Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…

Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article…

A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the…

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences.…

The purpose of this study is to test the validity of scores on the Homework Management Scale (HMS) using 681 rural and 306 urban high school students. Based on a randomized split of the rural sample, the author first conducts exploratory factor analysis on Group 1 (n = 341) and confirmatory factor analysis on Group 2 (n = 340). The results reveal…

Used logistic regression to model the observation-to-variable ratio required for the standard error scree procedure to identify the number of factors in simulated data correctly. Results demonstrate the ability of logistic regression to simplify summarizing and reporting findings from simulation studies that involve a large number of conditions.…

A linear regression approach to determining the number of factors in factor analytic solutions is presented. It provides objectivity while producing the same results as the visual scree test. Experience with the method to date indicates that the results are consistent with visual solutions. (SLD)

The effects of under and overrotation on common factor loading stability under three levels of common variance and three levels or error are examined. Four representative factor matrices were selected. Results suggested that matrices which account for large amounts of common variance tend to have stable factor loadings. (Author/RL)

General self-efficacy (GSE), individuals' belief in their ability to perform well in a variety of situations, has been the subject of increasing research attention. However, the psychometric properties (e.g., reliability, validity) associated with the scores on GSE measures have been criticized, which has hindered efforts to further establish the…