The AIC and aBIC Work Best For Identiying the Correct Number of Profiles in Latent Transition Analysis Applied to Typical Educational Settings
Peter A. Edelsbrunner, Maja Flaig, Michael Schneider
How can we best tell how many different learning patterns there are in our data?
Latent transition analysis is used to describe different learner patterns. However, it is often hard to tell how many patterns there are. Is there a pattern of learners who have little knowledge, another pattern of learners with a specific misconception, and another pattern of learners who have properly understood everything that we tried to teach them? Or are there some of these patterns but not all, or even additional ones? This is really hard to tell, and different indicators (called “relative fit indices”) are available for helping us determinate how many patterns there really are. We compare the performance of several relative fit indices. We find that the Bayesian information criterion (BIC), which is commonly used to determine the number of learning patterns, is not very accurate in finding the right number of patterns in comparison to other indices.