The present article provides a primer on using effect sizes in research. A small heuristic data set is used in order to make the discussion concrete. Additionally, various admonitions for best practice in reporting and interpreting effect sizes are presented. Among these is the admonition to not use Cohen's benchmarks for "small," "medium," and…

A general linear model (GLM) framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. The computation of structure coefficients in explanatory factor analysis and CFA is explained. Two heuristic data sets are used to…

This paper explains how Q-technique factor analysis can be used to identify types or clusters of people with similar views. Q-technique factor analysis can be implemented with commonly available statistical software such as the Statistical Package for the Social Sciences (SPSS). The paper addresses three questions: (1) How many types (factors) of…

This paper explains in user-friendly terms why multivariate statistics are so important in educational research. The basic logic of canonical correlation analysis is presented as a simple or bivariate Pearson "r" procedure. It is noted that all statistical tests implicitly involve the calculation of least squares weights, and that all…

Stepwise methods are frequently employed in educational and psychological research, both to select useful subsets of variables and to evaluate the order of importance of variables. Three problems with stepwise applications are explored in some detail. First, computer packages use incorrect degrees of freedom in their stepwise computations,…

The comparability of seven sets of factor structure studies reported in previous research with the Bem Sex-Role Inventory was evaluated. The inventory's primary factors were reproduced in the studies, although the masculine scale was somewhat less invariant than was the feminine counterpart. (SLD)

Various realizations have led to less frequent use of the "OVA" methods (analysis of variance--ANOVA--among others) and to more frequent use of general linear model approaches such as regression. However, too few researchers understand all the various coefficients produced in regression. This paper explains these coefficients and their…

The utility of combining confirmatory factor analysis and second-order methods is illustrated in a study of responses of 487 undergraduate and graduate students to the love instrument of C. Hendrick and S. Hendrick. Second-order confirmatory methods allow the researcher to explore complex realities more thoroughly. (SLD)

The application and utility of confirmatory second-order factor analytic methods are discussed. Factor analysis is central to concerns regarding measurement validity. Confirmatory methods are especially useful because they explicitly consider measurement error influences and because the methods are inherently theory-driven and theory-oriented.…

The relationship between analysis of variance (ANOVA) methods and their analogs (analysis of covariance and multiple analyses of variance and covariance--collectively referred to as OVA methods) and the more general analytic case is explored. A small heuristic data set is used, with a hypothetical sample of 20 subjects, randomly assigned to five…

Most training programs in education and psychology focus on classical test theory techniques for assessing score dependability. This paper discusses generalizability theory and explores its concepts using a small heuristic data set. Generalizability theory subsumes and extends classical test score theory. It is able to estimate the magnitude of…

The nature of percentile ranks scores is explored using concrete heuristic examples. It is explained why arithmetic operations require measurement on equal-interval scales, and that percentile ranks are not measured on equal-interval scales and therefore may not be added or averaged. The consequences of inappropriately adding percentile ranks are…