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…

Compared to traditional classification methods, developing a peer group using the National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data Systems (IPEDS) data allows institutions to add comparative dimensions, to update the peer group, and to track changes at peer institutions over time. Peer selection and…

Objective: The authors explored the relationship between self-reported vigorous exercise frequency and alcohol, tobacco, and other drug (ATOD) use behaviors among first-year college students who self-identified as drinkers. Participants: The authors recruited 391 freshman college students in Northeast Florida to participate in an alcohol abuse…

Although there is agreement that graduating students should be able to function effectively in an increasingly diverse society, there is reasonable difference of opinion regarding how that goal should be accomplished and how progress should be measured. The most pervasive and appealing conventional wisdom is that positive attitudes and behaviors…

Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…

Commonality analysis is a method of partitioning variance to determine the predictive ability unique to each predictor (or predictor set) and common to two or more of the predictors (or predictor sets). The purposes of the present paper are to (a) explain commonality analysis in a multiple regression context as an alternative for middle grades…

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…

Although many researchers are familiar with "testwise" alpha, "experimentwise" Type I error rates are also important and must be considered in many research situations. Experimentwise error rates can inflate rapidly when researchers use multiple univariate tests. Both analysis of variance (ANOVA) post hoc and multi-variate…

Discriminant analysis is a multivariate method of analysis with two purposes: (1) to describe differences among groups; or (2) to classify participants into groups. Either linear or quadratic rules can be used in both descriptive discriminant analysis (DDA) and predictive discriminant analysis (PDA). In both DDA and PDA the researcher wants to use…

Given that a minor condition holds (e.g., the number of variables is greater than the number of clusters), a nontrivial lower bound for the sum-of-squares error criterion in K-means clustering is derived. By calculating the lower bound for several different situations, a method is developed to determine the adequacy of cluster solution based on…

In this paper we examine the influence of gender, sibling characteristics and birth order on the schooling attainment of school-age Egyptian children. We use multivariate analysis to simultaneously examine three different schooling outcomes of a child having "no schooling", "less than the desired level of schooling", and an…

A random sample of Canadian foster parents were asked about the importance of culture in fostering. In response to the question "What values, beliefs and traditions were you raised with and feel are important?," a total of 74 different responses were received. These responses were grouped together by foster parents and the groupings…

Integrated Basic Education and Skills Training (I-BEST) was developed by the community and technical colleges in Washington State to increase the rate at which adult basic skills students enter and succeed in postsecondary occupational education and training. Under the I-BEST model, basic skills instructors and career-technical faculty jointly…

This paper introduces five commonly used approaches to analyzing multiple-choice test data. They are classical test theory, factor analysis, cluster analysis, item response theory, and model analysis. Brief descriptions of the goals and algorithms of these approaches are provided, together with examples illustrating their applications in physics…

In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…