Comparing Accuracy of Parallel Analysis and Fit Statistics for Estimating the Number of Factors With Ordered Categorical Data in Exploratory Factor Analysis
Bibliographic Data
| ID | 20282789 |
|---|---|
| Authors | Hyunjung Lee (0000-0003-0783-5484, Fordham University, Bronx, NY, USA, corresponding author), Heining Cham (0000-0002-2933-056X, Fordham University, Bronx, NY, USA) |
| Year | 2024 |
| Volume | 84 |
| Issue | 6 |
| Pages | 1173-1202 |
| Publication date | 2024-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/00131644241240435 |
| PMID | 39493799 |
| OpenAlex | W4394891058 |
| Language | EN |
| Citations received | 2 |
| References cited | 37 |
Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is to compare the parallel analysis with the performance of fit indices that researchers have started using as another strategy for determining the optimal number of factors in EFA. The Monte Carlo simulation was conducted with ordered categorical items because there are mixed results in previous simulation studies, and ordered categorical items are common in behavioral science. The results of this study indicate that the parallel analysis and the root mean square error of approximation (RMSEA) performed well in most conditions, followed by the Tucker–Lewis index (TLI) and then by the comparative fit index (CFI). The robust corrections of CFI, TLI, and RMSEA performed better in detecting misfit underfactored models than the original fit indices. However, they did not produce satisfactory results in dichotomous data with a small sample size. Implications, limitations of this study, and future research directions are discussed
Categorical variable · Exploratory data analysis · Exploratory factor analysis · Factor (programming language) · Psychometrics · Statistical analysis · Statistics · Advanced Statistical Methods and Models · Advanced Statistical Modeling Techniques · Computer Science · Mathematics · Psychology · Sensory Analysis and Statistical Methods
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Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors
Factor Analysis with Ordinal Indicators
On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA
Factor Retention Decisions in Exploratory Factor Analysis
Exploratory graph analysis
A new look at Horn’s parallel analysis with ordinal variables.
Determining the number of factors using parallel analysis and its recent variants.
A comparison of some methodologies for the factor analysis of non‐normal Likert variables
RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data
When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions.
Cutoff criteria for fit indexes in covariance structure analysis
A Rationale and Test for the Number of Factors in Factor Analysis
A Reliability Coefficient for Maximum Likelihood Factor Analysis
An Improvement on Horn's Parallel Analysis Methodology for Selecting the Correct Number of Factors to Retain
Type I and Type II Error Rates and Overall Accuracy of the Revised Parallel Analysis Method for Determining the Number of Factors
Accuracy of the Parallel Analysis Procedure With Polychoric Correlations
Use of Exploratory Factor Analysis in Published Research
Relative Accuracy of Two Modified Parallel Analysis Methods that Use the Proper Reference Distribution
Parallel Analysis with Unidimensional Binary Data
A Proposed Solution to the Problem With Using Completely Random Data to Assess the Number of Factors With Parallel Analysis
The Application of Electronic Computers to Factor Analysis
Accuracy of Revised and Traditional Parallel Analyses for Assessing Dimensionality with Binary Data
Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis
Improving Fit Indices in Structural Equation Modeling with Categorical Data
Model Fit and Item Factor Analysis
Recovery of Weak Common Factors by Maximum Likelihood and Ordinary Least Squares Estimation
On the Computation of the RMSEA and CFI from the Mean-And-Variance Corrected Test Statistic with Nonnormal Data in SEM
The Scree Test For The Number Of Factors
Comparative fit indexes in structural models
Comparison of five rules for determining the number of components to retain
Alternative Ways of Assessing Model Fit
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |