Bifactor and Bifactor S-1 Model Estimations with Non-Reverse-Coded Data

Abstract

The bifactor model is an extension of Spearman’s two-factor theory. The bifactor model has a strict assumption, which is named orthogonality. The bifactor S-1 model was developed by stretching the orthogonality assumption of the bifactor model. The bifactor S-1 model, contrary to the bifactor model, allows correlation between specific factors and enables items that do not form a common specific factor to be loaded only on the general factor. In psychology, data are mostly multidimensional due to the nature of psychological constructs. The Positive and Negative Affect Schedule (PANAS) which is one of the psychological tests and has two dimensions named positive affect and negative affect. In the literature studies on PANAS, negative affect dimensions were not reverse coded while implementing the bifactor model. Therefore, negative path coefficients were revealed. The purpose of this study is to ascertain whether or not the items in the negative affect factor should be reverse coded in the PANAS. Within the scope of the current study, bifactor and bifactor S-1 model analyses were implemented for the two data sets, which were reverse coded and non-reverse coded. As a result of this study, with reverse-coded data, the bifactor S-1 model was seen as the better model for the PANAS. Additionally, in the modeling of unique variances of items with specific factors, the bifactor S-1 model performed well and also resolved the problem of negative loading on the general factor. The point to take into consideration, which should be noted by researchers who will study the PANAS, is that negative items should be reverse coded.

Keywords

• PANAS
• bifactor S-1
• reverse coding

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