Many researchers have explored the relationships between the likelihood of graduating from college and demographic and pre-college factors such as gender, race/ethnicity, high school grade point average (GPA), and standardized test scores. However, additional factors such as a student's college major, home address, or use of learning support in…

This study explored the interaction between student characteristics and the online environment in predicting course performance and subsequent college persistence among students in a large urban U.S. university system. Multilevel modeling, propensity score matching, and the KHB decomposition method were used. The most consistent pattern observed…

Enabling undergraduate students to develop basic computing skills is an important issue in higher education. As a result, some universities have developed computer proficiency tests, which aim to assess students' computer literacy. Generally, students are required to pass such tests in order to prove that they have a certain level of computer…

This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…

Disproportionately large numbers of Black youth experience repeated encounters with the juvenile justice system, and often these same youth present with a variety of school-related difficulties. Data from a sample of 842 Black males, assessed in five Ohio juvenile courts using the Global Risk Assessment Device (GRAD), are used to describe the…

This paper examines some of the problems that arise when conducting multivariate analyses with incomplete data. The literature on the effectiveness of several missing data procedures (MDP) is summarized. The most widely used MDPs are: (1) listwise deletion; (2) pairwise deletion; (3) variable mean; (4) correlational methods. No MDP should be used…

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Although a complex process, canonical correlation has numerous potential advantages over other predictive techniques, the most important being that canonical correlation predicts several criterion variables simultaneously. The paper shows how the method can be used for industrial and vocational-technical education data. (Author)

The relationships between predictors of performance and subsequent measures of clinical performance in medical school were examined for two classes at Southern Illinois University of Medicine. Canonical redundancy analysis was used to evaluate the association between six academic and three biographical preselection characteristics and four…

Canonical variate analysis was employed to measure the direction and intensity of relationships between student ratings of instruction and students' course-oriented attitude change. Change was defined as the residual post-course attitude scores when pre-course attitude scores served as predictors. The Adjective Rating Scale (ARS), a measure of…

This study examined the relationship and relative importance of selected variables to successful performance of urban community college students on the American College Testing Program (ACT) College Outcome Measures Program (COMP). The importance of age, race, gender, type of degree, program or major category, admissions criteria and ACT…

Multivariate statistical analysis determined relationships between scores on the Otis-Lennon School Ability Test and the Stanford Achievement Test at grades 2, 4, and 6 for 272 students. The best predictor of achievement at a later grade was achievement at an earlier grade. Implications are discussed. (SLD)

Rectifies the unsatisfactory textbook treatment of the finite-sample proprieties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. Maintains that the bias of the ordinary least squares estimator is determined by the dynamic and correlation effects. (MJP)