What are Composite Scores?

Researchers use composite scores to represent hard-to-measure variables in the social sciences. An example would be a psychological construct such as anxiety. You could measure anxiety by asking, ‘How anxious do you feel?’ on a scale from 1 to 10. However, this might not be the most scientifically sound way to measure anxiety. Each person that you ask may have their own definition of anxiety that could affect their answer to the question. Some may rate anxiety based on thoughts, while others focus on physical sensations like a racing heart.

A better way to measure anxiety might be to instead collect information on the different symptoms the person is experiencing. Validated tools like the Beck Anxiety Inventory ask about the frequency or severity of symptoms like nervousness or heart pounding. The person rates each symptom, and you combine these ratings into a composite score of their anxiety level. You create this by summing the ratings or averaging them. In this case, it creates a single score to represent anxiety, based on a wide array of symptoms.

Statistically, the main advantage of this is that it reduces multiple data points into one. A single variable is easier to analyze and interpret than multiple variables. Fewer variables mean you need fewer analyses, which reduces the probability of Type I errors. Composite scoring loses information from individual items, making it harder to account for measurement error in subsequent analyses. However, there are more complex statistical techniques (such as structural equation modeling with latent variables) that can mitigate such issues.

So now that we know what a composite score is, when should we use them? First, check the documentation for the instrument you are using. If you use an instrument from other researchers, you’ll likely find a manual or article explaining its development and use. This documentation should explain any composite scores that can be calculated and provide instructions for doing so.

If you are using your own instrument or an instrument that does not have the documentation mentioned above, the decision to make composite scores (or not) should be based on the nature of the instrument and the goals of the research. Generally, they should only be made from sets of items that are strongly related, such as survey questions that all ask about the same concept or topic. Items that are not conceptually related should not be combined into a composite score. If you have a set of conceptually related items, you can run a Cronbach’s alpha or other reliability test to check their statistical relationship. A high reliability coefficient would justify creating a composite score for those items, while a low coefficient suggests you should not combine them.

Finally, consider the goals of your research and the specific questions you are trying to answer. What analysis will provide a clearer answer to your research questions: an analysis of a composite score, or separate analyses for each of the items? For example, let’s say you are doing a study about computer competency, so you ask participants multiple questions about their ability to perform various tasks on a computer (e.g., sending e-mails, using search engines, installing apps or programs). If your research question is just “How competent are people at using computers?” then a composite score may provide the most straightforward answer to this question. If you have research questions about the different domains of computer use (e.g., “How competent are people at using e-mail?”), then analyzing the items separately would be better.

Additional Resources:

Video: Create a Composite Score