Factor Retention in Exploratory Factor Analysis With Missing Data
Bibliographic Data
| ID | 20286355 |
|---|---|
| Authors | David Goretzko (0000-0002-2730-6347, Ludwig Maximilians University Munich, Munich, Germany, corresponding author) |
| Year | 2022 |
| Volume | 82 |
| Issue | 3 |
| Pages | 444-464 |
| Publication date | 2022-06-01 |
| 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 | 10.1177/00131644211022031 |
| PMID | 35444335 |
| OpenAlex | W3171495944 |
| Language | EN |
| Citations received | 9 |
| References cited | 34 |
Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process. Hence, in this study, we evaluated the performance of different factor retention criteria—the Factor Forest, parallel analysis based on a principal component analysis as well as parallel analysis based on the common factor model and the comparison data approach—in combination with different missing data methods, namely an expectation-maximization algorithm called Amelia, predictive mean matching, and random forest imputation within the multiple imputations by chained equations (MICE) framework as well as pairwise deletion with regard to their accuracy in determining the number of factors when data are missing. Data were simulated for different sample sizes, numbers of factors, numbers of manifest variables (indicators), between-factor correlations, missing data mechanisms and proportions of missing values. In the majority of conditions and for all factor retention criteria except the comparison data approach, the missing data mechanism had little impact on the accuracy and pairwise deletion performed comparably well as the more sophisticated imputation methods. In some conditions, especially small-sample cases and when comparison data were used to determine the number of factors, random forest imputation was preferable to other missing data methods, though. Accordingly, depending on data characteristics and the selected factor retention criterion, choosing an appropriate missing data method is crucial to obtain a valid estimate of the number of factors to extract
Data mining · Exploratory factor analysis · Factor analysis · Imputation (statistics) · Matching (statistics) · Missing data · Pairwise comparison · Principal component analysis · Random forest · Sample (material) · Sample size determination · Statistics · Structural equation modeling · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics · Psychometric Methodologies and Testing · Statistical Methods and Bayesian Inference
A comparison of methods to address item non-response when testing for differential item functioning in multidimensional patient-reported outcome measures
Short and sweet
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Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure.
Determining the number of factors using parallel analysis and its recent variants.
In Search of Underlying Dimensions
Fully conditional specification in multivariate imputation
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Evaluating the use of exploratory factor analysis in psychological research.
Mice
Investigating Parallel Analysis in the Context of Missing Data
The Application of Electronic Computers to Factor Analysis
Missing-Data Adjustments in Large Surveys
The Hull Method for Selecting the Number of Common Factors
Simulating Multivariate Nonnormal Data Using an Iterative Algorithm
Exploring the Sensitivity of Horn's Parallel Analysis to the Distributional Form of Random Data
The Scree Test For The Number Of Factors
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| Citations per year | 2.25 |
|---|---|
| Citation span | 2022 - 2025 (4) |
| Highly cited | No |