Given the American Psychological Association's strong recommendation to always report effect sizes in research, scholars have a responsibility to provide complete information regarding their findings. The purposes of this study were to (a) determine the frequencies with which different effect sizes were reported in published, peer-reviewed…

This article reviews the literature on interventions targeting the academic performance of students with attention-deficit/hyperactivity disorder (ADHD) and does so within the context of the statistical significance testing controversy. Both the arguments for and against null hypothesis statistical significance tests are reviewed. Recent standards…

Three criticisms of conventional uses of structural significance testing are elaborated; and alternatives for augmenting statistical significance tests are reviewed, which include emphasizing effect size, evaluating statistical significance in a sample size context, and evaluating result replicability. Among ways of estimating result…

As an extension of B. Thompson's 1998 invited address to the American Educational Research Association, this paper cites two additional common faux pas in research methodology and explores some research issues for the future. These two errors in methodology are the use of univariate analyses in the presence of multiple outcome variables (with the…

Three criticisms of overreliance on results from statistical significance tests are noted. It is suggested that: (1) statistical significance tests are often tautological; (2) some uses can involve comparisons that are not completely sensible; and (3) using statistical significance tests to evaluate both methodological assumptions (e.g., the…

Problems with using stepwise analytic methods are discussed, and better alternatives are illustrated. To make the illustrations concrete, an actual data set, involving responses of 91 medical school admissions directors to 30 variables, was used. The 30 variables involved perceptions of barriers to medical school with respect to characteristics of…

Dissertations are the cumulative, tangible "best evidence" of interests of doctoral faculty and students in serious and incisive scholarship. Thus, dissertations are thoroughly studied by the program review teams periodically hired by boards of higher education in most states. The present paper explores seven errors in quantitative…

Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing,…

Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commonly utilized parametric methods. Analysis of variance, analysis of covariance, multiple analysis of variance, and multiple analysis of covariance are heavily used by educational researchers. It is concluded that researchers would do well to consider…

This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…

Dissertations are an important component of the effort to generate knowledge. Thus, dissertation quality may be seen by accreditation and coordinating-board reviewers as a noteworthy reflection on the quality of doctoral programs themselves. The present study reviews methodological errors within Ph.D. dissertations. The illustrative errors are…