Question Randomizers Explained

Survey software is often equipped with tools that help to enhance your research and promote better data collection. Survey logic, for example, helps to reduce extraneous questions from your respondents so that they can complete the survey quickly and only see questions that are relevant to them. Likewise, question randomizers are another tool that researchers use to enhance the quality of their survey design.

As the name describes, question randomizers use a randomization algorithm to have a question appear at a random interval to the respondent. Sometimes randomizers may be used to display blocks of questions, while other times they may be used to randomize each question in the survey. Question randomization has several uses that make it valuable to researchers, and while not every survey can have random questions, it is something important to consider.

Reduces Bias Researchers like to believe that each question in a survey is independent of the other, but the truth is that where a question is displayed in a survey can have a fairly significant effect on how someone answers another question. For example, if one question asks “what do you think of feature X?” and the next question asks “what do you think of feature Y?” the respondent may rate Y lower or higher based on how they feel about feature X, but that doesn’t mean that independently they would have rated the feature at the same score. There are ample reasons that a previous question can have an effect on the results of a future question, and that affects the quality of your data.

Randomizing the question is a valuable solution because your question can be placed all throughout your survey – sometimes in the beginning, sometimes in the end, sometimes in the middle. Ideally, this randomization will average out the effects of these biases, giving you data that is more accurate. Some software platforms will also allow you to compare and contrast survey answers based on where they ended up, which can help you discover if there was a question bias as well.

Reduces Habituation/Central Tendency Problems Another significant issue that affects many researchers is habituation , and to a lesser extent central tendency . Both of these occur when a respondent is either bored with a survey or has answered enough questions that they start to give half-hearted answers that may not accurately reflect their thoughts. It’s a significant problem with survey data.

Question randomization reduces this problem to some extent. While it cannot necessarily stop central tendency or habituation, it can ensure that all questions are subjected to these problems relatively equally, by placing questions in different locations in the survey. This should, in essence, average out the effects of habituation and central tendency and lead to more analyzable data.

Shortens Surveys Finally, question randomizers can be used to make a survey shorter . Sometimes researchers find that they simply have too many questions in their survey. Rather than cut those questions out, they can target their survey to a much larger sample and then randomize which questions need to be shown to respondents.

Not all question blocks will be shown at any given time, thus making the survey much shorter while still including all of the questions for analysis. Your sample will have to be much larger to account for all respondents that will not be receiving the questions each time, but it can be an interesting way to collect a great deal of data at a single time period and compare against other data without necessarily cutting back the true length of your survey.

These are just a few of the benefits of question randomization. Short surveys with easy-to-understand questions may not benefit quite as much from the benefits listed above, but overall there are many reasons question randomizers can be used to improve your data. Question randomizers may not be ideal for every type of survey or every research study, but for those that can use them, they have a great deal of potential to help you improve your survey data and account for some of the issues that often lead to incorrect or bias survey results.