How To Analyse Questionnaire Results Dissertation

1. Relevance

Do not blindly follow the data you have collected; make sure your original research objectives inform which data does and does not make it into your analysis. All data presented should be relevant and appropriate to your aims. Irrelevant data will indicate a lack of focus and incoherence of thought. In other words, it is important that you show the same level of scrutiny when it comes to the data you include as you did in the literature review. By telling the reader the academic reasoning behind your data selection and analysis, you show that you are able to think critically and get to the core of an issue. This lies at the very heart of higher academia.

2. Analysis

It is important that you use methods appropriate both to the type of data collected and the aims of your research. You should explain and justify these methods with the same rigour with which your collection methods were justified. Remember that you always have to show the reader that you didn’t choose your method haphazardly, rather arrived at it as the best choice based on prolonged research and critical reasoning. The overarching aim is to identify significant patterns and trends in the data and display these findings meaningfully.

3. Quantitative work

Quantitative data, which is typical of scientific and technical research, and to some extent sociological and other disciplines, requires rigorous statistical analysis. By collecting and analysing quantitative data, you will be able to draw conclusions that can be generalised beyond the sample (assuming that it is representative – which is one of the basic checks to carry out in your analysis) to a wider population. In social sciences, this approach is sometimes referred to as the “scientific method,” as it has its roots in the natural sciences.

4. Qualitative work

Qualitative data is generally, but not always, non-numerical and sometimes referred to as ‘soft’. However, that doesn’t mean that it requires less analytical acuity – you still need to carry out thorough analysis of the data collected (e.g. through thematic coding or discourse analysis). This can be a time consuming endeavour, as analysing qualitative data is an iterative process, sometimes even requiring the application hermeneutics. It is important to note that the aim of research utilising a qualitative approach is not to generate statistically representative or valid findings, but to uncover deeper, transferable knowledge.

5. Thoroughness

The data never just ‘speaks for itself’. Believing it does is a particularly common mistake in qualitative studies, where students often present a selection of quotes and believe this to be sufficient – it is not. Rather, you should thoroughly analyse all data which you intend to use to support or refute academic positions, demonstrating in all areas a complete engagement and critical perspective, especially with regard to potential biases and sources of error. It is important that you acknowledge the limitations as well as the strengths of your data, as this shows academic credibility.

6. Presentational devices

It can be difficult to represent large volumes of data in intelligible ways. In order to address this problem, consider all possible means of presenting what you have collected. Charts, graphs, diagrams, quotes and formulae all provide unique advantages in certain situations. Tables are another excellent way of presenting data, whether qualitative or quantitative, in a succinct manner. The key thing to keep in mind is that you should always keep your reader in mind when you present your data – not yourself. While a particular layout may be clear to you, ask yourself whether it will be equally clear to someone who is less familiar with your research. Quite often the answer will be “no,” at least for your first draft, and you may need to rethink your presentation.

7. Appendix

You may find your data analysis chapter becoming cluttered, yet feel yourself unwilling to cut down too heavily the data which you have spent such a long time collecting. If data is relevant but hard to organise within the text, you might want to move it to an appendix. Data sheets, sample questionnaires and transcripts of interviews and focus groups should be placed in the appendix. Only the most relevant snippets of information, whether that be statistical analyses or quotes from an interviewee, should be used in the dissertation itself.

8. Discussion

In discussing your data, you will need to demonstrate a capacity to identify trends, patterns and themes within the data. Consider various theoretical interpretations and balance the pros and cons of these different perspectives. Discuss anomalies as well consistencies, assessing the significance and impact of each. If you are using interviews, make sure to include representative quotes to in your discussion.

9. Findings

What are the essential points that emerge after the analysis of your data? These findings should be clearly stated, their assertions supported with tightly argued reasoning and empirical backing.

10. Relation with literature

Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of agreement and difference. Are your findings consistent with expectations, or do they make up a controversial or marginal position? Discuss reasons as well as implications. At this stage it is important to remember what, exactly, you said in your literature review. What were the key themes you identified? What were the gaps? How does this relate to your own findings? If you aren’t able to link your findings to your literature review, something is wrong – your data should always fit with your research question(s), and your question(s) should stem from the literature. It is very important that you show this link clearly and explicitly.

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I shall assume that the questionnaires were completed and submitted for analysis in paper form. Online questionnaires are discussed in section 4.1. Here is a summary of the key stages in the process of analysing the data with useful tips – more extensive discussion follows:

  • Prepare a simple grid to collate the data provided in the questionnaires.
  • Design a simple coding system – careful design of questions and the form that answers take can simplify this process considerably.
    • It is relatively straightforward to code closed questions. For example, if answers are ranked according to a numerical scale, you will probably use the same scale as code.
    • To evaluate open questions, review responses and try to categorise them into a sufficiently small set of broad categories, which may then be coded. (There is an example of this below.)
  • Enter data on to the grid.
  • Calculate the proportion of respondents answering for each category of each question.
    • Many institutions calculate averages and standard deviations for ranked questions. Statistically, this is not necessarily a very sound approach (see the discussion on ‘evaluating data’ below).
  • If your data allow you to explore relationships in the data – for example, between the perceived difficulties that students experience with the course and the degree programme to which they are attached – a simple Chi-squared test may be appropriate.
    • For a review of this test and an example, see Munn and Drever (1999) and Burns (2000) – the page references are indexed.
  • You may wish to pool responses to a number of related questions. In this case, answers must conform to a consistent numerical code, and it is often best simply to sum the scores over questions, rather than compute an average score.

Preparing a grid

You will have a large number of paper questionnaires. To make it easier to interpret and store the responses, it is best to transfer data on to a single grid, which should comprise of no more than two or three sheets depending on the number of questions and student respondents. A typical grid looks like this:



Respondent 1          
Respondent 2          
Respondent 3          
Respondent 4          
Respondent 5          

Coding data

If the answers to a question are represented on the questionnaire as points on a scale from 1 to 5, usually you will enter these numbers directly into the grid. If the answers take a different form, you may wish to translate them into a numerical scale. For example, if students are asked to note their gender as male/female, you may ascribe a value of 1 to every male response and 0 to female responses – this will be helpful when it comes to computing summary statistics and necessary if you are interested in exploring correlations in the data. It will make it much easier to analyse the data if there is an entry for all questions. To do this, you will need to construct code to describe ‘missing data’, ‘don’t know’ answers or answers that do not follow instructions – for example, if some respondents select more than one category.

Coding open questions is not straightforward. You must first read through all of the comments made in response to the open questions and try to group them into meaningful categories. For example, if students are asked to ‘state what they least like about the course’, there are likely to be some very broad themes. A number may not find the subject matter interesting; others will have difficulties accessing reading material. It may be useful to have an ‘other’ category for those responses that you are unable to categorise meaningfully.

Evaluating data

Often, it is sufficient and best simply to calculate the proportions of all respondents answering in each category. (An Excel spreadsheet is much quicker than using a calculator!) It is clear that having a category for all respondents who either don’t know or didn’t answer is very important, as it provides useful information on the strength of feeling over a particular question.

Questionnaire results are often used to compute mean scores for individual questions or groups of questions. For example, the questionnaire may ask students to rate their lecturer on a five-point scale, with 5 denoting excellent, 4 good, 3 average, 2 poor and 1 very poor. The mean score is then used as an index of the overall quality of a lecturer with high scores indicating good quality. This is not a particularly useful or legitimate approach as it assumes that you are working on an evenly spaced scale, so that, for example, ‘very poor’ is twice as bad as ‘poor’, and ‘excellent’ twice as good as ‘good’.

Often analysts add up scores over a number of related questions. For example, you may ask students ten questions related to a lecturer’s skills, all ranked from 1 to 5 with 5 indicating a positive response, and add up the scores to derive some index of the overall ability of the lecturer. Again, except in carefully designed questionnaires, this approach is inappropriate. It assumes that each question is relevant and of equal importance. Comparing scores across different lecturers and modules, this assumption is unlikely to hold. If you are interested in summative indices of quality, it may be best simply to ask the students to rate the lecturer themselves on a ranked scale.

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