The purpose of this study was to investigate if students are prepared to release any personal data in order to inform learning analytics systems. Besides the well-documented benefits of learning analytics, serious concerns and challenges are associated with the application of these data driven systems. Most notably, empirical evidence regarding…

In Croatian classrooms it is possible to observe teaching scenarios that follow the features of constructivist and traditional teaching theories and many variants and combinations of teaching didactics that are student centered and those that are teacher centered. Teachers struggle to find their way in the selection and design of a media…

This study investigated whether college students' self-efficacy, level of learning strategy use, academic burnout, and school support predict course satisfaction and learning persistence. To this end, self-efficacy, level of learning strategy use, academic burnout, and school support were used as prediction variables, and course satisfaction and…

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

When investigating differences between two sets of scores, the t test is appropriate. If the two sets of data are from two groups of subjects, then the independent t test is appropriate. If the two sets are from the same subjects, the dependent t test is required. In this paper, the authors describe the use of a third test when part of a data set…

This paper relates common statistics from contingency table analysis to the more familiar R squared terminology in order to better understand the strength of the relation implied. The method of coding contingency tables was shown, as well as how R squared related to phi, V, and chi squared. (Author/CTM)

Considerable thought, research, and concern has been expanded in an effort to determine whether the assumption of a quadratic relation between a single predictor and a criterion violated the assumptions which Johnson and Neyman (1936) state for calculating regions of significance about interacting regressions. In particular, there has been special…

Using a Monte Carlo approach, comparison was made between traditional procedures and a multiple linear regression approach to test for differences between values of r sub 1 and r sub 2 when sample data were dependent and independent. For independent sample data, results from a z-test were compared to results from using multiple linear regression.…

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…

The purpose of this paper is to demonstrate how multiple linear regression provides a viable statistical methodology for dealing with meta-analysis in general, and specifically with the issues of nonindependence and design complexity, such as multiple treatments. Since the F-test and t-test are special cases of the general linear model,…

A dichotomous dependent variable is used to determine a combination of variables that will predict group membership. Dichotomous variables are frequently encountered in multiple regression analysis. However, several textbooks question the appropriateness of using multiple regression analysis when analyzing dichotomous dependent variables. The…

Three procedures used to control Type I error rate in stepwise regression analysis are forward selection, backward elimination, and true stepwise. In the forward selection method, a model of the dependent variable is formed by choosing the single best predictor; then the second predictor which makes the strongest contribution to the prediction of…

The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

A general procedure is presented for generating code values for a qualitative variable in multiple linear regression analyses that result in directly interpretable estimates of interest. The basic approach, in viewing ANOVA as a multiple regression problem, is to derive quantitative code values for the various levels of the qualitative ANOVA…

The current state of usage of regression models in analysis of variance (ANOVA) designs is empirically examined, and examples of several statistical errors made in usage are presented. The assumptions of the general linear model are that all predictors are known without error of measurement and are fixed with no replication or sample variation; in…