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Exploratory structural equation modeling: a streamlined step by step approach using the R Project software.

Maria Prokofieva¹, Daniel Zarate², Alex Parker¹

¹Institute for Health and Sport, Victoria University, Melbourne, Australia.

|July 28, 2023

Summary

Exploratory Structural Equation Modeling (ESEM) offers a more flexible approach than Confirmatory Factor Analysis (CFA) for understanding complex psychological scales. This tutorial simplifies ESEM implementation using R, enhancing its application in research.

Keywords:

ESEM Exploratory Structural Equation Modeling Longitudinal Study of Australian Children Mplus R software Syntax

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Area of Science:

Psychometrics
Structural Equation Modeling
Statistical Psychology

Background:

Confirmatory Factor Analysis (CFA) has limitations in assessing multifactorial scales due to its assumption of unique item loadings.
Exploratory Structural Equation Modeling (ESEM) integrates Exploratory Factor Analysis (EFA) and CFA, permitting cross-loadings for more realistic model assessment.
ESEM adoption is hindered by software limitations and coding complexity.

Purpose of the Study:

To present a streamlined, step-by-step tutorial for implementing Exploratory Structural Equation Modeling (ESEM).
To provide both R and Mplus syntax for ESEM analysis, addressing software and coding barriers.
To demonstrate ESEM by analyzing the factor structure of the Strengths and Difficulties Questionnaire (SDQ) using real-world data.

Main Methods:

Utilized the open-source statistical software R for a tutorial on ESEM.
Provided comparative syntax for both R and Mplus software packages.
Applied ESEM to analyze the Strengths and Difficulties Questionnaire (SDQ) factor structure using data from the Longitudinal Study of Australian Children (LSAC).

Main Results:

The tutorial successfully demonstrates a simplified sequence for ESEM analysis.
Open-source R facilitates accessible implementation of ESEM procedures.
Analysis of the SDQ using ESEM provides insights into its complex factor structure.

Conclusions:

ESEM offers a more nuanced understanding of multidimensional scale structures compared to traditional CFA.
This tutorial enhances the accessibility and application of ESEM in psychological research.
Optimized ESEM application can improve the epidemiological and clinical assessment of psychiatric conditions.