Dataset Information

Random intercept EFA of personality scales.

ABSTRACT: Previous research suggests that simple structure CFAs of Big Five personality measures fail to accurately reflect the scale's complex factorial structure, whereas EFAs generally perform better. Another strand of research suggests that acquiescence or uniform response bias masks the scale's "true" factorial structure. Random Intercept EFA (RI-EFA) captures acquiescence as well as the complex item-factor structure typical for personality measures. It is applied to the NEO-FFI and the BFI scale to test whether an accurate model-to-data fit can be achieved and whether the "clarity" of the factorial structure improves. The results lend confidence in the general effectiveness of RI-EFA whenever acquiescence bias is an issue. Example Mplus code is provided for replication.

SUBMITTER: Aichholzer J

PROVIDER: S-EPMC4251787 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Random intercept EFA of personality scales.

Aichholzer Julian J

Journal of research in personality 20141201

Previous research suggests that simple structure CFAs of Big Five personality measures fail to accurately reflect the scale's complex factorial structure, whereas EFAs generally perform better. Another strand of research suggests that acquiescence or uniform response bias masks the scale's "true" factorial structure. Random Intercept EFA (RI-EFA) captures acquiescence as well as the complex item-factor structure typical for personality measures. It is applied to the NEO-FFI and the BFI scale to ...[more]

PMID: 25484472

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Project description:BackgroundWhen study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions.MethodsUsing an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated.ResultsThe model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept.ConclusionThe models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Dataset Information

Random intercept EFA of personality scales.

Publications

Random intercept EFA of personality scales.

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