Clustering Individuals Based on Similarity in Idiographic Factor Loading Patterns.

ABSTRACT: Idiographic measurement models such as p-technique and dynamic factor analysis (DFA) assess latent constructs at the individual level. These person-specific methods may provide more accurate models than models obtained from aggregated data when individuals are heterogeneous in their processes. Developing clustering methods for the grouping of individuals with similar measurement models would enable researchers to identify if measurement model subtypes exist across individuals as well as assess if the different models correspond to the same latent concept or not. In this paper, methods for clustering individuals based on similarity in measurement model loadings obtained from time series data are proposed. We review literature on idiographic factor modeling and measurement invariance, as wel

SUBMITTER: Arizmendi CJ 

PROVIDER: S-EPMC11754526 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature