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How to... use questionnaires effectively

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The analysis and writing up of questionnaires

The goal with a questionnaire is not to produce a beautiful instrument but rather one that will yield data that will provide results. If you study articles published in Emerald journals you will note that relatively few report the questionnaire in detail, but give more space to its results. The latter have often been achieved by quite sophisticated statistical analysis which is a subject in its own right and beyond the scope of these pages. What we offer is a few preliminary observations on how to analyse and present.


Your work in analysis should start when you are thinking about the objectives of your questionnaire: you will have decided what statistical tests to apply, and you will have constructed your questionnaire so that it is easy to code.

You will also have established a cut-off date for the return of your questionnaires, at which point you will start the analysis. The first task is to go through the questionnaires and establish which ones are usable – those which are unfinished, or which have more than a couple of botched responses, must be discarded (if only a couple of botched responses, these can be marked as "non response").

Open questions will need to be analysed by content analysis. For the majority of closed-ended questions, you need to begin to:

  • record the answer to each question, using an Excel spreadsheet.
  • decide how you are going to cluster the data – what subgroups of your sample are you going to highlight.
  • decide how you are going to cross tabulate.
  • look at trends – do these relate back to your hypotheses?

Statistical packages such as SPSS and Minitab should only be used if there are sufficient responses – if the sample is less than 100, then the analysis is much better done by hand; if the sample is between 100 and 200, then it depends upon how many cross tabulations there are; for samples of more than 200, it is advisable to use statistical packages.

Types of data

Researchers should always try and gather data at the highest possible level of sophistication. The main types of data are listed below in order of scale:

  • Nominal data: this puts people into categories, i.e. gender, type of job, etc.
  • Ordinal data: ordinal data allows for ranking, e.g. the degree to which people possess a characteristic (but without a specific interval between the data)
  • Interval data: also allows for ranking, but the intervals between the scale are equal. 

Ratio data The most precise level of measurement, measures intervals but have a precise zero point (e.g. height, speed, time, etc.)


In articles written for Emerald publications, authors rarely concentrate on the details of the questionnaire construction but may describe such features as are necessary to the design of the research, such as:

  • the main categories of the questionnaire, as for example in "" (Susan Aldridge and Jennifer Rowley, Quality Assurance in Education, Vol. 6 No. 4), see the section on questionnaire design.
  • how the questionnaire was delivered (e.g. electronically, on paper, etc.).
  • number of responses.

When reporting results, authors describe the main points in the body of the text, with relevant data listed after the conclusion in the form of tables, bar charts, etc. as relevant.