Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow up-design with continuous indicators, varying sample size (N from 50 to 1,000), attrition rate (none/10% per wave), and profile separation (entropy; from 0.73 to 0.87). Results indicate that the most commonly used fit index, the Bayesian information criterion ("BIC"), and the consistent Akaike information criterion ("CAIC") consistently underestimate the real number of profiles. A combination of the "AIC" or the "AIC3" with the adjusted Bayesian Information Criterion ("aBIC") provides the most precise choice for selecting the number of profiles and is accurate with sample sizes of at least N = 200. The "AIC3" excels starting from N = 500. Results were mostly robust toward differing numbers of time points, profiles, indicator variables, and alternative profiles. We provide an online tool for computing these fit indices and discuss implications for research.