Recent and ongoing research has demonstrated the alarming likelihood of children from low-income homes and from ethnic minorities to read at much lower reading levels than their peers. Additionally, reading ability is related to the earliest of emergent literacy skills, which can be measured in young children before they enter formal schooling.…

The purpose of this study was to contribute to the research body of literature regarding dual enrollment, the impact and effectiveness of the program, and its relationship to college persistence. The goal was to provide information useful to governing bodies, educational leaders, parents and students. The theoretical construct, defined through the…

Nursing student attrition in community colleges negatively affects students, faculty, colleges, and the nursing profession. The purpose of this quantitative correlational retrospective research study was to examine the possible relationships between each of the independent variables of academic preparedness (as measured by NET mathematics and…

National education data indicate that young men of color and students living in poverty are not experiencing the same academic success as their female, White, or higher socioeconomic status peers, as evidenced by low reading achievement levels and high dropout rates. Of particular concern is the underachievement of Latino males, who currently have…

Objectives: To investigate the prevalence, trends, and characteristics of maltreatment and assault related hospital admissions and deaths among children; and identify common injuries and conditions associated with these admissions using routinely collected morbidity and mortality data. Methods: A retrospective cohort study of all children aged…

A simple formula for calculating semipartial correlations is presented and illustrated. (Author)

One of the innovative approaches in the use of hierarchical linear models (HLM) is to use HLM for Slopes as Outcomes models. This implies that the researcher considers that the regression slopes vary from cluster to cluster randomly as well as systematically with certain covariates at the cluster level. Among the covariates, group indicator…

Multiple regression analysis is used with considerable frequency by researchers as a means of predicting the impact of predictor variables on a dependent variable. Regression predictors are typically correlated, often intentionally. To better understand the relative contribution of each independent variable in regression (and other) analyses,…

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Studied two methods to reduce the impact of intraclass correlation (ICC) in group-randomized trials (GRTs) : modeling time and regression adjustment for covariates. Findings for 1,188 ICC estimates from 17 articles (21 studies) confirm that both methods can improve the efficiency of analyses shown to be valid across conditions in GRTs. (SLD)

Describes how the assumptions underlying the use of multiple regression are not satisfied in reliability generalization studies and introduces mixed effects modeling to overcome many shortcomings of traditional approaches. Provides an example using results from the Beck Depression Inventory. (SLD)

Studied the effectiveness of various analytical formulas for estimating "R" squared shrinkage in multiple regression analysis, focusing on estimators of the squared population multiple correlation coefficient and the squared population cross validity coefficient. Simulation results suggest that the most widely used Wherry (R. Wherry,…

A technique is proposed to estimate regression coefficients for interaction and quadratic latent variables that combines regression analysis with the measurement model portion of structural equation analysis. The proposed technique will provide coefficient estimates for regression models involving existing measures or new measures for which a…

Describes a Monte Carlo investigation of four methods for treating incomplete data: (1) resemblance based hot-deck imputation (RBHDI); (2) iterated stochastic regression imputation; (3) structured model expectation maximization; and (4) saturated model expectation maximization. Results favored the expectation maximization methods. (SLD)

Presents the concept of prediction via multiple regression (MR) and discusses the assumptions underlying multiple regression analyses. Also discusses shrinkage, cross-validation, and double cross-validation of prediction equations and describes how to calculate confidence intervals around individual predictions. (SLD)