In psychological research, there is a growing interest in using latent class analysis (LCA) for the investigation of quantitative constructs. The aim of this study is to illustrate how LCA can be applied to gain insights on a construct and to select items during test development. We show the added benefits of LCA beyond factor-analytic methods, namely being able (1) to describe groups of participants that differ in their response patterns, (2) to determine appropriate cutoff values, (3) to evaluate items, and (4) to evaluate the relative importance of correlated factors. As an example, we investigated the construct of Facebook addiction using the Facebook Addiction Test (F-AT), an adapted version of the Internet Addiction Test (I-AT). Applying LCA facilitates the development of new tests and short forms of established tests. We present a short form of the F-AT based on the LCA results and validate the LCA approach and the short F-AT with several external criteria, such as chatting, reading newsfeeds, and posting status updates. Finally, we discuss the benefits of LCA for evaluating quantitative constructs in psychological research.
- Behavior, Addictive/psychology
- Models, Statistical
- Psychological Tests