This page collates a couple of tips to avoid problems regarding the measuring instrument. The list is not complete and there are exceptions to every rule - generally though, the following tips avoid effort, hassle and resentment during the data analysis.
The questions you ask in the questionnaire are called Survey Questions. The wording of these questions significantly impacts the quality of your questionnaire. The most common mistake is to word the survey questions in a way that closely resembles the research question (“To what extent was your consumption decision influenced by the advertising material presented earlier?”). Another common mistake is that the survey questions hardly relate to the research project. In both cases, the research questions have usually not been conceptually separated from the survey questions.
Systematically working from the research question through to the survey questions usually leads to (a) comprehensible questions, which (b) deliver the necessary data for the analysis.
Tip: Make a note of your hypotheses and the corresponding constructs. You may be surprised what mantraps you have overlooked so far.
The validity of the questionnaire depends on two principles: the theoretical foundation and the comprehensibility. For the latter, there are loads of good tips in books. But no tool is as good at finding problems as a Pretest of the Questionnaire is.
Pretesters should not check spelling and grammar - this should be done prior to the pretest, otherwise problems with regard to content may not be detected.
In the ideal case, every participant answers all questions, and when asked for their age, you of course want participants to enter digits only. Such restrictions can be easily implemented in the online questionnaire.
The tip is: do not do it! There is usually a good reason for motivated participants to not answer a question, e.g. they cannot answer the question. Unmotivated participants usually drag down data quality anyway.
There are always people who just want to look at a questionnaire. If you force them to answer questions, they will enter something: usually junk data. However, in the data set you may not recognise this anymore - only the overall bad data quality. An empty data record on the other hand is easy to identify and delete.
Nevertheless, forcing an answer and allowing only defined characters are eligible when the responses are needed for filters or similar features.
If you follow some general recommendations1), the questionnaire is coherent for the participant, you reduce the number of dropouts and avoid irritation, which usually leads to better data.
For the wording of questions, Krosnick and Presser (2010, p. 264)5) give valuable recommendations:
There are countless terms that different people understand differently. For example, “rarely” and “often”. If you ask how often someone uses social online networks (such as Facebook), “rarely” means “only once a day” for one person and “only once a month” for another – depending on what the respondent perceives as “normal”. With a scale “never/rarely/occasionally/often”, you would therefore only measure how respondents see themselves in comparison to their peer group, but you do not get a valid measure of the amount of use. If you want to measure the amount of use, you should definitely specify absolute numbers, e.g. “about once a month”, “about once a week”, …
By the way, the same problem exists with supposedly clear terms. Facebook use, for example. Should not everyone know what is meant here? No, because one person opens Facebook in the morning, leaves the page open in the background all day and looks into it 10 times a day. Another person opens the Facebook app on their smartphone 10 times a day. Although both check the news equally often (you could validly ask about this), one would classify themselves as “daily”, the other as “several times a day” – a source of error of 20% on a five-point scale. And if 90% of the participants use Facebook often or very often anyway, the error can account for 80% (!) of the explained variance. You then need not expect significant correlations anymore.
The first impression can make the difference between a 5% and a 20% response rate – this section gives some recommendations on how not to chase away your participants by the layout alone. The following welcome screens of the same project (before and after optimization) illustrate the effect of a clean design.
Not everybody has an intimate relationship with HTML. But simple formatting such as paragraphs and headings is very easy to program with a bit of HTML code (Texts in the Questionnaire). You should definitely use these: the welcome text on the first page is the business card of the questionnaire. If this page looks professional and contains a logo of the institute and/or the university (Images in the Questionnaire), people are more likely to complete the questionnaire.
SoSci Survey offers loads of options to design the questionnaire. If you do not make use of these opportunities, a low response rate may be the result. Of course you also need to take care of the spelling and the display of the questions (Optimise the Display of Questions).
A brief checklist for a well-designed questionnaire (see also below A Short Welcome Page):
What is wrong with my writing?
Well. For starters, you use too many sentences.9)
A participant following a link to the questionnaire usually expects a brief welcome. The text should not be longer than 800 characters. Less is more – especially regarding the conversion rate! If you want or need to provide more information, put this on a separate page and link to this page in your welcome note. Some important parts for the first page:
The same advice also goes for the content of the text, as well as for invitation emails: Optimize your E-Mail-Invitations
In a lot of cases, you want to reduce social pressure in order to get the most honest responses possible (“there are no right or wrong answers…”). This sort of guideline is best placed where you ask sensitive questions - it would be skimmed over quickly on the welcome page. A good example of formulating this kind of guideline would be:
The answers you give to these questions can't be right or wrong. We are more interested in your perceptions, opinions and habits. It's not a bad thing if you're not completely sure about the answers you give - most questions can, and should be, answered going on your “gut feeling”.
People know standard forms from different instances. These forms are usually labelled very short and precise: “Date of Birth”, “Residence”, “Address”. What do you associate with these kind of forms? Petitioners, paperwork, and red tape.
If you want your participants to feel comfortable during your survey, ask them proper questions (“Where did you spend your last holidays?” instead of “Last holidays:”) and briefly explain the context of your questions (e.g. “Following we want to ask you a couple of questions regarding your last holidays.”) – especially when the topic of the questions changes (“The following questions relate to experiences on your last holidays”).
By asking questions, a hint of a real conversation is created and participants get a better idea of what you want to know. Of course you don't need to exaggerate: sociodemographic data can still be asked for in a very precise fashion. However, a friendly “Finally, we want to collect some information about yourself” surely doesn't harm.
Avoid using too many emphases (bold, italic, underlined).
Get to grips with the most important HTML tags.
<br>) and a new paragraph (<p>) are not the same. When using multiple paragraphs, make sure they are paragraphs – and not double line breaks.<ul> and <li>.<h1> to <h6> – bold formatting via <b> or <strong> is only rarely suitable for a heading, because the spacing is not adjusted, not to mention the accessibility for screen readers. If necessary, the font sizes for the headings can be adjusted in the layout using CSS.Do not make the typical typographic beginner mistakes – make sure that your text looks like a professional survey.