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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Why Survey Design Principles Are Important? (+ Examples)

It’s no secret that good survey design is key to getting accurate data. But what are the principles of good survey design? And how can you make sure your surveys yield the results you need?

In this blog post, we’ll discuss some of the most important principles of good survey design, and how to apply them to your own surveys. Read on to learn more!

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Why is survey design so important?

When it comes to surveys, the devil is in the details. Survey design is one of the most important aspects of survey research, and getting it wrong can lead to inaccurate results. Poorly designed surveys can produce biased data, which can in turn lead to faulty conclusions and misguided policy decisions.

Survey design can be complex, but it’s important to take the time to get it right. A well-designed survey will produce accurate results that can be used to make informed decisions about policy and programs.

By the end of this post you’ll know exactly what you should do to ensure you create surveys that add real value.

What are survey design principles & why they matter

Survey design principles help you to ensure that surveys are accurate and produce valid results. By following certain principles, survey designers can create surveys that are less likely to produce biased responses and more likely to be understood by respondents.

There are several key survey design principles, including:

• simplicity : keep surveys simple and straightforward so that respondents can easily understand them.
• clarity : make sure questions are clear and easy to understand.
• neutrality : avoid asking leading questions or phrasing questions in a way that could influence respondents’ answers
• brevity : keep surveys short so that respondents are not discouraged from completing them.
• comprehensiveness : make sure all relevant questions are asked.

These principles are important because they help to ensure the accuracy and validity of survey results.

Get Started: Choose the right survey tool

Choosing the right survey tool when asking your survey questions is crucial for creating a good survey questionnaire.

Surveys can be time-consuming and costly to design and administer, so it’s important to choose the right tool for the job. Additionally, surveys can generate sensitive data that should be handled with care.

Different survey tools are better suited for different types of research. Here are five factors to consider when choosing a survey tool:

• Purpose of the Survey

Essentially, what are you trying to learn?

Do you want to collect qualitative or quantitative data? Qualitative data is typically collected through open-ended questions, while quantitative data is collected through closed-ended questions.

If you’re not sure what type of data you need, consider your research goals. What do you hope to achieve with your research?

If you need to collect data that can be quantified, then you’ll want to use a tool that allows you to ask closed-ended questions. However, if your goal is to generate ideas or get feedback on a product or service, then an open-ended survey may be a better option.

• What features are important to you?

Different survey tools also offer different features. It’s important to choose a tool that offers the features you need to collect the data you want.

For example, if you want to collect data from a large number of people, you’ll want to use a tool that allows you to easily distribute your survey. Conversely, if you want to collect data from a specific group of people, you’ll want to use a tool that allows you to target your audience.

Additionally, some survey tools offer features that can make your life easier, such as the ability to skip questions or randomize answer choices. If you’re not sure which features are right for you, consider your research goals and objectives. What do you need to make your life easier?

• Question options

You should also consider the survey questions that you want all the answers for. This is extremely important to ensure you create an effective survey and the information gathered is quality data that you can trust.

Firstly, what type question are you interested in getting answers for? Do you want to ask open ended questions or closed ended questions? This will influence the specific questions you can ask, and the answer choices you’ll provide.

• How much does it cost?

Finally, it’s important to consider the costs associated with different survey tools. Some tools are free, while others come with a subscription fee. It’s important to choose a tool that fits within your budget.

Basics: Create an effective survey

Plan your survey carefully.

When planning a survey, it is important to consider the design of the survey. The design of the survey can impact the results of the survey. There are a few key considerations when designing a survey.

The first consideration is the length of the survey. A shorter survey is more likely to be completed than a longer one. Keep in mind that people may be less likely to complete a long survey online.

The second consideration is the number of questions asked. Too many questions can lead to fatigue and decreased response rates. Focus your questions on key factors of interest, to allow for a smooth survey process.

The third consideration is how the questions are worded. Questions should be clear and easy to understand, to get a accurate view of people’s opinions.

Engage your audience

People are more likely to respond to surveys that are engaging. This is because surveys that are engaging are more fun to take and people are less likely to make mistakes.

There are a few ways to make it more engaging. First, use interesting questions. Second, use graphics and images to break up the text. Third, make sure your survey is mobile-friendly.

People are more likely to respond to surveys that are easy to take. This is because people are less likely to get bored or frustrated when taking a survey on their phone.

Finally, make sure you offer respondents an incentive for taking your survey. This could be a discount on their next purchase or a free product.

Use Simple language

The question wording is important in any marketing campaign, and it’s no different for surveys. If people don’t understand your questions, they’re likely to provide inaccurate answers.

There are a few things you can do to make sure your over all survey design is clear and easy to understand. First, use simple language. Avoid technical terms and other industry specific jargon.

Second, use active voice. For example, “How often do you use Facebook?” is better than “How frequently is Facebook used by you?”.

Third, make sure your questions are specific. For example, “What features do you like best about our product?” is better than “What do you think of our product?”

Finally, never use a double barreled question. These are questions that ask about two things at the same time. For example, “How easy to use is our product and how satisfied are you with it?”

It’s important to avoid double-barreled questions because they can lead to ambiguous data. It’s better to ask two separate questions, “How easy to use is our product?” and “How satisfied are you with our product?”

Questions: Types and how to write them

Ask appropriate questions.

Clearly define the survey’s purpose and ask questions that are appropriate for your target audience. This seems like a no-brainer, but it can prove helpful for encouraging respondents to provide answers.

Asking irrelevant questions not only wastes respondents time, but it also reflects poorly on your brand. When crafting your questions, consider your target audience and what they care about.

It’s also important to avoid questions that are too personal or intrusive in your survey design. People are generally reluctant to answer sensitive questions, so it’s best to avoid asking questions that may make people uncomfortable or even confuse respondents.

If you’re not sure whether a question is appropriate, consider how you would feel if you were the one being asked. Would you be comfortable answering the question? If not, it’s probably best to avoid it.

Avoid bias questions

When creating surveys, question wording is important to avoid biased questions. This can lead to respondents abandoning your survey altogether.

Bias questions are those that can lead respondents to answer in a certain way. This can happen when the question is phrased in a way that suggests a particular answer. For example, the question “Do you support our troops?” is biased because it suggests that the respondent should support the troops.

There are several ways to avoid bias questions. One way is to use neutral language. Neutral language does not suggest any particular answer to the question. Another way to avoid biased questions is to use multiple choice questions instead of open-ended questions.

Multiple choice questions allow respondents to choose from a list of answers, which reduces the chances that they will be influenced by certain words in the question.

Avoid leading & loaded questions

A leading question is a question that suggests a particular answer. For example, “Don’t you agree that our product is the best on the market?”

Leading questions can bias your data and lead to inaccurate results. That’s why it’s important to avoid them.

There are a few things you can do to avoid leading questions. First, consider your wording carefully. Make sure your questions are neutral and don’t suggest a particular answer.

Second, avoid loaded questions. These are questions that contain emotionally charged words or phrases. For example, “How frustrated are you with our product?”

Finally, avoid yes/no questions. These types of questions can be leading because they force people to choose between two options. For example, “Do you like our product?”

It’s better to ask an open-ended question that allows respondents to answer in their own words. For example, “What do you think of our product?”

Both leading and loaded questions can influence the data you collect. To avoid bias, craft your questions carefully and make sure they are neutral. Avoid using words that suggest a particular answer or that contain assumptions.

Order your Questions by priority

Some questions are more important than others. To make sure you get the information you need, it’s important to use a logical order for your questions, regardless of whether you ask broad and general questions.

The first step is to identify the most important question. This is the question that will have the biggest impact on your business. Second, prioritize the remaining questions. Try to group similar questions together.

Finally, decide on the order in which you will ask the questions. The most important question should be asked first, followed by the next most important question. This will help you to get the information you need while keeping your survey short and engaging.

Length: How long should the survey be

Nobody likes taking surveys, especially long ones. In fact, the length of a survey is one of the most important factors that influence whether or not people will take your survey.

The general rule of thumb is to keep your survey as short as possible. Every question you add to your survey increases the chances that people will drop out.

There are a few ways to keep your survey short. First, consider what you really need to know. Do you need to ask every question on your list? Can some questions be combined?

Second, take advantage of question types that allow respondents to provide more than one answer. For example, the matrix question type allows respondents to answer multiple questions in one go.

Finally, consider using branching logic to skip irrelevant questions. This feature is available in most survey tools and allows you to display answer choices based on people’s responses to previous questions.

Sampling: How to select the right audience

Survey sampling is an important process in survey design. The goal of survey sampling is to select a representative sample of the population. This is essential for obtaining accurate results from a survey.

There are several methods for selecting a representative sample, which we’ll look at now.

Survey the most suitable audience

There are a few different ways to select people for your survey using random sampling. The most common method is to use a random number generator.

Another method is to use a list of all the people in your population, such as all the students in a school, and then select people at random from that list.

Finally, you can use a random sampling method called stratified sampling. This is a method of selecting respondents based on groups, or strata.

For example, if you want to create surveys for students, you could stratify by grade level and then select a certain number of students at random from each grade.

Test your survey on a focus group

Once you have designed your survey, it’s important to test the survey design on a focus group before sending it to your entire list. This will help you determine if there are any problems with the survey or if there are any areas that need improvement.

The focus group should be representative of your target audience. This will help you determine if the survey is relevant and engaging. Which should make the respondent’s job easier when providing answers.

Once you test your survey on a focus group, you can then send it to your entire list. This will help you get the most accurate data possible.

Use Random Sampling

If you want your survey results to be representative of a larger population, you need to use random sampling. This is a method of selecting potential respondents at random.

There are a few benefits of using random sampling. First, it allows you to make inferences about a population based on a smaller sample. Second, it helps to avoid bias in your results.

Avoid Selection bias

Some common pitfalls to avoid when using random sampling include self-selection bias and selection bias.

Self-selection bias occurs when people who are more likely to respond are more likely to be selected. For example, if you send out a survey by email and only people who have an email address can respond, you’re more likely to get responses from people who are younger and have higher incomes.

Selection bias occurs when the way that people are selected for a survey affects the results. For example, if you select people for your survey who live in a certain area, you’re more likely to get responses from people who live in that area.

Both self-selection bias and selection bias can lead to inaccurate results. That’s why it’s important to be aware of them and take steps to avoid them.

Mode: How should the survey be administered

Use an online survey tool.

If you want to collect accurate data from respondents, it’s important to use an online survey tool. There are a few benefits of using an online survey tool.

First, online tools can help you to reach a larger audience. This is because you can send your survey to people all over the world with just a few clicks.

Second, it’s possible to collect data from respondents quicker. This is because people can take your survey at their convenience and you don’t have to wait for them to return it to you.

Third, you can collect more accurate data. This is because people are less likely to make mistakes when they’re taking your survey online.

Finally, make sure the tool provides you with detailed results that you can use to improve your product or service. The right survey software can make significant differences in the accuracy of your data.

Pretest and pilot tools

When designing a survey, it’s important to ask the right questions in a logical order. Pretest and pilot are two tools that can help you determine which questions to include in your survey. The pretest is used to assess the clarity and accuracy of the questions and the pilot survey is used to test the survey’s feasibility.

The pretest should be administered to a small sample of respondents who are representative of the target population. The pilot should be administered to a larger sample of respondents who are also representative of the target population. This will help you determine if there are any problems with the survey, such as difficulty understanding the questions or completing the questionnaire.

It’s important to keep in mind that the pretest and pilot surveys are not meant to replace primary research. They are simply tools that can help you design a better survey.

Results: Make survey feedback matter

Collect useful information.

The goal of any survey is to collect useful information. This means that you need to ask questions that will help you to improve your product or service.

To do this, you need to first identify what information you need. Second, you need to choose the right type of questions to get that information.

Third, you need to make sure your questions are clear and concise. Fourth, you need to test your questions with a small group of people before you launch your survey.

Make it relevant to your target audience

It’s important your survey design is aimed at your target audience. The questions should be relevant to the group of people you are surveying.

The questions should also be appropriate for the age group, gender, and geographic location of your target audience. For example, if you are surveying teenagers, you would not want to include questions about retirement plans.

By keeping your target audience in mind, you can ensure that your survey is relevant and engaging. This will help you get the information you need to make decisions about your business.

Provide clear instructions (if needed)

If you are administering via paper, it’s important to provide clear instructions. The instructions should be easy to understand and follow. They should also be concise.

The instructions should explain how to answer questions accurately and how long it will take. They should also include any information that is necessary for the respondent, such as what personal information is needed or how the survey will be used.

To ensure high customer satisfaction among survey respondents, it’s important to provide a link to the survey. The link should be easy to find and accessible. The instructions should also be clear and concise.

Analysis: Interpreting the results

Analyze results and adjust as needed.

After you have collected the data you need, it’s important to analyze the results. This will help you determine if there are any ley factors that need improvement.

If you find that there are problems with the survey, such as a low response rate or poor question clarity, you should adjust the survey accordingly.

It’s also important to keep in mind that the results of the survey may not be representative of the entire population. This is why it’s important to use a large sample size when conducting a survey.

Monitor Response Rates

Once you have your survey design complete and administered your survey, it’s important to monitor the response rate. The response rate is the percentage of people who complete the survey.

A high response rate indicates that people are interested in taking your survey. A low response rate may indicate that the survey is too long or that the questions are not clear.

If you find that the response rate is low, you should adjust the survey accordingly. You may also want to consider using a different method of collecting data, such as focus groups or interviews.

Follow up with respondents

After you have collected and analyzed the data from your survey, you should follow up with respondents. This is a good way to thank them for their time and to get more feedback on your product or service.

You can also use this opportunity to ask respondents if they would be willing to participate in future surveys.

Now that you know the principles of good survey design, you’re ready to create your next survey that will help you to get the data you need. Just remember to use random sampling, avoid self-selection bias and selection bias, double-barreled questions and use an appropriate survey tool to get accurate survey results.

With these tips, you’ll be on your way to collecting accurate data that you can use to improve your business.

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A Comprehensive Guide to Survey Research Methodologies

For decades, researchers and businesses have used survey research to produce statistical data and explore ideas. The survey process is simple, ask questions and analyze the responses to make decisions. Data is what makes the difference between a valid and invalid statement and as the American statistician, W. Edwards Deming said:

“Without data, you’re just another person with an opinion.” - W. Edwards Deming

In this article, we will discuss what survey research is, its brief history, types, common uses, benefits, and the step-by-step process of designing a survey.

What is Survey Research

A survey is a research method that is used to collect data from a group of respondents in order to gain insights and information regarding a particular subject. It’s an excellent method to gather opinions and understand how and why people feel a certain way about different situations and contexts.

Brief History of Survey Research

Survey research may have its roots in the American and English “social surveys” conducted around the turn of the 20th century. The surveys were mainly conducted by researchers and reformers to document the extent of social issues such as poverty. ( 1 ) Despite being a relatively young field to many scientific domains, survey research has experienced three stages of development ( 2 ):

-       First Era (1930-1960)

-       Second Era (1960-1990)

-       Third Era (1990 onwards)

Over the years, survey research adapted to the changing times and technologies. By exploiting the latest technologies, researchers can gain access to the right population from anywhere in the world, analyze the data like never before, and extract useful information.

Survey Research Methods & Types

Survey research can be classified into seven categories based on objective, data sources, methodology, deployment method, and frequency of deployment.

Surveys based on Objective

Exploratory survey research.

Exploratory survey research is aimed at diving deeper into research subjects and finding out more about their context. It’s important for marketing or business strategy and the focus is to discover ideas and insights instead of gathering statistical data.

Generally, exploratory survey research is composed of open-ended questions that allow respondents to express their thoughts and perspectives. The final responses present information from various sources that can lead to fresh initiatives.

Predictive Survey Research

Predictive survey research is also called causal survey research. It’s preplanned, structured, and quantitative in nature. It’s often referred to as conclusive research as it tries to explain the cause-and-effect relationship between different variables. The objective is to understand which variables are causes and which are effects and the nature of the relationship between both variables.

Descriptive Survey Research

Descriptive survey research is largely observational and is ideal for gathering numeric data. Due to its quantitative nature, it’s often compared to exploratory survey research. The difference between the two is that descriptive research is structured and pre-planned.

 The idea behind descriptive research is to describe the mindset and opinion of a particular group of people on a given subject. The questions are every day multiple choices and users must choose from predefined categories. With predefined choices, you don’t get unique insights, rather, statistically inferable data.

Survey Research Types based on Concept Testing

Monadic concept testing.

Monadic testing is a survey research methodology in which the respondents are split into multiple groups and ask each group questions about a separate concept in isolation. Generally, monadic surveys are hyper-focused on a particular concept and shorter in duration. The important thing in monadic surveys is to avoid getting off-topic or exhausting the respondents with too many questions.

Sequential Monadic Concept Testing

Another approach to monadic testing is sequential monadic testing. In sequential monadic surveys, groups of respondents are surveyed in isolation. However, instead of surveying three groups on three different concepts, the researchers survey the same groups of people on three distinct concepts one after another. In a sequential monadic survey, at least two topics are included (in random order), and the same questions are asked for each concept to eliminate bias.

Based on Data Source

Primary data.

Data obtained directly from the source or target population is referred to as primary survey data. When it comes to primary data collection, researchers usually devise a set of questions and invite people with knowledge of the subject to respond. The main sources of primary data are interviews, questionnaires, surveys, and observation methods.

 Compared to secondary data, primary data is gathered from first-hand sources and is more reliable. However, the process of primary data collection is both costly and time-consuming.

Secondary Data

Survey research is generally used to collect first-hand information from a respondent. However, surveys can also be designed to collect and process secondary data. It’s collected from third-party sources or primary sources in the past.

 This type of data is usually generic, readily available, and cheaper than primary data collection. Some common sources of secondary data are books, data collected from older surveys, online data, and data from government archives. Beware that you might compromise the validity of your findings if you end up with irrelevant or inflated data.

Based on Research Method

Quantitative research.

Quantitative research is a popular research methodology that is used to collect numeric data in a systematic investigation. It’s frequently used in research contexts where statistical data is required, such as sciences or social sciences. Quantitative research methods include polls, systematic observations, and face-to-face interviews.

Qualitative Research

Qualitative research is a research methodology where you collect non-numeric data from research participants. In this context, the participants are not restricted to a specific system and provide open-ended information. Some common qualitative research methods include focus groups, one-on-one interviews, observations, and case studies.

Based on Deployment Method

Online surveys.

With technology advancing rapidly, the most popular method of survey research is an online survey. With the internet, you can not only reach a broader audience but also design and customize a survey and deploy it from anywhere. Online surveys have outperformed offline survey methods as they are less expensive and allow researchers to easily collect and analyze data from a large sample.

Paper or Print Surveys

As the name suggests, paper or print surveys use the traditional paper and pencil approach to collect data. Before the invention of computers, paper surveys were the survey method of choice.

Though many would assume that surveys are no longer conducted on paper, it's still a reliable method of collecting information during field research and data collection. However, unlike online surveys, paper surveys are expensive and require extra human resources.

Telephonic Surveys

Telephonic surveys are conducted over telephones where a researcher asks a series of questions to the respondent on the other end. Contacting respondents over a telephone requires less effort, human resources, and is less expensive.

What makes telephonic surveys debatable is that people are often reluctant in giving information over a phone call. Additionally, the success of such surveys depends largely on whether people are willing to invest their time on a phone call answering questions.

One-on-one Surveys

One-on-one surveys also known as face-to-face surveys are interviews where the researcher and respondent. Interacting directly with the respondent introduces the human factor into the survey.

Face-to-face interviews are useful when the researcher wants to discuss something personal with the respondent. The response rates in such surveys are always higher as the interview is being conducted in person. However, these surveys are quite expensive and the success of these depends on the knowledge and experience of the researcher.

Based on Distribution

The easiest and most common way of conducting online surveys is sending out an email. Sending out surveys via emails has a higher response rate as your target audience already knows about your brand and is likely to engage.

Buy Survey Responses

Purchasing survey responses also yields higher responses as the responders signed up for the survey. Businesses often purchase survey samples to conduct extensive research. Here, the target audience is often pre-screened to check if they're qualified to take part in the research.

Embedding Survey on a Website

Embedding surveys on a website is another excellent way to collect information. It allows your website visitors to take part in a survey without ever leaving the website and can be done while a person is entering or exiting the website.

Post the Survey on Social Media

Social media is an excellent medium to reach abroad range of audiences. You can publish your survey as a link on social media and people who are following the brand can take part and answer questions.

Based on Frequency of Deployment

Cross-sectional studies.

Cross-sectional studies are administered to a small sample from a large population within a short period of time. This provides researchers a peek into what the respondents are thinking at a given time. The surveys are usually short, precise, and specific to a particular situation.

Longitudinal Surveys

Longitudinal surveys are an extension of cross-sectional studies where researchers make an observation and collect data over extended periods of time. This type of survey can be further divided into three types:

-       Trend surveys are employed to allow researchers to understand the change in the thought process of the respondents over some time.

-       Panel surveys are administered to the same group of people over multiple years. These are usually expensive and researchers must stick to their panel to gather unbiased opinions.

-       In cohort surveys, researchers identify a specific category of people and regularly survey them. Unlike panel surveys, the same people do not need to take part over the years, but each individual must fall into the researcher’s primary interest category.

Retrospective Survey

Retrospective surveys allow researchers to ask questions to gather data about past events and beliefs of the respondents. Since retrospective surveys also require years of data, they are similar to the longitudinal survey, except retrospective surveys are shorter and less expensive.

Why Should You Conduct Research Surveys?

“In God we trust. All others must bring data” - W. Edwards Deming

 In the information age, survey research is of utmost importance and essential for understanding the opinion of your target population. Whether you’re launching a new product or conducting a social survey, the tool can be used to collect specific information from a defined set of respondents. The data collected via surveys can be further used by organizations to make informed decisions.

Furthermore, compared to other research methods, surveys are relatively inexpensive even if you’re giving out incentives. Compared to the older methods such as telephonic or paper surveys, online surveys have a smaller cost and the number of responses is higher.

 What makes surveys useful is that they describe the characteristics of a large population. With a larger sample size , you can rely on getting more accurate results. However, you also need honest and open answers for accurate results. Since surveys are also anonymous and the responses remain confidential, respondents provide candid and accurate answers.

Common Uses of a Survey

Surveys are widely used in many sectors, but the most common uses of the survey research include:

-       Market research : surveying a potential market to understand customer needs, preferences, and market demand.

-       Customer Satisfaction: finding out your customer’s opinions about your services, products, or companies .

-       Social research: investigating the characteristics and experiences of various social groups.

-       Health research: collecting data about patients’ symptoms and treatments.

-       Politics: evaluating public opinion regarding policies and political parties.

-       Psychology: exploring personality traits, behaviors, and preferences.

6 Steps to Conduct Survey Research

An organization, person, or company conducts a survey when they need the information to make a decision but have insufficient data on hand. Following are six simple steps that can help you design a great survey.

Step 1: Objective of the Survey

The first step in survey research is defining an objective. The objective helps you define your target population and samples. The target population is the specific group of people you want to collect data from and since it’s rarely possible to survey the entire population, we target a specific sample from it. Defining a survey objective also benefits your respondents by helping them understand the reason behind the survey.

Step 2: Number of Questions

The number of questions or the size of the survey depends on the survey objective. However, it’s important to ensure that there are no redundant queries and the questions are in a logical order. Rephrased and repeated questions in a survey are almost as frustrating as in real life. For a higher completion rate, keep the questionnaire small so that the respondents stay engaged to the very end. The ideal length of an interview is less than 15 minutes. ( 2 )

Step 3: Language and Voice of Questions

While designing a survey, you may feel compelled to use fancy language. However, remember that difficult language is associated with higher survey dropout rates. You need to speak to the respondent in a clear, concise, and neutral manner, and ask simple questions. If your survey respondents are bilingual, then adding an option to translate your questions into another language can also prove beneficial.

Step 4: Type of Questions

In a survey, you can include any type of questions and even both closed-ended or open-ended questions. However, opt for the question types that are the easiest to understand for the respondents, and offer the most value. For example, compared to open-ended questions, people prefer to answer close-ended questions such as MCQs (multiple choice questions)and NPS (net promoter score) questions.

Step 5: User Experience

Designing a great survey is about more than just questions. A lot of researchers underestimate the importance of user experience and how it affects their response and completion rates. An inconsistent, difficult-to-navigate survey with technical errors and poor color choice is unappealing for the respondents. Make sure that your survey is easy to navigate for everyone and if you’re using rating scales, they remain consistent throughout the research study.

Additionally, don’t forget to design a good survey experience for both mobile and desktop users. According to Pew Research Center, nearly half of the smartphone users access the internet mainly from their mobile phones and 14 percent of American adults are smartphone-only internet users. ( 3 )

Step 6: Survey Logic

Last but not least, logic is another critical aspect of the survey design. If the survey logic is flawed, respondents may not continue in the right direction. Make sure to test the logic to ensure that selecting one answer leads to the next logical question instead of a series of unrelated queries.

How to Effectively Use Survey Research with Starlight Analytics

Designing and conducting a survey is almost as much science as it is an art. To craft great survey research, you need technical skills, consider the psychological elements, and have a broad understanding of marketing.

The ultimate goal of the survey is to ask the right questions in the right manner to acquire the right results.

Bringing a new product to the market is a long process and requires a lot of research and analysis. In your journey to gather information or ideas for your business, Starlight Analytics can be an excellent guide. Starlight Analytics' product concept testing helps you measure your product's market demand and refine product features and benefits so you can launch with confidence. The process starts with custom research to design the survey according to your needs, execute the survey, and deliver the key insights on time.

• Survey research in the United States: roots and emergence, 1890-1960 https://searchworks.stanford.edu/view/10733873    
• How to create a survey questionnaire that gets great responses https://luc.id/knowledgehub/how-to-create-a-survey-questionnaire-that-gets-great-responses/    
• Internet/broadband fact sheet https://www.pewresearch.org/internet/fact-sheet/internet-broadband/    

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Survey Research Design

The survey research design is often used because of the low cost and easy accessible information.

This article is a part of the guide:

• Research Designs
• Quantitative and Qualitative Research
• Literature Review
• Quantitative Research Design

Browse Full Outline

• 1 Research Designs
• 2.1 Pilot Study
• 2.2 Quantitative Research Design
• 2.3 Qualitative Research Design
• 2.4 Quantitative and Qualitative Research
• 3.1 Case Study
• 3.2 Naturalistic Observation
• 3.3 Survey Research Design
• 3.4 Observational Study
• 4.1 Case-Control Study
• 4.2 Cohort Study
• 4.3 Longitudinal Study
• 4.4 Cross Sectional Study
• 4.5 Correlational Study
• 5.1 Field Experiments
• 5.2 Quasi-Experimental Design
• 5.3 Identical Twins Study
• 6.1 Experimental Design
• 6.2 True Experimental Design
• 6.3 Double Blind Experiment
• 6.4 Factorial Design
• 7.1 Literature Review
• 7.2 Systematic Reviews
• 7.3 Meta Analysis

Introduction

Conducting accurate and meaningful surveys is one of the most important facets of market research in the consumer driven 21st century.

Businesses, governments and media spend billions of dollars on finding out what people think and feel.

Accurate research can generate vast amounts of revenue; bad or inaccurate research can cost millions, or even bring down governments.

The survey research design is a very valuable tool for assessing opinions and trends. Even on a small scale, such as local government or small businesses, judging opinion with carefully designed surveys can dramatically change strategies.

Television chat-shows and newspapers are usually full of facts and figures gleaned from surveys but often no information is given as to where this information comes from or what kind of people were asked.

A cursory examination of these figures usually shows that the results of these surveys are often manipulated or carefully sifted to try and reflect distort the results to match the whims of the owners.

Businesses are often guilty of carefully selecting certain results to try and portray themselves as the answer to all needs.

When you decide to enter this minefield and design a survey, how do you avoid falling into the trap of inaccuracy and bias ? How do you ensure that your survey research design reflects the views of a genuine cross-section of the population?

The simple answer is that you cannot; even with unlimited budget, time and resources, there is no way of achieving 100% accuracy. Opinions, on all levels, are very fluid and can change on a daily or even hourly basis.

Despite this, surveys are still a powerful tool and can be an extremely powerful research tool. As long as you design your survey well and are prepared to be self-critical, you can still obtain an accurate representation of opinion.

Establishing the Aims of Your Research

This is the single most important step of your survey research design and can make or break your research; every single element of your survey must refer back to this design or it will be fatally flawed.

If your research is too broad, you will have to ask too many questions ; too narrow and you will not be researching the topic thoroughly enough.

Researching and Determining Your Sample Group

This is the next crucial step in determining your survey and depends upon many factors.

The first is accuracy; you want to try and interview as broad a base of people as possible. Quantity is not always the answer; if you were researching a detergent, for example, you would want to target your questions at those who actually use such products.

For a political or ethical survey, about which anybody can have a valid opinion, you want to try and represent a well balanced cross section of society.

It is always worth checking beforehand what quantity and breadth of response you need to provide significant results or your hard work may be in vain.

Before you start the planning, it is important that you consult somebody about the statistical side of your survey research design. This way, you know what number and type of responses you need to make it a valid survey and prevent inaccurate results.

Methodology

How do you make sure that your questionnaire reaches the target group? There are many methods of reaching people but all have advantages and disadvantages.

For a college or university study it is unlikely that you will have the facilities to use internet, e-mail or phone surveying so we will concentrate on only the likely methods you will use.

Face to Face

This is probably the most traditional method of the survey research design. It can be very accurate. It allows you to be selective about to whom you ask questions and you can explain anything that they do not understand.

In addition, you can make a judgment about who you think is wasting your time or giving stupid answers.

There are a few things to be careful of with this approach; firstly, people can be reluctant to give up their time without some form of incentive.

Another factor to bear in mind is that is difficult to ask personal questions face to face without embarrassing people. It is also very time consuming and difficult to obtain a representative sample.

Finally, if you are going to be asking questions door-to-door, it is essential to ensure that you have some official identification to prove who you are.

This does not necessarily mean using the postal service; putting in the legwork and delivering questionnaires around a campus or workplace is another method.

This is a good way of targeting a certain section of people and is excellent if you need to ask personal or potentially embarrassing questions.

The problems with this method are that you cannot be sure of how many responses you will receive until a long time period has passed.

You must also be wary of collecting personal data; most countries have laws about how much information you can keep about people so it is always wise to check with somebody more knowledgeable.

Structuring and Designing the Questionnaire

The design of your questionnaire depends very much upon the type of survey and the target audience.

If you are asking questions face to face it is easy to explain if people are unsure of a question. On the other hand, if your questionnaire is going to include many personal questions then mailing methods are preferable (but may violate local legislation).

You must keep your questionnaire as short as possible; people will either refuse to fill in a long questionnaire or get bored halfway through.

If you do have lots of information then it may be preferable to offer multiple-choice or rating questions to make life easier.

It is also polite, especially with mailed questionnaires, to send a short cover note explaining what you are doing and how the subject should return the surveys to you.

You should introduce yourself; explain why you are doing the research, what will happen with the results and who to contact if the subject has any queries.

Types of Question

Multiple choice questions allow many different answers, including don't know, to be assessed. The main strength of this type of question is that the form is easy to fill in and the answers can be checked easily and quantitatively ; this is useful for large sample groups.

Rating, on some scale, is a tried and tested form of question structure. This way is very useful when you are seeking to be a little more open-ended than is possible with multiple choice questions. It is a little harder to analyze your responses. It is important to make sure that the scale allows extreme views.

Questions asking for opinions must be open-ended and allow the subject to give their own response; you should avoid entrapment and appear to be as neutral as possible during the procedure. The major problem is that you have to devise a numerical way of analyzing and statistically evaluating the responses which can lead to a biased view, if care is not taken. These types of question should really be reserved for experienced researchers.

The order in which you ask the questions can be important. Try to start off with the most relevant questions first. Also friendly and non-threatening questions put the interviewee at ease. Questions should be simple and straightforward using everyday language rather than perfect grammar.

Try and group questions about similar topics together; this makes it a lot quicker for people to answer questions more quickly and easily.

Some researchers advocate mixing up and randomizing questions for accuracy but this approach tends to be more appropriate for advanced market research. For this type of survey the researcher is trying to disguise the nature of the research and filter out preconceptions.

It is also a good idea to try out a test survey; ask a small group to give genuine and honest feedback so that you can make adjustments.

Common mistakes when doing the survey research design.

Analyzing Your Results

This is where the fun starts and it will depend upon the type of questions used.

For multiple choice questions it is a matter of counting up the answers to each question and using statistics to ‘crunch the numbers' and test relevance.

Rating type questions require a little more work but they follow broadly the same principle.

For opinion questions you have to devise some way of judging the responses numerically.

The next step is to devise which statistical test you are going to use and start to enter some numbers to judge the significance of your data.

Conclusions

This is where you have to analyze the results. Be self critical whether your results showed what you expected or not. Any survey has flaws in its method so it is always a good idea to show that you are aware of these.

For example, a university represents only a narrow cross section of society; as long as you are aware of this then your results are valid. If your survey gave unexpected results explain the possible reasons for why this happened and suggestions for refining the techniques and structure of your survey next time.

As long as you have justified yourself and pointed out your own shortcomings then your results will be relevant and you should receive a good result.

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Martyn Shuttleworth (Jul 5, 2008). Survey Research Design. Retrieved Sep 03, 2024 from Explorable.com: https://explorable.com/survey-research-design

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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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Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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• Market Research

4 Main Benefits of Survey Research

• Written by Susan E. DeFranzo

Using survey software to administer survey research is a powerful tool that market researchers use to gather data. Advanced survey software providers have survey solutions for all modes of survey research, including: online surveys , paper surveys , phone surveys, to the more recent introduction of mobile surveys . Available survey solutions have led to widespread use of quantitative surveys, across all survey modes, to collect, analyze, and use data to formulate strategies for a more effective business model, create targeted marketing strategies, enhance customer service, and much more. Executed correctly, survey research can benefit market researchers with reliable and useable data, and improve research ROI.

The benefits of Survey Research

Surveys are relatively inexpensive. Online surveys and mobile surveys, in particular, have a very small cost per respondent. Even if incentives are given to respondents, the cost per response is often far less than the cost of administering a paper survey or phone survey, and the number of potential responses can be in the thousands.

Surveys are useful in describing the characteristics of a large population. No other research method can provide this broad capability, which ensures a more accurate sample to gather targeted results in which to draw conclusions and make important decisions.

Surveys can be administered in many modes, including: online surveys, email surveys, social media surveys, paper surveys, mobile surveys, telephone surveys, and face-to-face interview surveys.  For remote or hard-to-reach respondents, using a mixed mode of survey research may be necessary (e.g. administer both online surveys and paper surveys to collect responses and compile survey results into one data set, ready for analysis).

The anonymity of surveys allows respondents to answer with more candid and valid answers. To get the most accurate data, you need respondents to be as open and honest as possible with their answers. Surveys conducted anonymously provide an avenue for more honest and unambiguous responses than other types of research methodologies, especially if it is clearly stated that survey answers will remain completely confidential.

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• Am J Pharm Educ
• v.77(1); 2013 Feb 12

The Importance of Survey Research Standards

Jack e. fincham.

a School of Pharmacy, The University of Missouri Kansas City, Kansas City, MO

b Henry W. Bloch School of Management, The University of Missouri Kansas City, Kansas City, MO

Every discipline within fields of research has instituted guidelines and templates for research endeavors and subsequent publications of findings, with the ultimate result being an increase in quality and acceptance by researchers within and across disciplines. These significant efforts are by nature ongoing, as well they should. These enhancements and guideline developments have been instituted in basic science disciplines, clinical pharmacy, and pharmacy administration relevant and related to subsequent scholarly publication of research findings. Specific research endeavors have included bench research, clinical trials and randomized clinical trials, meta analyses, outcomes research, and large scale database analyses. A similar need for quality and standardization also exists for survey research and scholarship. The purpose of this paper is to clarify why this is important and crucial for the Journal and our academy.

INTRODUCTION

In the Research Standards section of Instructions to Authors ( http://archive.ajpe.org/instructions.asp ), the Journal provides guidelines for authors to consider when preparing a manuscript for submission to the Journal . These standards are important for a number of reasons, and may be seen as unique and groundbreaking with regard to other academic health professions journals. This paper is intended to add clarity to this sometimes controversial set of Journal guidelines.

Whether referring to sampling texts such as Cochran’s Sampling Techniques , 3rd edition, 1 or Kish’s Survey Sampling , 2 or using guidelines or tables generated based on these classics as found in Krejcie and Morgan, 3 Salant and Dillman, 4 Bartlett and colleagues, 5 and Dillman, 6 the researcher will find that small populations require a high number of data elements (ie, high response rates) to confidently generalize results because of the potential for sampling error. The recommended minimum sample size for a study depends upon desired confidence level (typically 95%) and how varied the population is with respect to the variable(s) of interest.

Using the conservative approach of a 50/50 split (in other words, an equal chance of one response versus another) on a dichotomous variable of interest at the conventional 95% confidence level for a population of 100, we would need a sample of 80 to ensure a sampling error of no more than +/- 5% at the 95% confidence level. For a population of 100, if a response rate of 50% was achieved for an item with a simple yes/no answer (eg, “Do you have a full-time biostatistician employed by the college?”) and responses were evenly split (50% yes and 50% no), it would not be prudent to extrapolate those findings to the overarching population (100) because the range of possible true percentages would be 25%-75% (that is, all, some, or none of the 50 nonrespondents could have a biostatistician at their college.) 7 (p55)

For a variable with a smaller standard deviation in response to a survey item, say an 80/20 split (eg, 80% agree, 20% disagree), a sample size of only 71 (rather than 80) would be required to maintain the same precision as in the previous example, ie, a 95% confidence level. However, according to Salant and Dillman, 4 (p55) “unless we know the split ahead of time, it is best to be conservative and use 50/50.” Continuous data sets may not require as many data points, however, “if a categorical variable will play a primary role in data analyses…the categorical sample size formulas should be used.” 5(p46) To estimate the sample size required for a continuous variable would necessitate a measure of variability in the population, which may not be easily discerned, thus “the sample size for the proportion is frequently preferred.” 8(p4) As well, “the effect of nonresponse on one variable can be very different than for others in the same survey.” 7 (p54)

Others have simply called for a census in small populations, again necessitating high response rates. 8,9 These considerations supported the rationale for the expectations set forth in the Viewpoint by Fincham. 10

There are 129 doctor of pharmacy degree programs in academic pharmacy in 1 of 3 classifications of accreditation: 109 full accreditation, 15 candidates, and 5 precandidates. 11 The recommended sample size for N=129 at +/- 5% sampling error and 95% confidence level is 97, or a 75% response rate for a 50/50 split. Modeling on a variable with an 80/20 split (ie, less variability in the population) would result in a recommended sample size of 85 or a 66% response rate. Because of the increase in the number of colleges and schools of pharmacy in the United States, the Journal will now accept a 70% response rate threshold for those survey projects collecting data on multiple variable types with the intent of generalizing results to the entire population.

The paper by Draugalis and Plaza 12 provides several examples of the importance of striving for a census and how much confidence readers would have in a published study with a data set with less than optimal response rates, including the annual AACP Faculty Salary Survey. As an example of the potential effects of nonresponse on specific variables in a study, consider the following from a published study on career planning and preparation strategies of pharmacy deans. 13 The subjects were 53 “new” deans with less than 5 years’ experience and 40 “experienced” deans previously in the database with greater than 5 years’ experience, for a cohort of 93 sitting permanent deans (ie, acting and interim deans were excluded) in 2009. Descriptive findings were presented for the total cohort as well as for separate groups on a number of variables when contrasts were desired. “Newly named deans spent an average of 17.1 +/- 8.7 years in the professoriate prior to assuming their first deanship, compared with established deans who had spent an average of 19.0 +/- 5.1 years ( p = 0.006).” If just 3 of the new dean respondents with no or few years in the professoriate had not participated in the study, the mean would have increased to 18.1, the comparison would not have been significant, and an important finding would have been missed. In the career path ladder variable, 9 of the 53 new deans fell in the nontraditional category. If any number of these subjects had actually been nonrespondents, and the closer to actually all 9 of them not participating, this would have skewed descriptive findings and obscured longitudinal comparisons.

High response rates to a research survey do not ensure the validity of the findings as there are other potential sources of error to consider. While attaining a high response rate is a necessary first step, it is not sufficient in and of itself. The specific research question determines the acceptable research methods. For example, in some inquiries, a survey of all colleges and schools of pharmacy may not be necessary or desirable. Depending on the research question, interviews or focus groups may be useful, but the results cannot be generalized to all institutions. Some projects may be intended to gather information only from certain types of institutions, such as private entities, or programs affiliated with a health sciences center. A demonstration project with descriptive findings may be useful to others and in a sense, the argument would be for a methodological development, with the method being generalizable and useful to others, but not the specific institutional findings pertinent to their research. Also, the accepted tools of modeling and decision analytic methods may be appropriate alternatives.

IMPORTANCE OF RESEARCH GUIDELINES AND STANDARDS

In several other research arenas, standards for research methods have been proposed, implemented, and well accepted. Other journals have set standards for research and publications appearing in such. In the 1990s, an international collaboration set in motion a process whereby research standards were developed to enhance the quality and validity of results from clinical trials. A thorough scrutiny of refereed journals accessed through MEDLINE, Embase, Cochrane Central, and associated reference lists was accomplished, and then experts determined the CONSORT checklist, which was subsequently proven to improve the methodology, quality, and external validity aspects of reports of randomized clinical trials. 14,15

Similarly a checklist has been published for qualitative research in hopes of promoting explicit, comprehensive reporting of such research. 16 A Canadian group has proposed developing a survey reporting guideline for health research beginning in 2013 (David Moher, Director, Evidence-based Practice Centre, University of Ottawa, Canada, personal communication, May 17, 2012).

The EQUATOR network (the resource center for good reporting of health research studies) also has been developed to address and make recommendations dealing with the “growing evidence demonstrating widespread deficiencies in the reporting of health research studies.” 17 The EQUATOR Web site provides a list of collected tools and guidelines available for assessing health research issues ( www.equator-network.org ).

Poor reporting guidelines lead to subsequent deficient outcome segments in written summaries of research. Bennett and colleagues have summarized this problem as follows: “There is limited guidance and no consensus regarding the optimal reporting of survey research. As in other areas of research poor reporting compromises both transparency and reliability, which are fundamental tenets of research.” 18 (p.8)

In addressing their concerns over established response rates, Mészáros and colleagues 19 point to the Journal of Dental Education and Academic Medicine as similar publications to the Journal that do not specify response rate criteria. Actually, the issue of response rates has been addressed repeatedly and specifically in these journals. As early as 1983, Creswell and Kuster 20 writing in the Journal of Dental Education noted that at that juncture, 40% of papers published over the previous 5 years were survey studies. Thirty years ago, they called for increased diligence in assessing appropriate sample sizes, adequate attention paid to survey response rates, and greater effort in improving the quality of survey-related research in the Journal of Dental Education.

In 2009, in an excellent analysis of survey research issues in the Journal of Dental Education , Chambers and Licari suggest that: “Evidence that is not grounded in theory is just data. There is a natural pull on the authors of surveys to interpret their findings as supporting policies or positions they favor.” 21(p288) The authors also speak to the importance of adequate response rates: “…that the precision of any claim based on a survey is strongly affected by sample size.” 21(p294) The authors point to sample saturation as a technique to reduce the impact of bias in surveys. This technique directly addresses the response rate issue by noting that the larger the sample size and the higher the response rate, the more accuracy can be attributed to the study results. A built in assumption is that even unknown missing data adversely affect the conclusions of the analyses. Subsequently, even contrary results that may have potentially come from the nonrespondents would result in a less likely scenario. In effect, the results would be different from what was obtained from the analyses of the data in hand.

Response rates matter a great deal, and this point has been made in the Journal of Dental Education over a 30-year period. The issue is not that the Journal of Dental Education has chosen not to develop standards for survey research papers, but rather that the American Journal of Pharmaceutical Education has taken a leadership role in this regard.

Although it is true that Academic Medicine does not explicitly list an acceptable response rate, the October 2011 issue provided summary guidance for survey research published in their journal. 22 In this excellent summary of good research practices relative to survey design and reporting, 5 references are listed. 23-27 These seminal references provide explicit information regarding sampling, research design, response rates and associated problems with biases, and acceptability indices in other components of survey research. In one of these “gold standard” references, Krosnick notes that: “It is important to recognize the inherent limitations of nonprobability sampling methods and to draw conclusions about populations or differences between populations tentatively when nonprobability sampling methods are used.” 25(p541) This point becomes even more significant when low response rates are achieved in nonprobability samples.

GUIDELINES AND STANDARDS AS A QUALITY CONTROL MECHANISM

Setting standards and suggesting guidelines are in no way a move on the part of the Journal editors to stifle research or unfairly limit the reporting of research findings; nor are they intended in any manner to arbitrarily curtail creativity. Many fine survey research papers are published in the Journal and contribute to the academy. There are simply no published studies that have pointed out the negative impact of such standard-setting processes on the research endeavors related to clinical, health services research, or sociological research.

Sexual Shame and Associations with Social Evaluation Among Chinese Adults: The Effect of Mianzi and Negative Body Consciousness

• Published: 02 September 2024

Cite this article

• Hongyu Peng 1 ,
• Yanchen Su 1 &
• Yong Zheng   ORCID: orcid.org/0000-0001-8485-0616 1  

Introduction

Although sexual shame is a very common emotional experience, it is hidden in Chinese culture and generally discussed in a non-academic manner. To our knowledge, this is the first study to empirically investigate associations between sexual shame and social evaluation among a Chinese sample, including the factors of mianzi (perceived evaluation by others) and negative body consciousness (body shame and body dissatisfaction).

Our study utilized a correlational, cross-sectional online survey design. Data were collected from October 6 to December 8, 2021. A sample of 1259 Chinese adults (aged 18–59 years, women = 690, men = 569) completed measures of sexual shame, mianzi , body shame, and body dissatisfaction. Descriptive analysis and hierarchical regression were performed.

After controlling for some variables (age, sex, sexual orientation, and adverse sexual experiences), higher severity of sexual shame in Chinese adults was predicted by a higher level of fear of losing mianzi , greater body shame, and greater body dissatisfaction. Sexual shame was not significantly predicted by the desire to gain mianzi.

Conclusions

Chinese adults’ self-perceived social evaluations may play important roles in predicting their sexual shame, bringing a relatively new perspective to the research.

Policy Implications

The findings provide evidence and future research directions for researchers, sex educators, and the general public interested in sexual attitudes in the context of Chinese culture. The study suggests ways to create interventions to address the potentially adverse effects of sexual shame and may be used to assist in the development of sex education programs.

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Data Availability

Data supporting the findings of this study are available from the corresponding author on request.

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This study was funded by the Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan006), China.

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Peng, H., Su, Y. & Zheng, Y. Sexual Shame and Associations with Social Evaluation Among Chinese Adults: The Effect of Mianzi and Negative Body Consciousness. Sex Res Soc Policy (2024). https://doi.org/10.1007/s13178-024-01028-w

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