Last updated: 2019-11-21
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Knit directory: mcfa-fit/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 332a2d6 | noah-padgett | 2019-11-22 | updated about page |
| html | 332a2d6 | noah-padgett | 2019-11-22 | updated about page |
| html | b534b90 | noah-padgett | 2019-09-29 | updated publish |
| Rmd | 982c8f1 | noah-padgett | 2019-05-18 | roc analyses completed |
| html | 982c8f1 | noah-padgett | 2019-05-18 | roc analyses completed |
| html | 451ccf7 | noah-padgett | 2019-05-07 | Build site. |
| Rmd | 8b2f44d | noah-padgett | 2019-05-07 | intial commit |
| Rmd | 3ffee99 | noah-padgett | 2019-05-07 | Start workflowr project. |
This study is a continuation from R. Noah Padgett’s Master’s Thesis. The project is generally about the performance of commonly used fit statistics in multilevel CFA models. The work is part of a large simulation study that will be reported in multiple manuscripts throughout the next year (at least that is the hope). This study is related solely to how the fit indices varied across conditions and estimation methods.
Within a multilevel confirmatory factor analysis framework, we investigated the ability of commonly used fit indices to discriminate between correctly specified models and misspecified models. Receiver operating characteristics (ROC) analyses were used to evaluate the performance of fit indices. Combining ROC analyses with checks of the convergence rates across Monte Carlo replications and ANOVA for investigating the variation in fit scores across replications, we found converging evidence for the utility of the investigated fit indices.
Optimal thresholds based on maximizing sensitivity and specificity for detection of the true model were identified by the highest sensitivity and specificity and found to vary across different robust estimation methods (i.e., MLR, ULSMV, and WLSMV). The estimation method and sample size influenced the performance of common fit indices to detect misspecification of the level-1 model. All fit indices investigated performed poorly for detecting misspecification of the level-2 model when the level-2 sample size was below 100. We offer recommendations of commonly reported fit indices to use (and not use), cut-off criteria to use for specific estimation methods, and cautions about the use of recommended cut-off criteria for ML-CFA.