Refine
Year of publication
- 2020 (3) (remove)
Document Type
- Article (3)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3) (remove)
Keywords
- confirmatory factor analysis (3) (remove)
Institute
- Psychologie (3)
The paper reports an investigation on whether valid results can be achieved in analyzing the structure of datasets although a large percentage of data is missing without replacement. Two types of confirmatory factor analysis (CFA) models were employed for this purpose: the missing data CFA model with an additional latent variable for representing the missing data and the semi-hierarchical CFA model that also includes the additional latent variable and reflects the hierarchical structure assumed to underlie the data. Whereas, the missing data CFA model assumes that the model is equally valid for all participants, the semi-hierarchical CFA model is implicitly specified differently for subgroups of participants with and without omissions. The comparison of these models with the regular one-factor model in investigating simulated binary data revealed that the modeling of missing data prevented negative effects of missing data on model fit. The investigation of the accuracy in estimating the factor loadings yielded the best results for the semi-hierarchical CFA model. The average estimated factor loadings for items with and without omissions showed the expected equal sizes. But even this model tended to underestimate the expected values.
The purpose of this study was to examine the psychometric properties (i.e., factorial validity, measurement invariance, and reliability) of the Grit-Original scale (Grit-O) within the Netherlands. The Grit-O scale was subjected to a competing measurement modeling strategy that sequentially compared both independent cluster model confirmatory factor analytical- and exploratory structural equation modeling approaches. The results showed that both a two first order, bi-factor structure as well as a less restrictive two factor ESEM factorial structure best-fitted the data. The instrument showed to be reliable at both a lower- (Cronbach’s alpha) and upper-level (composite reliability) limit. However, measurement invariance between genders could only be established for the B-ICM-CFA model. Finally, concurrent validity was established through relating the GRIT-O to task performance. The linear use of the Grit-O scale should therefore carefully be considered.