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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing 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
• Analyze 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: analyze the survey results, step 6: write up the survey results, other interesting articles, 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 in 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
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

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

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

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 college student in the US. 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 generalize 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. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, 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 mail 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, and at risk for biases like self-selection bias .

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 analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

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 mall 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 store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

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 analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed 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 analyzed 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. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

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.

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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 mail, online, or in person.

There are many methods of analyzing 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 clean 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 organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing 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 analyzed 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 analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 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 5 or 7 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 analyze your data.

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 sampling methods or criteria for selecting subjects
• 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 )

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Dissertation surveys: Questions, examples, and best practices

Collect data for your dissertation with little effort and great results.

Dissertation surveys are one of the most powerful tools to get valuable insights and data for the culmination of your research. However, it’s one of the most stressful and time-consuming tasks you need to do. You want useful data from a representative sample that you can analyze and present as part of your dissertation. At SurveyPlanet, we’re committed to making it as easy and stress-free as possible to get the most out of your study.

With an intuitive and user-friendly design, our templates and premade questions can be your allies while creating a survey for your dissertation. Explore all the options we offer by simply signing up for an account—and leave the stress behind.

How to write dissertation survey questions

The first thing to do is to figure out which group of people is relevant for your study. When you know that, you’ll also be able to adjust the survey and write questions that will get the best results.

The next step is to write down the goal of your research and define it properly. Online surveys are one of the best and most inexpensive ways to reach respondents and achieve your goal.

Before writing any questions, think about how you’ll analyze the results. You don’t want to write and distribute a survey without keeping how to report your findings in mind. When your thesis questionnaire is out in the real world, it’s too late to conclude that the data you’re collecting might not be any good for assessment. Because of that, you need to create questions with analysis in mind.

You may find our five survey analysis tips for better insights helpful. We recommend reading it before analyzing your results.

Once you understand the parameters of your representative sample, goals, and analysis methodology, then it’s time to think about distribution. Survey distribution may feel like a headache, but you’ll find that many people will gladly participate.

Find communities where your targeted group hangs out and share the link to your survey with them. If you’re not sure how large your research sample should be, gauge it easily with the survey sample size calculator.

Need help with writing survey questions? Read our guide on well-written examples of good survey questions .

Dissertation survey examples

Whatever field you’re studying, we’re sure the following questions will prove useful when crafting your own.

At the beginning of every questionnaire, inform respondents of your topic and provide a consent form. After that, start with questions like:

• Please select your gender:
• What is the highest educational level you’ve completed?
• High school
• Bachelor degree
• Master’s degree
• On a scale of 1-7, how satisfied are you with your current job?
• Please rate the following statements:
• I always wait for people to text me first.
• Strongly Disagree
• Neither agree nor disagree
• Strongly agree
• My friends always complain that I never invite them anywhere.
• I prefer spending time alone.
• Rank which personality traits are most important when choosing a partner. Rank 1 - 7, where 1 is the most and 7 is the least important.
• Flexibility
• Independence
• How openly do you share feelings with your partner?
• Almost never
• Almost always
• In the last two weeks, how often did you experience headaches?

Dissertation survey best practices

There are a lot of DOs and DON’Ts you should keep in mind when conducting any survey, especially for your dissertation. To get valuable data from your targeted sample, follow these best practices:

Use the consent form.

The consent form is a must when distributing a research questionnaire. A respondent has to know how you’ll use their answers and that the survey is anonymous.

Avoid leading and double-barreled questions

Leading and double-barreled questions will produce inconclusive results—and you don’t want that. A question such as: “Do you like to watch TV and play video games?” is double-barreled because it has two variables.

On the other hand, leading questions such as “On a scale from 1-10 how would you rate the amazing experience with our customer support?” influence respondents to answer in a certain way, which produces biased results.

Use easy and straightforward language and questions

Don’t use terms and professional jargon that respondents won’t understand. Take into consideration their educational level and demographic traits and use easy-to-understand language when writing questions.

Mix close-ended and open-ended questions

Too many open-ended questions will annoy respondents. Also, analyzing the responses is harder. Use more close-ended questions for the best results and only a few open-ended ones.

Strategically use different types of responses

Likert scale, multiple-choice, and ranking are all types of responses you can use to collect data. But some response types suit some questions better. Make sure to strategically fit questions with response types.

Ensure that data privacy is a priority

Make sure to use an online survey tool that has SSL encryption and secure data processing. You don’t want to risk all your hard work going to waste because of poorly managed data security. Ensure that you only collect data that’s relevant to your dissertation survey and leave out any questions (such as name) that can identify the respondents.

Create dissertation questionnaires with SurveyPlanet

Overall, survey methodology is a great way to find research participants for your research study. You have all the tools required for creating a survey for a dissertation with SurveyPlanet—you only need to sign up . With powerful features like question branching, custom formatting, multiple languages, image choice questions, and easy export you will find everything needed to create, distribute, and analyze a dissertation survey.

Happy data gathering!

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7+ Reasons to Use Surveys in Your Dissertation

Writing a dissertation is a serious milestone. Your degree depends on it, so it takes a lot of effort and time to figure out what direction to choose. Everything starts with the topic: you read background literature, consult with your supervisor and seek approval before you start writing the first draft. After that, you need to decide how you will collect the data that is supposed to contribute to the research field.

This is where it gets complicated. If you have never tried conducting primary research (i.e. working with human subjects), it can seem quite scary. Analyzing articles may sound like the safest and the coolest option. Yet, there might not be enough information for you to claim that your research is somehow novel.

To make sure it is, you might need to conduct primary research, and the survey method is the most widespread tool to do that. The number of advantages surveys present is huge. However, there are various perks depending on what approach you pursue. So, let’s go through all of them before you decide to pay for essay and order a dissertation that will go on and on about analyzing literature and nothing else except it.

In the quantitative primary research, students have to calculate the data received from typical a, b, c, d questionnaires. The latter provides precise answers and helps prove or reject the formulated hypothesis. For the research to be legit, there are several stages to go through like:

• Discarding irrelevant or subjective questions/answers included in questionnaires.
• Setting criteria for credible answers.
• Composing an explanation of how you will manage ethical concerns (for participants and university committee).

However, all this is done to prevent issues in the future. Provided you have taken care of all the points above, you will get to enjoy the following benefits.

Data Collection Is Less Tedious

There are numerous services, like Survey Monkey, that the best write my essay services use. It can help you distribute your questionnaire among potential participants. These platforms simplify the data collection process. You don’t have to arrange calls or convince someone that they can safely share the information. Just upload the consent letter each participant has to sign and let the platform guide them further.

Data Analysis Is Fast

In quantitative analysis, all you have to take care of is mainly data entry. It requires focus and accuracy, but the rest can be done with the help of software. Whether it’s ordinary Excel or something like SPSS, you don’t have to reread loads of text. Just make sure you download the collected data from the platform correctly, remove irrelevant fields, and feed the rest to your computer.

Numbers Rule

Numbers don’t lie (unless you miscalculated them, of course). They give a clear answer: it’s either ‘yes’ or ‘no’. Moreover, they leave more room for creating good visuals and making your paper less boring. Just make sure you explain the numbers properly and compare the results between various graphs and charts.

No Room For Subjectivity

A quantitative dissertation is mostly a technical paper. It’s not about creativity and your ability to impress like in admission essays students usually delegate to admission essay writing services to avoid babbling about things they deem senseless. It’s about following particular procedures. And there is also a less abstract analysis.

Qualitative-oriented surveys are about conducting full-fledged personal interviews, working with focus groups, or distributing open-ended questionnaires requiring short but unique answers. Let’s talk about what makes this approach worth trying!

First-Hand Experience

The ability to gain a unique perspective is what distinguishes interviews from other surveys. Close-ended questions may be too rigid and make participants omit a lot of information that might help the research. In an interview, you may also correct some of your questions, and add more details to them, thus improving the outcomes.

More Diverse and Honest Answers

When participants are limited by only several options, they might choose something they cannot fully relate to. So, there is no guarantee that the results will be authentic. Meanwhile, with open-ended questions, participants share a lot of details.

Sure, some of them may be less relevant to your topic, but the researcher gains a deeper understanding of the issues lying beneath the topic. Of course, all of it is guaranteed only if the researcher provides anonymity and a safe space for the interviewees to share their thoughts freely.

No Need For Complex Software

In contrast to quantitative analysis, here, you won’t have to use formulae and learn how to perform complex tests. You might not even need Excel, except for storing some data about your participants. However, no calculations will be needed, which is also a relief for those who are not used to working with such kind of data.

Both types of research have also other advantages:

• With surveys, you have more chances to fill the literature gap you’ve discovered.
• Primary research may not be quite easy, but it’s highly valued at the doctoral level of education.
• You receive a lot of new information and stay away from retelling literature that has been published before.
• Primary research is less boring.

However, there is a must-remember thing: not every supervisor or university committee approves of surveys and primary research in general. It depends on numerous aspects like topic and subject, the conditions of research, your approach to handling human subjects, etc.

It means that the methodology you are going to use should be approved by your professor first. Otherwise, you may have to discard some parts of your draft and lose time gathering data you won’t be able to use. So, take care and good luck!

7+ Reasons to Use Surveys in Your Dissertation FAQ

What are the benefits of using surveys in a dissertation, surveys can provide a large amount of data in a short amount of time, they are cost-effective and can allow for anonymity, they can reach a wide audience, and they can be used to obtain feedback from the participants., how can i ensure that my survey results are accurate, make sure to ask questions that are clear and concise and that there are no bias in the questions. make sure to have a good sample size and to have a response rate that is high enough to provide accurate results., how can i analyze the survey results, depending on the type of survey, there are various analysis techniques that can be used. these include descriptive statistics, inferential statistics, correlation analysis, and regression analysis., what are the limitations of surveys, surveys can be subject to sampling errors, response bias, and interviewer effects. they may also not be able to capture the full range of opinions and attitudes of the population., like what you see share with a friend..

Sarath Shyamson

Sarath Shyamson is the customer success person at BlockSurvey and also heads the outreach. He enjoys volunteering for the church choir and loves spending time with his two year old son.

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results/findings/analysis chapter is
• What you need to include in your results chapter
• How to structure your results chapter
• A few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Survey vs. questionnaire: what’s the difference, published by branford mcallister on march 11, 2022 march 11, 2022.

Last Updated on: 29th August 2022, 08:09 am

Surveys are very common and effective (mostly) in scholarly research. They are an excellent way to collect data related to human behavior and opinions. And, a survey can support both qualitative and quantitative research and analysis.

In this article I explain the differences between surveys and questionnaires, discuss sampling, and talk about considerations for each of these concepts.

Survey vs. Questionnaire 

Yes, there is a difference! A  questionnaire  is an instrument (like an interview protocol, an observation plan, or an experiment)—a written set of questions. 

Survey  is a broader term that encompasses both the instrument (questionnaire) and the process of employing the instrument—collecting and analyzing the responses from those questions. 

So, you might say, the questionnaire is one component of the survey. Planning a survey is a different task from constructing a questionnaire. The potential errors and bias, and their impacts on reliability are different.

• In surveys, we’re concerned with coverage error (ensuring all prospective groups or characteristics have an opportunity to participate to avoid selection bias ), and nonresponse error (low response rate). 
• For questionnaires, we’re concerned with clarity, length, and construct validity , which relates to measurement error (accurately measuring the constructs the questionnaire purports to measure).

Why Is This Difference Important?

Terms matter, and using them properly contributes to your credibility. And, from a practical perspective, understanding the tasks ensures that your research is rigorous, unbiased, and valid. The reliability of your research depends on how you handle both the questionnaire and the survey, and the reliability issues are different for each.

So, let’s delve into each in a little more detail.

Survey Research

The survey is the overall process of using a questionnaire to collect data. 

There are some very important considerations when choosing the survey method. Any choice about your research methodology should fit the purpose and the research objectives. These questions should take you in the right direction:

• What is your research problem? 
• What’s the gap in the current research?
• What must you learn to address that research question? 
• What kind of data do you need?
• What’s the population about which you wish to infer some attribute? 
• What method best generates those data? Quantitative or qualitative research? Survey, interviews, observation, experiment?

Once you choose the survey method, then the next set of questions help scope your effort. Needless to say, especially for busy, starving, stressed students, there are real-world constraints in terms of costs, time, and effort (which is why we sample). 

• How much time do you have?
• How much money do you have to invest in it?
• How feasible is your plan? Can you reach members of the population? Are there any insurmountable hurdles to reaching your population?

You have options: 

• Self-administered survey using mail or hand-delivery?
• Internet-based?
• Access your population through a professional or social group, association, or online social media platform (LinkedIn, Facebook)?
• Use a survey service (e.g., SurveyMonkey)? 

There are advantages and disadvantages to each option. While self-administered surveys provide control and flexibility, they suffer from low response rates and potentially high costs. A service may guarantee a specified sample size of respondents who meet your criteria, and may help construct the questionnaire. But, they also incur some costs to the researcher. Using an association leads to convenience sampling (discussed a bit later).

Once you decide on the survey mode, many of your considerations relate to choosing or designing an instrument, or with sampling. Let’s tackle sampling now.

Sampling is a very important aspect of survey research (and, for that matter, most scholarly research). Some simple definitions:

• Sampling : It would be great to obtain data for the entire population. But, due to constraints on resources, you may need to sample and infer characteristics of the entire population.
• Population : The entire set of all objects (or participants) sharing characteristics or qualifications (e.g., all undergraduate students in the U.S.); and to which the researcher intends to infer something of interest.
• Target population : A subset of the population, delimited by some additional characteristics (e.g., undergraduate students in public universities); or, feasibility or access issue.
• Sample frame : The subset of the target population from which an actual sample will be drawn, to which the researcher has access (e.g., undergraduate students in California state universities). The sample frame may be the same as the target population.
• Sample : A subset of the sample frame—those who meet the participant criteria and are contacted to complete the questionnaire; selection is based on a sampling technique (e.g., random sample of candidates within the sample frame).
• Participant criteria : The people who comprise your sample must meet the criteria (characteristics and qualifications) you establish.

Sampling Techniques 

• The purest form of sampling is a completely random sample from the sample frame. This requires the researcher to develop some mechanism for randomly selecting participants (or rely on a service to do it). 
• Convenience sampling is using a mechanism to contact qualified candidates in the sample frame, such as LinkedIn; or people attending a conference. 
• Stratified sampling divides the target population and sample frame into distinct cells: combinations of attributes (e.g., race, gender) proportionate to the population; then samples randomly within the cells.

A final consideration, no matter what sampling technique you use: How you will find and contact participants?

Sample Size 

In quantitative studies for which you will test hypotheses and make inferences about population attributes, there are online tools to compute minimum sample size, including G*Power . Here’s an example:

Say you’re comparing GRE scores by race and gender, using ANOVA, medium effect size, α = .05, power = .90. Using G*Power, you compute a minimum sample size of n min = 270.

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Overcoming Response Error

With a minimum sample size ( n min ) calculated, you must ensure that either the survey service obtains the minimum sample size, or you must send out a sufficient number of questionnaires to account for the response rate . AND, you should consider the likelihood of incomplete, invalid, or corrupted questionnaires.

Continuing with the example:

Survey response rate (from a similar study documented in a journal article) is 30%. Rate of corrupted, incomplete, invalid questionnaires is 10%.

n min = 270 (min required sample size of valid questionnaires)

270 = .90 × n 2 ⇒ n 2 = 300 ( n 2 is the number of questionnaire returned)

300 = .30 × n 3 ⇒ n 3 = 1000 ( n 3 is the number of questionnaires sent out)

So, to ensure you have the minimum number of valid, complete questionnaires (270), you would need to send out 1000 questionnaires to prospective participants.

Minimum Sample Size

Some survey services may guarantee your minimum sample size. But, ( huge point! ), be sure to consider the rate of questionnaire validity, and call that the minimum sample size for the survey service .

There’s little you can do after data collection to obtain more data if later you find that some of it is corrupt. And, it’s tragic to get to data analysis only to find out your sample is too small.

Let’s turn now to the instrument used in the survey method.

Questionnaires

The style of a questionnaire should fit the purpose and the research objectives. That means, using the kind of vocabulary that your target population is comfortable with. And, choosing a format that serves your data collection needs.

Here are some considerations:

• In either case, you must provide evidence of instrument validity, and the details of development and purpose and prior use. If previously used, this information should be available in a citable source. 
• One measurement of internal construct validity is Cronbach’s alpha. For off-the-shelf questionnaires, did the author report Cronbach’s alpha? If self-developed, you as the researcher must report on Cronbach’s alpha.
• For self-developed questionnaires, perform a pilot study of your instrument to ensure it is understandable, with no confusing questions or potential bias; that the length is appropriate; and to compute Cronbach’s alpha.
• Provide an introduction to your study, which is clear and professional, and addresses your purpose.
• Consider return envelopes and postage, anonymity of the participants, deadlines, and incentives. ( Another important point! Consider the Institutional Review Board [IRB] policies for any agency with a stake in the research, such as a university or an organization targeted for a survey.)
• Will it be cross-sectional (one point in time across sample frame) or longitudinal (data collected over time)?
• If a structured questionnaire, what kind of responses are needed, which determines what kind of scale?
• Will the responses be categorical/nominal? Dichotomous (e.g., yes or no)? Ordinal (such as a Likert scale)? Or numerical (a measured or counted quantity such as age or test scores)? 
• When writing the questions, consider objectivity and avoid language or questions that might be perceived to be biased.
• Consider complexity and length of time to complete.
• Use consistent ordinal responses (positive is high and negative is low); or reverse scoring will be needed.

Survey vs. Questionnaire: What’s It All Mean?

Surveys and questionnaires are different animals. A questionnaire is a component of a survey. And, there are different considerations for each, different sources of error and bias, impacting reliability and validity. 

But, why is this all important?

The survey method, with a properly designed questionnaire and rigorous sampling, is very useful in scholarly research.

Performing good survey research boils down to these three principles:

• The purpose of the research, which drives research methodology, the survey process, and the questionnaire as a data collection instrument.
• Rigorous planning for each component of that research, to meticulously address each potential source of bias and error.
• Disciplined execution to obtain, through sampling, a valid data set and to perform analysis to make inferences about the population.

Happy surveying!

Branford McAllister

Branford McAllister received his PhD from Walden University in 2005. He has been an instructor and PhD mentor for the University of Phoenix, Baker College, and Walden University; and a professor and lecturer on military strategy and operations at the National Defense University. He has technical and management experience in the military and private sector, has research interests related to leadership, and is an expert in advanced quantitative analysis techniques. He is passionately committed to mentoring students in post-secondary educational programs. Book a Free Consultation with Branford McAllister

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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Surveys & questionnaires

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

Structured Interviews may follow a similar form of open questioning.  

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

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

Face to face surveys  

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

Telephone surveys

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

Online surveys

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

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

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

Online surveys will receive typed responses reducing translation by the researcher 

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

Limitations

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

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

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

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

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

Narrow sampling may skew the results of the survey. 

Example questions

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

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

Example studies

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

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

• World Health Organization. (2007.) Expert opinion on barriers and facilitating factors for the implementation of existing mental health knowledge in mental health services. Geneva: World Health Organization. https://apps.who.int/iris/handle/10665/44808
• Green, J. 1961-author., & Thorogood, N. (2018). Qualitative methods for health research. SAGE. Retrieved from http://ezproxy.deakin.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cat00097a&AN=deakin.b4151167&authtype=sso&custid=deakin&site=eds-live&scope=site   
• Hancock, B., Ockleford, E., & Windridge, K. (2009). An introduction to qualitative research. NHS. https://www.rds-yh.nihr.ac.uk/wp-content/uploads/2013/05/5_Introduction-to-qualitative-research-2009.pdf   
• JANSEN, H. The Logic of Qualitative Survey Research and its Position in the Field of Social Research Methods. Forum Qualitative Sozialforschung, 11(2), Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/1450/2946  
• Neilsen Norman Group, (2019). 28 Tips for Creating Great Qualitative Surveys. Retrieved from https://www.nngroup.com/articles/qualitative-surveys/   
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• Last Updated: Mar 19, 2024 9:32 AM
• URL: https://deakin.libguides.com/qualitative-study-designs

9 Survey Tools for Academic Research in 2024

• Important Features

Survey Panels

• Additional Tools

1. SurveyKing

2. alchemer, 3. surveymonkey, 4. qualtrics, 5. questionpro, 6. sawtooth, 7. conjointly, 8. typeform, 9. google forms.

• Employee Feedback
• Creating the Survey
• Identity Protection
• Research Tools

Need a research survey tool? Features include MaxDiff, conjoint, and more!

These nine survey tools are perfect for academic research because they offer unique question types, solid reporting options, and support staff to help make your project a success. This article includes a detailed review of each of these nine survey tools. In addition to these survey tools, we include information about other research tools and survey panels.

Below is a quick summary of these nine survey tools. We list the lowest price to upgrade, which usually has the featured s needed for research projects. We also include a summary of the unique features of each tool. Most survey software has a monthly subscription; we denote when a tool requires annual pricing is required.

Important Features of Research Survey Software

Academic research surveys often require advanced question types to capture the necessary data. Many of the tools we mention in this article include these questions. However, some projects also require specialized features or the ability to purchase a panel. To help guide your decision in choosing the best piece of software for your project, we’ll summarize some of the most critical aspects.

Research Questions

Standard multiple-choice questions can only get you so far. Here are some question types you should be aware of:

• MaxDiff – measure the relative importance of an attribute. It goes beyond a standard ranking or rating by forcing respondents to pick the least and most valued items from a list. Rankings and other types only can you what is liked, not what is disliked. A statistical model will give you the probability of a user selecting an item as the most important. Latent class analysis can help you identify groups of respondents who value different attributes.
• Conjoint – Similar to MaxDiff in terms of finding importance, respondents evaluate a complete product (multiple attributes combined). This simulates real word purchasing decisions. A statistical model is also used to compute the importance of each item.
• Van Westendorp – Asks respondents to evaluate four price points. This shapes price curves and gives you a range of acceptable prices.
• Gabor Granger – Asks users whether or not they would purchase an item at specific price points. Price points are shown in random order to simulate real-world buying conditions. The results include a demand curve, giving you the revenue-maximizing price.
• Likert Scale – Measure attitudes and opinions related to a topic. It’s essential to use a mobile-ready Likert scale tool to increase response rates; many tools use a matrix for Likert scales, which could be more user-friendly.
• Semantic differential scale – a multirow rating scale that contains grammatically opposite adjectives at each end. It is used similarly to a Likert scale but is much easier for respondents to evaluate.
• Image heat map – Respondents click on places they like on an image. The results include a heat map showing the density of clicks. This is useful for product packaging.
• Net Promoter Score – Respondents choose a rating from 0-10. Many companies use this industry-standard question to benchmark their brand perception. This question type is necessary if your academic project measures brand reputation.

Anonymous Survey Links

Many academic surveys can deal with sensitive subjects or target sensitive groups. For this reason, assuring anonymity for respondents is crucial. Choosing a platform with an anonymous link is essential to increase trust with respondents and increase your response rates.

Data Segmentation

Comparing two groups within your survey data is essential for many research projects. This is called cross tabulation . For example, consider a survey where you ask for gender along with product satisfaction. You may notice that males are not satisfied with the product while females are.

You can take this further and compute the statistical significance between the groups. In other words, make the differences that exist between two data sets due to random chance or not. Your comparison is statistically significant if it’s not due to random chance.

Some lower-end survey tools may not offer any segmentation features. If this is the case, you need to download your survey data into a spreadsheet and create pivots of set-up custom formulas.

Skip Logic and Piping

If your academic project has questions that only a specific subset of respondents need to answer, then some logic will help streamline your survey.

Skip logic will take you to a new page based on answers to previous questions. Display logic will show a question to a user based on previous questions; perfect for follow-up.

Answer piping will allow you to carry forward answers from one question into another. So, for example, ask someone which brand names they have heard of, then pipe those answers into a ranking question.

Data Cleaning

Making sure your responses are high quality is a big part of any survey research project. For example, if people speed through the survey or mark all the first answers for questions, those would be low-quality responses and should be removed from your data set. Some tools highlight these low-quality responses, which can be a helpful feature.

For platforms that do not offer a data cleaning feature, it’s generally possible to export the data to Excel, create formulas for time spent, answer straight-lining, then remove the needed data. You can also include a  trap question  to help filter out low-quality responses.

Great Support

Many academic projects require statistical analysis or additional options for the survey. Using a tool with a support staff that can explain a statistical model’s intricacies, help build custom models, or adds features on request will ensure your project is a success. With SurveyKing, custom-built features are billed at $50 per hour, making custom projects feasible for small budgets.

Asking classmates to take your survey, posting it on social media, or distributing QR code surveys around campus is a great way to collect responses for your project. But if you need more responses with those methods, purchasing additional answers might be required.

A panel provider will enable you to target a specific demographic, job role, or hobby type. When setting up a survey with a penal provider, you always want to include screening questions (on the first page) to ensure they meet your criteria, as panel filters may not be 100% accurate. Generally, panel responses start around $2.50 per completed response.  Cint  is one of the largest panel providers and works well with any survey platform.

Additional Research Tools

Before deep diving into the survey software list, here are some additional tools and resources that might assist in your project. These can help shape your survey by conducting preliminary research or using it as a substitute if conducting a study is not feasible.

• Hotjar  – They offer simple surveys and many tools to help capture feedback and data points from a website. A feedback widget customized for websites in addition to a heat map tool to show where users click the most or to identify rage clicks. A tool like this could be helpful if your academic projects revolve around launching or optimizing a website.
• Think with Google  – Used to help marketers understand their audience. The site contains links to Google Trends to search for the popularity of key terms over time. They also have a tool that helps you identify your audience based on popular YouTube channels. Finally, they have a “Grow My Store Tool” that recommends tips for improving an online store.
• Google Scholar  – A specific search engine used for scholarly literature. This can help locate research papers related to the survey you are creating.
• MIT Theses  – Contains over 58,000 theses and dissertations from all MIT departments. The database is organized by department and lets you search for keywords.

SurveyKing is the best tool for academic research surveys because of a wide variety of question types like MaxDiff, excellent reporting features, a solid support staff, and a low cost of $19 per month.

The survey builder is straightforward to use. Question types include MaxDiff, conjoint, Gabor Granger, Van Westendorp, a mobile optimized Likert scale, and semantic differential.

The MaxDiff question also includes anchored MaxDiff and collecting open-ended feedback for the feature most valued by a respondent. In addition, cluster analysis is available to help similar group data together; some respondents might value specific attributes, while other groups value others.

The reporting section is also a standout feature. It is easy to create filters and segment reports. In addition, the Excel export is well formatted easily for question types like ranking and Likert Scale, making it easy to upload into SPSS. The reporting section also gives the probability for MaxDiff, one of the few tools to offer that.

The anonymous link on SurveyKing is a valuable feature. A snippet at the top of each anonymous survey is where users can click to understand whether their identities are protected.

The software also offers a Net Promoter Score module which can come in handy for projects that deep dive into brand reputation.

Some downsides to SurveyKing include no answer piping, no image heat maps, no continuous sum question, and no premade data cleaning feature.

As a platform with lots of advanced question types and a reasonable cost, Alchemer is an excellent tool for academic research. Question types include MaxDiff, conjoint, semantic differential, image heat map, text highlighter, continuous sum, cascading dropdowns, rankings, and card grouping.

Reporting on Alchemer is a standout feature. Not only can you create filters and segment reports, but you can also create those filters and segments using advanced criteria. So if you ask a question about gender and hobby, you can make advanced criteria that match a specific gender and hobby.

In addition, their reporting section also can do chi-square tests to calculate the significant difference between the two groups. Finally, they also have a section where you can create and run your R scripts. This can be useful for various academic research projects as you can create custom statistical models in the software without needing to export your data.

Alchemer is less user-friendly than some other tools. The platform is a little clunky; things like MaxDiff require respondents to hit the submit button to get to the next set. Radio buttons need respondents to click inside of them instead of the area around them.

The pricing is reasonable for a student; $249 a month for access to the research questions. However, if you can organize your project quickly, you may only need one month of access.

As the most recognized brand for online surveys, SurveyMonkey is a reliable option for academic research. While the platform does not have any research questions, it offers all the standard question types and a clean user interface to build your surveys.

One advanced question type they do have is the image heat map. Their parent company  Momentive  does offer things like MaxDiff and conjoint studies, but you would need to contact sales to get a quote, meaning this could be out of budget for students.

The reporting on SurveyMonkey is good. You can easily create filters and segments. You can also save that criterion to create a view. The views enable you to toggle between rules quickly.

One of the main downsides to SurveyMonkey is the cost. For the image heat map and to create advanced branching rules, you need to upgrade to their Premier plan, which costs $1,428 annually. To get statistical significance, you would need their Primer plan, which is $468 annually.

As the survey tool known for experience management, Qualtrics has some nice features for research projects. For example, they offer both MaxDiff and conjoint in addition to tools like drill-down, continuous sun, image heat map, and a text highlighter.

Reporting on the tool offers the ability to create filters and segments. For segments, it’s called a report breakout, and it appears there is no ability to create a breakout with advanced criteria. However, filers do allow you for advanced criteria.

There is a custom report builder option to create custom PDF reports. You can add as many elements as needed and customize the information displayed, whether a chart type or a data table.

Overall, Qualtrics could be more user-friendly and may require training. The survey builder and reporting screens could be more cohesive. For example, to add more answer options, you need to click the “plus” symbol on the left-hand side of the question instead of just hitting enter or clicking a button right below the current answer choice. In addition, the reporting section will display things like mean and standard deviation for simple multiple-choice questions before showing simple response counts.

One drawback to Qualtrics is the pricing. For example, you would need to pay $1,440 for an annual plan to use the research questions. But many universities have a licensing agreement with Qualtrics so students can use the platform. When you sign up for a new account, you can select academic use, enter your Edu email, and they will check if your university has a license agreement.

A survey platform with all the needed research questions, including Gabor Granger and Van Westendorp, QuestionPro is a quality research tool.

The reporting on QuestionPro is comprehensive. They offer segment reports with statistical significance using a t-test. In addition, they offer TURF analysis to show answer combinations with the highest reach.

For conjoint, offer a market simulation tool that can forecast new product market share based on your data. That tool can also calculate how much  premium  consumers will pay for a brand name.

QuestionPro is a little easier to use than Qualtrics. The UI is cleaner but still clumsy. You must navigate to a different section in the builder for things like quotas instead of just having it near skip logic rules. The distribution page has the link at the top but an email body below. The reporting has a lot of different pages to click through for each option. Small things like this mean there is a learning curve to use the platform efficiently.

The biggest downside of QuestionPro is the price. All of their research questions, even Net Promoter Score, would require a custom quote under the research plan. There another plan with upgraded feature types is $1,188 annually.

When it comes to advanced research projects, Sawtooth is a great resource. While their survey builder is a little limited in question types, they offer different forms of MaxDiff and conjoint. They also provide consulting services, which could help if your academic project is highly specialized.

For MaxDiff, they offer a bandit  version, which can be used for MaxDiff studies with over 50 attributes. Each set of detailed attributes that are most relevant to the user. This can save panel costs because you can build a suitable statistical model with 300 bandit responses compared with 500 or 1000 standard MaxDiff responses.

Their MaxDiff feature also comes with a TURF analysis option that can show you the possible market research of various attributes.

For conjoint, they offer adaptive choice-based conjoint and menu-based conjoint. Adaptive choice tailors the product cards toward each respondent based on early responses or screening questions. Menu-based conjoint is for more complex projects, allowing respondents to build their products based on various attributes and prices.

Sawtooth has a high price point and may be out of the research for many academic projects. The lowest plan is $4,500 annually. If you need advanced tools like bandit MaxDiff or adaptive conjoint, you must pay $11,990 annually. They do have a package just for MaxDiff starting at $2,420.

Conjointly is a platform geared towards research projects, namely market research. Not only do they have the standard research questions, but they also have a bunch of unique ones: claims testing, Kano Model testing, and monadic testing. There are also question types like feature placement matrix, which combines MaxDiff and Gabor Granger into a single question.

You can either use your respondents or select from a survey panel. The survey panel option comes with predefined audiences, which makes scouring respondents a breeze.

One unique feature is that they monitor in real-time speeders and other criteria for low-quality respondents. If a respondent is speeding through the survey, a warning message is displayed asking them to repeat questions before being disqualified. If a question has a lot of information to digest, the system automatically pauses, forcing the respondent to thoroughly read the question before answering.

The pricing is a little steep at $1,795 annually. Response panels for USA residents appear to start around $4 per completed response. The survey builder and reporting section could be cleaner, with different options in many places. It may take time to get up to speed.

While Typeform doesn’t have any research questions, it is a very well-designed and easy-to-use tool that can assist with your academic survey. For example, it could gather preliminary data for a MaxDiff study.

Typeform offers a lot of integrations with other applications. For example, if your project requires exporting data to a spreadsheet, then Google Sheets or Excel integration might be helpful. Likewise, if your research project is part of a class project, then the Slack or Microsoft Teams integration might help to notify other team members when you get responses.

One unique feature of Typeform is the calculator feature. Add, subtract, and multiply numbers to the @score or @price variable. These variables can be recalled to show scores or used in a payment form.

The reporting in Typeform is basic. There is no option to create a filter or a segment report. Any data analysis would need to be done in Google Sheets or Excel.

For $29 a month, you can get 100 responses, or $59 a month, you can collect 1,000 responses each month.

One of the widely used survey tools, Google Forms , is a decent platform for an academic research survey. Unfortunately, the software doesn’t offer any research questions. Still, the few questions it has, like multiple choice, rantings, and open-ended feedback, are enough to collect essential feedback for simple projects or preliminary data for more complex studies.

Skip logic is straightforward to set up on Google Forms. For example, you can select what section to skip based on question answers or choose what to skip once a section is complete. Of course, you can’t create complex rules, but these simple rules can cover many bases.

Overall the user interface is elegant and straightforward. The form design is also elegant, meaning the respondent experience is excellent. Unlike other survey tools, which can have a clunky interface, there is no worry about that with Google Forms; respondents can quickly navigate your form and submit answers.

The spreadsheet export is very well formatted and can be easily imported into SPSS for advanced analysis. However, the export has the submission date and time but has yet to have the time started, so calculating speeders is impossible.

ABOUT THE AUTOR

Allen is the founder of SurveyKing. A former CPA and government auditor, he understands how important quality data is in decision making. He continues to help SurveyKing accomplish their main goal: providing organizations around the world with low-cost high-quality feedback tools.

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• Knowledge Base
• Dissertation

How to Write a Dissertation | A Guide to Structure & Content

A dissertation or thesis is a long piece of academic writing based on original research, submitted as part of an undergraduate or postgraduate degree.

The structure of a dissertation depends on your field, but it is usually divided into at least four or five chapters (including an introduction and conclusion chapter).

The most common dissertation structure in the sciences and social sciences includes:

• An introduction to your topic
• A literature review that surveys relevant sources
• An explanation of your methodology
• An overview of the results of your research
• A discussion of the results and their implications
• A conclusion that shows what your research has contributed

Dissertations in the humanities are often structured more like a long essay , building an argument by analysing primary and secondary sources . Instead of the standard structure outlined here, you might organise your chapters around different themes or case studies.

Other important elements of the dissertation include the title page , abstract , and reference list . If in doubt about how your dissertation should be structured, always check your department’s guidelines and consult with your supervisor.

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Table of contents

Acknowledgements, table of contents, list of figures and tables, list of abbreviations, introduction, literature review / theoretical framework, methodology, reference list.

The very first page of your document contains your dissertation’s title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo. Many programs have strict requirements for formatting the dissertation title page .

The title page is often used as cover when printing and binding your dissertation .

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The acknowledgements section is usually optional, and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you.

The abstract is a short summary of your dissertation, usually about 150-300 words long. You should write it at the very end, when you’ve completed the rest of the dissertation. In the abstract, make sure to:

• State the main topic and aims of your research
• Describe the methods you used
• Summarise the main results
• State your conclusions

Although the abstract is very short, it’s the first part (and sometimes the only part) of your dissertation that people will read, so it’s important that you get it right. If you’re struggling to write a strong abstract, read our guide on how to write an abstract .

In the table of contents, list all of your chapters and subheadings and their page numbers. The dissertation contents page gives the reader an overview of your structure and helps easily navigate the document.

All parts of your dissertation should be included in the table of contents, including the appendices. You can generate a table of contents automatically in Word.

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If you have used a lot of tables and figures in your dissertation, you should itemise them in a numbered list . You can automatically generate this list using the Insert Caption feature in Word.

If you have used a lot of abbreviations in your dissertation, you can include them in an alphabetised list of abbreviations so that the reader can easily look up their meanings.

If you have used a lot of highly specialised terms that will not be familiar to your reader, it might be a good idea to include a glossary . List the terms alphabetically and explain each term with a brief description or definition.

In the introduction, you set up your dissertation’s topic, purpose, and relevance, and tell the reader what to expect in the rest of the dissertation. The introduction should:

• Establish your research topic , giving necessary background information to contextualise your work
• Narrow down the focus and define the scope of the research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your objectives and research questions , and indicate how you will answer them
• Give an overview of your dissertation’s structure

Everything in the introduction should be clear, engaging, and relevant to your research. By the end, the reader should understand the what , why and how of your research. Not sure how? Read our guide on how to write a dissertation introduction .

Before you start on your research, you should have conducted a literature review to gain a thorough understanding of the academic work that already exists on your topic. This means:

• Collecting sources (e.g. books and journal articles) and selecting the most relevant ones
• Critically evaluating and analysing each source
• Drawing connections between them (e.g. themes, patterns, conflicts, gaps) to make an overall point

In the dissertation literature review chapter or section, you shouldn’t just summarise existing studies, but develop a coherent structure and argument that leads to a clear basis or justification for your own research. For example, it might aim to show how your research:

• Addresses a gap in the literature
• Takes a new theoretical or methodological approach to the topic
• Proposes a solution to an unresolved problem
• Advances a theoretical debate
• Builds on and strengthens existing knowledge with new data

The literature review often becomes the basis for a theoretical framework , in which you define and analyse the key theories, concepts and models that frame your research. In this section you can answer descriptive research questions about the relationship between concepts or variables.

The methodology chapter or section describes how you conducted your research, allowing your reader to assess its validity. You should generally include:

• The overall approach and type of research (e.g. qualitative, quantitative, experimental, ethnographic)
• Your methods of collecting data (e.g. interviews, surveys, archives)
• Details of where, when, and with whom the research took place
• Your methods of analysing data (e.g. statistical analysis, discourse analysis)
• Tools and materials you used (e.g. computer programs, lab equipment)
• A discussion of any obstacles you faced in conducting the research and how you overcame them
• An evaluation or justification of your methods

Your aim in the methodology is to accurately report what you did, as well as convincing the reader that this was the best approach to answering your research questions or objectives.

Next, you report the results of your research . You can structure this section around sub-questions, hypotheses, or topics. Only report results that are relevant to your objectives and research questions. In some disciplines, the results section is strictly separated from the discussion, while in others the two are combined.

For example, for qualitative methods like in-depth interviews, the presentation of the data will often be woven together with discussion and analysis, while in quantitative and experimental research, the results should be presented separately before you discuss their meaning. If you’re unsure, consult with your supervisor and look at sample dissertations to find out the best structure for your research.

In the results section it can often be helpful to include tables, graphs and charts. Think carefully about how best to present your data, and don’t include tables or figures that just repeat what you have written  –  they should provide extra information or usefully visualise the results in a way that adds value to your text.

Full versions of your data (such as interview transcripts) can be included as an appendix .

The discussion  is where you explore the meaning and implications of your results in relation to your research questions. Here you should interpret the results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data and discuss any limitations that might have influenced the results.

The discussion should reference other scholarly work to show how your results fit with existing knowledge. You can also make recommendations for future research or practical action.

The dissertation conclusion should concisely answer the main research question, leaving the reader with a clear understanding of your central argument. Wrap up your dissertation with a final reflection on what you did and how you did it. The conclusion often also includes recommendations for research or practice.

In this section, it’s important to show how your findings contribute to knowledge in the field and why your research matters. What have you added to what was already known?

You must include full details of all sources that you have cited in a reference list (sometimes also called a works cited list or bibliography). It’s important to follow a consistent reference style . Each style has strict and specific requirements for how to format your sources in the reference list.

The most common styles used in UK universities are Harvard referencing and Vancouver referencing . Your department will often specify which referencing style you should use – for example, psychology students tend to use APA style , humanities students often use MHRA , and law students always use OSCOLA . M ake sure to check the requirements, and ask your supervisor if you’re unsure.

To save time creating the reference list and make sure your citations are correctly and consistently formatted, you can use our free APA Citation Generator .

Your dissertation itself should contain only essential information that directly contributes to answering your research question. Documents you have used that do not fit into the main body of your dissertation (such as interview transcripts, survey questions or tables with full figures) can be added as appendices .

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Dissertation Format and Submission

• Format Guidelines
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Instrument Permission documents

Instrument Permissions FAQ

Download a pdf of this faq  , download the template permission letter, permissions to use and reproduce instruments in a thesis/dissertation frequently asked questions, why might i need permission to use an instrument in my thesis/dissertation.

• Determine whether you need permission
• Identify the copyright holder
• Ask for permission
• Keep a record
• What if I can't locate the copyright holder?

If you want to use surveys, questionnaires, interview questions, tests, measures, or other instruments created by other people, you are required to locate and follow usage permissions. The instrument may be protected by copyright and/or licensing restrictions.

Copyright Protection

Copyright provides authors of original creative work with limited control over the reproduction and distribution of that work. Under United States law, all original expressions that are “fixed in a tangible medium” are automatically protected by copyright at the time of their creation. In other words, it is not necessary to formally state a declaration of copyright, to use the © symbol, or to register with the United States Copyright Office.

Therefore, you must assume that any material you find is copyrighted, unless you have evidence otherwise. This is the case whether you find the instrument openly on the web, in a library database, or reproduced in a journal article. It is your legal and ethical responsibility to obtain permission to use, modify, and/or reproduce the instrument.

If you use and/or reproduce material in your thesis/dissertation beyond the limits outlined by the “fair use” doctrine, which allows for limited use of a work, without first gaining the copyright holder’s permission, you may be infringing copyright.

Licensing/Terms of Use

Some instruments are explicitly distributed under a license agreement or terms of use. Unlike copyright, which applies automatically, users must agree to these terms in order to use the instrument. In exchange for abiding by the terms, the copyright holder grants the licensee specific and limited rights, such as the right to use the instrument in scholarly research, or to reproduce the instrument in a publication.

When you ask a copyright holder for permission to use or reproduce an instrument, you are in effect asking for a license to do those things.

How do I know if I need permission to use instruments in my thesis/dissertation research? (Adapted from Hathcock & Crews )

Follow the four-step process below:

1. Determine whether you need permission

There are different levels of permissions for using an instrument:

a)  No permission required

i. The copyright holder has explicitly licensed the use of instrument for any purpose, without requiring you to obtain permission.

ii. If you are only using a limited portion of the instrument, your use may be covered under the Fair Use Doctrine. See more here:  https://uhcl.libguides.com/copyright/fairuse .

iii. If the instrument was developed by the federal government or under a government grant it may be in the public domain, and permission is therefore not required.

iv. If the document was created before 1977, it may be in the public domain, and permission is therefore not required. See the Stanford Public Domain Flowchart at https://fairuse.stanford.edu/wp-content/uploads/2014/06/publicdomainflowchart.png .

b)  Non-commercial/educational use: The copyright holder has licensed the instrument only for non-commercial research or educational purposes, without requiring you to obtain the permission of the copyright holder. Any other usage requires permission.

Sample Permission for Educational Use:

Test content may be reproduced and used for non-commercial research and educational purposes without seeking written permission. Distribution must be controlled, meaning only to the participants engaged in the research or enrolled in the educational activity. Any other type of reproduction or distribution of test content is not authorized without written permission from the author and publisher. Always include a credit line that contains the source citation and copyright owner when writing about or using any test.

Source: Marta Soto, “How Permissions Work in PsycTests,” APA Databases & Electronic Resources Blog. American Psychological Association. http://blog.apapubs.org/2016/12/21/how-permissions-work-in-psyctests/ .

Even if you are not required to obtain permission to use the instrument, consider contacting the author for ideas on how to administer and analyze the test. Authors often welcome further use of their work, and may request you send them a copy of your final work.

c)  Permission required:  Instruments that require you to obtain the permission of the copyright holder, regardless of whether the use is for educational or commercial purposes. This may be because the copyright holder

• has important directions for how the test must be administered and analyzed
• wants to make sure the most current version is being used
• charges users a fee in order to administer the test

If you cannot locate the permissions, you are required to identify the copyright holder and contact them to ask about permission to use the instrument.

2. Identify the copyright holder  (Adapted from Crews )

The next step is to identify who owns the copyright. The copyright holder is usually the creator of the work. If the copyright owner is an individual, you will need to do the usual Internet and telephone searches to find the person. Be ready to introduce yourself and to explain carefully what you are seeking.

Some authors transfer copyright to another entity, such as a journal publisher or an organization. In these cases, you must obtain permission from that entity to use or reproduce the instrument. You can often identify the owner by locating a © copyright notice, but as mentioned above, not all copyrighted works have a notice.

Check the following sources to locate instruments, their copyright holders, and their permission statements:

• Mental Measurements Yearbook: https://uhcl.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=mmt
• PsycTESTS: https://uhcl.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=pst
• Neumann Library Tests & Measures help: https://uhcl.libguides.com/PSYC/tests
• Library assistance e-mail: [email protected]

​You may need to contact the author or publisher directly to find out who owns the copyright. Publishers often have websites that prescribe a method for contacting the copyright owner, so search the publisher website for a permissions department or contact person. Be sure to confirm the exact name and address of the addressee, and call/e-mail the person or publishing house to confirm the copyright ownership.

• The copyright owner may prefer or require that permission requests be made using a certain medium (i.e. fax, mail, web form, etc.). If you do not follow instructions, you may not get a reply.
• Telephone calls may be the quickest method for getting a response from the owner, but they should be followed up with a letter or e-mail in order to document the exact scope of the permission. E-mail permissions are legally acceptable in most cases, but getting a genuine signature is usually best.
• The request should be sent to the individual copyright holder (when applicable) or permissions department of the publisher in question. Be sure to include your return address, telephone and fax numbers, e-mail address, and the date at the top of your letter or message. If you send the permission request by mail, include a self-addressed, stamped return envelope.
• Make the process easy for the copyright owner. The less effort the owner has to put forth, the more likely you will get the permission you need. If you are using conventional mail, include a second copy of your request for the owner’s records.
• State clearly who you are, your institutional affiliation (e.g., University of Houston-Clear Lake), and the general nature of your thesis/dissertation research.

Do not send permissions letters to all possible rightsholders simultaneously. Taking the time to find the person who most likely holds the copyright will better yield success. If you do not have much information about who actually owns the copyright, be honest with your contacts, and they may be able to help you find the right person.

3. Ask for permission  (Adapted from  Crews )

Once you have identified the copyright holder, you must determine the scope of your permission request. Some copyright owners furnish their own permission form that you may download from their website.

If the copyright owner does not provide a permission agreement form, you may write your own letter ( click here to download a template ). Requests should be made in writing; e-mail is fine for this purpose. A most effective letter will include detailed information concerning your request for permission to use the work. Include the following information:

• Who: Introduce yourself. Tell who you are, your degree program, and a brief overview of your research.
• Why: Tell why you are contacting that person or entity for permission.
• What: Be as specific as possible when you cite and describe the instrument you wish to use. Include whether you plan to use the entire instrument, or if you plan on modifying or adapting any of the questions.
• How: Tell how you plan to use the instrument. Specify the parameters of your research study, and include any important information about the way you will administer the instrument and/or analyze the results.
• When: Expected length of the project and time to complete the thesis/dissertation.

Important : Obtaining permission to use an instrument is not the same as obtaining permission to reproduce the instrument in your appendix. If you intend on providing a copy of the instrument in an appendix, ask for separate permissions to do that.

Click here to download a template letter . Feel free to modify and adapt this template for your purposes.

4. Keep a record

After securing permission to use and/or reproduce the instrument, save a copy of the correspondence and the agreement. Documentation allows you to demonstrate to others that you have the legal right to use the owner's work. In the unlikely event that your use of the work is ever challenged, you will need to demonstrate your good faith efforts. That challenge could arise far in the future, so keep a permanent file of the records. Moreover, you might need to contact that same copyright owner again for a later use of the work, and your notes from the past will make the task easier.

Upload a copy of your permission letter in Vireo with your thesis/dissertation, or include it as an appendix in the document itself.

What if I can't locate the copyright holder?  (Adapted from Hathcock  & Crews & Pantalony )

In some cases, you may never get a response from the copyright holder or you may never even be able to identify who they are or how to contact them. It can be difficult to know how to proceed when you reach a dead end. Unfortunately, no matter how diligently you have tried to get permission, these efforts cannot completely eliminate the risk of infringement should you proceed to use the work.

Assuming you have diligently investigated your alternatives, do not want to change your project, and remain in need of the elusive copyright permission, the remaining alternative is to explore a risk-benefit analysis. You need to balance the benefits of using that particular material in your given project against the risks that a copyright owner may see your project, identify the materials, and assert the owner’s legal claims against you. Numerous factual circumstances may be important in this evaluation. The “benefit” may depend upon the importance of your project and the importance of using that particular material. The “risks” may depend upon whether your project will be published or available on the Internet for widespread access—as theses and dissertations will. You ought to investigate whether the work is registered with the U.S. Copyright Office and weigh the thoroughness of your search for the copyright owner and your quest for appropriate permission.

Undertaking this analysis can be sensitive and must be advanced with caution and with careful documentation. You may be acting to reduce the risk of liability, but you have not eliminated liability. A copyright owner may still hold rights to the material. Members of the University of Houston-Clear Lake community should consult with their chair or the Neumann Library to discuss their options.

Portions of this FAQ are used and adapted from:

Crews, Kenneth and Rina Elster Pantalony. “Special Cases.” Columbia University Copyright Advisory Services. https://copyright.columbia.edu/basics/special-cases.html . Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Crews, Kenneth. “Asking for Permission.” Columbia University Advisory Services. https://copyright.columbia.edu/basics/permissions-and-licensing.html . Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Hathcock, April. “Getting Permission.” NYU Libraries Copyright Library Guide, https://guides.nyu.edu/c.php?g=276785&p=1845968 . Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

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