Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures.
While academic self-efficacy is widely considered an individual cognitive variable, it may be influenced by a sense of belonging and connection to others in the school community. Using a correlation and multiple regression design, the study in this article examined the relationship between perceptions of school belonging, educational aspirations, and academic self-efficacy among 40 African American male high school students. Results indicated that feeling encouraged to participate and educational aspirations were significant, positive predictors of academic self-efficacy. Other components of perceptions of school belonging were not significant in predicting academic self-efficacy. Recommendations for future research and practical suggestions for school counselors are discussed.
Douglas, Roussos, and Stout introduced the concept of differential bundle functioning (DBF) for identifying the underlying causes of differential item functioning (DIF). In this study, reference group was simulated to have higher mean ability than the focal group on a nuisance dimension, resulting in DIF for each of the multidimensional items that, when examined together, produced DBF. The empirical power and the Type I error of the Simultaneous Item Bias Test for DBF analysis were examined under various sample sizes, ratios of reference to focal group sizes, correlations between target and nuisance dimensions, magnitudes of DIF/ DBF, test lengths, percentages of test items in the bundle, and item discriminations. Power was generally high in cells with larger DIF magnitudes, higher percentages of items in the bundle, larger sample sizes, and with the nuisance dimension having a higher discrimination than the target dimension. Type I error rates approximated the nominal alpha rate for all conditions.
Little is known about the use and accuracy of model selection criteria when selecting among a set of competing multilevel models. The practices of applied researchers and the performance of five model selection criteria are examined when selecting the correct multilevel model using simulation techniques.
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