A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category thresholds. Results revealed that factor loadings and robust standard errors were generally most accurately estimated using cat-LS, especially with fewer than 5 categories; however, factor correlations and model fit were assessed equally well with ML. Cat-LS was found to be more sensitive to sample size and to violations of the assumption of normality of the underlying continuous variables. Normal theory ML was found to be more sensitive to asymmetric category thresholds and was especially biased when estimating large factor loadings. Accordingly, we recommend cat-LS for data sets containing variables with fewer than 5 categories and ML when there are 5 or more categories, sample size is small, and category thresholds are approximately symmetric. With 6-7 categories, results were similar across methods for many conditions; in these cases, either method is acceptable.

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have been proposed, but the theoretical and empirical differences between the 2 have not been explored. In this article, we investigate the behavior of these 2 corrections. We show that the virtually unknown correction due to Li and Bentler (2006) , which we label the sample-corrected robust RMSEA, is a consistent estimate of the population ML RMSEA yet drastically reduces bias due to nonnormality in small samples. On the other hand, the popular correction implemented in several SEM programs, which we label the population-corrected robust RMSEA, has poor properties because it estimates a quantity that decreases with increasing nonnormality. We recommend the use of the sample-corrected RMSEA with nonnormal data and its wide implementation.

A variety of indices are commonly used to assess model fit in structural equation modeling. However, fit indices obtained from the normal theory maximum likelihood fit function are affected by the presence of nonnormality in the data. We present a nonnormality correction for 2 commonly used incremental fit indices, the comparative fit index and the Tucker-Lewis index. This correction uses the Satorra-Bentler scaling constant to modify the sample estimate of these fit indices but does not affect the population value. We argue that this type of nonnormality correction is superior to the correction that changes the population value of the fit index implemented in some software programs. In a simulation study, we demonstrate that our correction performs well across a variety of sample sizes, model types, and misspecification types.

Research has shown that preschoolers monitor others' prior accuracy and prefer to learn from individuals who have the best track record. We investigated the scope of preschoolers' attributions based on an individual's prior accuracy. Experiment 1 revealed that 5-year-olds (but not 4-year-olds) used an individual's prior accuracy at labelling to predict her knowledge of words and broader facts; they also showed a 'halo effect' predicting she would be more prosocial. Experiment 2 confirmed that, overall, 4-year-olds did not make explicit generalizations of knowledge. These findings suggest that an individual's prior accuracy influences older preschoolers' expectations of that individual's broader knowledge as well as their impressions of how she will behave in social interactions.

The study of children's social learning is a topic of central importance to our understanding of human development. Learning from others allows children to acquire information efficiently; however, not all information conveyed by others is accurate or worth learning. A large body of research conducted over the past decade has shown that preschoolers learn selectively from some individuals over others. In the present article we summarize our work and that of others on the developmental origins of selective social learning during infancy. The results of these studies indicate that infants are sensitive to a number of cues, including competence, age, and confidence, when deciding from whom to learn. We highlight the important implications of this research in improving our understanding of the cognitive and social skills necessary for selective learning, and point out promising avenues for future research.