[Various methods of dynamic functional analysis in human sciences and economics]

Area of Science:

• Behavioral Science
• Psychometrics
• Econometrics

Context:

• Measuring human conduct requires incorporating temporal and developmental dimensions.
• Traditional analyses struggle with limited repeated observations in longitudinal studies.
• Practicable research designs often lack sufficient data for traditional time series or large-sample analyses.

Purpose:

• Introduce dynamic factor analysis (DFA) as a method for analyzing pooled time series data.
• Address the challenge of insufficient data in longitudinal research for traditional statistical methods.
• Provide a framework for detecting structures and relationships within time series data.

Summary:

• Dynamic factor analysis merges multivariate time series analysis and common factor models.
• It allows common factors to influence observed variables concurrently and with delays.
• This approach facilitates the analysis of dynamic, process-relevant elements in pooled short time series.

Impact:

• Enables robust analysis of human behavior in naturalistic settings with limited data.
• Offers a solution for researchers facing constraints in longitudinal study designs.
• Applicable across psychology, social sciences, econometrics, and economics for time series analysis.