Using Constrained Factor Mixture Analysis to Validate Mixed-Worded Psychological Scales: The Case of the Rosenberg Self-Esteem Scale in the Dominican Republic

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Detecting non-content-based response styles in survey data: An application of mixture factor analysis

It is common for some participants in self-report surveys to be careless, inattentive, or lacking in effort. Data quality can be severely compromised by responses that are not based on item content (non-content-based [nCB] responses), leading to strong biases in the results of data analysis and misinterpretation of individual scores. In this study, we propose a specification of factor mixture analysis (FMA) to detect nCB responses. We investigated the usefulness and effectiveness of the FMA model in detecting nCB responses using both simulated data (Study 1) and real data (Study 2). In the first study, FMA showed reasonably robust sensitivity (.60 to .86) and excellent specificity (.96 to .99) on mixed-worded scales, suggesting that FMA had superior properties as a screening tool under different sample conditions. However, FMA performance was poor on scales composed of only positive items because of the difficulty in distinguishing acquiescent patterns from valid responses representing high levels of the trait. In Study 2 (real data), FMA detected a minority of cases (6.5%) with highly anomalous response patterns. Removing these cases resulted in a large increase in the fit of the unidimensional model and a substantial reduction in spurious multidimensionality.

Validation of the Chinese version of the Rosenberg Self-Esteem Scale: evidence from a three-wave longitudinal study

BACKGROUND

The 10-item Rosenberg Self-Esteem Scale (RSES) is a widely used tool for individuals to self-report their self-esteem; however, the factorial structures of translated versions of the RSES vary across different languages. This study aimed to validate the Chinese version of the RSES in the Chinese mainland using a longitudinal design.

METHODS

A group of healthcare university students completed the RSES across three waves: baseline, 1-week follow-up, and 15-week follow-up. A total of 481 valid responses were collected through the three-wave data collection process. Exploratory factor analysis (EFA) was performed on the baseline data to explore the potential factorial structure, while confirmatory factor analysis (CFA) was performed on the follow-up data to determine the best-fit model. Additionally, the cross-sectional and longitudinal measurement invariances were tested to assess the measurement properties of the RSES for different groups, such as gender and age, as well as across different time points. Convergent validity was assessed against the Self-Rated Health Questionnaire (SRHQ) using Spearman's correlation. Internal consistency was examined using Cronbach's alpha and McDonald's omega coefficients, while test-retest reliability was assessed using intraclass correlation coefficient.

RESULTS

The results of EFA revealed that Items 5, 8, and 9 had inadequate or cross-factor loadings, leading to their removal from further analysis. Analysis of the remaining seven items using EFA suggested a two-factor solution. A comparison of several potential models for the 10-item and 7-item RSES using CFA showed a preference for the 7-item form (RSES-7) with two factors. Furthermore, the RSES-7 exhibited strict invariance across different groups and time points, indicating its stability and consistency. The RSES-7 also demonstrated adequate convergent validity, internal consistency, and test-retest reliability, which further supported its robustness as a measure of self-esteem.

CONCLUSIONS

The findings suggest that the RSES-7 is a psychometrically sound and brief self-report scale for measuring self-esteem in the Chinese context. More studies are warranted to further verify its usability.