An illustration of multilevel factor analysis.

Area of Science:

• Psychiatry
• Psychometrics
• Statistical Modeling

Background:

• Repeated measures psychiatric data present challenges in identifying latent structures.
• Variance within and between individuals complicates traditional factor analysis.
• Existing methods for separating variance sources are problematic.

Purpose of the Study:

• To provide a didactic, step-by-step guide for applying multilevel factor analysis (MFA) to psychiatric data.
• To address the complexities of analyzing latent structures in repeated measures psychiatric assessments.
• To offer a methodological solution for researchers in psychiatric and psychometric fields.

Main Methods:

• Exploratory multilevel factor analysis (MFA) is proposed for analyzing repeated measures data.
• The guide details the application of MFA in the context of psychiatric research.
• Discussion includes challenges in applying MFA to psychiatric samples and life event appraisals.

Main Results:

• Multilevel factor analysis (MFA) offers a robust approach to disentangle within- and between-individual variance.
• The proposed step-by-step guide facilitates the application of MFA for psychiatric researchers.
• Identifies specific challenges in factor analyzing psychiatric data using MFA, particularly for life event appraisals.

Conclusions:

• Exploratory multilevel factor analysis (MFA) is a valuable technique for analyzing the latent structure of repeated measures psychiatric data.
• The guide serves as a practical resource for implementing MFA in psychiatric research.
• Further research is needed to address specific challenges in applying MFA to complex psychiatric constructs like life event appraisals.