Sensitivity Analysis in Covariance Structure Analysis: A Numerical Investigation in the Case of Confirmatory Factor Analysis

1993 ◽  
pp. 197-210
Keyword(s):  
Sensitivity Analysis ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Numerical Investigation ◽  
Structure Analysis ◽  
Covariance Structure ◽  
Covariance Structure Analysis ◽  
Confirmatory Factor

scholarly journals RGLS and RLS in Covariance Structure Analysis

2021 ◽  
Author(s):  
Bang Quan Zheng
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Least Squares ◽  
Goodness Of Fit ◽  
Weight Matrix ◽  
Equation Modeling ◽  
Covariance Structure Analysis ◽  
Factor Analysis Model ◽  
Monte Carlo Simulation Study ◽  
Confirmatory Factor

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and GLS statistics are based on large sample statistical theory. However, violation of asymptotic sample size is ubiquitous in real applications of structural equation modeling (SEM), and ML and GLS goodness-of-fit tests in SEM often make incorrect decisions on the true model. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Proposed by Arruda and Bentler (2017), RGLS replaces a GLS weight matrix with a regularized one. Rediscovered by Hayakawa (2019), RLS replaces this weight matrix with one that derives from an ML function. Both of these methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A confirmatory factor analysis Monte Carlo simulation study with normal data was carried out to examine the statistical performance of these two methods at different sample sizes. Based on empirical rejection frequencies and empirical distributions of test statistics, we find that RLS and RGLS have equivalent performance when N≥70; whereas when N<70, RLS outperforms RGLS. Both methods clearly outperform ML and GLS with N≤400.

A study of algorithms for covariance structure analysis with specific comparisons using factor analysis

Psychometrika ◽  
1979 ◽  
Vol 44 (1) ◽  
pp. 99-113 ◽  
Author(s):  
S. Y. Lee ◽  
R. I. Jennrich
Keyword(s):  
Factor Analysis ◽  
Structure Analysis ◽  
Covariance Structure ◽  
Covariance Structure Analysis

Evidence-based subtypes in functional dyspepsia with confirmatory factor analysis: Psychosocial and physiopathological correlates

2001 ◽  
Vol 120 (5) ◽  
pp. A51-A52 ◽  
Author(s):  
B FISCHLER ◽  
J VANDENBERGHE ◽  
P PERSOONS ◽  
V GUCHT ◽  
D BROEKAERT ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Functional Dyspepsia ◽  
Evidence Based ◽  
Confirmatory Factor
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