how to analyse a questionnaire for a dissertation

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

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Using a questionnaire survey for your dissertation

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Find out how to use a dissertation questionnaire for your masters.

Prof Martyn Denscombe, author of " The Good Research Guide, 6th edition ", gives expert advice on using a questionnaire survey for your postgraduate dissertation.

Questionnaire surveys are a well-established way of collecting data. They work with relatively small-scale research projects so design and deliver research questionnaires quickly and cheaply. When it comes to conducting research for a master’s dissertation, questionnaire surveys feature prominently as the method of choice.

Using the post for bulky and lengthy surveys is normal. Sometimes questionnaires go by hand. The popularity of questionnaire surveys is principally due to the benefits of using online web-based questionnaires. There are two main aspects to this.

Designing questionnaires

First, the software for producing and delivering web questionnaires. Simple to use features such as drop-down menus and tick-box answers, is user-friendly and inexpensive.

Second, online surveys make it possible to contact people across the globe without travelling anywhere. Given the time and resource constraints faced when producing a dissertation, makes online surveys all the more enticing. Social media such as Facebook, Instagram and WhatsApp is great for contacting people to participate in the survey.

In the context of a master’s dissertation, however, the quality of the survey data is a vital issue. The grade for the dissertation will depend on being able to defend the use of the data from the survey. This is the basis for advanced, master’s level academic enquiry.

Pro's and con's

It is not good enough to simply rely on getting 100 or so people to complete your questionnaire. Be aware of the pros and cons of questionnaire surveys. You need to justify the value of the data you have collected in the face of probing questions, such as:

• Who are the respondents and how they were selected?
• How representative are the respondents of the whole group being studied?
• What response rate was achieved by the survey?
• Are the questions suitable in relation to the topic and the particular respondents?
• What likelihood is there that respondents gave honest answers to the questions?

This is where The Good Research Guide, 6th edition becomes so valuable.

It identifies the key points that need to be addressed in order to conduct a competent questionnaire survey. It gets right to the heart of the matter, with plenty of practical guidance on how to deal with issues.

In a straightforward style, using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects and outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe's advice on using a Case Study for your postgraduate dissertation.

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Your postgraduate student guide to using a research questionnaire for your dissertation

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Frequently asked questions.

There are a few key steps in creating a dissertation for use in your thesis. Firstly, you should think about the topic you are studying and who you need to respond to your questionnaire. You then need to think about how you can deliver the questions, this can be in the form of in-person interviews or emails. You can begin to formulate your questions and format.

Visit the Studying for a PhD section for more information and advice.

Prof Martyn Denscombe, author of “ The Good Research Guide, 6th edition ”, gives expert advice on using a questionnaire survey for your postgraduate dissertation.

Questionnaire surveys are a well-established way of collecting data. They can be used with relatively small-scale research projects, and research questionnaires can be designed and delivered quite quickly and cheaply. It is not surprising, therefore, that when it comes to conducting research for a master’s dissertation, questionnaire surveys feature prominently as the research method of choice.

Occasionally such thesis surveys will be sent out by post, and sometimes the questionnaires will be distributed by hand. But the popularity of questionnaire surveys in the context of master’s dissertations is principally due to the benefits of using online web-based questionnaires. There are two main aspects to this.

First, the software for producing and delivering web questionnaires, with their features such as drop-down menus and tick-box answers, is user-friendly and inexpensive.

Second, online surveys make it possible to contact people across the globe without travelling anywhere which, given the time and resource constraints faced when producing a dissertation, makes online surveys all the more enticing. (And, for the more adventurous students, there are also developing possibilities for the use of social media such as Facebook and SMS texts for contacting people to participate in the survey.)

In the context of a master’s dissertation, however, the quality of the survey data is a vital issue. The grade for the dissertation will depend on being able to defend the use of the data from the survey as the basis for advanced, master’s level academic enquiry. Which means it is not good enough to simply rely on getting 100 or so people to complete your questionnaire. Students are expected to be aware of the pros and cons of questionnaire surveys and to be able to justify the value of the data they have collected in the face of probing questions such as:

• Who are the respondents and how they were selected?
• How representative are the respondents of the whole group being studied?
• What response rate was achieved by the survey?
• Are the questions suitable in relation to the topic and the particular respondents?
• What likelihood is there that respondents gave honest answers to the questions?

This is where The Good Research Guide, 6th edition becomes so valuable.

It not only identifies the key points that need to be addressed in order to conduct a competent questionnaire survey, it gets right to the heart of the matter with plenty of practical guidance on how to deal with the issues. In a straightforward style, using plain language, this bestselling book covers a range of alternative strategies and methods for conducting small-scale social research projects and outlines some of the main ways in which the data can be analysed.

Read Prof Martyn Denscombe’s advice on using a Case Study for your postgraduate dissertation

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How To Analyze Survey Data 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

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). 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. 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
• Free results chapter template

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 for writing 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.

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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?

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How to Analyse Secondary Data for a Dissertation

Secondary data refers to data that has already been collected by another researcher. For researchers (and students!) with limited time and resources, secondary data, whether qualitative or quantitative can be a highly viable source of data.  In addition, with the advances in technology and access to peer reviewed journals and studies provided by the internet, it is increasingly popular as a form of data collection.  The question that frequently arises amongst students however, is: how is secondary data best analysed?

The process of data analysis in secondary research

Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective.  In simple terms there are three steps:

• Step One: Development of Research Questions
• Step Two: Identification of dataset
• Step Three: Evaluation of the dataset.

Let’s look at each of these in more detail:

Step One: Development of research questions

Using secondary data means you need to apply theoretical knowledge and conceptual skills to be able to use the dataset to answer research questions.  Clearly therefore, the first step is thus to clearly define and develop your research questions so that you know the areas of interest that you need to explore for location of the most appropriate secondary data.

Step Two: Identification of Dataset

This stage should start with identification, through investigation, of what is currently known in the subject area and where there are gaps, and thus what data is available to address these gaps.  Sources can be academic from prior studies that have used quantitative or qualitative data, and which can then be gathered together and collated to produce a new secondary dataset.  In addition, other more informal or “grey” literature can also be incorporated, including consumer report, commercial studies or similar.  One of the values of using secondary research is that original survey works often do not use all the data collected which means this unused information can be applied to different settings or perspectives.

Key point: Effective use of secondary data means identifying how the data can be used to deliver meaningful and relevant answers to the research questions.  In other words that the data used is a good fit for the study and research questions.

Step Three: Evaluation of the dataset for effectiveness/fit

A good tip is to use a reflective approach for data evaluation.  In other words, for each piece of secondary data to be utilised, it is sensible to identify the purpose of the work, the credentials of the authors (i.e., credibility, what data is provided in the original work and how long ago it was collected).  In addition, the methods used and the level of consistency that exists compared to other works. This is important because understanding the primary method of data collection will impact on the overall evaluation and analysis when it is used as secondary source. In essence, if there is no understanding of the coding used in qualitative data analysis to identify key themes then there will be a mismatch with interpretations when the data is used for secondary purposes.  Furthermore, having multiple sources which draw similar conclusions ensures a higher level of validity than relying on only one or two secondary sources.

A useful framework provides a flow chart of decision making, as shown in the figure below.

Following this process ensures that only those that are most appropriate for your research questions are included in the final dataset, but also demonstrates to your readers that you have been thorough in identifying the right works to use.

Writing up the Analysis

Once you have your dataset, writing up the analysis will depend on the process used.  If the data is qualitative in nature, then you should follow the following process.

Pre-Planning

• Read and re-read all sources, identifying initial observations, correlations, and relationships between themes and how they apply to your research questions.
• Once initial themes are identified, it is sensible to explore further and identify sub-themes which lead on from the core themes and correlations in the dataset, which encourages identification of new insights and contributes to the originality of your own work.

Structure of the Analysis Presentation

Introduction.

The introduction should commence with an overview of all your sources. It is good practice to present these in a table, listed chronologically so that your work has an orderly and consistent flow. The introduction should also incorporate a brief (2-3 sentences) overview of the key outcomes and results identified.

The body text for secondary data, irrespective of whether quantitative or qualitative data is used, should be broken up into sub-sections for each argument or theme presented. In the case of qualitative data, depending on whether content, narrative or discourse analysis is used, this means presenting the key papers in the area, their conclusions and how these answer, or not, your research questions. Each source should be clearly cited and referenced at the end of the work. In the case of qualitative data, any figures or tables should be reproduced with the correct citations to their original source. In both cases, it is good practice to give a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

Do not use direct quotes from secondary data unless they are:

• properly referenced, and
• are key to underlining a point or conclusion that you have drawn from the data.

All results sections, regardless of whether primary or secondary data has been used should refer back to the research questions and prior works. This is because, regardless of whether the results back up or contradict previous research, including previous works shows a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Summary of results

The summary of the results section of a secondary data dissertation should deliver a summing up of key findings, and if appropriate a conceptual framework that clearly illustrates the findings of the work. This shows that you have understood your secondary data, how it has answered your research questions, and furthermore that your interpretation has led to some firm outcomes.

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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 .

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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

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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.

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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.

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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;

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Biden gave debate performance Democrats feared, but Trump did not win new votes

Ahead of the debate, the Trump team released an ad they planned to run after the event arguing that a vote for Biden is really a vote for Vice President Kamala Harris, seemingly predicting Biden’s performance.

“You know who’s waiting behind him, right?” the ad’s narrator said.

“Listen, people can debate on style points, but ultimately this election and who is the president of the United States has to be about substance, and the contrast is clear,” Harris said on CNN after the debate, trying to turn the focus to Trump. “He would not disavow what happened on Jan. 6. He would not give a clear answer on whether he would stand by the election results this November.”

Harris also referenced the Trump-appointed Supreme Court justices helping to overturn Roe v. Wade — but Trump himself seemed to waffle on a particular position on abortion, arguing repeatedly that the issue was in the hands of states now and falsely saying it was a universal goal he’d delivered.

[ Fact-checking the Biden-Trump debate ]

“Everybody wanted to get it back to the states,” Trump said. “Everybody without exception — Democrats, Republicans, liberals, conservatives — everybody wanted it back.”

Biden landed jabs, repeatedly arguing that Trump was not telling the truth, that historians had voted Trump the worst president in history, and calling him a “whiner” who could not accept his loss in 2020 and would refuse to do so again this year.

“The idea if you lose again, you accepting anything — you can’t stand the loss,” Biden said. “Something snapped in you when you lost the last time.”

There was also this historic exchange, after Trump called Biden “a criminal” for demanding Ukraine replace a corrupt prosecutor:

“The idea that I did anything wrong relative to what you’re talking about is outrageous. It’s simply a lie, No. 1,” Biden said. “No. 2, the idea that you have a right to seek retribution against any American just because you’re president is wrong. It’s simply wrong. No president has ever spoken like that before. No president in our history has spoken like that before. No. 3, the crimes you are still charged with. And think of all the civil penalties you have. How many billions of dollars do you owe in civil penalties? Or for molesting a woman in public, for doing a whole range of things, of having sex with a porn star on the night while your wife was pregnant. I mean, what what what are you talking about? You have the morals of an alley cat.”

“I didn’t have sex with a porn star,” Trump replied, accusing Biden of using the Justice Department to go after him — even though Trump was convicted by the Manhattan district attorney in New York City. “Because he thought it was going to damage me. But when the public found out about these cases, because they understand it better than he does, he has no idea what these cases are. But when he — when they found out about these cases, you know what they did? My poll numbers went up. Way up.”

But Biden also appeared to freeze at one point in an answer before saying, “We finally beat Medicare.” And in one answer about Ukraine, Biden used Trump’s name when he appeared to be referring to Russian President Vladimir Putin.

Afterward, his allies focused statements largely on policy, and some did not even try to sugarcoat Biden’s performance.

Former Sen. Claire McCaskill, D-Mo., said on MSNBC that she wished she were in the role of debate surrogate so she could look to the positive, but in the role of network commentator she spoke directly about how poorly she thought Biden had performed.

“My job now is to be really honest. Joe Biden had one thing he had to do tonight, and he didn’t do it. He had one thing he had to accomplish, and that was to reassure America that he was up to the job at his age,” McCaskill said. “And he failed at that tonight.”

McCaskill said she was hearing from elected officials, including those in offices where “you might know where they serve.”

“I don’t know if things can be done to fix this,” she said, noting that Harris and California Gov. Gavin Newsom were effective surrogates Thursday night.

“Those two people are signaling to a whole lot of Americans that are paying attention, how come they’re not running? How come the Democratic Party doesn’t have them at the top of the ticket instead of using them to shore up what have become after tonight some pretty glaring weaknesses … in our president,” McCaskill said.

Newsom was in Georgia as one of the top surrogates in the spin room for the Biden-Harris campaign, appearing along with Sen. Raphael Warnock, D-Ga.

As with the vice president and Newsom, Warnock and other surrogates speaking on Biden’s behalf argued for the election continuing to be about issues and policies.

“I would be concerned if the president didn’t have a record to run on, but the fact of the matter is that this is a man who has passed historic legislation,” Warnock told reporters after the debate.

Democrats also tried to keep the focus on Trump.

Rep. Jasmine Crockett, D-Texas, said Trump “was acting as if he was some used car salesman and could just tell us whatever and pretend as if it was fact.”

The question may be less whether Biden’s showing Thursday will drive voters to Trump, however, or whether it will boost voter apathy, driving them stay home, to cast a ballot for Robert F. Kennedy Jr., or — as is possible in Nevada — to vote for none of the candidates whose name appear on the ballot.

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• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

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

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

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• 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 .

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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.

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Watch CBS News

5 things to know about CBS News' 2024 Battleground Tracker election poll analysis

By Kabir Khanna

June 28, 2024 / 4:23 PM EDT / CBS News

The CBS News Battleground Tracker is back, explaining what's on voters' minds and regularly providing detailed snapshots of the U.S. presidential election in each state throughout the 2024 campaign. In addition to the specific polls we conduct in key states in a given week , the Battleground Tracker map includes our best estimates and presidential race ratings in every state. This includes states we've polled extensively and states where we've surveyed fewer voters but have lots of other data. Scroll down to the end to see CBS News' latest battleground state estimates (you can also hover over the map to see estimates) .

What exactly is the Battleground Tracker, and where do the numbers come from? Here are five things to know.

1. Going state by state to understand this election

We take a state-by-state approach to describing the race and measuring public opinion, since the presidency is won in the Electoral College, not by the national popular vote. Indeed, relying too much on national polling can be misleading, as 2016 reminded us .

The Battleground Tracker looks at each individual state, focusing on the most competitive ones. And we translate each candidate's current support to the electoral vote scoreboard. Our state-by-state approach also gives a sense of what voters in different parts of the country think and feel about this year's candidates, national issues and local matters.

2. More than just a poll

While surveying voters across the country is an integral part of the Battleground Tracker, this is more than your typical poll. It's really a big data project. We combine polling, voter files (from L2 Data (L2 is the firm used by CBS News for voter files), U.S. Census data, and historical trends to get a clear picture of what's going on in each state.

Here's what the data tells us:

• We know which candidates different types of voters are supporting from our polling, which includes much larger sample sizes — tens of thousands — than a typical poll;
• We know how many people like them are in each state and county, as well as their turnout history, from voter files and U.S. Census data;
• And we know each state's previous election results, which enables us to anchor our 2024 estimates to recent history.

Our model combines all this data using multilevel regression with post-stratification (scroll down for more details on this). A feature of this technique is that we use trends across the country to inform our picture of a specific state. If we find Hispanic voters across the Southwest shifting support, for instance, we use the information to more precisely estimate specific states in which Hispanic voters live. The same applies for many other types of voters. The survey lets them tell us what they are thinking, and we map that to how many of them live in each state.

We collaborate on data collection and modeling with global opinion research firm YouGov , building on our successful efforts in 2018 , 2020 , and 2022 .

3. Think snapshots, not forecasts

We tell you where races stand today, explaining why and what might change. We fully anticipate movement before the first vote is cast, so we'll update everything regularly in the months ahead.

Unlike an electoral forecast, we're estimating each candidate's current support, incorporating all the data we've collected up to this point. For instance, if we estimate President Biden at 49% in a state with a margin of error of 3 points, we're confident that his support there is between 46% and 52% today — not that the final result will be in that range.

There's nothing here to account for forward-looking uncertainty — nothing about the economy changing or dramatic debates, for example. We fully expect movement before the first vote is cast, so we'll update everything regularly throughout the fall. A race that's leaning toward a party today could be reclassified as a toss-up if it becomes more competitive.

4. Electoral scenarios

That brings us to scenarios. Down the road, the Battleground Tracker will offer plausible scenarios for how the election might unfold. We'll do this using a combination of statistical simulation and by tweaking some of the assumptions underlying our model, resulting in a range of possible outcomes.

Here's an example. One of the most challenging things to figure out will be turnout: who's actually going to bother to vote? Modeling who is likely to vote is a perennial challenge that is sensitive to assumptions.

In our baseline model, we estimate which voters are casting ballots based on both what they tell us they're planning to do and historical patterns in their states. In our scenarios, we'll slightly alter the model's parameters to explore what could happen if, for example, large swaths of voters stay home (perhaps for fear of getting sick) or if there's a surge in voting by mail (also possible given intense interest in this election). We'll roll out our scenarios later in the campaign, so check back for them!

5. Models have solid track record

While we take a different approach than traditional polling, the Battleground Tracker is based on rigorous methods from the fields of political science, survey research, and statistics. Moreover, we have a strong track record employing similar models at CBS News over the past few years.

Our 2018 model performed particularly well, steadily tracking Democrats' improvement in key races and the eventual blue wave in the U.S. House. In fact, our high-turnout scenario accurately estimated the final seat breakdown, when it came to pass that historic turnout powered Democratic gains.

Our accurate race ratings in 2020 were based on a similar model. We estimated that Democrats had built a lead heading into Election Day, but that Republicans could catch up with a late turnout surge. Every state we classified as leaning Democratic wound up going to Mr. Biden, and each we rated as leaning Republican went to Trump. Of our six toss ups, Trump won four and Mr. Biden won two.

And most recently, this methodology enabled us to accurately estimate the 2022 midterm elections . Our model consistently pointed to narrow Republican gains in the House, rather than the red wave that many other analysts expected.

NEW Battleground Tracker estimates out today Rs still ahead in race for House heading into final week of campaign — baseline model sees slight shift to GOP Eight in ten voters say things are "out of control" in U.S. Explore *range* of possibilities here https://t.co/rAA68pkcsw pic.twitter.com/EG7x52dgn5 — Kabir K. / kabirkhanna.bsky.social (@kabir_here) October 30, 2022

More on the statistical model we use

If you want to know more about the data and statistical model we use — and don't mind a bit of jargon — then keep reading...

First, we survey thousands of registered voters across the country and make sure to draw larger samples in battleground states, which we expect to be more competitive. The most important survey questions we ask for estimation purposes are how likely they are to vote and which candidate they would vote for today.

We then determine how people's vote intentions are related to their characteristics like age, gender, race, education, past vote, where they live, and so on. Each voter has a certain combination, which we'll call a "profile" for shorthand. For example, one possible profile is a 45-year-old, Asian female, who holds a college degree, voted for Biden four years ago, and lives in Allegheny County, Pennsylvania. Change any one of these characteristics and you get a different profile. We run a multilevel regression — a way to model the relationship between different variables — on the survey data to estimate how many voters of each specific profile intend to vote for each major candidate. The regression includes the voter characteristics above, as well as state and district effects (the levels in "multilevel").

The next step is estimating how many people of each voter profile live in each state. For this we use a combination of U.S. census data and voter files, which includes counts of voters at very granular levels, such as voting precincts. In each state, we multiply the total number of voters of a given profile by the proportion of voters with that profile choosing a candidate (the "post-stratification" step). Aggregating across all voter profiles in a state, we finally get the estimate of that candidate's statewide vote share. In Maine and Nebraska — the two states that award electoral votes by congressional district — we also estimate candidate support in each district.

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Kabir Khanna, Ph.D., is Deputy Director, Elections & Data Analytics at CBS News. He conducts surveys, develops statistical models, and projects races at the network Decision Desk. His scholarly research centers on political behavior and methodology. He holds a Ph.D. in political science from Princeton University.

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4 takeaways from the first presidential debate

Domenico Montanaro

President Biden and former President Donald Trump participate in the first presidential debate of the 2024 elections at CNN's studios in Atlanta on June 27. Andrew Caballero-Reynolds/AFP via Getty Images hide caption

If some people who listened to the radio in 1960 thought Richard Nixon won the presidential debate with John F. Kennedy, then maybe people reading the transcript of Thursday night’s match-up would think President Biden won.

But elections aren’t won in transcripts. The reality is, fairly or not, debates are often about optics — how the candidates present themselves, defend their records and parry attacks.

Fact check: What did Biden and Trump claim about immigration in the debate?

And that’s why so many Democrats are ringing the fire alarms after the first general-election presidential debate of 2024. The Biden campaign said the president had a cold to explain why he sounded so hoarse and weak. But Biden’s stumbles right from the beginning played into his biggest vulnerability — his age and whether the 81-year-old is up to the challenge of handling four more years in office.

There were issues for Trump, too, as he continued to spread falsehoods and bathe in the kinds of conspiratorial grievances that have turned off many voters.

Not much has changed the dynamics of this race; will anything that happened Thursday night make a difference either?

Here are four takeaways from the first Biden-Trump debate of this campaign:

1. First and foremost, let’s talk about the elephant in the room – Democrats have to be wondering if they’d be better off with someone else as their nominee.

Neither candidate is the official nominee yet. The national political conventions haven’t happened — but it’s next to impossible that Democrats would replace Biden.

Still, given he delivered the kind of performance Democrats feared, party leaders, strategists and many voters, frankly, had to be wondering during this debate what it would be like if any of a handful of other Democrats were standing on that stage.

Biden got a bit stronger as the debate went on, especially on foreign policy. He had some one-liners, like calling Trump a “whiner” when Trump wouldn’t definitively say that he would accept the results of the 2024 election. But Biden often wasn’t able to show vigor or consistently convey what he wanted to say. He simply couldn’t deliver the kinds of happy-warrior blows with that toothy smile audiences have seen from Biden in years past.

“Sometimes the spin don’t spin,” one Democratic strategist texted midway through the debate when asked for reaction.

2. If how Biden sounded wasn’t bad enough, the visuals might have been equally as bad.

An important rule of thumb for candidates — and moderators — in debates is to be conscious of how things look, of how you look, of what people are seeing at home. And what people saw — and this was predictable — was a split screen.

What to know about the key policies that got airtime in the presidential debate

Biden wasn’t able to use that to his advantage at all, even as Trump doled out falsehood after falsehood. Instead, he looked genuinely shocked and confused, which is never a good look.

Trump and his base might not care about late-night comedy, but this week’s monologues are going to sting Democratic voters.

3. The format — and hands-off moderators — benefited Trump.

The muting of the candidates was likely intended to make the debate calmer and not allow Trump to run roughshod over the moderators or his opponent. But it had the effect of making Trump seem more sedate than usual.

Trump employed rounds of verbal jujitsu, in which he threw back his own vulnerabilities and directed them toward Biden. He was even able at one point, during a strange exchange about golf handicaps, to say, “Let’s not act like children.”

The moderation, or lack thereof, also allowed Trump to spread falsehoods and hyperbole without being interrupted or corrected. CNN indicated before the debate that the moderators were not going to play a strong role in fact checking the candidates, and they lived up to that.

They left it to the candidates, essentially, and with Biden unable to deliver in real time and the moderators declining to, the audience was left with a salad bowl full of rotten eggs and moldy lettuce that passed for facts.

4. This debate might not move the needle much, if at all.

Despite Biden’s struggles, which will understandably get the headlines, Trump had some difficult moments, too, especially in the second half of the debate.

In addition to spreading myriad falsehoods, he did little to credibly defend his conduct on and before the Jan. 6 siege on the Capitol; he used the kind of hyperbolic and vituperative language that has long turned off swing voters; and showed why many are concerned about some of his positions on the issues, especially on abortion and how the U.S. should be represented on the world stage.

So despite Biden’s shortcomings, millions will still likely vote for Biden, anyway, because he’s not Trump.

The bottom line is: Americans have said they are unhappy with their choices, and, in this – the biggest moment of the 2024 presidential campaign yet — it was clear why.

Correction June 28, 2024

A previous version of this story referenced this week's live SNL episode but in fact the show is on its summer hiatus.