Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis…

Group research projects frequently are used to teach undergraduate research methods. This study uses multivariate analyses to examine the characteristics of higher-achieving groups (those that earn higher grades on group research projects) and to estimate the effects of participating in higher-achieving groups on subsequent individual learning…

This study explores race as a potential predictor of research methods anxiety among a sample of undergraduates. While differences in academic achievement based on race and ethnicity have been well documented, few studies have examined racial differences in anxiety with regard to specific subject matter in undergraduate curricula. This exploratory…

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…

Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across…

This study examines how GRE scores can be used to better understand Education doctoral candidates' methodology choices for the dissertation as well as their persistence behaviors. Candidates' of one online doctoral education program were examined. Results of a MANOVA suggested that there is no difference in GRE scores based on doctoral candidates'…

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…

This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…

A review of the development of canonical correlation is presented and insufficiencies in applications from the literature are summarized. A list of analytical procedures for use in canonical correlation is detailed. The relationship between analysis and interpretation is emphasized. An example is presented. (JKS)

Conventional canonical methods distinguish between the two variable sets being analyzed, but the methods do not attempt to optimize the variance from a given variable set that will be contained in the final solution. In this respect canonical methods are said the be "symmetric." This paper proposes two non-symmetric, canonical-like…

Virtually all parametric statistical procedures have been shown to be special cases of canonical correlation analysis, which is a useful research methodology particularly when augmented by the calculation of canonical structure, index, and invariance coefficients. A logic for conducting stepwise canonical correlation analysis based upon evaluation…

Some interpretations of relative variable importance in the contexts of multivariate analysis of variance (MANOVA) and discriminant analysis (DA) are presented. Some indices potentially useful for the interpretations are presented, and the assessment of variable importance is illustrated using real data sets. Both descriptive discriminant analysis…

Virtually all parametric statistical procedures have been shown to be special cases of canonical correlation analysis. This article proposes a logic for conducting stepwise canonical correlation analyses, based upon evaluation of canonical communality coefficients. The procedure is a direct analogue of stepwise multiple regression. (Author/RD)

The overidentification of structural equation models with latent variables is discussed. The use of two- and three-indicator models is not recommended since such models do not allow a testing of the crucial assumption of unidimensionality among indicators in most cases. Models with four or more indicators may be more sensitive to departures from…

A growing controversy surrounds the strict interpretation of statistical significance tests in social research. Statistical significance tests fail in particular to provide estimates for the stability of research results. Methods that do provide such estimates are known as invariance or cross-validation procedures. Invariance analysis is largely…