How to Avoid Central Tendency Bias

Central tendency bias is a serious data collection problem. It is the tendency to not give an extreme answer, and instead pick an answer that is closer to the center of the options. Central tendency bias is often seen in things like subjective grading – teachers (or employees, or a customer’s appraisal of a product) rarely want to claim that someone has already mastered something, and so they avoid giving a perfect score for anyone.

Similarly, most people rarely give completely negative scores, because they may give someone a feeling of hopelessness. Avoiding these extreme responses makes your data less meaningful, because it groups your means closer together. Central tendency bias is also the result of having multiple questions on a survey, which has shown a tendency to cause people to choose the less extreme answers.

First and foremost, shortening your survey has shown the ability to reduce central tendency in the results. However, that may not be feasible, in which case you should consider the following ideas: Forced Rating One idea is to force comparable ratings, rather than using a scale with the same responses on each.

For example, rating a feature in terms of priority to the customer in terms of 1, 2, 3 all the way until the last priority item. Forcing people to choose a priority level means that something (or someone) is going to be ranked the “best,” thereby ensuring that two items that are unequal in value are not seen as equal as a result of this bias. Mixed Questions Another thing you can do is change the way your questions are asked.

Often (but not always) central tendency bias occurs because the questions are seen all in a row, and cause people to eventually lose interest in the idea of giving out an extreme score. Mixing the questions up to help them appear more interesting to the person filling them out may reduce this problem.

You should be able to find out if there is a central tendency bias by testing the survey beforehand. It’s important that you discover if this bias exists so that you can take the necessary steps to stop it, because your data is only as useful as it is accurate, and if this bias persists, you are going to collect data that may not represent the respondent’s true feelings.