Stability and change in ethnic labeling among adolescents from Asian and Latin American immigrant families

An important question for the acculturation of adolescents from immigrant families is whether they retain ethnic labels that refer to their national origin (e.g., Mexican, Chinese) or adopt labels that are dominant in American society (e.g., Latino, Asian American, American). Approximately 380 adolescents from Asian and Latin American immigrant families selected ethnic labels during each of the 4 years of high school (age span = 14.87–17.82 years). Results indicated no normative trend either toward or away from identifying most closely with pan-ethnic or American ethnic labels. Significant numbers of adolescents changed their ethnic labels from year to year, however and these changes were associated with fluctuations in adolescents' ethnic affirmation and exploration and proficiency in their heritage languages.     

Ready to learn? Children's pre-academic achievement in pre-Kindergarten programs

We examined children's growth in school-related learning and social skills over the pre-Kindergarten (pre-K) year in state-funded programs designed to prepare children for kindergarten. We expected that children's gains in academic and social skills could be attributed to variations in the structural and classroom process dimensions of program quality. Nearly 3000 (n = 2800) children were randomly selected, four per classroom, from approximately 700 randomly selected, state-funded pre-Kindergarten classrooms in eleven states. Enrollment in pre-K appeared related to gains in academic skills. Children showed larger gains in academic outcomes when they experienced higher-quality instruction or closer teacher–child relationships. Gains were not related to characteristics of the child or program (i.e., ratio, teacher qualifications and program location and length). These findings have implications for a range of state and local policy and program development efforts as well as for theories of contextual influences on development.     

Summary Intervals for Model-Based Classification Accuracy and Consistency Indices

When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the linear factor model have been recently proposed, but parameter uncertainty of the CA and CC indices has not been investigated. This article demonstrates how to estimate percentile bootstrap confidence intervals and Bayesian credible intervals for CA and CC indices, which have the added benefit of incorporating the sampling variability of the parameters of the linear factor model to summary intervals. Results from a small simulation study suggest that percentile bootstrap confidence intervals have appropriate confidence interval coverage, although displaying a small negative bias. However, Bayesian credible intervals with diffused priors have poor interval coverage, but their coverage improves once empirical, weakly informative priors are used. The procedures are illustrated by estimating CA and CC indices from a measure used to identify individuals low on mindfulness for a hypothetical intervention, and R code is provided to facilitate the implementation of the procedures.     

Grade retention and school entry age in Spain: a structural problem

Educational Assessment, Evaluation and Accountability - Grade retention has been the focus of the education debate in Spain for decades. On average, more than 30% of students have repeated at least...