A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals.

Authors

Guangjian Zhang (ORCID: https://orcid.org/0000-0001-8279-7313)
Dayoung Lee (ORCID: https://orcid.org/0000-0001-7543-7096)

Institutions

University of Notre Dame (US)

Publication Details

Journal: Psychological Methods
Published: 2024-09-23
DOI: https://doi.org/10.1037/met0000609
Citations: 2
Primary Topic: Advanced Statistical Modeling Techniques
Type: article
Field-Weighted Citation Impact: 0.70

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article

A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals.

Guangjian Zhang, Dayoung Lee

Sep 23, 2024

2 citations

Psychological Methods

Advanced Statistical Modeling Techniques

0.70

article

A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals.

Guangjian Zhang, Dayoung Lee

September 23, 2024article en

2 citations

Abstract

No abstract available for this paper.

Psychological MethodsVol. 30(6)

University of Notre Dame (US)

Openalex Percentile: Top 28%

Advanced Statistical Modeling Techniques

0.70

DOI Source OpenAlex

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A computationally efficient and robust method to estimate exploratory factor analysis models with correlated residuals. — Guangjian Zhang, Dayoung Lee · Psychological Methods (2024) | TGRS Research Map | TGRS