Latent Profile Analysis
As one type of Latent Variable Mixture Modeling (LVMM), Latent Profile Analysis (LPA) is based on the framework of structural equation modeling (SEM). LPA is used for identifying unobserved but distinct patterns of responses to a set of observed continuous indicators in a sample of individuals, and these unobserved but distinct response patterns are known as latent profiles.
Commonly attributed to Lazarsfeld and Henry (1968), LPA is a relatively new clustering approach for capturing patterns of continuous observed variables within a sample of individuals. In contrast to other approaches for a similar purpose (e.g., median splits, K-means clustering, and qualitative comparative analysis), LPA is a probabilistic and model-based technique, and it relies on objective model fit indices to identify the most appropriate number and nature of the profiles (Meyer et al. 2013). Additionally, LPA also allows researchers to include covariates and outcomes...
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