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Researchers are often interested in identifying homogeneous subgroups within heterogeneous samples on the basis of a set of measures, such as profiles of individuals’ motivation (i.e., their values, competence beliefs, and achievement goals). Latent Profile Analysis (LPA) is a statistical method for identifying such groups, or latent profiles, and is a special case of the general mixture model where all measured variables are continuous (Harring & Hodis, 2016; Pastor, Barron, Miller, & Davis, 2007). The tidyLPA package allows users to specify different models that determine whether and how different parameters (i.e., means, variances, and covariances) are estimated, and to specify and compare different solutions based on the number of profiles extracted.

tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software