ABSTRACT
PROBLEM: Urban children who become competent adults despite circumstances that place their development and mental health at risk are considered to be resilient. Less is known about the risk and protective factors that characterize resilience among Hispanic/Latinos living in rural areas. METHODS: Data for regression analyses were collected when children (n = 603; 54% Hispanic/Latino) enrolled in the study in fifth grade (M = 10.4 years of age), and again 5 years later when they were in high school (M = 15 years of age). FINDINGS: Statistically significant predictors of competence and self-worth in high schoolers included gender, ethnicity, and mother's education, as well as stress, temperament (task persistence), and competences measured in grade school. CONCLUSIONS: Parents' perception of child's temperament is a significant predictor of future competence and self-worth among rural adolescents.
Subject(s)
Adolescent Behavior , Mental Competency , Rural Population , Self Concept , Students/psychology , Adolescent , Adolescent Behavior/ethnology , Adult , Child , Educational Measurement , Female , Hispanic or Latino/psychology , Hispanic or Latino/statistics & numerical data , Humans , Longitudinal Studies , Male , Mothers/education , Mothers/psychology , Predictive Value of Tests , Regression Analysis , Resilience, Psychological , Risk Factors , Sex Factors , Social Class , Stress, Psychological/epidemiology , Stress, Psychological/ethnology , Students/statistics & numerical data , Texas/epidemiologyABSTRACT
The goal of the current study was to introduce a new stopping rule for computerized adaptive testing. The predicted standard error reduction stopping rule (PSER) uses the predictive posterior variance to determine the reduction in standard error that would result from the administration of additional items. The performance of the PSER was compared to that of the minimum standard error stopping rule and a modified version of the minimum information stopping rule in a series of simulated adaptive tests, drawn from a number of item pools. Results indicate that the PSER makes efficient use of CAT item pools, administering fewer items when predictive gains in information are small and increasing measurement precision when information is abundant.
ABSTRACT
Multiple membership random effects models (MMREMs) have been developed for use in situations where individuals are members of multiple higher level organizational units. Despite their availability and the frequency with which multiple membership structures are encountered, no studies have extended the MMREM approach to hierarchical growth curve modeling (GCM). This study introduces a cross-classified multiple membership growth curve model (CCMM-GCM) for modeling, for example, academic achievement trajectories in the presence of student mobility. Real data are used to demonstrate and compare growth curve model estimates using the CCMM-GCM and a conventional GCM that ignores student mobility. Results indicate that the CCMM-GCM represents a promising option for modeling growth for multiple membership data structures.
