1. Make sure your questions are answerable. Anticipate cases where questions may not be answerable. For example, a question about family medical history should include an 'unknown' response for adoptees and others who wouldn't know. If someone has difficulty answering a survey question, that frustration lingers and they may guess a response, guess future responses, or quit entirely. Adding a not-applicable or open ended 'other' question, or a means to skip a question are all ways to mitigate this problem.
2. Avoid logical negatives like 'not', 'against', or 'isn't' when possible. Some readers will fail to see the word 'not', and some will get confused by the logic and will answer a question contrary to their intended answer. If logical negatives are unavoidable, highlight them in BOLD, LARGE AND CAPITAL.
3. Minimize knowledge assumptions. Not everyone knows what initialisms like FBI or NHL stand for. Not everyone knows what the word 'initialism' means. Lower the language barrier by using as simple language as possible without losing meaning. Use full names like National Hockey League, or define them regularly if the terms are used very often.
4. If a section of your survey, such as demographic questions, is not obviously related to the context of the rest of the survey, preface that section with a reason why are you asking them. Respondents may otherwise resent being asked questions they perceive as irrelevant.
5. Each question comes at a cost. Don't include questions haphazardly or allow other researchers to piggyback questions onto your survey. Every increase in survey length increases the risk of missed or invalid answers. Engagement will drop off over time.
6. Be specific about your questions, don't leave them open to interpretation. Minimize words with context specific definitions like 'framework', and avoid slang and non-standard language. Provide definitions for anything that could be ambiguous. This includes time frames and frequencies. For example, instead of 'very long' or 'often', use '3 months' or 'five or more times per day'.
7. Base questions on specific time frames like 'In the past week how many hours have you...', as opposed to imagined time frames like 'In a typical week how many hours have you...'. The random noise involved in people doing that activity more or less than typical should balance out in your sample. Time frames should be long enough to include relevant events and short enough to recall.
8. For sensitive questions (drug use, trauma, illegal activity), start with the negative or less socially desirable answers first and move towards the milder ones. That gives respondents a comparative frame of reference that makes their own response seem less undesirable.
9. Pilot your questions on potential respondents. If the survey is for an undergrad course, have some undergrads answer and critique the survey before a full release. Re-evaluate any questions that get skipped in the pilot. Remember, if you could predict the responses you will get from a survey, you wouldn't need to do the survey at all.
10. Hypothesize first, then determine the analysis and data format you'll need, and THEN write or find your questions.
11. Some numerical responses, like age and income, are likely to be rounded. Some surveys ask such questions as categories instead of open-response numbers, but information is lost this way. There are statistical methods to mitigate both problems, but only if you acknowledge the problems first.
12. Match your numerical categories to the respondent population. For example, if you are asking the age of respondents in a university class, use categories like 18 or younger, 19-20, 21-22, 23-25, 26 or older. These categories would not be appropriate for a general population survey.
13. For pick-one category responses, including numerical categories, make sure no categories overlap (i.e. mutually exclusive), and that all possible values are covered (i.e. exhaustive.)
14. When measuring a complex psychometric variable, (e.g. depression), try to find a set of questions that have already been tested for reliability on a comparable population (e.g. CES-D). Otherwise, consult a psychometrics specialist. Reliability refers to the degree to responses to a set of questions 'move together', or are measuring the same thing. Reliability can be computed after the survey is done.
15. Ordinal answers in which a neutral answer is possible should include one. This prevents neutral people from guessing. However, not every ordinal answer will have a meaningful neutral response.
16. Answers that are degrees between opposites should be balanced. For each possible response, its opposite should also be included. For example, strongly agree / somewhat agree / no option / somewhat disagree / strongly disagree is a balanced scale.
17. Limit mental overheard - the amount of information that people need to keep in mind at the same time in order to answer your question. Try to limit the list of possible responses to 5-7 items. When this isn't possible, don't ask people to interact with every item. People aren't going to be able to rank 10 different objects 1st through 10th meaningfully, but they will be able to list the top or bottom 2-3. An ordered-response question rarely needs more than 5 levels from agree to disagree.
18. Layout matters. Make every response field unambiguously next to its most relevant text. For an ordinal response question, make sure that ordering structure is apparent by lining up all the answers along one line or column of the page.
19. Randomize response order where appropriate. All else being equal, earlier responses in a list are chosen more often, especially when there are many items. To smooth out this bias, scramble the order of responses differently for each survey. This is only appropriate when responses are not ordinal. Example of a appropriate question: 'Which of the following topics in this course did you find the hardest?'
20. A missing value for a variable does not invalidate a survey. Even if the variable is used in an analysis, the value can substituted with a set of plausible values by a method called imputation. A missing value is not as bad as a guessed value, because then the uncertainty can be identified.
Main Source: Fink, Arlene (1995). "How to Ask Survey Questions" - The Survey Kit Vol. 2, Sage Publications.
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