When conducting an analysis of quantitative data, one important consideration is the use of composite scores. However, many students either do not know what composite scores are or if composite scoring is appropriate for their data. In this blog, we will explain what composite scores are and when it is appropriate to perform composite scoring.
A composite score is a single variable or data point that represents a combination of information from multiple variables or data points. In other words, it is a single score derived from multiple pieces of information.
Composite scores are commonly used in the social sciences to represent variables or concepts that are difficult or impossible to directly measure with a single data point. An example would be a psychological construct such as anxiety. Perhaps you could measure anxiety with a single piece of information, such as asking the person, “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 people might rate their anxiety based on the thoughts or emotions they are experiencing, while others may rate their anxiety based on physical sensations (such as their heart racing or having shortness of breath).
A better way to measure anxiety might be to instead collect information on the different symptoms the person is experiencing. Scientifically validated instruments such as the Beck Anxiety Inventory typically ask how often or to what degree the person experiences different symptoms (e.g., feeling nervous, unable to relax, heart pounding, difficulty breathing). The person would provide a rating for each symptom, and these ratings can then be combined into a composite score that represents their overall degree of anxiety. The composite score is usually created by adding all of the ratings together (i.e., the sum) or by taking the average (mean) of the ratings. In this case, the composite score allows us to create a single score to represent anxiety that is determined based on a wide array of symptoms.
From a statistical standpoint, the main advantage of using a composite score is that it allows us to reduce multiple data points into a single data point. A single variable is generally simpler to analyze than multiple variables, and the results of the analysis are easier to interpret. Also, fewer variables could mean fewer analyses are needed, which reduces the probability of Type I errors. A statistical disadvantage of composite scoring is that information from the individual items that make up the composite score is lost, and subsequent analyses of the composite score are less able to account for measurement error. 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 composite scores? First, check the documentation for the instrument you are using. If you are using an instrument that other researchers already created, there will likely be a user manual or a journal article that describes how the instrument was developed and how it should be used. This documentation should contain information on what (if any) composite scores may be calculated from the data, as well as instructions for how to calculate the scores.
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, composite scores 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 items that seem conceptually related, you can run a Cronbach’s alpha or other reliability test to see if the items are statistically related. If you run this test and get a high reliability coefficient, then you could justify creating a composite score for those items. If your reliability coefficient is low, you probably should not combine the items.
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.
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