dissertation questionnaire analysis

How to Analyze Questionnaire Data: A Step by Step Guide

Approaching your questionnaire with the right principles in mind and tools in hand will produce easily-understood results packed with actionable insights.

In this guide you'll be led through the basics behind questionnaire data, then move on to a step-by-step approach for analyzing your responses. 

What is Questionnaire Data?

Types of questionnaire data, how to analyze questionnaire data.

Survey data , aka questionnaire data, is data collected during a survey campaign. This data can be analyzed and broken down, yielding statistics and insights that can be used to boost business.

What is Questionnaire Data Analysis?

The end all be all of customer feedback collection, whether questionnaires, online reviews, or other data, should always be the improvement of your overall customer experience for the benefit of existing and future customers.

The modern market has shown customer experience (CX) to be the number one differentiator between competitors. A large amount of this is by virtue of active customer experience management's attentiveness to existing customers -- companies who are able to convert existing customers into 'Promoters' (on the NPS scale ) improve their lifetime value by 6 to 14 times according to Bain & Co .

This is especially relevant when it comes to customer surveys as surveys are invariably distributed to existing and/or past users. The data they collect and the insights they derive apply directly to the customer journey.

By actively listening to the voices of your customers and analyzing survey data you are getting strategic tips from the best, and most honest, possible source. 

Questionnaire data, or survey data, comes in one of two formats: close-ended data and open-ended data .

Close-ended Questionnaire Data

Close-ended data is what people think of first when they imagine a survey result. It is data that translates directly into numbers. The 'big three' feedback questions ( NPS, CSAT and CES surveys ) all start with a close-ended question. They vary in format, with CSAT being a yes/no binary, NPS a 1-10 scale and CES a 1-5, but the responses can be tabulated in a straightforward manner and analyzed using basic software such as Excel.

From there, close-ended data can be interpreted using basic statistics to derive clear insights. This is basic survey analysis , and there are a ton of tools out there to help you quickly and effectively break down, cross tabulate, and display your results.

However, you aren't getting the most out of your surveys unless you pair your close-ended approach with open-ended questions , which draw out otherwise unseen but invaluable data .

Open-ended Questionnaire Data

Open-ended data is the 'why' behind your close-ended metrics, and for this reason it is key to excellent questionnaire analysis .

You know those additional written comments at the end of surveys? Those are open-ended questions. Throwing out these responses means missing out on the context behind whatever rating the customer is giving you.

The next logical question is, 'How can I measure text-based responses?'. 

Until a few years ago, each dataset's answers would require manual tabulation, which is both tedious and inaccurate. Now, with the power of machine learning and utilizing  techniques such as sentiment analysis and keyword extraction you can interpret your open-ended responses right alongside your close-ended metrics at scale, and in real time .

Having the right customer feedback analysis tools at your disposal can help make sure your survey analysis approaches, both close and open ended, are properly paired and integrated. This is crucial as losing which open-ended comment is tied to which close-ended score can mean losing the depth behind that data, making accurate analysis impossible.

That in mind, let's move on to the main course, our step-by-step approach to survey data analysis.

• Interrogate your question
• Cross tabulate quantitative results
• Expand with open-ended questions
• Analyze your open-ended data
• Visualize your results
• Interpret actionable insights

We landed on these particular steps because they convey a clear journey from the inception of your survey campaign to the implementation of your survey's insights.

1. Interrogate your question

An easy first mistake some businesses make is not knowing what they are looking for out of their survey. This of course directly affects the question(s) you are going to ask within your survey.

So, to form the best possible question and get clear answers, interrogate what you are looking for.  Are you curious as to customer opinion of your price point? Or is it something else entirely.

Deciding on the main goal or goals of your survey before distributing it ensures that you will, at bare minimum, answer your main concerns. That is not to say drilling down on what you are asking limits the possibilities of your survey. With additional comment or thought bubbles for customers to fill out, yielding open-ended response data, you are sure to uncover other, related but hidden, trends. But clarity as to purpose makes sure you don't confuse yourself, or worse, your customers with your survey.

2. Cross tabulate quantitative results

Cross tabulate is just a fancy word for filtering your survey so that you can compare customer groups aka subgroups. Think of it as the process of sorting your data by demographic so that you can unearth trends.

Take a look at this table for instance which reflects the answers to whether attendees of a conference think they will attend again next year, breaking the answers into three sub groups (Administrators, Teachers, and Students):

What at first might have remained hidden if you only looked at the total percentage that wanted to return now becomes clear.

Administrators, as reflected in their 40% 'No' responses and their 46% 'Yes' responses (compared to 86% Students and 80% Teachers) clearly didn't get what they were looking for out of the conference.

Curious questionnaire/survey analysis is good practice -- by taking a deeper look at the data, in this survey's case, uncovered a hidden trend. However, referencing our first step, this wouldn't be possible without asking the right question and keeping track of the three distinct demographic groups.

With this discovery in hand, it would be wise to continue to compare and contrast your data. This could also be a form of benchmarking -- meaning viewing your data in contrast to other surveys. You could compare the number of attendees this year to those in the ten years previous, and, if possible, isolate the subgroups from those years (if they were surveyed). Doing so would let you know which years were most popular with each subgroup.

Now it's one thing to know the Administrators, in this year's case, were the least likely to come back, and quite another to know what made them feel this way. Here's where those pesky open-ended questions come in, and why they are so critical to obtain and dissect.

3. Expand with open-ended questions

While this is third in our list it really needs to be a priority from the jump. Taking every step possible to solicit written feedback will truly take your questionnaire/survey campaigns to the next level.

Attaching open-ended questionnaires to your survey campaigns will add depth to your data and inform you of the 'why' behind your scores.

Luckily, it's easier than ever with advancements in artificial intelligence. Which brings us to our next step, accurately and effectively analyzing your data.

4. Analyze your open-ended data

Machine learning-backed software, such as Monkeylearn takes heaps of text data and transforms it into objective insights.

Analyzing your data using sentiment analysis and keyword extraction text analysis techniques can make your questionnaire analysis best in class.

These, and other open-ended analysis techniques such as topic analysis make sure you get the absolute most of your data, deepening and adding context to your extant quantitative data. These include plug-and-play templates, designed for no-code users to be able to access and mold questionnaire data - Monkeylearn even offers a ready-made survey data template - book your demo today and try it out for free.

5. Visualize your results

Insights are worthless if they cannot be conveyed to the appropriate decision-makers. Look no further than complete visualization suites to get the graphs, stats, and charts that keep modern businesses ahead of the curve.

Monkeylearn's all-in-one dataviz suite, as seen below, embraces the ideal that best-practice visualization means having up-to-the-minute data visualization at your fingertips at all times. 

If you have all the right graphs, and the ability to transform them at all times, you are able to deliver whatever graphs you need to your strategy times, rest assured that they are up to date and accurate.

6. Interpret accurate insights

Here is where we double down on the difference between insight and market data. Insights are the end product of any well-run questionnaire/survey campaign. But they require diligence in regards to what kind of questions you are asking and how deep you are digging to get actionable answers.

Great survey analysis/questionnaire campaigns ensure the applicability of their end data by maintaining a clear idea from the start of what kind of consumer insights they are looking for , while taking care to find the reasons behind their data via open-ended analysis along the way.

Just like that, if applied with care, you have an effective methodology for questionnaire analysis.\ Monkeylearn is here to help with the most powerful survey analysis software. Sign up for a free demo with one of our data analysis experts to get a custom model built for your business, or jump right in with a free trial today.

Rachel Wolff

March 24th, 2022

Posts you might like...

Survey Types for Success in 2022

How customer surveys are performed has changed radically for businesses. And this trend will continue long into the future. Fillable…

What Is Survey Data Processing?

Designing and distributing the perfect survey can take a lot of precious resources. So, it's important that you get a return on this…

Survey Responses: How & What to Ask to Get The Responses You Need

According to data from SurveyMonkey , two thirds of customers say they complete more than half of the surveys they receive. While that…

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

Before you go, check this out!

We have lots more on the site to show you. You've only seen one page. Check out this post which is one of the most popular of all time.

How To Analyze Data From A Questionnaire For A Research Paper?

This post provides some tips and information about the process of analyzing survey data. Some of it is from Dave’s vlog and some of it is my own. Just a note about survey research.

Surveys can be quantitative with all questions/items that can be analyzed statistically or it can be mainly or in part qualitative. Qualitative research using a survey would include open-ended questions that the respondent has to write out in sentences or paragraphs. This post mainly addresses issues in quantitative survey research.  If you need help on writing a paper or editing your thesis, you should check out this detailed post.

A disclaimer about Dave’s vlog on this topic: This is one of Dave’s more technical vlogs, and you do have to have some baseline knowledge of research analysis methods to benefit from some of the content, but Dave did provides a great summary of key things that are important to keep in mind as you design your survey research and prepare to analyze survey data, whether is be more a smaller class project or your dissertation. You can view Dave’s full vlog here: 

First of all, before you begin your analysis, you must think about your research question and how the survey / questionnaire relates to your research question. How are you going to operationalize the variables specified in your research question? That is, how is the survey data going to describe phenomena that you are interested in observing and measuring? Also, if you made some hypotheses, how are you going to determine whether they are confirmed or rejected by the data? 

This post was written by Stephanie A. Bosco-Ruggiero (PhD candidate in Social Work at Fordham University Graduate School of Social Service) on behalf of Dave Maslach for the R3ciprocity project (Check out the YouTube Channel or the writing feedback software ). R3ciprocity helps students, faculty, and research folk by providing a real and authentic look into doing research. It provides solutions and hope to researchers around the world.

Creating a data analysis plan

Specifically focus on your research questions before you do anything else and come up with a data analysis plan. If your research is purely quantitative (no open ended questions requiring content analysis) outline the statistical procedures you are going to use to answer your research question. Do you want to use bivariate or multivariate analyses? That is, do you want to measure the association between two variables, or do you want to observe how more than two variables impact an outcome or relate to each other? Some common bivariate analyses are Pearson chi-square or bivariate correlation. For a more rigorous multivariate analysis you might use a multiple linear regression or a cluster analysis. 

There is a lot of regression analysis to cover, so we are not going to cover regression here. People spend many courses trying to understand regression analysis. Most of it is thinking about how regression assumptions do and do not hold.

Avoiding confirmation bias

The key thing is to specify as much of the analysis before you touch the data. Why, you ask? We have a tendency as humans to look to confirm our hypotheses, and the goal in science is to objectively confirm or reject (falsify) your hypotheses. By specifying as much of the analysis upfront as possible, you prevent yourself from being human and selecting analytical methods that will more likely confirm your hypothesis as you proceed through your research.

Now, sometimes you do have to adjust your data analysis plan (more about that at the end) and that is ok in some instances, but don’t change your research questions and data analysis plan continuously as you go through your research because you want to come up with some kind of predetermine finding or don’t like what you’ve come up with your original plan.  

(This is Dave: Personally, I think you are OK to adjust as you go as long as you are upfront and clear with this in your analysis. If anyone has gone through the review process of a major journal, you will know that revise and improving clarity is a major part of writing papers. Yes, we know that there is debate about HARKing and such right now, but writing a paper is virtually impossible to do without this trial and error process. If we knew what the answer was upfront, which is what pre-specification presumes, then it would not be research.)

This pitfall of wanting to change our questions or plan to find something interesting or confirm our hypotheses is known as confirmation bias. We all want to find something interesting in our data, and all the better if our analyses confirm what we thought would happen, but we can’t will our results. They are what they are. By creating a data analysis plan early on, you are more likely to stick to it and not make too many adjustments based on what you’re seeing in the data, or learning, along the way. At some point, you just have to say, I will find what I find even if it’s not that interesting.

Here are tips Dave shared about things you should think about and steps you should take as you go through the process of planning your study and analyzing your data. 

• Consider construct validity. First you want to ask yourself whether your survey items are measuring your concept precisely enough. That is, is your main idea and hypothesis being accurately measured by the set of questions in your survey tool? If you adjust your hypotheses along the way, you may want to consider using a different survey tool if your original tool no longer measures the concept you are studying. To determine construct validity you want to become familiar with past studies and tools used to analyze and observe your key ideas or research question. Read more about construct validity here .
• Run your frequencies and plot your data. So you’ve gathered 100 completed surveys and you have them in hand or the data online. After you enter the data into a data analysis software platform (e.g. R, SAS, SPSS), run your frequencies. Simply look at your numbers. Can you glean anything from the descriptive data? Is there an imbalance in who answered your survey (e.g. by gender). Just take a look at the data and become familiar with the raw results. You can plot your continuous data as well. Dave says, “Always plot the data!” Try to think about simple histograms or scatterplots (x-y plots), or line plots. Then look at the data and see what it’s telling you. Take a look at the outliers and anomalies that show up in the data. Plotting allows you to see how the data lines up comparatively to what you would expect to see. 
• Explore your data. In order to run certain statistical tests, your data has to be appropriate for that test. First think about the basics. If your data is categorical (e.g. colors, states) you can’t run tests that are appropriate only for continuous variables (e.g interval or ratio data). You also need to confirm that your continuous data is appropriate for certain tests. Is it normally distributed? Does it contain numerous outliers? Learn about the requirements for running an ANOVA, regression, chi-square, etc. Then you have to decide which are your independent and dependent variables. If you are doing multivariate analyses, carefully consider which independent variables you want to include in the model. Also, do you want to create any interaction variables? Which control variables do you want to include so you can clearly understand which independent variables cause your dependent variable to vary. 

4. Run your analyses. Run your bivariate and/or multivariate analyses. When conducting a multiple linear regression, use a stepwise regression so you can add variables to the model one by one. If you remove or add a variable, do your findings suddenly become significant. Think about why this might be. A particular variable might also make your model unstable. Figure out which variable is causing the problem, and find out why. Is it intercorrelated with another variable? It’s ok to run a bunch of analyses that you never report on (Dave: Maybe. We always have to be clear on what we report on and don’t run. It’s just far more efficient use of your time to document, document, document.)

You just want to become familiar with your data and various results. If you want to run a bunch of bivariate analyses to become familiar with what you are going to see in your multivariate analyses, go right ahead. It doesn’t mean you have to report on every single test you run. Also, you are not deviating from your analysis plan by running more tests than you need. You are only deviating from your plan if you keep changing the variables you are looking, make a major change to your research methodology, or completely change the focus of your study or how you are going to analyze your data (e.g. scrapping a regression analysis plan to do a factor analysis, moving from a cross sectional analysis to a longitudinal study). 

5. If you conduct a one way ANOVA or regressions, run a post hoc analysis . If you find a difference in means between your variables, find out where the significant differences are. To do this run a post-hoc test , also known as a multiple comparisons test. For example, if you have groups of freshman, sophomore, and junior high school students taking a standardized test, and your ANOVA results are significant, run a post-hoc test to determine if all three groups have significantly different scores or whether the different lies between two specific groups. You have to choose your post-hoc statistic carefully (e.g. think Tukey) based on the characteristics of your data. 

6. Double check your work and output. We have all made mistakes at one time or another in analyzing our data or interpreting our results. Double check everything you’ve done after you’ve run all of your analyses. Do some of the results seem really off, or the data is not performing as expected? Trace your steps and make sure you entered all of the correct variables and ran the right tests. You can even have a student assistant double check your work, or have a colleague look at any puzzling results. 

7. Think about how your findings are different or similar to other studies’ findings. You should have conducted a literature review in the study planning stages to find out who has studied your concept, or closely related concepts, prior, and what they discovered. Are you going to confirm past findings or try to refute them? What should you include or not include in the analysis? How many research questions should you have and have you made them straightforward enough that they are easily analyzed? Take a look at your frequencies and think about whether the data is lining up with what was found in previous studies.  

8. Continuously write up your results: Obviously, people from a range of disciplines read this blog, so we can’t describe exactly how you’re going to write up your results because there are different formatting requirements in each discipline. We can tell you, however, that whatever the format, you are going to need to understand and write up your results and interpret them in a discussion section (or something similar). As soon as you look at your output you can start writing notes about about what your’re seeing and what it might mean. How does it relate to prior finding in this area of research? If your hypothesis was rejected or the null could not be rejected, think about why. If you found something completely new that has not been found before in your field, discuss why at the present time or in your particular study these results might have come about. 

9. Leftover data. Dave advises that you don’t need to use all of the data in the survey in your analysis. Save some for future research. You don’t want to go overboard in reporting every single result. Stick to what you wanted to look at according to your research questions, hypotheses, and data analysis plan. Of course, dissertation data and analyses can provide the perfect content for several peer reviewed research manuscripts (journal articles). Save all of your data!  (Dave: Indeed, a good research project should have room for 3 to many more studies with the data).

10. Think about future studies. What did you find that was particularly interesting from your data that you might want to explore further. Jot down some ideas for future studies that look at different angles of what you studied or that take your research to the next level. You might look at a similar set of research questions using a different research methodology or set of tests, or you might focus in on particular finding and explore it using qualitative survey techniques (e.g. focus groups, interviews.) 

Check out Dave’s vlog about Mistakes most PhDs make in their Doctoral Research and Scientific Careers: 

Here are a few more tips to consider as you conduct your quantitative survey research: 

• Keep careful track of who you administered your questionnaire to (create IDs for anonymity), if you changed any items along the way, and your process for distributing your survey. You’ll have to write all of this up later on. 
• Don’t forget about Ethics Approval! Every university and research institution has an Ethics Board or IRB. It is super important that you do that before you start doing research. Our research has consequences, some of them can be rather nasty, and we have to think about what those consequences are. If you need help, check out this video on the IRB Approval process:
• Be upfront about the limitations of your research. Don’t try to hide the limitation. Any good study disclosed limitations so the reader understands where there may be a lack of validity or reliability in the numbers. For example, if you have a small sample or it is skewed in some way, note that. You want your reader to understand whether these results might be similar for a different population. 
• If you don’t actually find much that is interesting or all of your hypotheses must be rejected, don’t despair, you are not the only one. Report your results and if you’ve done a good job you will get a good grade or your degree. Journals are full of studies that found nothing significant and nothing particularly interesting. (Dave: Yes and No. You have to think about why that is interesting to not find results.) This is research too and others in your field will learn from your results. Your results might even help you come up with a new theory. As Dave says, “That is the beauty of science….don’t be afraid to explore different avenues, you should be writing down and clear about the stuff you’ve done, be very systematic.” He also advised that a spurious result can be really interesting. 
• If you do need to change your data analysis plan, that is ok. As long as your new plan is helping you come up with results that best explain your research questions that is fine. You do have to write up a new data analysis plan and stick to your new plan, don’t keep changing it up. It’s great if you can stick to your original plan, but that often does not happen. 
• Do keep a codebook. A codebook is your menu of variables, their names, and their numerical codes. You should include all of your created interaction variables.  Also keep careful note or  log of all of your recoded variables and their new responses and codes. You will thank yourself later…..
• Did you benefit from this post? Do you know of anyone at all that could use feedback on their writing or editing of their documents? I would be so grateful if you read this post on how to get feedback on your writing using R3ciprocity.com or let others know about the R3ciprocity Project. THANK YOU in advance! You are the bees knees.

If you enjoyed this blog, check out these other blogs on r3ciprocity.com: 

Striving in Your Career: Challenges and Opportunities of Always Striving for More at Work and the Benefits of Emotional Intelligence

When Do Most PhDs Quit?

How To Stay Calm And Productive When Writing Your Dissertation

R3ciprocity_Team

Recent Posts

The PhD Journey: Embracing Challenge and Prioritizing Wellbeing

Embarking on a PhD journey is a remarkable endeavor that symbolizes not only academic dedication, but also a deep commitment to advancing knowledge in one’s field. However, this path is often...

Rethinking Success and Embracing the Journey: Lessons from the R3ciprocity Project

The Surprising Lesson of Going It Alone When I launched the R3ciprocity Project, I anticipated an outpouring of support from various groups like scientists, researchers, universities, and...

How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD Cand). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter – exciting! But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step.  

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips and tricks for an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

Psst... there’s more!

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

You Might Also Like:

20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Utility Menu

Harvard University Program on Survey Research

• Questionnaire Design Tip Sheet

This PSR Tip Sheet provides some basic tips about how to write good survey questions and design a good survey questionnaire.

PSR Resources

• Managing and Manipulating Survey Data: A Beginners Guide
• Finding and Hiring Survey Contractors
• How to Frame and Explain the Survey Data Used in a Thesis
• Overview of Cognitive Testing and Questionnaire Evaluation
• Sampling, Coverage, and Nonresponse Tip Sheet
• Introduction to Surveys for Honors Thesis Writers
• PSR Introduction to the Survey Process
• Related Centers/Programs at Harvard
• General Survey Reference
• Institutional Review Boards
• Select Funding Opportunities
• Survey Analysis Software
• Professional Standards
• Professional Organizations
• Major Public Polls
• Survey Data Collections
• Major Longitudinal Surveys
• Other Links

• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

• +44 7897 053596
• [email protected]

Get an experienced writer start working

Review our examples before placing an order, learn how to draft academic papers, a step-by-step guide to dissertation data analysis.

How to Write a Dissertation Conclusion? | Tips & Examples

What is PhD Thesis Writing? | Beginner’s Guide

A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

3-Step  Dissertation Process!

Get 3+ Topics

Dissertation Proposal

Get Final Dissertation

Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

Testimonials

Very satisfied students

This is our reason for working. We want to make all students happy, every day. Review us on Sitejabber

Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

How Does It Work ?

Fill the Form

Writer Starts Working

3+ Topics Emailed!

Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

How Do You Select the Right Data Analysis

How to Write Data Analysis For A Dissertation?

How to Develop a Conceptual Framework in Dissertation?

What is a Hypothesis in a Dissertation?

Get an Immediate Response

Discuss your requirments with our writers

WhatsApp Us Email Us Chat with Us

Get 3+ Free   Dissertation Topics within 24 hours?

Your Number

Academic Level Select Academic Level Undergraduate Masters PhD

Area of Research

admin farhan

Related posts.

What Is a Covariate? Its Role in Statistical Modeling

What is Conventions in Writing | Definition, Importance & Examples

Understanding TOK Concepts: A Beginner’s Guide

Comments are closed.

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!

Sign up now

Free unlimited surveys, questions and responses.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Table of contents

Questionnaires vs surveys, questionnaire methods, open-ended vs closed-ended questions, question wording, question order, step-by-step guide to design, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyse data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives, placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method , administering questionnaires, data cleaning and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalise your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimising these will help you avoid sampling bias .

Prevent plagiarism, run a free check.

Questionnaires can be self-administered or researcher-administered . Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

• Cost-effective
• Easy to administer for small and large groups
• Anonymous and suitable for sensitive topics

But they may also be:

• Unsuitable for people with limited literacy or verbal skills
• Susceptible to a nonreponse bias (most people invited may not complete the questionnaire)
• Biased towards people who volunteer because impersonal survey requests often go ignored

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents.

Researcher-administered questionnaires can:

• Help you ensure the respondents are representative of your target audience
• Allow clarifications of ambiguous or unclear questions and answers
• Have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

• Costly and time-consuming to perform
• More difficult to analyse if you have qualitative responses
• Likely to contain experimenter bias or demand characteristics
• Likely to encourage social desirability bias in responses because of a lack of anonymity

Your questionnaire can include open-ended or closed-ended questions, or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalisable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Likert-type questions collect ordinal data using rating scales with five or seven points.

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale . Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio data, you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer ‘multiracial’ for the question on race rather than selecting from a restricted list.

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle to productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarising responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorise answers, and you may also need to involve other researchers in data analysis for high reliability .

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way ( reliable ) and measure exactly what you’re interested in ( valid ).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Use a mix of both positive and negative frames to avoid bias , and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counterargument within the question as well.

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

• The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favour flexible work-from-home policies even after it’s safe to return to offices?
• Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

Keep your questions focused

Ask about only one idea at a time and avoid double-barrelled questions. Double-barrelled questions ask about more than one item at a time, which can confuse respondents.

This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

Instead, you should ask two separate questions to gauge respondents’ opinions.

Strongly Agree Agree Undecided Disagree Strongly Disagree

Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

You can organise the questions logically, with a clear progression from simple to complex. Alternatively, you can randomise the question order between respondents.

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioural or opinion questions, and ending with more complex, sensitive, or controversial questions.

The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

• How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
• Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
• Do you approve or disapprove of the way Joe Biden is handling his job as president?

It’s important to minimise order effects because they can be a source of systematic error or bias in your study.

Randomisation

Randomisation involves presenting individual respondents with the same questionnaire but with different question orders.

When you use randomisation, order effects will be minimised in your dataset. But a randomised order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

Follow this step-by-step guide to design your questionnaire.

Step 1: Define your goals and objectives

The first step of designing a questionnaire is determining your aims.

• What topics or experiences are you studying?
• What specifically do you want to find out?
• Is a self-report questionnaire an appropriate tool for investigating this topic?

Once you’ve specified your research aims, you can operationalise your variables of interest into questionnaire items. Operationalising concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

Step 2: Use questions that are suitable for your sample

Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

• Are the respondents familiar with the language and terms used in your questions?
• Would any of the questions insult, confuse, or embarrass them?
• Do the response items for any closed-ended questions capture all possible answers?
• Are the response items mutually exclusive?
• Do the respondents have time to respond to open-ended questions?

Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

Step 3: Decide on your questionnaire length and question order

Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

If respondents are not being incentivised or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomise questions. Randomising questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

Step 4: Pretest your questionnaire

When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection , and analysis.

You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered .

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

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

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

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

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Questionnaire Design | Methods, Question Types & Examples. Scribbr. Retrieved 29 April 2024, from https://www.scribbr.co.uk/research-methods/questionnaire-design/

Is this article helpful?

Pritha Bhandari

Other students also liked, doing survey research | a step-by-step guide & examples, what is a likert scale | guide & examples, reliability vs validity in research | differences, types & examples.

Dissertation Questionnaire

A dissertation is a document usually a requirement for a doctoral degree especially in the field of philosophy. This long essay discusses a particular subject matter uses questionnaires   and other sources of data and is used to validate its content. The  questionnaire’s importance is evident in the processes of data gathering as it can make the dissertation factual, effective and usable.

Having a well-curated and formatted document to follow when making a dissertation can be very beneficial to an individual who is currently immersed in the data gathering stage of the specific research study. We have gathered downloadable samples and templates of questionnaires so it will be easier for you to curate your own.

Dissertation Timeline Gantt Chart Template

Size: 55 KB

Dissertation Research Gantt Chart Template

Size: 43 KB

Dissertation Project Gantt Chart Template

Size: 41 KB

Dissertation Plan Gantt Chart Template

Size: 51 KB

Dissertation Research Questionnaire

Size: 18 KB

Dissertation Proposal Questionnaire

Size: 131 KB

Sample Dissertation Questionnaire

Size: 10 KB

What Is a Dissertation Questionnaire?

A dissertation questionnaire can be defined as follows:

• It is a document used in the processes of data gathering.
• Questionnaires in PDF used for a dissertation contain questions that can help assess the current condition of the community which is the subject of study within the dissertation.
• It specifies the questions that are needed to be answered to assure that there is a basis in terms of the results that will be presented in a dissertation.

How to Write a Dissertation Questionnaire

Writing an efficient and comprehensive dissertation questionnaire can greatly affect the entire dissertation. You can make one by following these steps:

• Be specific with the kind of dissertation that you are creating and align the purposes of the dissertation questionnaire that you need to make to your study.
• List down the information needed from the community who will provide the answers to your questions.
• Open a software where you can create a questionnaire template. You may also download  survey questionnaire examples   and templates to have a faster time in formatting the document.
• The purpose of the dissertation questionnaire.
• The guidelines and instructions in answering the dissertation questions.
• The name of the person to who will use the questionnaire results to his/her dissertation.
• The institution to whom the dissertation will be passed.
• List down the questions based on your needs.

Undergraduate Dissertation Questionnaire

Size: 12 KB

Project Management Dissertation

Size: 54 KB

Guidelines for Writing a Dissertation Questionnaire

There are no strict rules in writing a dissertation questionnaire. However, there are some tips that can help you to create a dissertation questionnaire that is relevant to the study that you are currently doing. Some guidelines:

• Make sure that you are well aware of the data that is needed in your dissertation so you can properly curate questions that can supply your information needs.
• It will be best to use a dissertation questionnaire format that is organized, easy to understand, and properly structured. This will help the people who will answer the dissertation questionnaire quickly know how they can provide the items that you would like to know.
• Always make sure that your instructions in answering the questions are precise and directly stated.
• You may look at  questionnaires in Word   for comparisons. Doing this will help you assess whether there are still areas of improvement that you may tap with the content and format of the dissertation questionnaire that you have created.

Keeping this guidelines in mind and implementing them accordingly will allow you to create a dissertation questionnaire that is beneficial to the processes that you need to have an outstanding dissertation.

Questionnaire Generator

Text prompt

• Instructive
• Professional

Create a fun quiz to find out which historical figure you're most like in your study habits

Design a survey to discover students' favorite school subjects and why they love them.