Economic inequality is associated with preferences for smaller, immediate gains over larger, delayed ones. Such temporal discounting may feed into rising global inequality, yet it is unclear whether it is a function of choice preferences or norms, or rather the absence of sufficient resources for immediate needs. It is also not clear whether these reflect true differences in choice patterns between income groups. We tested temporal discounting and five intertemporal choice anomalies using local currencies and value standards in 61 countries (N = 13,629). Across a diverse sample, we found consistent, robust rates of choice anomalies. Lower-income groups were not significantly different, but economic inequality and broader financial circumstances were clearly correlated with population choice patterns.

The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PA_PCA) or principal axis, and exploratory graph analysis with Louvain clustering (EGA_LV). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PA_PCA (PAP_CA-FS) and EGA_LV (EGA_LV-FS). Additionally, we examined the direct multilevel solution provided by EGA_LV. All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGA_LV and PA_PCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAP_CA-FS and EGA_LV-FS showed a close to perfect accuracy across all the conditions, while EGA_LV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGA_LV (group factors) and EGA_LV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Bi-factor exploratory modeling has recently emerged as a promising approach to multidimensional psychological measurement. However, state-of-the-art methods relying on target rotation require researchers to select an arbitrary cut-off for defining the target matrix. Unfortunately, the consequences of such choice on factor recovery remain uninvestigated under realistic conditions (e.g., factors differing in their average loadings). Built upon the iterative target rotation based on Schmid-Leiman algorithm (SLi), a novel method is here introduced (SLiD). SLiD settles an empirical, factor-specific cut-off based on the first prominent one-lagged difference of sorted squared normalized factor loadings. SLiD and SLi with arbitrary cut-off (ranging from .05 to .20) performance were evaluated via Monte Carlo simulation manipulating sample size, number of specific factors, number of indicators, and cross-loading magnitude. Results indicate that SLiD performed the best for all conditions. For SLi, and due to the presence of minor factors, smaller cut-offs (i.e., .05) outperformed higher ones (i.e., .20).

There has been increased interest in assessing the quality and usefulness of short versions of the Raven’s Progressive Matrices. A recent proposal, composed of the last twelve matrices of the Standard Progressive Matrices (SPM-LS), has been depicted as a valid measure of g. Nonetheless, the results provided in the initial validation questioned the assumption of essential unidimensionality for SPM-LS scores. We tested this hypothesis through two different statistical techniques. Firstly, we applied exploratory graph analysis to assess SPM-LS dimensionality. Secondly, exploratory bi-factor modelling was employed to understand the extent that potential specific factors represent significant sources of variance after a general factor has been considered. Results evidenced that if modelled appropriately, SPM-LS scores are essentially unidimensional, and that constitute a reliable measure of g. However, an additional specific factor was systematically identified for the last six items of the test. The implications of such findings for future work on the SPM-LS are discussed.

As general factor modeling continues to grow in popularity, researchers have become interested in assessing how reliable general factor scores are. Even though omega hierarchical estimation has been suggested as a useful tool in this context, little is known about how to approximate it using modern bi-factor exploratory factor analysis methods. This study is the first to compare how omega hierarchical estimates were recovered by six alternative algorithms: Bi-quartimin, bi-geomin, Schmid-Leiman (SL), empirical iterative empirical target rotation based on an initial SL solution (SLiD), direct SL (DSL), and direct bi-factor (DBF). The algorithms were tested in three Monte-Carlo simulations including bi-factor and second-order structures and presenting complexities such as cross-loadings or pure indicators of the general factor and structures without a general factor. Results showed that SLiD provided the best approximation to omega hierarchical under most conditions. Overall, neither SL, bi-quartimin, nor bi-geomin produced an overall satisfactory recovery of omega hierarchical. Lastly, the performance of DSL and DBF depended upon the average discrepancy between the loadings of the general and the group factors. The re-analysis of eight classical datasets further illustrated how algorithm selection could influence judgments regarding omega hierarchical.