specific question, they can ask for clarification. In many organisations, it is relatively easy to assemble a group of employees in a conference room or lunch room, especially if the survey is approved by corporate executives.

A more recent type of questionnaire survey is an online or web survey. These surveys are administered over the Internet using interactive forms. Respondents may receive an email request for participation in the survey with a link to a website where the survey may be completed. Alternatively, the survey may be embedded into an email, and can be completed and returned via email. These surveys are very inexpensive to administer, results are instantly recorded in an online database, and the survey can be easily modified if needed. However, if the survey website is not password-protected or designed to prevent multiple submissions, the responses can be easily compromised. Furthermore, sampling bias may be a significant issue since the survey cannot reach people who do not have computer or Internet access, such as many of the poor, senior, and minority groups, and the respondent sample is skewed toward a younger demographic who are online much of the time and have the time and ability to complete such surveys. Computing the response rate may be problematic if the survey link is posted on LISTSERVs or bulletin boards instead of being emailed directly to targeted respondents. For these reasons, many researchers prefer dual-media surveys (e.g., postal survey and online survey), allowing respondents to select their preferred method of response.

Constructing a survey questionnaire is an art. Numerous decisions must be made about the content of questions, their wording, format, and sequencing, all of which can have important consequences for the survey responses.

Response formats. Survey questions may be structured or unstructured. Responses to structured questions are captured using one of the following response formats:

Dichotomous response , where respondents are asked to select one of two possible choices, such as true/false, yes/no, or agree/disagree. An example of such a question is: Do you think that the death penalty is justified under some circumstances? (circle one): yes / no.

Nominal response , where respondents are presented with more than two unordered options, such as: What is your industry of employment?: manufacturing / consumer services / retail / education / healthcare / tourism and hospitality / other.

Ordinal response , where respondents have more than two ordered options, such as: What is your highest level of education?: high school / bachelor’s degree / postgraduate degree.

Interval-level response , where respondents are presented with a 5-point or 7-point Likert scale, semantic differential scale, or Guttman scale. Each of these scale types were discussed in a previous chapter.

Continuous response , where respondents enter a continuous (ratio-scaled) value with a meaningful zero point, such as their age or tenure in a firm. These responses generally tend to be of the fill-in-the blanks type.

Question content and wording. Responses obtained in survey research are very sensitive to the types of questions asked. Poorly framed or ambiguous questions will likely result in meaningless responses with very little value. Dillman (1978) [1] recommends several rules for creating good survey questions. Every single question in a survey should be carefully scrutinised for the following issues:

Is the question clear and understandable ?: Survey questions should be stated in very simple language, preferably in active voice, and without complicated words or jargon that may not be understood by a typical respondent. All questions in the questionnaire should be worded in a similar manner to make it easy for respondents to read and understand them. The only exception is if your survey is targeted at a specialised group of respondents, such as doctors, lawyers and researchers, who use such jargon in their everyday environment. Is the question worded in a negative manner ?: Negatively worded questions such as ‘Should your local government not raise taxes?’ tend to confuse many respondents and lead to inaccurate responses. Double-negatives should be avoided when designing survey questions.

Is the question ambiguous ?: Survey questions should not use words or expressions that may be interpreted differently by different respondents (e.g., words like ‘any’ or ‘just’). For instance, if you ask a respondent, ‘What is your annual income?’, it is unclear whether you are referring to salary/wages, or also dividend, rental, and other income, whether you are referring to personal income, family income (including spouse’s wages), or personal and business income. Different interpretation by different respondents will lead to incomparable responses that cannot be interpreted correctly.

Does the question have biased or value-laden words ?: Bias refers to any property of a question that encourages subjects to answer in a certain way. Kenneth Rasinky (1989) [2] examined several studies on people’s attitude toward government spending, and observed that respondents tend to indicate stronger support for ‘assistance to the poor’ and less for ‘welfare’, even though both terms had the same meaning. In this study, more support was also observed for ‘halting rising crime rate’ and less for ‘law enforcement’, more for ‘solving problems of big cities’ and less for ‘assistance to big cities’, and more for ‘dealing with drug addiction’ and less for ‘drug rehabilitation’. A biased language or tone tends to skew observed responses. It is often difficult to anticipate in advance the biasing wording, but to the greatest extent possible, survey questions should be carefully scrutinised to avoid biased language.

Is the question double-barrelled ?: Double-barrelled questions are those that can have multiple answers. For example, ‘Are you satisfied with the hardware and software provided for your work?’. In this example, how should a respondent answer if they are satisfied with the hardware, but not with the software, or vice versa? It is always advisable to separate double-barrelled questions into separate questions: ‘Are you satisfied with the hardware provided for your work?’, and ’Are you satisfied with the software provided for your work?’. Another example: ‘Does your family favour public television?’. Some people may favour public TV for themselves, but favour certain cable TV programs such as Sesame Street for their children.

Is the question too general ?: Sometimes, questions that are too general may not accurately convey respondents’ perceptions. If you asked someone how they liked a certain book and provided a response scale ranging from ‘not at all’ to ‘extremely well’, if that person selected ‘extremely well’, what do they mean? Instead, ask more specific behavioural questions, such as, ‘Will you recommend this book to others, or do you plan to read other books by the same author?’. Likewise, instead of asking, ‘How big is your firm?’ (which may be interpreted differently by respondents), ask, ‘How many people work for your firm?’, and/or ‘What is the annual revenue of your firm?’, which are both measures of firm size.

Is the question too detailed ?: Avoid unnecessarily detailed questions that serve no specific research purpose. For instance, do you need the age of each child in a household, or is just the number of children in the household acceptable? However, if unsure, it is better to err on the side of details than generality.

Is the question presumptuous ?: If you ask, ‘What do you see as the benefits of a tax cut?’, you are presuming that the respondent sees the tax cut as beneficial. Many people may not view tax cuts as being beneficial, because tax cuts generally lead to lesser funding for public schools, larger class sizes, and fewer public services such as police, ambulance, and fire services. Avoid questions with built-in presumptions.

Is the question imaginary ?: A popular question in many television game shows is, ‘If you win a million dollars on this show, how will you spend it?’. Most respondents have never been faced with such an amount of money before and have never thought about it—they may not even know that after taxes, they will get only about $640,000 or so in the United States, and in many cases, that amount is spread over a 20-year period—and so their answers tend to be quite random, such as take a tour around the world, buy a restaurant or bar, spend on education, save for retirement, help parents or children, or have a lavish wedding. Imaginary questions have imaginary answers, which cannot be used for making scientific inferences.

Do respondents have the information needed to correctly answer the question ?: Oftentimes, we assume that subjects have the necessary information to answer a question, when in reality, they do not. Even if a response is obtained, these responses tend to be inaccurate given the subjects’ lack of knowledge about the question being asked. For instance, we should not ask the CEO of a company about day-to-day operational details that they may not be aware of, or ask teachers about how much their students are learning, or ask high-schoolers, ‘Do you think the US Government acted appropriately in the Bay of Pigs crisis?’.

Question sequencing. In general, questions should flow logically from one to the next. To achieve the best response rates, questions should flow from the least sensitive to the most sensitive, from the factual and behavioural to the attitudinal, and from the more general to the more specific. Some general rules for question sequencing:

Start with easy non-threatening questions that can be easily recalled. Good options are demographics (age, gender, education level) for individual-level surveys and firmographics (employee count, annual revenues, industry) for firm-level surveys.

Never start with an open ended question.

If following a historical sequence of events, follow a chronological order from earliest to latest.

Ask about one topic at a time. When switching topics, use a transition, such as, ‘The next section examines your opinions about…’

Use filter or contingency questions as needed, such as, ‘If you answered “yes” to question 5, please proceed to Section 2. If you answered “no” go to Section 3′.

Other golden rules . Do unto your respondents what you would have them do unto you. Be attentive and appreciative of respondents’ time, attention, trust, and confidentiality of personal information. Always practice the following strategies for all survey research:

People’s time is valuable. Be respectful of their time. Keep your survey as short as possible and limit it to what is absolutely necessary. Respondents do not like spending more than 10-15 minutes on any survey, no matter how important it is. Longer surveys tend to dramatically lower response rates.

Always assure respondents about the confidentiality of their responses, and how you will use their data (e.g., for academic research) and how the results will be reported (usually, in the aggregate).

For organisational surveys, assure respondents that you will send them a copy of the final results, and make sure that you follow up with your promise.

Thank your respondents for their participation in your study.

Finally, always pretest your questionnaire, at least using a convenience sample, before administering it to respondents in a field setting. Such pretesting may uncover ambiguity, lack of clarity, or biases in question wording, which should be eliminated before administering to the intended sample.

Interview survey

Interviews are a more personalised data collection method than questionnaires, and are conducted by trained interviewers using the same research protocol as questionnaire surveys (i.e., a standardised set of questions). However, unlike a questionnaire, the interview script may contain special instructions for the interviewer that are not seen by respondents, and may include space for the interviewer to record personal observations and comments. In addition, unlike postal surveys, the interviewer has the opportunity to clarify any issues raised by the respondent or ask probing or follow-up questions. However, interviews are time-consuming and resource-intensive. Interviewers need special interviewing skills as they are considered to be part of the measurement instrument, and must proactively strive not to artificially bias the observed responses.

The most typical form of interview is a personal or face-to-face interview , where the interviewer works directly with the respondent to ask questions and record their responses. Personal interviews may be conducted at the respondent’s home or office location. This approach may even be favoured by some respondents, while others may feel uncomfortable allowing a stranger into their homes. However, skilled interviewers can persuade respondents to co-operate, dramatically improving response rates.

A variation of the personal interview is a group interview, also called a focus group . In this technique, a small group of respondents (usually 6–10 respondents) are interviewed together in a common location. The interviewer is essentially a facilitator whose job is to lead the discussion, and ensure that every person has an opportunity to respond. Focus groups allow deeper examination of complex issues than other forms of survey research, because when people hear others talk, it often triggers responses or ideas that they did not think about before. However, focus group discussion may be dominated by a dominant personality, and some individuals may be reluctant to voice their opinions in front of their peers or superiors, especially while dealing with a sensitive issue such as employee underperformance or office politics. Because of their small sample size, focus groups are usually used for exploratory research rather than descriptive or explanatory research.

A third type of interview survey is a telephone interview . In this technique, interviewers contact potential respondents over the phone, typically based on a random selection of people from a telephone directory, to ask a standard set of survey questions. A more recent and technologically advanced approach is computer-assisted telephone interviewing (CATI). This is increasing being used by academic, government, and commercial survey researchers. Here the interviewer is a telephone operator who is guided through the interview process by a computer program displaying instructions and questions to be asked. The system also selects respondents randomly using a random digit dialling technique, and records responses using voice capture technology. Once respondents are on the phone, higher response rates can be obtained. This technique is not ideal for rural areas where telephone density is low, and also cannot be used for communicating non-audio information such as graphics or product demonstrations.

Role of interviewer. The interviewer has a complex and multi-faceted role in the interview process, which includes the following tasks:

Prepare for the interview: Since the interviewer is in the forefront of the data collection effort, the quality of data collected depends heavily on how well the interviewer is trained to do the job. The interviewer must be trained in the interview process and the survey method, and also be familiar with the purpose of the study, how responses will be stored and used, and sources of interviewer bias. They should also rehearse and time the interview prior to the formal study.

Locate and enlist the co-operation of respondents: Particularly in personal, in-home surveys, the interviewer must locate specific addresses, and work around respondents’ schedules at sometimes undesirable times such as during weekends. They should also be like a salesperson, selling the idea of participating in the study.

Motivate respondents: Respondents often feed off the motivation of the interviewer. If the interviewer is disinterested or inattentive, respondents will not be motivated to provide useful or informative responses either. The interviewer must demonstrate enthusiasm about the study, communicate the importance of the research to respondents, and be attentive to respondents’ needs throughout the interview.

Clarify any confusion or concerns: Interviewers must be able to think on their feet and address unanticipated concerns or objections raised by respondents to the respondents’ satisfaction. Additionally, they should ask probing questions as necessary even if such questions are not in the script.

Observe quality of response: The interviewer is in the best position to judge the quality of information collected, and may supplement responses obtained using personal observations of gestures or body language as appropriate.

Conducting the interview. Before the interview, the interviewer should prepare a kit to carry to the interview session, consisting of a cover letter from the principal investigator or sponsor, adequate copies of the survey instrument, photo identification, and a telephone number for respondents to call to verify the interviewer’s authenticity. The interviewer should also try to call respondents ahead of time to set up an appointment if possible. To start the interview, they should speak in an imperative and confident tone, such as, ‘I’d like to take a few minutes of your time to interview you for a very important study’, instead of, ‘May I come in to do an interview?’. They should introduce themself, present personal credentials, explain the purpose of the study in one to two sentences, and assure respondents that their participation is voluntary, and their comments are confidential, all in less than a minute. No big words or jargon should be used, and no details should be provided unless specifically requested. If the interviewer wishes to record the interview, they should ask for respondents’ explicit permission before doing so. Even if the interview is recorded, the interviewer must take notes on key issues, probes, or verbatim phrases

During the interview, the interviewer should follow the questionnaire script and ask questions exactly as written, and not change the words to make the question sound friendlier. They should also not change the order of questions or skip any question that may have been answered earlier. Any issues with the questions should be discussed during rehearsal prior to the actual interview sessions. The interviewer should not finish the respondent’s sentences. If the respondent gives a brief cursory answer, the interviewer should probe the respondent to elicit a more thoughtful, thorough response. Some useful probing techniques are:

The silent probe: Just pausing and waiting without going into the next question may suggest to respondents that the interviewer is waiting for more detailed response.

Overt encouragement: An occasional ‘uh-huh’ or ‘okay’ may encourage the respondent to go into greater details. However, the interviewer must not express approval or disapproval of what the respondent says.

Ask for elaboration: Such as, ‘Can you elaborate on that?’ or ‘A minute ago, you were talking about an experience you had in high school. Can you tell me more about that?’.

Reflection: The interviewer can try the psychotherapist’s trick of repeating what the respondent said. For instance, ‘What I’m hearing is that you found that experience very traumatic’ and then pause and wait for the respondent to elaborate.

After the interview is completed, the interviewer should thank respondents for their time, tell them when to expect the results, and not leave hastily. Immediately after leaving, they should write down any notes or key observations that may help interpret the respondent’s comments better.

Biases in survey research

Despite all of its strengths and advantages, survey research is often tainted with systematic biases that may invalidate some of the inferences derived from such surveys. Five such biases are the non-response bias, sampling bias, social desirability bias, recall bias, and common method bias.

Non-response bias. Survey research is generally notorious for its low response rates. A response rate of 15-20 per cent is typical in a postal survey, even after two or three reminders. If the majority of the targeted respondents fail to respond to a survey, this may indicate a systematic reason for the low response rate, which may in turn raise questions about the validity of the study’s results. For instance, dissatisfied customers tend to be more vocal about their experience than satisfied customers, and are therefore more likely to respond to questionnaire surveys or interview requests than satisfied customers. Hence, any respondent sample is likely to have a higher proportion of dissatisfied customers than the underlying population from which it is drawn. In this instance, not only will the results lack generalisability, but the observed outcomes may also be an artefact of the biased sample. Several strategies may be employed to improve response rates:

Advance notification: Sending a short letter to the targeted respondents soliciting their participation in an upcoming survey can prepare them in advance and improve their propensity to respond. The letter should state the purpose and importance of the study, mode of data collection (e.g., via a phone call, a survey form in the mail, etc.), and appreciation for their co-operation. A variation of this technique may be to ask the respondent to return a prepaid postcard indicating whether or not they are willing to participate in the study.

Relevance of content: People are more likely to respond to surveys examining issues of relevance or importance to them.

Respondent-friendly questionnaire: Shorter survey questionnaires tend to elicit higher response rates than longer questionnaires. Furthermore, questions that are clear, non-offensive, and easy to respond tend to attract higher response rates.

Endorsement: For organisational surveys, it helps to gain endorsement from a senior executive attesting to the importance of the study to the organisation. Such endorsement can be in the form of a cover letter or a letter of introduction, which can improve the researcher’s credibility in the eyes of the respondents.

Follow-up requests: Multiple follow-up requests may coax some non-respondents to respond, even if their responses are late.

Interviewer training: Response rates for interviews can be improved with skilled interviewers trained in how to request interviews, use computerised dialling techniques to identify potential respondents, and schedule call-backs for respondents who could not be reached.

Incentives : Incentives in the form of cash or gift cards, giveaways such as pens or stress balls, entry into a lottery, draw or contest, discount coupons, promise of contribution to charity, and so forth may increase response rates.

Non-monetary incentives: Businesses, in particular, are more prone to respond to non-monetary incentives than financial incentives. An example of such a non-monetary incentive is a benchmarking report comparing the business’s individual response against the aggregate of all responses to a survey.

Confidentiality and privacy: Finally, assurances that respondents’ private data or responses will not fall into the hands of any third party may help improve response rates

Sampling bias. Telephone surveys conducted by calling a random sample of publicly available telephone numbers will systematically exclude people with unlisted telephone numbers, mobile phone numbers, and people who are unable to answer the phone when the survey is being conducted—for instance, if they are at work—and will include a disproportionate number of respondents who have landline telephone services with listed phone numbers and people who are home during the day, such as the unemployed, the disabled, and the elderly. Likewise, online surveys tend to include a disproportionate number of students and younger people who are constantly on the Internet, and systematically exclude people with limited or no access to computers or the Internet, such as the poor and the elderly. Similarly, questionnaire surveys tend to exclude children and the illiterate, who are unable to read, understand, or meaningfully respond to the questionnaire. A different kind of sampling bias relates to sampling the wrong population, such as asking teachers (or parents) about their students’ (or children’s) academic learning, or asking CEOs about operational details in their company. Such biases make the respondent sample unrepresentative of the intended population and hurt generalisability claims about inferences drawn from the biased sample.

Social desirability bias . Many respondents tend to avoid negative opinions or embarrassing comments about themselves, their employers, family, or friends. With negative questions such as, ‘Do you think that your project team is dysfunctional?’, ‘Is there a lot of office politics in your workplace?’, ‘Or have you ever illegally downloaded music files from the Internet?’, the researcher may not get truthful responses. This tendency among respondents to ‘spin the truth’ in order to portray themselves in a socially desirable manner is called the ‘social desirability bias’, which hurts the validity of responses obtained from survey research. There is practically no way of overcoming the social desirability bias in a questionnaire survey, but in an interview setting, an astute interviewer may be able to spot inconsistent answers and ask probing questions or use personal observations to supplement respondents’ comments.

Recall bias. Responses to survey questions often depend on subjects’ motivation, memory, and ability to respond. Particularly when dealing with events that happened in the distant past, respondents may not adequately remember their own motivations or behaviours, or perhaps their memory of such events may have evolved with time and no longer be retrievable. For instance, if a respondent is asked to describe his/her utilisation of computer technology one year ago, or even memorable childhood events like birthdays, their response may not be accurate due to difficulties with recall. One possible way of overcoming the recall bias is by anchoring the respondent’s memory in specific events as they happened, rather than asking them to recall their perceptions and motivations from memory.

Common method bias. Common method bias refers to the amount of spurious covariance shared between independent and dependent variables that are measured at the same point in time, such as in a cross-sectional survey, using the same instrument, such as a questionnaire. In such cases, the phenomenon under investigation may not be adequately separated from measurement artefacts. Standard statistical tests are available to test for common method bias, such as Harmon’s single-factor test (Podsakoff, MacKenzie, Lee & Podsakoff, 2003), [3] Lindell and Whitney’s (2001) [4] market variable technique, and so forth. This bias can potentially be avoided if the independent and dependent variables are measured at different points in time using a longitudinal survey design, or if these variables are measured using different methods, such as computerised recording of dependent variable versus questionnaire-based self-rating of independent variables.

• Dillman, D. (1978). Mail and telephone surveys: The total design method . New York: Wiley. ↵
• Rasikski, K. (1989). The effect of question wording on public support for government spending. Public Opinion Quarterly , 53(3), 388–394. ↵
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88(5), 879–903. http://dx.doi.org/10.1037/0021-9010.88.5.879. ↵
• Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology , 86(1), 114–121. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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 ...

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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

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

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Chapter 3 -- Survey Research Design and Quantitative Methods of Analysis for Cross-sectional Data

Almost everyone has had experience with surveys. Market surveys ask respondents whether they recognize products and their feelings about them. Political polls ask questions about candidates for political office or opinions related to political and social issues. Needs assessments use surveys that identify the needs of groups. Evaluations often use surveys to assess the extent to which programs achieve their goals. Survey research is a method of collecting information by asking questions. Sometimes interviews are done face-to-face with people at home, in school, or at work. Other times questions are sent in the mail for people to answer and mail back. Increasingly, surveys are conducted by telephone. SAMPLE SURVEYS Although we want to have information on all people, it is usually too expensive and time consuming to question everyone. So we select only some of these individuals and question them. It is important to select these people in ways that make it likely that they represent the larger group. The population is all the individuals in whom we are interested. (A population does not always consist of individuals. Sometimes, it may be geographical areas such as all cities with populations of 100,000 or more. Or we may be interested in all households in a particular area. In the data used in the exercises of this module the population consists of individuals who are California residents.) A sample is the subset of the population involved in a study. In other words, a sample is part of the population. The process of selecting the sample is called sampling . The idea of sampling is to select part of the population to represent the entire population. The United States Census is a good example of sampling. The census tries to enumerate all residents every ten years with a short questionnaire. Approximately every fifth household is given a longer questionnaire. Information from this sample (i.e., every fifth household) is used to make inferences about the population. Political polls also use samples. To find out how potential voters feel about a particular race, pollsters select a sample of potential voters. This module uses opinions from three samples of California residents age 18 and over. The data were collected during July, 1985, September, 1991, and February, 1995, by the Field Research Corporation (The Field Institute 1985, 1991, 1995). The Field Research Corporation is a widely-respected survey research firm and is used extensively by the media, politicians, and academic researchers. Since a survey can be no better than the quality of the sample, it is essential to understand the basic principles of sampling. There are two types of sampling-probability and nonprobability. A probability sample is one in which each individual in the population has a known, nonzero chance of being selected in the sample. The most basic type is the simple random sample . In a simple random sample, every individual (and every combination of individuals) has the same chance of being selected in the sample. This is the equivalent of writing each person's name on a piece of paper, putting them in plastic balls, putting all the balls in a big bowl, mixing the balls thoroughly, and selecting some predetermined number of balls from the bowl. This would produce a simple random sample. The simple random sample assumes that we can list all the individuals in the population, but often this is impossible. If our population were all the households or residents of California, there would be no list of the households or residents available, and it would be very expensive and time consuming to construct one. In this type of situation, a multistage cluster sample would be used. The idea is very simple. If we wanted to draw a sample of all residents of California, we might start by dividing California into large geographical areas such as counties and selecting a sample of these counties. Our sample of counties could then be divided into smaller geographical areas such as blocks and a sample of blocks would be selected. We could then construct a list of all households for only those blocks in the sample. Finally, we would go to these households and randomly select one member of each household for our sample. Once the household and the member of that household have been selected, substitution would not be allowed. This often means that we must call back several times, but this is the price we must pay for a good sample. The Field Poll used in this module is a telephone survey. It is a probability sample using a technique called random-digit dialing . With random-digit dialing, phone numbers are dialed randomly within working exchanges (i.e., the first three digits of the telephone number). Numbers are selected in such a way that all areas have the proper proportional chance of being selected in the sample. Random-digit dialing makes it possible to include numbers that are not listed in the telephone directory and households that have moved into an area so recently that they are not included in the current telephone directory. A nonprobability sample is one in which each individual in the population does not have a known chance of selection in the sample. There are several types of nonprobability samples. For example, magazines often include questionnaires for readers to fill out and return. This is a volunteer sample since respondents self-select themselves into the sample (i.e., they volunteer to be in the sample). Another type of nonprobability sample is a quota sample . Survey researchers may assign quotas to interviewers. For example, interviewers might be told that half of their respondents must be female and the other half male. This is a quota on sex. We could also have quotas on several variables (e.g., sex and race) simultaneously. Probability samples are preferable to nonprobability samples. First, they avoid the dangers of what survey researchers call "systematic selection biases" which are inherent in nonprobability samples. For example, in a volunteer sample, particular types of persons might be more likely to volunteer. Perhaps highly-educated individuals are more likely to volunteer to be in the sample and this would produce a systematic selection bias in favor of the highly educated. In a probability sample, the selection of the actual cases in the sample is left to chance. Second, in a probability sample we are able to estimate the amount of sampling error (our next concept to discuss). We would like our sample to give us a perfectly accurate picture of the population. However, this is unrealistic. Assume that the population is all employees of a large corporation, and we want to estimate the percent of employees in the population that is satisfied with their jobs. We select a simple random sample of 500 employees and ask the individuals in the sample how satisfied they are with their jobs. We discover that 75 percent of the employees in our sample are satisfied. Can we assume that 75 percent of the population is satisfied? That would be asking too much. Why would we expect one sample of 500 to give us a perfect representation of the population? We could take several different samples of 500 employees and the percent satisfied from each sample would vary from sample to sample. There will be a certain amount of error as a result of selecting a sample from the population. We refer to this as sampling error . Sampling error can be estimated in a probability sample, but not in a nonprobability sample. It would be wrong to assume that the only reason our sample estimate is different from the true population value is because of sampling error. There are many other sources of error called nonsampling error . Nonsampling error would include such things as the effects of biased questions, the tendency of respondents to systematically underestimate such things as age, the exclusion of certain types of people from the sample (e.g., those without phones, those without permanent addresses), or the tendency of some respondents to systematically agree to statements regardless of the content of the statements. In some studies, the amount of nonsampling error might be far greater than the amount of sampling error. Notice that sampling error is random in nature, while nonsampling error may be nonrandom producing systematic biases. We can estimate the amount of sampling error (assuming probability sampling), but it is much more difficult to estimate nonsampling error. We can never eliminate sampling error entirely, and it is unrealistic to expect that we could ever eliminate nonsampling error. It is good research practice to be diligent in seeking out sources of nonsampling error and trying to minimize them.   DATA ANALYSIS Examining Variables One at a Time (Univariate Analysis) The rest of this chapter will deal with the analysis of survey data . Data analysis involves looking at variables or "things" that vary or change. A variable is a characteristic of the individual (assuming we are studying individuals). The answer to each question on the survey forms a variable. For example, sex is a variable-some individuals in the sample are male and some are female. Age is a variable; individuals vary in their ages. Looking at variables one at a time is called univariate analysis . This is the usual starting point in analyzing survey data. There are several reasons to look at variables one at a time. First, we want to describe the data. How many of our sample are men and how many are women? How many are black and how many are white? What is the distribution by age? How many say they are going to vote for Candidate A and how many for Candidate B? How many respondents agree and how many disagree with a statement describing a particular opinion? Another reason we might want to look at variables one at a time involves recoding. Recoding is the process of combining categories within a variable. Consider age, for example. In the data set used in this module, age varies from 18 to 89, but we would want to use fewer categories in our analysis, so we might combine age into age 18 to 29, 30 to 49, and 50 and over. We might want to combine African Americans with the other races to classify race into only two categories-white and nonwhite. Recoding is used to reduce the number of categories in the variable (e.g., age) or to combine categories so that you can make particular types of comparisons (e.g., white versus nonwhite). The frequency distribution is one of the basic tools for looking at variables one at a time. A frequency distribution is the set of categories and the number of cases in each category. Percent distributions show the percentage in each category. Table 3.1 shows frequency and percent distributions for two hypothetical variables-one for sex and one for willingness to vote for a woman candidate. Begin by looking at the frequency distribution for sex. There are three columns in this table. The first column specifies the categories-male and female. The second column tells us how many cases there are in each category, and the third column converts these frequencies into percents. Table 3.1 -- Frequency and Percent Distributions for Sex and Willingness to Vote for a Woman Candidate (Hypothetical Data) Sex Voting Preference Category  Freq.  Percent  Category  Freq.  Percent  Valid Percent  Male  380  40.0  Willing to Vote for a Woman  460  48.4  51.1  Female  570  60.0  Not Willing to Vote for a Woman  440  46.3  48.9  Total  950  100.0  Refused  50  5.3  Missing  Total  950  100.0  100.0  In this hypothetical example, there are 380 males and 570 females or 40 percent male and 60 percent female. There are a total of 950 cases. Since we know the sex for each case, there are no missing data (i.e., no cases where we do not know the proper category). Look at the frequency distribution for voting preference in Table 3.1. How many say they are willing to vote for a woman candidate and how many are unwilling? (Answer: 460 willing and 440 not willing) How many refused to answer the question? (Answer: 50) What percent say they are willing to vote for a woman, what percent are not, and what percent refused to answer? (Answer: 48.4 percent willing to vote for a woman, 46.3 percent not willing, and 5.3 percent refused to tell us.) The 50 respondents who didn't want to answer the question are called missing data because we don't know which category into which to place them, so we create a new category (i.e., refused) for them. Since we don't know where they should go, we might want a percentage distribution considering only the 900 respondents who answered the question. We can determine this easily by taking the 50 cases with missing information out of the base (i.e., the denominator of the fraction) and recomputing the percentages. The fourth column in the frequency distribution (labeled "valid percent") gives us this information. Approximately 51 percent of those who answered the question were willing to vote for a woman and approximately 49 percent were not. With these data we will use frequency distributions to describe variables one at a time. There are other ways to describe single variables. The mean, median, and mode are averages that may be used to describe the central tendency of a distribution. The range and standard deviation are measures of the amount of variability or dispersion of a distribution. (We will not be using measures of central tendency or variability in this module.)   Exploring the Relationship Between Two Variables (Bivariate Analysis) Usually we want to do more than simply describe variables one at a time. We may want to analyze the relationship between variables. Morris Rosenberg (1968:2) suggests that there are three types of relationships: "(1) neither variable may influence one another .... (2) both variables may influence one another ... (3) one of the variables may influence the other." We will focus on the third of these types which Rosenberg calls "asymmetrical relationships." In this type of relationship, one of the variables (the independent variable ) is assumed to be the cause and the other variable (the dependent variable ) is assumed to be the effect. In other words, the independent variable is the factor that influences the dependent variable. For example, researchers think that smoking causes lung cancer. The statement that specifies the relationship between two variables is called a hypothesis (see Hoover 1992, for a more extended discussion of hypotheses). In this hypothesis, the independent variable is smoking (or more precisely, the amount one smokes) and the dependent variable is lung cancer. Consider another example. Political analysts think that income influences voting decisions, that rich people vote differently from poor people. In this hypothesis, income would be the independent variable and voting would be the dependent variable. In order to demonstrate that a causal relationship exists between two variables, we must meet three criteria: (1) there must be a statistical relationship between the two variables, (2) we must be able to demonstrate which one of the variables influences the other, and (3) we must be able to show that there is no other alternative explanation for the relationship. As you can imagine, it is impossible to show that there is no other alternative explanation for a relationship. For this reason, we can show that one variable does not influence another variable, but we cannot prove that it does. We can only show that it is more plausible or credible to believe that a causal relationship exists. In this section, we will focus on the first two criteria and leave this third criterion to the next section. In the previous section we looked at the frequency distributions for sex and voting preference. All we can say from these two distributions is that the sample is 40 percent men and 60 percent women and that slightly more than half of the respondents said they would be willing to vote for a woman, and slightly less than half are not willing to. We cannot say anything about the relationship between sex and voting preference. In order to determine if men or women are more likely to be willing to vote for a woman candidate, we must move from univariate to bivariate analysis. A crosstabulation (or contingency table ) is the basic tool used to explore the relationship between two variables. Table 3.2 is the crosstabulation of sex and voting preference. In the lower right-hand corner is the total number of cases in this table (900). Notice that this is not the number of cases in the sample. There were originally 950 cases in this sample, but any case that had missing information on either or both of the two variables in the table has been excluded from the table. Be sure to check how many cases have been excluded from your table and to indicate this figure in your report. Also be sure that you understand why these cases have been excluded. The figures in the lower margin and right-hand margin of the table are called the marginal distributions. They are simply the frequency distributions for the two variables in the whole table. Here, there are 360 males and 540 females (the marginal distribution for the column variable-sex) and 460 people who are willing to vote for a woman candidate and 440 who are not (the marginal distribution for the row variable-voting preference). The other figures in the table are the cell frequencies. Since there are two columns and two rows in this table (sometimes called a 2 x 2 table), there are four cells. The numbers in these cells tell us how many cases fall into each combination of categories of the two variables. This sounds complicated, but it isn't. For example, 158 males are willing to vote for a woman and 302 females are willing to vote for a woman. Table 3.2 -- Crosstabulation of Sex and Voting Preference (Frequencies)   Sex Voting Preference Male  Female  Total  Willing to Vote for a Woman 158  302  460  Not Willing to Vote for a Woman 202  238  440  Total 360  540  900  We could make comparisons rather easily if we had an equal number of women and men. Since these numbers are not equal, we must use percentages to help us make the comparisons. Since percentages convert everything to a common base of 100, the percent distribution shows us what the table would look like if there were an equal number of men and women. Before we percentage Table 3.2, we must decide which of these two variables is the independent and which is the dependent variable. Remember that the independent variable is the variable we think might be the influencing factor. The independent variable is hypothesized to be the cause, and the dependent variable is the effect. Another way to express this is to say that the dependent variable is the one we want to explain. Since we think that sex influences willingness to vote for a woman candidate, sex would be the independent variable. Once we have decided which is the independent variable, we are ready to percentage the table. Notice that percentages can be computed in different ways. In Table 3.3, the percentages have been computed so that they sum down to 100. These are called column percents . If they sum across to 100, they are called row percents . If the independent variable is the column variable, then we want the percents to sum down to 100 (i.e., we want the column percents). If the independent variable is the row variable, we want the percents to sum across to 100 (i.e., we want the row percents). This is a simple, but very important, rule to remember. We'll call this our rule for computing percents . Although we often see the independent variable as the column variable so the table sums down to 100 percent, it really doesn't matter whether the independent variable is the column or the row variable. In this module, we will put the independent variable as the column variable. Many others (but not everyone) use this convention. It would be helpful if you did this when you write your report. Table 3.3 -- Voting Preference by Sex (Percents) Voting Preference Male Female Total Willing to Vote for a Woman 43.9  55.9  51.1  Not Willing to Vote for a Woman 56.1  44.1  100.0  Total Percent 100.0  100.0  100.0  (Total Frequency) (360)  (540)  (900)  Now we are ready to interpret this table. Interpreting a table means to explain what the table is saying about the relationship between the two variables. First, we can look at each category of the independent variable separately to describe the data and then we compare them to each other. Since the percents sum down to 100 percent, we describe down and compare across. The rule for interpreting percents is to compare in the direction opposite to the way the percents sum to 100. So, if the percents sum down to 100, we compare across, and if the percents sum across to 100, compare down. If the independent variable is the column variable, the percents will always sum down to 100. We can look at each category of the independent variable separately to describe the data and then compare them to each other-describe down and then compare across. In Table 3.3, row one shows the percent of males and the percent of females who are willing to vote for a woman candidate--43.9 percent of males are willing to vote for a woman, while 55.9 percent of the females are. This is a difference of 12 percentage points. Somewhat more females than males are willing to vote for a woman. The second row shows the percent of males and females who are not willing to vote for a woman. Since there are only two rows, the second row will be the complement (or the reverse) of the first row. It shows that males are somewhat more likely to be unwilling to vote for a woman candidate (a difference of 12 percentage points in the opposite direction). When we observe a difference, we must also decide whether it is significant. There are two different meanings for significance-statistical significance and substantive significance. Statistical significance considers whether the difference is great enough that it is probably not due to chance factors. Substantive significance considers whether a difference is large enough to be important. With a very large sample, a very small difference is often statistically significant, but that difference may be so small that we decide it isn't substantively significant (i.e., it's so small that we decide it doesn't mean very much). We're going to focus on statistical significance, but remember that even if a difference is statistically significant, you must also decide if it is substantively significant. Let's discuss this idea of statistical significance. If our population is all men and women of voting age in California, we want to know if there is a relationship between sex and voting preference in the population of all individuals of voting age in California. All we have is information about a sample from the population. We use the sample information to make an inference about the population. This is called statistical inference . We know that our sample is not a perfect representation of our population because of sampling error . Therefore, we would not expect the relationship we see in our sample to be exactly the same as the relationship in the population. Suppose we want to know whether there is a relationship between sex and voting preference in the population. It is impossible to prove this directly, so we have to demonstrate it indirectly. We set up a hypothesis (called the null hypothesis ) that says that sex and voting preference are not related to each other in the population. This basically says that any difference we see is likely to be the result of random variation. If the difference is large enough that it is not likely to be due to chance, we can reject this null hypothesis of only random differences. Then the hypothesis that they are related (called the alternative or research hypothesis ) will be more credible.

In the first column of Table 3.4, we have listed the four cell frequencies from the crosstabulation of sex and voting preference. We'll call these the observed frequencies (f o ) because they are what we observe from our table. In the second column, we have listed the frequencies we would expect if, in fact, there is no relationship between sex and voting preference in the population. These are called the expected frequencies (f e ). We'll briefly explain how these expected frequencies are obtained. Notice from Table 3.1 that 51.1 percent of the sample were willing to vote for a woman candidate, while 48.9 percent were not. If sex and voting preference are independent (i.e., not related), we should find the same percentages for males and females. In other words, 48.9 percent (or 176) of the males and 48.9 percent (or 264) of the females would be unwilling to vote for a woman candidate. (This explanation is adapted from Norusis 1997.) Now, we want to compare these two sets of frequencies to see if the observed frequencies are really like the expected frequencies. All we do is to subtract the expected from the observed frequencies (column three). We are interested in the sum of these differences for all cells in the table. Since they always sum to zero, we square the differences (column four) to get positive numbers. Finally, we divide this squared difference by the expected frequency (column five). (Don't worry about why we do this. The reasons are technical and don't add to your understanding.) The sum of column five (12.52) is called the chi square statistic . If the observed and the expected frequencies are identical (no difference), chi square will be zero. The greater the difference between the observed and expected frequencies, the larger the chi square. If we get a large chi square, we are willing to reject the null hypothesis. How large does the chi square have to be? We reject the null hypothesis of no relationship between the two variables when the probability of getting a chi square this large or larger by chance is so small that the null hypothesis is very unlikely to be true. That is, if a chi square this large would rarely occur by chance (usually less than once in a hundred or less than five times in a hundred). In this example, the probability of getting a chi square as large as 12.52 or larger by chance is less than one in a thousand. This is so unlikely that we reject the null hypothesis, and we conclude that the alternative hypothesis (i.e., there is a relationship between sex and voting preference) is credible (not that it is necessarily true, but that it is credible). There is always a small chance that the null hypothesis is true even when we decide to reject it. In other words, we can never be sure that it is false. We can only conclude that there is little chance that it is true. Just because we have concluded that there is a relationship between sex and voting preference does not mean that it is a strong relationship. It might be a moderate or even a weak relationship. There are many statistics that measure the strength of the relationship between two variables. Chi square is not a measure of the strength of the relationship. It just helps us decide if there is a basis for saying a relationship exists regardless of its strength. Measures of association estimate the strength of the relationship and are often used with chi square. (See Appendix D for a discussion of how to compute the two measures of association discussed below.) Cramer's V is a measure of association appropriate when one or both of the variables consists of unordered categories. For example, race (white, African American, other) or religion (Protestant, Catholic, Jewish, other, none) are variables with unordered categories. Cramer's V is a measure based on chi square. It ranges from zero to one. The closer to zero, the weaker the relationship; the closer to one, the stronger the relationship. Gamma (sometimes referred to as Goodman and Kruskal's Gamma) is a measure of association appropriate when both of the variables consist of ordered categories. For example, if respondents answer that they strongly agree, agree, disagree, or strongly disagree with a statement, their responses are ordered. Similarly, if we group age into categories such as under 30, 30 to 49, and 50 and over, these categories would be ordered. Ordered categories can logically be arranged in only two ways-low to high or high to low. Gamma ranges from zero to one, but can be positive or negative. For this module, the sign of Gamma would have no meaning, so ignore the sign and focus on the numerical value. Like V, the closer to zero, the weaker the relationship and the closer to one, the stronger the relationship. Choosing whether to use Cramer's V or Gamma depends on whether the categories of the variable are ordered or unordered. However, dichotomies (variables consisting of only two categories) may be treated as if they are ordered even if they are not. For example, sex is a dichotomy consisting of the categories male and female. There are only two possible ways to order sex-male, female and female, male. Or, race may be classified into two categories-white and nonwhite. We can treat dichotomies as if they consisted of ordered categories because they can be ordered in only two ways. In other words, when one of the variables is a dichotomy, treat this variable as if it were ordinal and use gamma. This is important when choosing an appropriate measure of association. In this chapter we have described how surveys are done and how we analyze the relationship between two variables. In the next chapter we will explore how to introduce additional variables into the analysis.   REFERENCES AND SUGGESTED READING Methods of Social Research Riley, Matilda White. 1963. Sociological Research I: A Case Approach . New York: Harcourt, Brace and World. Hoover, Kenneth R. 1992. The Elements of Social Scientific Thinking (5 th Ed.). New York: St. Martin's. Interviewing Gorden, Raymond L. 1987. Interviewing: Strategy, Techniques and Tactics . Chicago: Dorsey. Survey Research and Sampling Babbie, Earl R. 1990. Survey Research Methods (2 nd Ed.). Belmont, CA: Wadsworth. Babbie, Earl R. 1997. The Practice of Social Research (8 th Ed). Belmont, CA: Wadsworth. Statistical Analysis Knoke, David, and George W. Bohrnstedt. 1991. Basic Social Statistics . Itesche, IL: Peacock. Riley, Matilda White. 1963. Sociological Research II Exercises and Manual . New York: Harcourt, Brace & World. Norusis, Marija J. 1997. SPSS 7.5 Guide to Data Analysis . Upper Saddle River, New Jersey: Prentice Hall. Data Sources The Field Institute. 1985. California Field Poll Study, July, 1985 . Machine-readable codebook. The Field Institute. 1991. California Field Poll Study, September, 1991 . Machine-readable codebook. The Field Institute. 1995. California Field Poll Study, February, 1995 . Machine-readable codebook.

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Qualitative study design: Surveys & questionnaires

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Surveys & questionnaires

Qualitative surveys use open-ended questions to produce long-form written/typed answers. Questions will aim to reveal opinions, experiences, narratives or accounts. Often a useful precursor to interviews or focus groups as they help identify initial themes or issues to then explore further in the research. Surveys can be used iteratively, being changed and modified over the course of the research to elicit new information. 

Structured Interviews may follow a similar form of open questioning.  

Qualitative surveys frequently include quantitative questions to establish elements such as age, nationality etc. 

Qualitative surveys aim to elicit a detailed response to an open-ended topic question in the participant’s own words.  Like quantitative surveys, there are three main methods for using qualitative surveys including face to face surveys, phone surveys, and online surveys. Each method of surveying has strengths and limitations.

Face to face surveys  

• Researcher asks participants one or more open-ended questions about a topic, typically while in view of the participant’s facial expressions and other behaviours while answering. Being able to view the respondent’s reactions enables the researcher to ask follow-up questions to elicit a more detailed response, and to follow up on any facial or behavioural cues that seem at odds with what the participants is explicitly saying.
• Face to face qualitative survey responses are likely to be audio recorded and transcribed into text to ensure all detail is captured; however, some surveys may include both quantitative and qualitative questions using a structured or semi-structured format of questioning, and in this case the researcher may simply write down key points from the participant’s response.

Telephone surveys

• Similar to the face to face method, but without researcher being able to see participant’s facial or behavioural responses to questions asked. This means the researcher may miss key cues that would help them ask further questions to clarify or extend participant responses to their questions, and instead relies on vocal cues.

Online surveys

• Open-ended questions are presented to participants in written format via email or within an online survey tool, often alongside quantitative survey questions on the same topic.
• Researchers may provide some contextualising information or key definitions to help ‘frame’ how participants view the qualitative survey questions, since they can’t directly ask the researcher about it in real time. 
• Participants are requested to responses to questions in text ‘in some detail’ to explain their perspective or experience to researchers; this can result in diversity of responses (brief to detailed).
• Researchers can not always probe or clarify participant responses to online qualitative survey questions which can result in data from these responses being cryptic or vague to the researcher.
• Online surveys can collect a greater number of responses in a set period of time compared to face to face and phone survey approaches, so while data may be less detailed, there is more of it overall to compensate.

Qualitative surveys can help a study early on, in finding out the issues/needs/experiences to be explored further in an interview or focus group. 

Surveys can be amended and re-run based on responses providing an evolving and responsive method of research. 

Online surveys will receive typed responses reducing translation by the researcher 

Online surveys can be delivered broadly across a wide population with asynchronous delivery/response. 

Limitations

Hand-written notes will need to be transcribed (time-consuming) for digital study and kept physically for reference. 

Distance (or online) communication can be open to misinterpretations that cannot be corrected at the time. 

Questions can be leading/misleading, eliciting answers that are not core to the research subject. Researchers must aim to write a neutral question which does not give away the researchers expectations. 

Even with transcribed/digital responses analysis can be long and detailed, though not as much as in an interview. 

Surveys may be left incomplete if performed online or taken by research assistants not well trained in giving the survey/structured interview. 

Narrow sampling may skew the results of the survey. 

Example questions

Here are some example survey questions which are open ended and require a long form written response:

• Tell us why you became a doctor? 
• What do you expect from this health service? 
• How do you explain the low levels of financial investment in mental health services? (WHO, 2007) 

Example studies

• Davey, L. , Clarke, V. and Jenkinson, E. (2019), Living with alopecia areata: an online qualitative survey study. British Journal of Dermatology, 180 1377-1389. Retrieved from https://onlinelibrary-wiley-com.ezproxy-f.deakin.edu.au/doi/10.1111%2Fbjd.17463    
• Richardson, J. (2004). What Patients Expect From Complementary Therapy: A Qualitative Study. American Journal of Public Health, 94(6), 1049–1053. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=13270563&site=eds-live&scope=site  
• Saraceno, B., van Ommeren, M., Batniji, R., Cohen, A., Gureje, O., Mahoney, J., ... & Underhill, C. (2007). Barriers to improvement of mental health services in low-income and middle-income countries. The Lancet, 370(9593), 1164-1174. Retrieved from https://www-sciencedirect-com.ezproxy-f.deakin.edu.au/science/article/pii/S014067360761263X?via%3Dihub  

Below has more detail of the Lancet article including actual survey questions at: 

• World Health Organization. (2007.) Expert opinion on barriers and facilitating factors for the implementation of existing mental health knowledge in mental health services. Geneva: World Health Organization. https://apps.who.int/iris/handle/10665/44808
• Green, J. 1961-author., & Thorogood, N. (2018). Qualitative methods for health research. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b4151167&authtype=sso&custid=deakin&site=eds-live&scope=site   
• JANSEN, H. The Logic of Qualitative Survey Research and its Position in the Field of Social Research Methods. Forum Qualitative Sozialforschung, 11(2), Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/1450/2946  
• Neilsen Norman Group, (2019). 28 Tips for Creating Great Qualitative Surveys. Retrieved from https://www.nngroup.com/articles/qualitative-surveys/   
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Survey Research Design: Definition, How to Conduct a Survey & Examples

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Survey research is a quantitative research method that involves collecting data from a sample of individuals using standardized questionnaires or surveys. The goal of survey research is to measure the attitudes, opinions, behaviors, and characteristics of a target population. Surveys can be conducted through various means, including phone, mail, online, or in-person.

If your project involves live interaction with numerous people in order to obtain important data, you should know the basic rules of survey research beforehand. Today we’ll talk about this research type, review the step-by-step guide on how to do a survey research and try to understand main advantages and potential pitfalls. The following important questions will be discussed below:

• Purpose and techniques of information collection.
• Kinds of responses.
• Analysis techniques, assumptions, and conclusions.

Do you wish to learn best practices of survey conducting? Stay with our research paper service and get prepared for some serious reading!

What Is Survey Research: Definition

Let’s define the notion of survey research first. It revolves around surveys you conduct to retrieve certain data from your respondents. The latter is to be carefully selected from some population that for particular reasons possess the data necessary for your research. For example, they can be witnesses of some event that you should investigate. Surveys contain a set of predefined questions, closed- or open-ended. They can be sent to participants who would answer them and thus provide you with data for your research. There are many methods for organizing surveys and processing the obtained information.

Purpose of Survey Research Design

Purpose of survey research is to collect proper data and thus get insights for your research. You should pick participants with relatable experience. It should be done in order to get relevant information from them. Questions in your survey should be formulated in a way that allows getting as much useful data as possible. The format of a survey should be adjusted to the situation. It will ensure your respondents will be ready to give their answers. It can be a questionnaire sent over email or questions asked during a phone call.

Surveys Research Methods

Which survey research method to choose? Let’s review the most popular approaches and when to use them. There are two critical factors that define how a survey will be conducted

• Tool to send questions
• online: using web forms or email questionnaires.
• phone: reaching out to respondents individually. Sometimes using an automated service.
• face-to-face: interviewing respondents in the real world. This makes room for more in-depth questions.
• Time to conduct research
• short-term periods.
• long-term periods.

Let’s explore the time-related methods in detail.

Cross-Sectional Survey Design Research

The first type is cross sectional survey research. Design of this survey type includes collecting various insights from an audience within a specific short time period. It is used for descriptive analysis of a subject. The purpose is to provide quick conclusions or assumptions. Which is why this approach relies on fast data gathering and processing techniques.  Such surveys are typically implemented in sectors such as retail, education, healthcare etc, where the situation tends to change fast. So it is important to obtain operational results as soon as possible.

Longitudinal Survey Research

Let’s talk about survey research designs . Planning a design beforehand is crucial. It is crucial in case you are pressed on time or have a limited budget. Collecting information using a properly designed survey research is typically more effective and productive compared with a casually conducted study.  Preparation of a survey design includes the following major steps:

• Understand the aim of your research. So that you can better plan the entire path of a survey and avoid obvious issues.
• Pick a good sample from a population. Ensure precision of the results by selecting members who could provide useful insights and opinions.
• Review available research methods. Decide about the one most suitable for your specific case.
• Prepare a questionnaire. Selection of questions would directly affect the quality of your longitudinal analysis . So make sure to pick good questions. Also, avoid unnecessary ones to save time and counter possible errors.
• Analyze results and make conclusions.

Advantages of Survey Research

As a rule, survey research involves getting data from people with first-hand knowledge about the research subject. Therefore, when formulated properly, survey questions should provide some unique insights and thus describe the subject better. Other benefits of this approach include:

• Minimum investment. Online and automated call services require very low investment per respondent.
• Versatile sources. Data can be collected by numerous means, allowing more flexibility.
• Reliable for respondents. Anonymous surveys are secure. Respondents are more likely to answer honestly if they understand it will be confidential.

Types of Survey Research

Let’s review the main types of surveys. It is important to know about most popular templates. So that you wouldn’t have to develop your own ones from scratch for your specific case. Such studies are usually categorized by the following aspects:

• Objectives.
• Data source.
• Methodology.

We’ll examine each of these aspects below, focusing on areas where certain types are used. 

Types of Survey Research Depending on Objective

Depending on your objective and the specifics of the subject’s context, the following survey research types can be used:

• Predictive This approach foresees asking questions that automatically predict the best possible response options based on how they are formulated. As a result, it is often easier for respondents to provide their answers as they already have helpful suggestions.
• Exploratory This approach is focused more on the discovery of new ideas and insights rather than collecting statistically accurate information. The results can be difficult to categorize and analyze. But this approach is very useful for finding a general direction for further research.
• Descriptive This approach helps to define and describe your respondents' opinions or behavior more precisely. By predefining certain categories and designing survey questions, you obtain statistical data. This descriptive research approach is often used at later research stages. It is used in order to better understand the meaning of insights obtained at the beginning.

Types of Survey Research Depending on Data Source

The following research survey types can be defined based on which sources you obtain the data from:

• Primary In this case, you collect information directly from the original source, e.g., learn about a natural disaster from a survivor. You aren’t using any intermediary instances. And, as a result, don't get any information twisted or lost on its way. This is the way to obtain the most valid and trustworthy results. But at the same time, it is often not so easy to access such sources.
• Secondary This involves collecting data from existing research on the same subject that has been published. Such information is easier to access. But at the same time, it is usually too general and not tailored for your specific needs.

Types of Survey Research Depending on Methodology

Finally, let’s review survey research methodologies based on the format of retrieved and processed data. They can be:

• Quantitative An approach that focuses on gathering numeric or measurable data from respondents. This provides enough material for statistical analysis. And then leads to some meaningful conclusions. Collection of such data requires properly designed surveys that include numeric options. It is important to take precautions to ensure that the data you’ve gathered is valid.
• Qualitative Such surveys rely on opinions, impressions, reflections, and typical reactions of target groups. They should include open-ended questions to allow respondents to give detailed answers. It allows providing information that they consider most relevant. Qualitative research is used to understand, explain or evaluate some ideas or tendencies.

It is essential to differentiate these two kinds of research. That's why we prepared a special blog, which is about quantitative vs qualitative research .

How to Conduct a Survey Research: Main Steps

Now let’s find out how to do a survey step by step. Regardless of methods you use to design and conduct your survey, there are general guidelines that should be followed. The path is quite straightforward: 

• Assess your goals and options for accessing necessary groups.
• Formulate each question in a way that helps you obtain the most valuable data.
• Plan and execute the distribution of the questions.
• Process the results.

Let’s take a closer look at all these stages.

Step 1. Create a Clear Survey Research Question

Each survey research question should add some potential value to your expected results. Before formulating your questionnaire, it is better to invest some time analyzing your target populations. This will allow you to form proper samples of respondents. Big enough to get some insights from them but not too big at the same time. A good way to prepare questions is by constructing case studies for your subject. Analyzing case study examples in detail will help you understand which information about them is necessary.

Step 2. Choose a Type of Survey Research

As we’ve already learned, there are several different types of survey research. Starting with a close analysis of your subject, goals and available sources will help you understand which kinds of questions are to be distributed.  As a researcher, you’ll also need to analyze the features of the selected group of respondents. Pick a type that makes it easier to reach out to them. For example, if you should question a group of elderly people, online forms wouldn’t be efficient compared with interviews.

Step 3. Distribute the Questionnaire for Your Survey Research

The next step of survey research is the most decisive one. Now you should execute the plan you’ve created earlier. And then conduct the questioning of the entire group that was selected. If this is a group assignment, ask your colleagues or peers for help. Especially if you should deal with a big group of respondents. It is important to stick to the initial scenario but leave some room for improvisation in case there are difficulties with reaching out to respondents. After you collect all necessary responses, this data can be processed and analyzed.

Step 4. Analyze the Results of Your Research Survey

The data obtained during the survey research should be processed. So that you can use it for making assumptions and conclusions. If it is qualitative, you should conduct a thematic analysis to find important ideas and insights that could confirm your theories or expand your knowledge of the subject. Quantitative data can be analyzed manually or with the help of some program. Its purpose is to extract dependencies and trends from it to confirm or refute existing assumptions.

Step 5. Save the Results of Your Survey Research

The final step is to compose a survey research paper in order to get your results ordered. This way none of them would be lost especially if you save some copies of the paper. Depending on your assignment and on which stage you are at, it can be a dissertation, a thesis or even an illustrative essay where you explain the subject to your audience.  Each survey you’ve conducted must get a special section in your paper where you explain your methods and describe your results.

Survey Research Example

We have got a few research survey examples in case you would need some real world cases to illustrate the guidelines and tips provided above. Below is a sample research case with population and the purposes of researchers defined.

Example of survey research design The Newtown Youth Initiative will conduct a qualitative survey to develop a program to mitigate alcohol consumption by adolescent citizens of Newtown. Previously, cultural anthropology research was performed for studying mental constructs to understand young people's expectations from alcohol and their views on specific cultural values. Based on its results, a survey was designed to measure expectancies, cultural orientation among the adolescent population. A secure web page has been developed to conduct this survey and ensure anonymity of respondents. The Newtown Youth Initiative will partner with schools to share the link to this page with students and engage them to participate. Statistical analysis of differences in expectancies and cultural orientation between drinkers and non-drinkers will be performed using the data from this survey.

Survey Research: Key Takeaways

Today, we have explored the research survey notion and reviewed the main features of this research activity and its usage in the social sciences topics . Important techniques and tips have been reviewed. A step by step guide for conducting such studies has also been provided.

Found it difficult to reach out to your target group? Or are you just pressed with deadlines? We've got your back! Check out our writing services and leave a ‘ write paper for me ’ request. We are a team of skilled authors with vast experience in various academic fields.

Frequently Asked Questions About Survey Research

1. what is a market research survey.

A market research survey can help a company understand several aspects of their target market. It typically involves picking focus groups of customers and asking them questions in order to learn about demand for specific products or services and understand whether it grows. Such feedback would be crucial for a company’s development. It can help it to plan its further strategic steps.

2. How does survey research differ from experimental research methods?

The main difference between experiment and survey research is that the latter means field research, while experiments are typically performed in laboratory conditions. When conducting surveys, researchers don’t have full control on the process and should adapt to the specific traits of their target groups in order to obtain answers from them. Besides, results of a study might be harder to quantify and turn into statistical values.

4. What is the difference between survey research and descriptive research?

The purpose of descriptive studies is to explain what the subject is and which features it has. Survey research may include descriptive information but is not limited by that. Typically it goes beyond descriptive statistics and includes qualitative research or advanced statistical methods used to draw inferences, find dependencies or build trends. On the other hand, descriptive methods don’t necessarily include questioning respondents, obtaining information from other sources.

3. What is good sample size for a survey?

It always depends on a specific case and researcher’s goals. However, there are some general guidelines and best practices for this activity. Good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000 people. In any case, you should be mindful of your time and budget limitations when planning your actions. In case you’ve got a team to help you, it might be possible to process more data.

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• Asian Americans, Charitable Giving and Remittances

Methodology

Table of contents.

• Charitable giving in the U.S.
• Charitable giving in ancestral homelands
• Which Asian Americans send remittances?
• Why do Asian Americans send remittances?
• Remittance flows from the U.S. to Asian origin countries
• Acknowledgments
• Sample design
• Data collection
• Weighting and variance estimation
• Appendix: Supplemental tables

The data in this report is drawn from a national cross-sectional survey conducted for Pew Research Center by Westat. The sampling design of the survey was an address-based sampling (ABS) approach, supplemented by list samples, to reach a nationally representative group of respondents. The survey was fielded July 5, 2022, through Jan. 27, 2023.

Self-administered screening interviews were conducted with a total of 36,469 U.S. adults either online or by mail, resulting in 7,006 interviews with Asian American adults. It is these 7,006 Asian Americans who are the focus of this report. After accounting for the complex sample design and loss of precision due to weighting, the margin of sampling error for these respondents is plus or minus 2.1 percentage points at the 95% level of confidence.

The survey was administered in two stages. In the first stage, a short screening survey was administered to a national sample of U.S. adults to collect basic demographics and determine a respondent’s eligibility for the extended survey of Asian Americans. Screener respondents were considered eligible for the extended survey if they self-identified as Asian (alone or in combination with any other race or ethnicity). Note that all individuals who self-identified as Asian were asked to complete the extended survey.

To maintain consistency with the Census Bureau’s definition of “Asian,” individuals responding as Asian but who self-identified with origins that did not meet the bureau’s official standards prior to the 2020 decennial census were considered ineligible and were not asked to complete the extended survey or were removed from the final sample. Those excluded were people solely of Southwest Asian descent (e.g., Lebanese, Saudi), those with Central Asian origins (e.g., Afghan, Uzbek) as well as various other non-Asian origins. The impact of excluding these groups is small, as together they represent about 1%-2% of the national U.S. Asian population, according to Pew Research Center tabulations of the 2021 American Community Survey.

Eligible survey respondents were asked in the extended survey how they identified ethnically (for example: Chinese, Filipino, Indian, Korean, Vietnamese, or some other ethnicity with a write-in option). Note that survey respondents were asked about their ethnicity rather than nationality. For methodological purposes (such as the sample design, weighting and variance estimation) respondents were classified based on their ethnicity. For example, those classified as Chinese in the survey methodology are those self-identifying as of Chinese ethnicity, rather than necessarily being a citizen or former citizen of the People’s Republic of China. Since this is an ethnicity, classification of survey respondents as Chinese also includes those who are Taiwanese. This report, however, classifies respondents based on their Asian origin to discuss differences in views among origin groups in their financial and philanthropic ties to their places of Asian origin. For example, it details the responses of self-identified Chinese-origin respondents separately from those of self-identified Taiwanese-origin respondents. In the remainder of this methodology statement, references to Chinese respondents refer to those who are ethnically Chinese and therefore includes those who report being Taiwanese.

The research plan for this project was submitted to Westat’s institutional review board (IRB), which is an independent committee of experts that specializes in helping to protect the rights of research participants. Due to the minimal risks associated with this questionnaire content and the population of interest, this research underwent an expedited review and received approval (approval #FWA 00005551).

Throughout this methodology statement, the terms “extended survey” and “extended questionnaire” refer to the extended survey of Asian Americans that is the focus of this report, and “eligible adults” and “eligible respondents” refer to those individuals who met its eligibility criteria, unless otherwise noted.

The survey had a complex sample design constructed to maximize efficiency in reaching Asian American adults while also supporting reliable, national estimates for the population as a whole and for the five largest ethnic groups (Chinese, Filipino, Indian, Korean and Vietnamese). Asian American adults include those who self-identify as Asian, either alone or in combination with other races or Hispanic identity.

The main sample frame of the 2022-2023 Asian American Survey is an address-based sample (ABS). The ABS frame of addresses was derived from the USPS Computerized Delivery Sequence file. It is maintained by Marketing Systems Group (MSG) and is updated monthly. MSG geocodes their entire ABS frame, so block, block group, and census tract characteristics from the decennial census and the American Community Survey (ACS) could be appended to addresses and used for sampling and data collection.

All addresses on the ABS frame were geocoded to a census tract. Census tracts were then grouped into three strata based on the density of Asian American adults, defined as the proportion of Asian American adults among all adults in the tract. The three strata were defined as:

• High density: Tracts with an Asian American adult density of 10% or higher
• Medium density: Tracts with a density of 3% to less than 10%
• Low density: Tracts with a density less than 3%

Mailing addresses in census tracts from the lowest density stratum, strata 3, were excluded from the sampling frame. As a result, the frame excluded 54.1% of the 2020 census tracts, making up 49.1% of the U.S. adult population, including 9.1% of adults who self-identified as Asian alone or in combination with other races or Hispanic ethnicity. For the largest five Asian ethnic subgroups, Filipinos had the largest percentage of excluded adults with 6.8%, while Indians had the lowest with 4.2% of the adults. Addresses were then sampled from the two remaining strata. This stratification and the assignment of differential sampling rates to the strata were critical design components because of the rareness of the Asian American adult population.

Despite oversampling of the high- and medium-density Asian American strata in the ABS sample, the ABS sample was not expected to efficiently yield the required number of completed interviews for some ethnic subgroups. Therefore, the ABS sample was supplemented with samples from the specialized surnames list frames maintained by the MSG. These list frames identify households using commercial databases linked to addresses and telephone numbers. The individuals’ surnames in these lists could be classified by likely ethnic origin. Westat requested MSG to produce five list frames: Chinese, Filipino, Indian, Korean and Vietnamese. The lists were subset to include only cases with a mailing address. Addresses sampled from the lists, unlike those sampled from the ABS frame, were not limited to high- and medium-density census tracts.

Once an address was sampled from either the ABS frame or the surname lists, an invitation was mailed to the address. The invitation requested that the adult in the household with the next birthday complete the survey.

To maximize response, the survey used a sequential mixed-mode protocol in which sampled households were first directed to respond online and later mailed a paper version of the questionnaire if they did not respond online.

The first mailing was a letter introducing the survey and providing the information necessary (URL and unique PIN) for online response. A pre-incentive of $2 was included in the mailing. This and remaining screener recruitment letters focused on the screener survey, without mentioning the possibility of eligibility for a longer survey and associated promised incentive, since most people would only be asked to complete the short screening survey. It was important for all households to complete the screening survey, not just those who identify as Asian American. As such, the invitation did not mention that the extended survey would focus on topics surrounding the Asian American experience. The invitation was generic to minimize the risk of nonresponse bias due to topic salience bias.

After one week, Westat sent a postcard reminder to all sampled individuals, followed three weeks later by a reminder letter to nonrespondents. Approximately 8.5 weeks after the initial mailing, Westat sent nonrespondents a paper version screening survey, which was a four-page booklet (one folded 11×17 paper) and a postage-paid return envelope in addition to the cover letter. If no response was obtained from those four mailings, no further contact was made.

Eligible adults who completed the screening interview on the web were immediately asked to continue with the extended questionnaire. If an eligible adult completed the screener online but did not complete the extended interview, Westat sent them a reminder letter. This was performed on a rolling basis when it had been at least one week since the web breakoff. Names were not collected until the end of the web survey, so these letters were addressed to “Recent Participant.”

If an eligible respondent completed a paper screener, Westat mailed them the extended survey and a postage-paid return envelope. This was sent weekly as completed paper screeners arrived. Westat followed these paper mailings with a reminder postcard. Later, Westat sent a final paper version via FedEx to eligible adults who had not completed the extended interview online or by paper.

A pre-incentive of $2 (in the form of two $1 bills) was sent to all sampled addresses with the first letter, which provided information about how to complete the survey online. This and subsequent screener invitations only referred to the pre-incentive without reference to the possibility of later promised incentives.

Respondents who completed the screening survey and were found eligible were offered a promised incentive of $10 to go on and complete the extended survey. All participants who completed the extended web survey were offered their choice of a $10 Amazon.com gift code instantly or $10 cash mailed. All participants who completed the survey via paper were mailed a $10 cash incentive.

In December 2022 a mailing was added for eligible respondents who had completed a screener questionnaire, either by web or paper but who had not yet completed the extended survey. It was sent to those who had received their last mailing in the standard sequence at least four weeks earlier. It included a cover letter, a paper copy of the extended survey, and a business reply envelope, and was assembled in a 9×12 envelope with a $1 bill made visible through the envelope window.

In the last month of data collection, an additional mailing was added to boost the number of Vietnamese respondents. A random sample of 4,000 addresses from the Vietnamese surname list and 2,000 addresses from the ABS frame who were flagged as likely Vietnamese were sent another copy of the first invitation letter, which contained web login credentials but no paper copy of the screener. This was sent in a No. 10 envelope with a wide window and was assembled with a $1 bill visible through the envelope window.

The mail and web screening and extended surveys were developed in English and translated into Chinese (Simplified and Traditional), Hindi, Korean, Tagalog and Vietnamese. For web, the landing page was displayed in English initially but included banners at the top and bottom of the page that allowed respondents to change the displayed language. Once in the survey, a dropdown button at the top of each page was available to respondents to toggle between languages.

The paper surveys were also formatted into all six languages. Recipients thought to be more likely to use a specific language option, based on supplemental information in the sampling frame or their address location, were sent a paper screener in that language in addition to an English screener questionnaire. Those receiving a paper extended instrument were sent the extended survey in the language in which the screener was completed. For web, respondents continued in their selected language from the screener.

Household-level weighting

The first step in weighting was creating a base weight for each sampled mailing address to account for its probability of selection into the sample. The base weight for mailing address k is called BW k and is defined as the inverse of its probability of selection. The ABS sample addresses had a probability of selection based on the stratum from which they were sampled. The supplemental samples (i.e., Chinese, Filipino, Indian, Korean and Vietnamese surname lists) also had a probability of selection from the list frames. Because all of the addresses in the list frames are also included in the ABS frame, these addresses had multiple opportunities for these addresses to be selected, and the base weights include an adjustment to account for their higher probability of selection.

Each sampled mailing address was assigned to one of four categories according to its final screener disposition. The categories were 1) household with a completed screener interview, 2) household with an incomplete screener interview, 3) ineligible (i.e., not a household, which were primarily postmaster returns), and 4) addresses for which status was unknown (i.e., addresses that were not identified as undeliverable by the USPS but from which no survey response was received).

The second step in the weighting process was adjusting the base weight to account for occupied households among those with unknown eligibility (category 4). Previous ABS studies have found that about 13% of all addresses in the ABS frame were either vacant or not home to anyone in the civilian, non-institutionalized adult population. For this survey, it was assumed that 87% of all sampled addresses from the ABS frame were eligible households. However, this value was not appropriate for the addresses sampled from the list frames, which were expected to have a higher proportion of households as these were maintained lists. For the list samples, the occupied household rate was computed as the proportion of list cases in category 3 compared to all resolved list cases (i.e., the sum of categories 1 through 3). The base weights for the share of category 4 addresses (unknown eligibility) assumed to be eligible were then allocated to cases in categories 1 and 2 (known households) so that the sum of the combined category 1 and 2 base weights equaled the number of addresses assumed to be eligible in each frame. The category 3 ineligible addresses were given a weight of zero.

The next step was adjusting for nonresponse for households without a completed screener interview to create a final household weight. This adjustment allocated the weights of nonrespondents (category 2) to those of respondents (category 1) within classes defined by the cross-classification of sampling strata, census region, and sample type (e.g., ABS and list supplemental samples). Those classes with fewer than 50 sampled addresses or large adjustment factors were collapsed with nearby cells within the sample type. Given the large variance in the household weights among the medium density ABS stratum, final household weights for addresses within this stratum were capped at 300.

Weighting of extended survey respondents

The extended interview nonresponse adjustment began by assigning each case that completed the screener interview to one of three dispositions: 1) eligible adult completed the extended interview; 2) eligible adult did not complete the extended interview; and 3) not eligible for the extended interview.

An initial adult base weight was calculated for the cases with a completed extended interview as the product of the truncated number of adults in the household (max value of 3) and the household weight. This adjustment accounted for selecting one adult in each household.

The final step in the adult weighting was calibrating the adult weights for those who completed the extended interview so that the calibrated weights (i.e., the estimated number of adults) aligned with benchmarks for noninstitutionalized Asian adults from the 2016-2020 American Community Surveys Public Use Microdata Sample (PUMS). Specifically, raking was used to calibrate the weights on the following dimensions:

• Ethnic group (Chinese, Filipino, Indian, Japanese, Korean, Vietnamese, other single Asian ethnicities, and multiple Asian ethnicities)
• Collapsed ethnic group (Chinese, Filipino, Indian, Korean, Vietnamese, all other single and multiple Asian ethnicities) by age group
• Collapsed ethnic group by sex
• Collapsed ethnic group by census region
• Collapsed ethnic group by education
• Collapsed ethnic group by housing tenure
• Collapsed ethnic group by nativity
• Income group by number of persons in the household

The control totals used in raking were based on the entire population of Asian American adults (including those who live in the excluded stratum) to correct for both extended interview nonresponse and undercoverage from excluding the low-density stratum in the ABS frame.

Variance estimation

Because the modeled estimates used in the weighting are themselves subject to sampling error, variance estimation and tests of statistical significance were performed using the grouped jackknife estimator ( JK 2). One hundred sets of replicates were created by deleting a group of cases within each stratum from each replicate and doubling the weights for a corresponding set of cases in the same stratum. The entire weighting and modeling process was performed on the full sample and then separately repeated for each replicate. The result is a total of 101 separate weights for each respondent that have incorporated the variability from the complex sample design. 6

Response rates

Westat assigned all sampled cases a result code for their participation in the screener, and then they assigned a result for the extended questionnaire for those who were eligible for the survey of Asian Americans. Two of the dispositions warrant some discussion. One is the category “4.313 No such address.” This category is for addresses that were returned by the U.S. Postal Service as not being deliverable. This status indicates the address, which was on the USPS Delivery Sequence File at the time of sampling, currently is not occupied or no longer exists. The second category is “4.90 Other.” This category contains 588 addresses that were never mailed because they had a drop count of greater than four. Drop points are addresses with multiple households that share the same address. The information available in the ABS frame on drop points is limited to the number of drop points at the address, without information on the type of households at the drop point, or how they should be labeled for mailing purposes. In this survey, all drop points were eligible for sampling, but only those with drop point counts of four or fewer were mailed. Westat treated drop point counts of five or more as out of scope, and no mailing was done for those addresses.

Westat used the disposition results to compute response rates consistent with AAPOR definitions. The response rates are weighted by the base weight to account for the differential sampling in this survey. The AAPOR RR3 response rate to the screening interview was 17.0%. 7 The RR1 response rate to the extended Asian American interview (77.9%) is the number of eligible adults completing the questionnaire over the total sampled for that extended questionnaire. The overall response rate is the product of the screener response rate and the conditional response rate for the extended questionnaire. The overall response rate for the Asian American sample in the Pew Research Center survey was 13.3% (17.0% x 77.9%).

• For additional details on jackknife replication, refer to Rust, K.F., and J.N.K. Rao. 1996. “ Variance estimation for complex surveys using replication techniques .” Statistical Methods in Medical Research. ↩
• The weighted share of unscreened households assumed to be eligible for the screener interview (occupied “e”) was 87%. ↩

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