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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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Harvard University Program on Survey Research
- How to Frame and Explain the Survey Data Used in a Thesis
Surveys are a special research tool with strengths, weaknesses, and a language all of their own. There are many different steps to designing and conducting a survey, and survey researchers have specific ways of describing what they do.
This handout, based on an annual workshop offered by the Program on Survey Research at Harvard, is geared toward undergraduate honors thesis writers using survey data.
PSR Resources
- Managing and Manipulating Survey Data: A Beginners Guide
- Finding and Hiring Survey Contractors
- Overview of Cognitive Testing and Questionnaire Evaluation
- Questionnaire Design Tip Sheet
- Sampling, Coverage, and Nonresponse Tip Sheet
- Introduction to Surveys for Honors Thesis Writers
- PSR Introduction to the Survey Process
- Related Centers/Programs at Harvard
- General Survey Reference
- Institutional Review Boards
- Select Funding Opportunities
- Survey Analysis Software
- Professional Standards
- Professional Organizations
- Major Public Polls
- Survey Data Collections
- Major Longitudinal Surveys
- Other Links
- AI & NLP
- Churn & Loyalty
- Customer Experience
- Customer Journeys
- Customer Metrics
- Feedback Analysis
- Product Experience
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- Surveys & Feedback Collection
- Try Thematic
Welcome to the community
How to analyze survey data: best practices for actionable insights from survey analysis
Just started using a new survey tool ? Collected all of your survey data? Great. Confused about what to do next and how to achieve the optimal survey analysis? Don’t be.
If you’ve ever stared at an Excel sheet filled with thousands of rows of survey data and not known what to do, you’re not alone. Use this post as a guide to lead the way to execute best practice survey analysis.
Customer surveys can have a huge impact on your organization. Whether that impact is positive or negative depends on how good your survey is (no pressure). Has your survey been designed soundly ? Does your survey analysis deliver clear, actionable insights? And do you present your results to the right decision makers? If the answer to all those questions is yes, only then new opportunities and innovative strategies can be created.
What is survey analysis?
Survey analysis refers to the process of analyzing your results from customer (and other) surveys. This can, for example, be Net Promoter Score surveys that you send a few times a year to your customers.
Why do you need for best in class survey analysis?
Data on its own means nothing without proper analysis. Thus, you need to make sure your survey analysis produces meaningful results that help make decisions that ultimately improve your business.
There are multiple ways of doing this, both manual and through software, which we’ll get to later.
Types of survey data
Data exists as numerical and text data, but for the purpose of this post, we will focus on text responses here.
Close-ended questions
Closed-ended questions can be answered by a simple one-word answer, such as “yes” or “no”. They often consist of pre-populated answers for the respondent to choose from; while an open-ended question asks the respondent to provide feedback in their own words.
Closed-ended questions come in many forms such as multiple choice, drop down and ranking questions.
In this case, they don’t allow the respondent to provide original or spontaneous answers but only choose from a list of pre-selected options. Closed-ended questions are the equivalent of being offered milk or orange juice to drink instead of being asked: “What would you like to drink?”
These types of questions are designed to create data that are easily quantifiable, and easy to code, so they’re final in their nature. They also allow researchers to categorize respondents into groups based on the options they have selected.
Open-ended questions
An open-ended question is the opposite of a closed-ended question. It’s designed to produce a meaningful answer and create rich, qualitative data using the subject’s own knowledge and feelings.
Open-ended questions often begin with words such as “Why” and “How”, or sentences such as “Tell me about…”. Open-ended questions also tend to be more objective and less leading than closed-ended questions.
How to analyze survey data
How do you find meaningful answers and insights in survey responses?
To improve your survey analysis, use the following 5 steps:
- Start with the end in mind – what are your top research questions?
- Filter results by cross-tabulating subgroups
- Interrogate the data
- Analyze your results
- Draw conclusions
1. Check off your top research questions
Go back to your main research questions which you outlined before you started your survey. Don’t have any? You should have set some out when you set a goal for your survey. (More on survey planning below).
A top research question for a business conference could be: “How did the attendees rate the conference overall?”.
The percentages in this example show how many respondents answered a particular way, or rather, how many people gave each answer as a proportion of the number of people who answered the question.
Thus, 60% or your respondents (1098 of those surveyed) are planning to return. This is the majority of people, even though almost a third are not planning to come back. Maybe there’s something you can do to convince the 11% who are not sure yet!
2. Filter results by cross-tabulating subgroups
At the start of your survey, you will have set up goals for what you wanted to achieve and exactly which subgroups you wanted to analyze and compare against each other.
This is the time to go back to those and check how they (for example the subgroups; enterprises, small businesses, self-employed) answered, with regards to attending again next year.
For this, you can cross-tabulate, and show the answers per question for each subgroup.
Here, you can see that most of the enterprises and the self-employed must have liked the conference as they’re wanting to come back, but you might have missed the mark with the small businesses.
By looking at other questions and interrogating the data further, you can hopefully figure out why and address this, so you have more of the small businesses coming back next year.
You can also filter your results based on specific types of respondents, or subgroups. So just look at how one subgroup (women, men) answered the question without comparing.
Then you apply the cross tab to look at different attendees to look at female enterprise attendees, female self-employed attendees etc. Just remember that your sample size will be smaller every time you slice the data this way, so check that you still have a valid enough sample size.
3. Interrogate the data
Look at your survey questions and really interrogate them. The following are some questions we use for this:
- What are the most common responses to questions X?
- Which responses are affecting/impacting us the most?
- What’s different about this month/this year?
- What did respondents in group Y say?
- Which group of respondents are most affected by issue Z?
- Have customers noticed our efforts in solving issue Z?
- What do people say about Z?
For example, look at question 1 and 2. The difference between the two is that the first one returns the volume, whereas in the second one we can look at the volume relating to a particular satisfaction score. If something is very common, it may not affect the score. But if, for example, your Detractors in an NPS survey mention something a lot, that particular theme will be affecting the score in a negative way. These two questions are important to take hand in hand.
You can also compare different slices of the data, such as two different time periods, or two groups of respondents. Or, look at a particular issue or a theme, and ask questions such as “have customers noticed our efforts in solving a particular issue?”, if you’re conducting a continuous survey over multiple months or years.
For tips on how to analyze results, see below. This is a whole topic in itself, and here are our best tips. For best practice on how to draw conclusions you can find in our post How to get meaningful, actionable insights from customer feedback .
4 best practices for analyzing survey data
Make sure you incorporate these tips in your analysis, to ensure your survey results are successful.
1. Ensure sample size is sufficient
To always make sure you have a sufficient sample size, consider how many people you need to survey in order to get an accurate result.
You most often will not be able to, and shouldn’t for practicality reasons, collect data from all of the people you want to speak to. So you’d take a sample (or subset) of the people of interest and learn what we can from that sample.
Clearly, if you are working with a larger sample size, your results will be more reliable as they will often be more precise. A larger sample size does often equate to needing a bigger budget though.
The way to get around this issue is to perform a sample size calculation before starting a survey. Then, you can have a large enough sample size to draw meaningful conclusions, without wasting time and money on sampling more than you really need.
Consider how much margin of error you’re comfortable working with first, as your sample size is always an estimate of how the overall population think and behave.
2. Statistical significance – and why it matters
How do you know you can “trust” your survey analysis ie. that you can use the answers with confidence as a basis for your decision making? In this regard, the “significant” in statistical significance refers to how accurate your data is. Or rather, that your results are not based on pure chance, but that they are in fact, representative of a sample. If your data has statistical significance, it means that to a large extent, the survey results are meaningful.
It also shows that your respondents “look like” the total population of people about whom you want to draw conclusions.
3. Focus on your insights, not the data
When presenting to your stakeholders, it’s imperative to highlight the insights derived from your data, rather than the data itself.
You’ll do yourself a disservice. Don’t even present the information from the data. Don’t wait for your team to create insights out of the data, you’ll get a better response and better feedback if you are the one that demonstrates the insights to begin with, as it goes beyond just sharing percentages and data breakouts.
4. Complement with other types of data
Don’t stop at the survey data alone. When presenting your insights, to your stakeholders or board, it’s always helpful to use different data points and which might include even personal experiences. If you have personal experience with the topic, use it! If you have qualitative research that supports the data, use it!
So, if you can overlap qualitative research findings with your quantitative data, do so.
Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.
3 ways to code open-ended responses
When you analyze open-ended responses, you need to code them. Coding open-ended questions have 3 approaches, here’s a taster:
- Manual coding by someone internally. If you receive 100-200 responses per month, this is absolutely doable. The big disadvantage here is that there is a high likelihood that whoever codes your text will apply their own biases and simply not notice particular themes, because they subconsciously don’t think it’s important to monitor.
- Outsource to an agency. You can email the results and they would simply send back coded responses.
- Automating the coding. You use an algorithm to simulate the work of a professional human coder.
Whichever way you code text, you want to determine which category a comment falls under. In the below example, any comment about friends and family both fall into the second category. Then, you can easily visualize it as a bar chart.
Code frames can also be combined with a sentiment.
Below, we’re inserting the positive and the negative layer under customer service theme.
So, next, you apply this code frame. Below are snippets from a manual coding job commissioned to an agency.
In the first snippet, there’s a code frame. Under code 1, they code “Applied courses”, and under code “2 Degree in English”. In the second snippet, you can see the actual coded data, where each comment has up to 5 codes from the above code frame. You can imagine that it’s actually quite difficult to analyze data presented in this way in Excel, but it’s much easier to do it using software.
The best survey analysis software tools
Traditional survey analysis is highly manual, error-prone, and subject to human bias. You may think of this as the most economical solution, but in the long run, it often ends up costing you more (due to time it takes to set up and analyze, human resource, and any errors or bias which result in inaccurate data analysis, leading to faulty interpretation of the data. So, the question is:
Do you need software?
When you’re dealing with large amounts of data, it is impossible to manage it all properly manually. Either because there’s simply too much of it or if you’re looking to avoid any bias, or if it’s a long-term study, for example. Then, there is no other option but to use software”
On a large scale, software is ideal for analyzing survey results as you can automate the process by analyzing large amounts of data simultaneously. Plus, software has the added benefit of additional tools that add value.
Below we give just a few examples of types of software you could use to analyze survey data. Of course, these are just a few examples to illustrate the types of functions you could employ.
1. Thematic software
As an example, with Thematic’s software solution you can identify trends in sentiment and particular themes. Bias is also avoided as it is a software tool, and it doesn’t over-emphasize or ignore specific comments to come to unquantified conclusions.
Below is an example we’ve taken from the tool, to visualize some of Thematic’s features.
Our visualizations tools show far more detail than word clouds, which are more typically used.
You can see two different slices of data. The blue bars are United Airlines 1 and 2-star reviews, and the orange bars are the 4 and 5-star reviews. It’s a fantastic airline, but you can identify the biggest issue as mentioned most frequently by 1-2 stars reviews, which is their flight delays. But the 4 and 5-star reviews have frequent praise for the friendliness of the airline.
You can find more features, such as Thematic’s Impact tool, Comparison, Dashboard and Themes Editor here.
If you’re a DIY analyzer, there’s quite a bit you can do in Excel. Clearly, you do not have the sophisticated features of an online software tool, but for simple tasks, it does the trick. You can count different types of feedback (responses) in the survey, calculate percentages of the different responses survey and generate a survey report with the calculated results. For a technical overview, see this article.
You can also build your own text analytics solution, and rather fast.
How to build a Text Analytics solution in 10 minutes
The following is an excerpt from a blog written by Alyona Medelyan, PhD in Natural Language Processing & Machine Learning.
As she mentions, you can type in a formula, like this one, in Excel to categorize comments into “Billing”, “Pricing” and “Ease of use”:
It can take less than 10 minutes to create this, and the result is so encouraging! But wait…
Everyone loves simplicity. But in this case, simplicity sucks
Various issues can easily crop up with this approach, see the image below:
Out of 7 comments, here only 3 were categorized correctly. “Billing” is actually about “Price”, and three other comments missed additional themes. Would you bet your customer insights on something that’s at best 50 accurate?
Developed by QRS International, Nvivo is a tool where you can store, organize, categorize and analyze your data and also create visualisations. Nvivo lets you store and sort data within the platform, automatically sort sentiment, themes and attribute, and exchange data with SPSS for further statistical analysis. There’s a transcription tool for quick transcription of voice data.
It’s a no-frills online tool, great for academics and researchers.
4. Interpris
Interpris is another tool from QRS International, where you can import and store free text data directly from platforms such as Survey Monkey and store all your data in one place. It has numerous features, for example automatically detecting and categorizing themes.
Favoured by government agencies and communities, it’s good for employee engagement, public opinion and community engagement surveys.
Other tools worth mentioning (for survey analysis but not open-ended questions) are SurveyMonkey, Tableau and DataCracker.
There are numerous tools on the market, and they all have different features and benefits. Choosing a tool that is right for you will depend on your needs, the amount of data and the time you have for your project and, of course, budget. The important part to get right is to choose a tool that is reliable and provides you with quick and easy analysis, and flexible enough to adapt to your needs.
An idea is to check the list of existing clients of the product, which is often listed on their website. Crucially, you’ll want to test the tool, or at the least, get a demo from the sales team, ideally using your own data so that you can use the time to gather new insights.
A few tips on survey design
Good surveys start with smart survey design. Firstly, you need to plan for survey design success. Here are a few tips:
Our 9 top tips for survey design planning
1. keep it short.
Only include questions that you are actually going to use. You might think there are lots of questions that seem useful, but they can actually negatively affect your survey results. Another reason is that often we ask redundant questions that don’t contribute to the main problem we want to solve. The survey can be as short as three questions.
2. Use open-ended questions first
To avoid enforcing your own assumptions, use open-ended questions first. Often, we start with a few checkboxes or lists, which can be intimidating for survey respondents. An open-ended question feels more inviting and warmer – it makes people feel like you want to hear what they want to say and actually start a conversation. Open-ended questions give you more insightful answers, however, closed questions are easier to respond to, easier to analyze, but they do not create rich insights.
The best approach is to use a mix of both types of questions, as It’s more compelling to answer different types of questions for respondents.
3. Use surveys as a way to present solutions
Your surveys will reveal what areas in your business need extra support or what creates bottlenecks in your service. Use your surveys as a way of presenting solutions to your audience and getting direct feedback on those solutions in a more consultative way.
4. Consider your timing
It’s important to think about the timing of your survey. Take into account when your audience is most likely to respond to your survey and give them the opportunity to do it at their leisure, at the time that suits them.
5. Challenge your assumptions
It’s crucial to challenge your assumptions, as it’s very tempting to make assumptions about why things are the way they are. There is usually more than meets the eye about a person’s preferences and background which can affect the scenario.
6. Have multiple survey-writers
To have multiple survey writer can be helpful, as having people read each other’s work and test the questions helps address the fact that most questions can be interpreted in more than one way.
7. Choose your survey questions carefully
When you’re choosing your survey questions, make it really count. Only use those that can make a difference to your end outcomes.
8. Be prepared to report back results and take action
As a respondent you want to know your responses count, are reviewed and are making a difference. As an incentive, you can share the results with the participants, in the form of a benchmark, or a measurement that you then report to the participants.
9. What’s in it for them?
Always think about what customers (or survey respondents) want and what’s in it for them. Many businesses don’t actually think about this when they send out their surveys.
If you can nail the “what’s in it for me”, you automatically solve many of the possible issues for the survey, such as whether the respondents have enough incentive or not, or if the survey is consistent enough.
For a good survey design, always ask:
- What insight am I hoping to get from this question?
- Is it likely to provide useful answers?
For more pointers on how to design your survey for success, check out our blog on 4 Steps to Customer Survey Design – Everything You Need to Know .
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Dissertation survival guide: methodology & data analysis.
- Choosing a topic
- Literature searching
- Evaluating your sources
- Methodology & Data Analysis
- Referencing
- Posters and presentations
Decide on your methodology
Writing a dissertation usually involves doing some original research. This may use qualitative methods such as interviews, or quantitative methods such as surveys. What method is most suitable for you will depend on what you need to find out.
We have lots of books (print and online) on research methods, so don’t just stick to the items on your reading lists. See below for some selected titles that are available from the library. You could also talk to your supervisor or academic skills tutors about suitable methodology in your subject area.
Design your research tools
Next, you need to design your research tools before collecting your data.
Try searching Summon for topics such as "qualitative research methods", "quantitative research methods", "survey design", and more for ideas of how you could collect data for your research. Or, see some recommended books below.
There are also lots of videos and courses on LinkedIn Learning to help you learn about research methods. Try this for example: Quantitative vs. qualitative research .
- Qualtrics If you plan to use a survey to collect data for your dissertation, all staff and students have access to Qualtrics, an online survey service. When accessing Qualtrics for the first time, select "No, I don't have a preexisting account here". You can then create an account using your University of Huddersfield email address.
Recommended books on research methodology
Start analysing your results.
So, you have your data, but what does it mean? This is where you put YOUR data into the context of the literature you’ve already found. There are various tools available from the University that can help you analyse what you have found.
- Data analysis software There are three software packages available to download for home use from the link above: - Nvivo: qualitative data analysis tool. Used for transcribing and analysing interviews and other qualitative data. - SPSS and Minitab, both statistical analysis packages that are most helpful for analysing quantitative data.
- LinkedIn Learning If you need help using any of these software packages, search for courses and videos in LinkedIn Learning that can help you get started.
- Digital Skills training For help, either in a group or one-to-one training, contact the Digital Skills team.
If you've used a qualitative method such as interviewing, you will need to transcribe these to analyse them. Manually transcribing interviews can be a long and laborious process. There are three tools available to you as Huddersfield students that can help speed up the process.
Remember, they are not perfect and the accuracy will depend on the quality of your audio recordings. Recent feedback from students suggests that they are between 85% - 95% accurate so some editing will be required. Unfortunately no automatic transcription tool is 100% accurate.
- Word on the Web Available as part of your Office 365 package, Word Online's transcribe feature converts speech (recorded directly in Word or from an uploaded audio file) to a text transcript. After your conversation, interview, or meeting, you can revisit parts of the recording by playing back the timestamped audio and edit the transcription to make corrections. You can save the full transcript as a Word document or insert snippets of it into existing documents.
- Otter.ai: Otter Voice Notes This tool can be accessed online with your University email account. There is also an app available for apple and android devices. Transcripts can be downloaded as a text file and opened in Word. The standard account allows for up to 600 minutes a month of transcription for free.
- MS Stream This is also available as part of your Office 365 package. You can upload as many videos as you like. Captions can be automatically created, edited within Streams and then downloaded as a transcript and opened in Word. Stream is idea for producing transcripts of video recordings.
For more information, please see the Transcribe Audio and Subtitle Video pages on Brightspace. For help and advice, please ask [email protected] .
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Best practices for using surveys & survey data
Our blog about surveys, tips for business, & more
Tutorials & how-to guides for using SurveyMonkey
How to analyze survey data
You’ve collected your survey results and have a survey data analysis plan in place. Now it’s time to dig in, start sorting, and analyze the data.
The results are back from your online surveys. Now it’s time to tap the power of survey data analysis to make sense of the results and present them in ways that are easy to understand and act on.After you’ve collected statistical survey results and have a data analysis plan , it’s time to begin the process of calculating survey results you got back. Here’s how our survey research scientists make sense of quantitative data (versus qualitative data ). They structure their reporting around survey responses that will answer research questions. Even for the experts, it can be hard to parse the insights in raw data.
In order to reach your survey goals, you’ll want to start with relying on the survey methodology suggested by our experts. Then once you have results, you can effectively analyze them using all the data analysis tools available to you including statistical analysis, data analytics, and charts and graphs that capture your survey metrics.
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Survey data analysis made easy
Sound survey data analysis is key to getting the information and insights you need to make better business decisions. Yet it’s important to be aware of potential challenges that can make analysis more difficult or even skew results.
Asking too many open-ended questions can add time and complexity to your analysis because it produces qualitative results that aren’t numerically based. Meanwhile, closed-ended questions generate results that are easier to analyze. Analysis can also be hampered by asking leading or biased questions or posing questions that are confusing or too complex. Being equipped with the right tools and know-how helps assure that survey analysis is both easy and effective.
Read more about using closed-ended vs. open-ended questions .
See how SurveyMonkey makes analyzing results a breeze
With its many data analysis techniques , SurveyMonkey makes it easy for you to turn your raw data into actionable insights presented in easy-to-grasp formats. Features such as automatic charts and graphs and word clouds help bring data to life. For instance, Sentiment Analysis allows you to get an instant summary of how people feel from thousands or even millions of open text responses. You can review positive, neutral, and negative sentiments and a glance or filter by sentiment to identify areas that need attention. For even deeper insights, you can filter a question by sentiment. Imagine being able to turn all those text responses into a quantitative data set.
Word clouds let you quickly interpret open-ended responses through a visual display of the most frequently used words. You can customize the look of your word clouds in a range of ways from selecting colors or fonts for specific words to easily hiding non-relevant words.
Our wide range of features and tools can help you address analysis challenges, and quickly generate graphics and robust reports. Check out how a last-minute report request can be met in a snap through SurveyMonkey.
Ready to get started?
To begin calculating survey results more effectively, follow these 6 steps:
- Take a look at your top survey questions
Determine sample size
- Use cross tabulation to filter your results
Benchmarking, trending, and comparative data
- Crunch the numbers
- Draw conclusions
Calculating results using your top survey questions
First, let’s talk about how you’d go about calculating survey results from your top research questions. Did you feature empirical research questions? Did you consider probability sampling ? Remember that you should have outlined your top research questions when you set a goal for your survey.
For example, if you held an education conference and gave attendees a post-event feedback survey , one of your top research questions may look like this: How did the attendees rate the conference overall? Now take a look at the answers you collected for a specific survey question that speaks to that top research question:
Do you plan to attend this conference next year?
Notice that in the responses, you’ve got some percentages (71%, 18%) and some raw numbers (852, 216). The percentages are just that—the percent of people who gave a particular answer. Put another way, the percentages represent the number of people who gave each answer as a proportion of the number of people who answered the question. So, 71% of your survey respondents (852 of the 1,200 surveyed) plan on coming back next year.
This table also shows you that 18% say they are planning to return and 11% say they are not sure.
Having a good understanding of sample size is also key to making sure you are accurately and effectively analyzing your survey results. Sample size is how many people you need to take your survey and complete responses to make it statistically viable. Even if you’re a statistician, determining survey sample size can be a challenge. But SurveyMonkey takes the guesswork and complexity out of the process with its easy-to-use margin of error calculator that helps you determine how many people you need to survey to ensure your results help you avoid your margin or error.
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Cross-tabulation and filtering results
Recall that when you set a goal for your survey and developed your analysis plan, you thought about what subgroups you were going to analyze and compare. Now is when that planning pays off. For example, say you wanted to see how teachers, students, and administrators compared to one another their responses about attending next year’s conference. To figure this out, you want to dive into response rates by means of cross tabulation , or use cross tab reports, where you show the results of the conference question by subgroup:
From this table you see that a large majority of the students (86%) and teachers (80%) plan to come back next year. However, the administrators who attended your conference look different, with under half (46%) of them intending to come back! Hopefully, some of our other questions will help you figure out why this is the case and what you can do to improve the conference for administrators so more of them will return year after year.
A filter is another method of data analysis when you’re modeling data. Filtering means narrowing your focus to one particular subgroup, and filtering out the others. So, instead of comparing subgroups to one another, here we’re just looking at how one subgroup answered the question. Combining filters can give you pinpoint accuracy in your data.
For instance, you could limit your focus to just women, or just men, then re-run the crosstab by type of attendee to compare female administrators, female teachers, and female students. One thing to be wary of as you slice and dice your results: Every time you apply a filter or cross tab, your sample size decreases. To make sure your results are statistically significant, it may be helpful to use a sample size calculator .
How graphs add clarity to data analysis
Graphs can be a regular go-to tool when you aim to quickly demonstrate the results of your data analysis in a way that is easy for anyone to understand. It’s easy to create graphs with SurveyMonkey that provide clarity and context to your analysis which, in turn, makes using the data in more targeted and actionable ways.
Cross tabulations, otherwise known as crosstab reports, are useful tools for taking a deeper dive into your data. Crosstabs structure your data into a table that groups respondents based on shared background information or survey responses, allowing you to compare each group’s answers to one another. This helps you better understand each group of respondents and uncover how they differ from each other.
Let’s say on your conference feedback survey, one key question is, “Overall how satisfied were you with the conference?”
Your results show that 75% of the attendees were satisfied with the conference. That sounds pretty good. But wouldn’t you like to have some context? Something to compare it against? Is that better or worse than last year? How does it compare to other conferences?
Benchmarking can provide answers to these questions and more by readily allowing you to make comparisons to past and current data to identify trends in your industry and marketplace, and see how you stack up against them.
Well, say you did ask this question in your conference feedback survey after last year’s conference. You’d be able to make a trend comparison. Professional pollsters make poor comedians, but one favorite line is “trend is your friend.” If last year’s satisfaction rate was 60%, you increased satisfaction by 15 percentage points! What caused this increase in satisfaction? Hopefully the responses to other questions in your survey will provide some answers.
If you don’t have data from prior years’ conferences, make this the year you start collecting feedback after every conference. This is called benchmarking. You establish a benchmark or baseline number and, moving forward, you can see whether and how this has changed. You can benchmark not just attendees’ satisfaction, but other questions as well. You’ll be able to track, year after year, what attendees think of the conference. This is called longitudinal data analysis .
You can even track data for different subgroups. Say for example that satisfaction rates are increasing year over year for students and teachers, but not for administrators. You might want to look at administrators’ responses to various questions to see if you can gain insight into why they are less satisfied than other attendees.
Crunching the numbers
You know how many people said they were coming back, but how do you know if your survey has yielded answers that you can trust and answers that you can use with confidence to inform future decisions? It’s important to pay attention to the quality of your data and to understand the components of statistical significance.
In everyday conversation, the word “significant” means important or meaningful. In survey analysis and statistics, significant means “an assessment of accuracy.” This is where the inevitable “plus or minus” comes into survey work. In particular, it means that survey results are accurate within a certain confidence level and not due to random chance. Drawing an inference based on results that are inaccurate (i.e., not statistically significant) is risky. The first factor to consider in any assessment of statistical significance is the representativeness of your sample—that is, to what extent the group of people who were included in your survey “look like” the total population of people about whom you want to draw conclusions.
You have a problem if 90% of conference attendees who completed the survey were men, but only 15% of all your conference attendees were male. The more you know about the population you are interested in studying, the more confident you can be when your survey lines up with those numbers. At least when it comes to gender, you’re feeling pretty good if men make up 15% of survey respondents in this example.
If your survey sample is a random selection from a known population, statistical significance can be calculated in a straightforward manner. A primary factor here is sample size . Suppose 50 of the 1,000 people who attended your conference replied to the survey. Fifty (50) is a small sample size and results in a broad margin of error . In short, your results won’t carry much weight.
Say you asked your survey respondents how many of the 10 available sessions they attended over the course of the conference. And your results look like this:
You might want to analyze the average. As you may recall, there are three different kinds of averages: mean, median and mode.
In the table above, the average number of sessions attended is 6.1. The average reported here is the mean, the kind of average that’s probably most familiar to you. To determine the mean you add up the data and divide that by the number of figures you added. In this example, you have 100 people saying they attended one session, 50 people for four sessions, 100 people for five sessions, etc. So, you multiply all of these pairs together, sum them up, and divide by the total number of people.
The median is another kind of average. The median is the middle value, the 50% mark. In the table above, we would locate the number of sessions where 500 people were to the left of the number and 500 to the right. The median is, in this case, six sessions. This can help you eliminate the influence of outliers, which may adversely affect your data.
The last kind of average is mode. The mode is the most frequent response. In this case the answer is six. 260 survey participants attended six sessions, more than attended any other number of sessions.
Means and other types of averages can also be used if your results were based on Likert scales .
Drawing conclusions
When it comes to reporting on survey results , think about the story the data tells.
Say your conference overall got mediocre ratings. You dig deeper to find out what’s going on. The data show that attendees gave very high ratings to almost all the aspects of your conference — the sessions and classes, the social events, and the hotel—but they really disliked the city chosen for the conference. (Maybe the conference was held in Chicago in January and it was too cold for anyone to go outside!)
That is part of the story right there—great conference overall, lousy choice of locations. Miami or San Diego might be a better choice for a winter conference.
One aspect of data analysis and reporting you have to consider is causation vs. correlation.
Questions about different types of data analysis techniques
People digest and understand information in a range of different ways. Fortunately, SurveyMonkey offers a ton of different ways for you to analyze survey data so you can assess and present the information in ways that will be most useful to meet your goals and create graphs, charts, and reports that make your results easy to understand.
Here are some of the common questions that we can help you navigate as you build up your survey analysis chops:
What is longitudinal analysis?
Longitudinal data analysis (often called “trend analysis”) is basically tracking how findings for specific questions change over time. Once a benchmark is established, you can determine whether and how numbers shift. Suppose the satisfaction rate for your conference was 50% three years ago, 55% two years ago, 65% last year, and 75% this year. Congratulations are in order! Your longitudinal data analysis shows a solid, upward trend in satisfaction.
What is the difference between correlation and causation?
Causation is when one factor causes another, while correlation is when two variables move together, but one does not influence or cause the other. For example, drinking hot chocolate and wearing mittens are two variables that are correlated — they tend to go up and down together. However, one does not cause the other. In fact, they are both caused by a third factor, cold weather.
Cold weather influences both hot chocolate consumption and the likelihood of wearing mittens. Cold weather is the independent variable and hot chocolate consumption and the likelihood of wearing mittens are the dependent variables. In the case of our conference feedback survey, cold weather likely influenced attendees dissatisfaction with the conference city and the conference overall.
Finally, to further examine the relationship between variables in your survey you might need to perform a regression analysis.
What is regression analysis?
Regression analysis is an advanced method of data visualization and analysis that allows you to look at the relationship between two or more variables. There are many types of regression analysis and the one(s) a survey scientist chooses will depend on the variables he or she is examining. What all types of regression analysis have in common is that they look at the influence of one or more independent variables on a dependent variable. In analyzing our survey data we might be interested in knowing what factors most impact attendees’ satisfaction with the conference. Is it a matter of the number of sessions? The keynote speaker? The social events? The site? Using regression analysis, a survey scientist can determine whether and to what extent satisfaction with these different attributes of the conference contribute to overall satisfaction.
This, in turn, provides insight into what aspects of the conference you might want to alter next time around. Say, for example, you paid a high honorarium to get a top flight keynote speaker for your opening session. Participants gave this speaker and the conference overall high marks. Based on these two facts you might think that having a fabulous (and expensive) keynote speaker is the key to conference success. Regression analysis can help you determine if this is indeed the case. You might find that the popularity of the keynote speaker was a major driver of satisfaction with the conference. If so, next year you’ll want to get a great keynote speaker again. But say the regression shows that, while everyone liked the speaker, this did not contribute much to attendees’ satisfaction with the conference. If that is the case, the big bucks spent on the speaker might be best spent elsewhere.
If you take the time to carefully analyze the soundness of your survey data, you’ll be on your way to using the answers to help you make informed decisions.
Survey data can be one of your most powerful tools
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What is survey data collection.
Survey data collection uses surveys to gather information from specific respondents. Survey data collection can replace or supplement other data collection types, including interviews, focus groups, and more. The data collected from surveys can be used to boost employee engagement, understand buyer behavior, and improve customer experiences.
Causation is when one factor causes another, while correlation is when two variables move together, but one does not influence or cause the other. For example, drinking hot chocolate and wearing mittens are two variables that are correlated — they tend to go up and down together. However, one does not cause the other. In fact, they are both caused by a third factor, cold weather. Cold weather influences both hot chocolate consumption and the likelihood of wearing mittens. Cold weather is the independent variable and hot chocolate consumption and the likelihood of wearing mittens are the dependent variables. In the case of our conference feedback survey, cold weather likely influenced attendees dissatisfaction with the conference city and the conference overall. Finally, to further examine the relationship between variables in your survey you might need to perform a regression analysis.
Regression analysis is an advanced method of data visualization and analysis that allows you to look at the relationship between two or more variables. There a many types of regression analysis and the one(s) a survey scientist chooses will depend on the variables he or she is examining. What all types of regression analysis have in common is that they look at the influence of one or more independent variables on a dependent variable. In analyzing our survey data we might be interested in knowing what factors most impact attendees’ satisfaction with the conference. Is it a matter of the number of sessions? The keynote speaker? The social events? The site? Using regression analysis, a survey scientist can determine whether and to what extent satisfaction with these different attributes of the conference contribute to overall satisfaction.
This, in turn, provides insight into what aspects of the conference you might want to alter next time around. Say, for example, you paid a high honorarium to get a top flight keynote speaker for your opening session. Participants gave this speaker and the conference overall high marks. Based on these two facts you might think that having a fabulous (and expensive) keynote speaker is the key to conference success. Regression analysis can help you determine if this is indeed the case. You might find that the popularity of the keynote speaker was a major driver of satisfaction with the conference. If so, next year you’ll want to get a great keynote speaker again. But say the regression shows that, while everyone liked the speaker, this did not contribute much to attendees’ satisfaction with the conference. If that is the case, the big bucks spent on the speaker might be best spent elsewhere. If you take the time to carefully analyze the soundness of your survey data, you’ll be on your way to using the answers to help you make informed decisions.
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Dissertation surveys: Questions, examples, and best practices
Collect data for your dissertation with little effort and great results.
Dissertation surveys are one of the most powerful tools to get valuable insights and data for the culmination of your research. However, it’s one of the most stressful and time-consuming tasks you need to do. You want useful data from a representative sample that you can analyze and present as part of your dissertation. At SurveyPlanet, we’re committed to making it as easy and stress-free as possible to get the most out of your study.
With an intuitive and user-friendly design, our templates and premade questions can be your allies while creating a survey for your dissertation. Explore all the options we offer by simply signing up for an account—and leave the stress behind.
How to write dissertation survey questions
The first thing to do is to figure out which group of people is relevant for your study. When you know that, you’ll also be able to adjust the survey and write questions that will get the best results.
The next step is to write down the goal of your research and define it properly. Online surveys are one of the best and most inexpensive ways to reach respondents and achieve your goal.
Before writing any questions, think about how you’ll analyze the results. You don’t want to write and distribute a survey without keeping how to report your findings in mind. When your thesis questionnaire is out in the real world, it’s too late to conclude that the data you’re collecting might not be any good for assessment. Because of that, you need to create questions with analysis in mind.
You may find our five survey analysis tips for better insights helpful. We recommend reading it before analyzing your results.
Once you understand the parameters of your representative sample, goals, and analysis methodology, then it’s time to think about distribution. Survey distribution may feel like a headache, but you’ll find that many people will gladly participate.
Find communities where your targeted group hangs out and share the link to your survey with them. If you’re not sure how large your research sample should be, gauge it easily with the survey sample size calculator.
Need help with writing survey questions? Read our guide on well-written examples of good survey questions .
Dissertation survey examples
Whatever field you’re studying, we’re sure the following questions will prove useful when crafting your own.
At the beginning of every questionnaire, inform respondents of your topic and provide a consent form. After that, start with questions like:
- Please select your gender:
- What is the highest educational level you’ve completed?
- High school
- Bachelor degree
- Master’s degree
- On a scale of 1-7, how satisfied are you with your current job?
- Please rate the following statements:
- I always wait for people to text me first.
- Strongly Disagree
- Neither agree nor disagree
- Strongly agree
- My friends always complain that I never invite them anywhere.
- I prefer spending time alone.
- Rank which personality traits are most important when choosing a partner. Rank 1 - 7, where 1 is the most and 7 is the least important.
- Flexibility
- Independence
- How openly do you share feelings with your partner?
- Almost never
- Almost always
- In the last two weeks, how often did you experience headaches?
Dissertation survey best practices
There are a lot of DOs and DON’Ts you should keep in mind when conducting any survey, especially for your dissertation. To get valuable data from your targeted sample, follow these best practices:
Use the consent form.
The consent form is a must when distributing a research questionnaire. A respondent has to know how you’ll use their answers and that the survey is anonymous.
Avoid leading and double-barreled questions
Leading and double-barreled questions will produce inconclusive results—and you don’t want that. A question such as: “Do you like to watch TV and play video games?” is double-barreled because it has two variables.
On the other hand, leading questions such as “On a scale from 1-10 how would you rate the amazing experience with our customer support?” influence respondents to answer in a certain way, which produces biased results.
Use easy and straightforward language and questions
Don’t use terms and professional jargon that respondents won’t understand. Take into consideration their educational level and demographic traits and use easy-to-understand language when writing questions.
Mix close-ended and open-ended questions
Too many open-ended questions will annoy respondents. Also, analyzing the responses is harder. Use more close-ended questions for the best results and only a few open-ended ones.
Strategically use different types of responses
Likert scale, multiple-choice, and ranking are all types of responses you can use to collect data. But some response types suit some questions better. Make sure to strategically fit questions with response types.
Ensure that data privacy is a priority
Make sure to use an online survey tool that has SSL encryption and secure data processing. You don’t want to risk all your hard work going to waste because of poorly managed data security. Ensure that you only collect data that’s relevant to your dissertation survey and leave out any questions (such as name) that can identify the respondents.
Create dissertation questionnaires with SurveyPlanet
Overall, survey methodology is a great way to find research participants for your research study. You have all the tools required for creating a survey for a dissertation with SurveyPlanet—you only need to sign up . With powerful features like question branching, custom formatting, multiple languages, image choice questions, and easy export you will find everything needed to create, distribute, and analyze a dissertation survey.
Happy data gathering!
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Surveys for dissertation
Last updated: 10 apr 2022.
We are starting a series of articles created for students that are looking for online survey and questionnaire tool for their dissertations. While planning your survey you can select from multiple research techniques. Each one can be a subject of a separate article. In this article, we will focus on CAWI (Computer-Assisted Web Interview). Thanks to Internet and survey tools development (like SurveyLab) CAWI is gaining popularity not only among students but also among big organizations and corporations. The most important advantages of CAWI are low cost of the research, a clear process that is easy to control, and short time needed to create a survey and collect responses.
How to start - research goal
Before you start work on your questionnaire design, define the goal you want to accomplish. It may occur that survey research is not the best way to do it. It is always good to write your goal on a blank sheet of paper. It will help you to clarify your thoughts.
Target group
Define your target group. It means estimate how many responses you need to be able to start data analysis and what is your respondent profile (e.g. people living in big cities - over 200 k citizens, that are active tourists - spend at least one weekend outside the city).
Questionnaire
Now you can start work on your questionnaire. This phase is very important as questionnaire quality will impact survey results and report quality. There are a few rules you should follow :
- Remember that your respondents won't be able to ask you additional questions and you won't be able to provide additional explanations. Therefore your questionnaire should be clear and easy to understand.
- Your questionnaire and questions quality will impact report and survey results.
- Use easy to understand language, avoid technical jargon, slang, or abbreviations.
- Don't try to add every question to your survey. Ask yourself if you need each question that has been added.
- Your questionnaire should be logical, it means questions should be grouped by subjects.
- Don't forget about statistical and demographic questions. It will help you to analyze your database using such criteria as gender, age, city, occupation, ...
- Test your questionnaire, send it to your friends, test if the survey is clear and easy to complete.
Project start
Select the best date to start data collection. For example December, 24 won't be the best idea to start data collection. Make sure that selected date is best for your target group.
Response analysis
SurveyLab was designed to make hard work for you. The system will automatically collect responses, aggregate them and present them in a single report. When selecting a survey tool make sure it will be able to export data in the format you need it. SurveyLab allows you to download survey results in CSV, Excel, or SPSS format.
author: Jakub Wierusz
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How to Analyze Survey Results Like a Data Pro
Updated: November 23, 2021
Published: October 04, 2021
Obtaining customer feedback is difficult. You need strong survey questions that effectively derive customer insights. Not to mention a distribution system that shares the survey with the right customers at the right time. However, survey data doesn't just sort and analyze itself. You need a team dedicated to sifting through survey results and highlighting key trends and behaviors for your marketing, sales, and customer service teams. In this post, we'll discuss not only how to analyze survey results, but also how to present your findings to the rest of your organization.
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How to Analyze Survey Results
How to present survey results, how to write a survey report, survey report template examples, 1. understand the four measurement levels..
Before analyzing data, you should understand the four levels of measurement. These levels determine how survey questions should be measured and what statistical analysis should be performed. The four measurement levels are nominal scales, ordinal scales, interval scales, and ratio scales.
Nominal Scale
Nominal scales classify data without any quantitative value, similar to labels. An example of a nominal scale is, "Select your car's brand from the list below." The choices have no relationship to each other. Due to the lack of numerical significance, you can only keep track of how many respondents chose each option and which option was selected the most.
Ordinal Scale
Ordinal scales are used to depict the order of values. For this scale, there's a quantitative value because one rank is higher than another. An example of an ordinal scale is, "Rank the reasons for using your laptop." You can analyze both mode and median from this type of scale, and ordinal scales can be analyzed through cross-tabulation analysis .
Interval Scale
Interval scales depict both the order and difference between values. These scales have quantitative value because data intervals remain equivalent along the scale, but there's no true zero point. An example of an interval scale is in an IQ test. You can analyze mode, median, and mean from this type of scale and analyze the data through ANOVA , t-tests , and correlation analyses . ANOVA tests the significance of survey results, while t-tests and correlation analyses determine if datasets are related.
Ratio Scale
Ratio scales depict the order and difference between values, but unlike interval scales, they do have a true zero point. With ratio scales, there's quantitative value because the absence of an attribute can still provide information. For example, a ratio scale could be, "Select the average amount of money you spend online shopping." You can analyze mode, median, and mean with this type of scale and ratio scales can be analyzed through t-tests, ANOVA, and correlation analyses as well.
2. Select your survey question(s).
Once you understand how survey questions are analyzed, you should take note of the overarching survey question(s) that you're trying to solve. Perhaps, it's "How do respondents rate our brand?"
Then, look at survey questions that answer this research question, such as "How likely are you to recommend our brand to others?" Segmenting your survey questions will isolate data that are relevant to your goals.
Additionally, it's important to ask both close-ended and open-ended questions.
Close-Ended Questions
A close-ended survey question gives a limited set of answers. Respondents can't explain their answer and they can only choose from pre-determined options. These questions could be yes or no, multiple-choice, checkboxes, dropdown, or a scale question. Asking a variety of questions is important to get the best data.
Open-Ended Questions
An open-ended survey question will ask the respondent to explain their opinion. For example, in an NPS survey, you'll ask how likely a customer is to recommend your brand. After that, you might consider asking customers to explain their choice. This could be something like "Why or why wouldn't you recommend our product to your friends/family?"
3. Analyze quantitative data first.
Quantitative data is valuable because it uses statistics to draw conclusions. While qualitative data can bring more interesting insights about a topic, this information is subjective, making it harder to analyze. Quantitative data, however, comes from close-ended questions which can be converted into a numeric value. Once data is quantified, it's much easier to compare results and identify trends in customer behavior .
It's best to start with quantitative data when performing a survey analysis. That's because quantitative data can help you better understand your qualitative data. For example, if 60% of customers say they're unhappy with your product, you can focus your attention on negative reviews about user experience. This can help you identify roadblocks in the customer journey and correct any pain points that are causing churn.
4. Use cross-tabulation to better understand your target audience.
If you analyze all of your responses in one group, it isn't entirely effective for gaining accurate information. Respondents who aren't your ideal customers can overrun your data and skew survey results. Instead, if segment responses using cross-tabulation, you can analyze how your target audience responded to your questions.
Split Up Data by Demographics
Cross-tabulation records the relationships between variables. It compares two sets of data within one chart. This reveals specific insights based on your participants' responses to different questions. For example, you may be curious about customer advocacy among your customers based in Boston, MA. You can use cross-tabulation to see how many respondents said they were from Boston and said they would recommend your brand.
By pulling multiple variables into one chart, we can narrow down survey results to a specific group of responses. That way, you know your data is only considering your target audience.
Below is an example of a cross-tabulation chart. It records respondents' favorite baseball teams and what city they reside in.
If the statistical significance or p-value for a data point is equal to or lower than 0.05, it has moderate statistical significance since the probability for error is less than 5%. If the p-value is lower than 0.01, that means it has high statistical significance because the probability for error is less than 1%.
6. Consider causation versus correlation.
Another important aspect of survey analysis is knowing whether the conclusions you're drawing are accurate. For instance, let's say we observed a correlation between ice cream sales and car thefts in Boston. Over a month, as ice cream sales increased so did reports of stolen cars. While this data may suggest a link between these variables, we know that there's probably no relationship.
Just because the two are correlated doesn't mean one causes the other. In cases like these, there's typically a third variable — the independent variable — that influences the two dependent variables. In this case, it's temperature. As the temperature increases, more people buy ice cream. Additionally, more people leave their homes and go out, which leads to more opportunities for crime.
While this is an extreme example, you never want to draw a conclusion that's inaccurate or insufficient. Analyze all the data before assuming what influences a customer to think, feel, or act a certain way.
7. Compare new data with past data.
While current data is good for keeping you updated, it should be compared to data you've collected in the past. If you know 33% of respondents said they would recommend your brand, is that better or worse than last year? How about last quarter?
If this is your first year analyzing data, make these results the benchmark for your next analysis. Compare future results to this record and track changes over quarters, months, years, or whatever interval you prefer. You can even track data for specific subgroups to see if their experiences improve with your initiatives.
Now that you've gathered and analyzed all of your data, the next step is to share it with coworkers, customers, and other stakeholders. However, presentation is key in helping others understand the insights you're trying to explain.
The next section will explain how to present your survey results and share important customer data with the rest of your organization.
1. Use a graph or chart.
Graphs and charts are visually appealing ways to share data. Not only are the colors and patterns easy on the eyes, but data is often easier to understand when shared through a visual medium. However, it's important to choose a graph that highlights your results in a relevant way.
4. Empowerment Keynote Presentation
This presentation template makes a great research report template due to its clean lines, contrasting graphic elements, and ample room for visuals. The headers in this template virtually jump off the page to grab the readers' attention. There's aren't many ways to present quantitative data using this template example, but it works well for qualitative survey reports like focus groups or product design studies where original images will be discussed.
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Surveys can be quantitative with all questions/items that can be analyzed statistically ... whether is be more a smaller class project or your dissertation.
Surveys are a special research tool with strengths, weaknesses, and a language all ... is geared toward undergraduate honors thesis writers using survey data.
3. Interrogate the data · What are the most common responses to questions X? · Which responses are affecting/impacting us the most? · What's different about this
In this video, I detail how I would go about analyzing a questionnaire / survey for a research paper or dissertation.
This may use qualitative methods such as interviews, or quantitative methods such as surveys. What method is most suitable for you will depend
To begin calculating survey results more effectively, follow these 6 steps: · Take a look at your top survey questions · Determine sample size · Use cross
Dissertation submitted in accordance with the requirements of the University of ... To analyse the ethical development of students in three academic
Dissertation surveys: Questions, examples, and best practices. Collect data for your dissertation with little effort and great results.
Online surveys and questionnaires for students and academy. ... able to start data analysis and what is your respondent profile (e.g. people
Graphs and charts are visually appealing ways to share data. Not only are the colors and patterns easy on the eyes, but data is often easier to