Latino English Language Learners represent the largest group of language minority students attending public school in the United States. Research is needed to address the early literacy problems which impede this population from acquiring literacy in the English language with efficacy and to determine the most effective assessment and intervention…

Each year, thousands of American students enter postsecondary institutions unprepared for college-level work and are subsequently placed in remedial or developmental courses. Several recent studies have examined the impact of these courses on student outcomes, but such studies focus exclusively on students who need just one or two classes; the…

A longitudinal research study was conducted at a public university in Pennsylvania on a sample of 129 at-risk students from a state-funded program (Act 101) designed to provide support services for economically and educationally disadvantaged students. This research employed a non-experimental, ex post facto methodology to assess the impact of…

Immigration to the United States presents both challenges and opportunities that affect students' academic achievement. Using a 5-year longitudinal, mixed-methods approach, we identified varying academic trajectories of newcomer immigrant students from Central America, China, the Dominican Republic, Haiti, and Mexico. Latent class growth curve…

Among the computer-based methods used for the construction of trees such as AID, THAID, CART, and FACT, the only one that uses an algorithm that first grows a tree and then prunes the tree is CART. The pruning component of CART is analogous in spirit to the backward elimination approach in regression analysis. This idea provides a tool in…

The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis is the GLM is explained, using a heuristic data set to demonstrate how canonical correlation analysis subsumes various multivariate and univariate methods. The paper shows how each of these analyses produces a synthetic variable, like the Yhat…

The paper stresses the importance of consulting beta weights and structure coefficients in the interpretation of regression results. The effects of multilinearity and suppressors and their effects on interpretation of beta weights are discussed. It is concluded that interpretations based on beta weights only can lead the unwary researcher to…

The business of science is formulating generalizable insight. No one study, taken singly, establishes the basis for such insight. Meta-analysis, however, can be used to determine if results generalize and to estimate the mean and the variance of effect sizes across studies (J. Hunter and F. Schmidt, 1990). Meta-analysis inquiries treat studies…

The Johnson-Neyman (J-N) technique (P. Johnson and N. Neyman, 1936) is used to determine areas of significant difference in a criterion variable between two or more groups in situations of linear regression. In using this technique, researchers have encountered difficulties with results, possibly related to the J-N technique's sensitivity to…

Regression models were developed for predicting internal and external turnaround time for interlibrary loan requests. Variables included document characteristics (publication age, language, document type, and subject); request characteristics (patron type, staffing levels, number of daily requests, and number of concurrent requests from a patron);…

Statistical issues concerning the analysis of multilevel data common in educational studies are discussed. Computer simulation results are presented to argue for a modification of two currently popular approaches to multilevel or contextual analytical procedures. (JKS)

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

We consider the problem of classifying an unknown observation into one of several populations using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for discriminant procedures that can be utilized regardless of the group-conditional distributions that…

Various statistical methods have been available to deal with missing data problems, but the difficulty is that they are based on somewhat restrictive assumptions that missing patterns are known or can be modeled with auxiliary information. This paper treats the presence of missing cases from the viewpoint that generalization as a sample does not…

This paper identifies specific problems with stepwise regression, notes criticisms of stepwise methods by statisticians, suggests appropriate ways in which stepwise procedures can be used, and gives examples of how this can be done. Although the stepwise method has been routinely criticized by statisticians, it is still frequently used in the…