Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation
Saved in:
| Title: | Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation |
|---|---|
| Language: | English |
| Authors: | Clark, D. Angus, Bowles, Ryan P. |
| Source: | Grantee Submission. 2018 53(4):544-558. |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2018 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305A110293 R324A150063 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Factor Analysis, Goodness of Fit, Factor Structure, Monte Carlo Methods, Comparative Analysis, Item Analysis, Correlation, Structural Equation Models |
| DOI: | 10.1080/00273171.2018.1461058 |
| ISSN: | 0027-3171 |
| Abstract: | In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA. |
| Abstractor: | As Provided |
| IES Funded: | Yes |
| Entry Date: | 2021 |
| Accession Number: | ED614019 |
| Database: | ERIC |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA. |
|---|---|
| ISSN: | 0027-3171 |
| DOI: | 10.1080/00273171.2018.1461058 |
