Quantitative education research is often perceived to be "objective" or "neutral." However, quantitative research has been and continues to be used to perpetuate inequities; these inequities arise as both intended effects and unintended side effects of traditional quantitative research. In this review of the literature, we…

The purposes of this manuscript are to identify common statistical mistakes in sport management, and to provide scholars with suggestions on how to develop and improve the quality of quantitative research. We have reviewed articles published from 2001 to 2017 in the "Journal of Sport Management," "Sport Management Review,"…

Multivariate analysis of variance (MANOVA) is a statistical method used to examine group differences on multiple outcomes. This article reports results of a review of MANOVA in gifted education journals between 2011 and 2017 (N = 56). Findings suggest a number of conceptual and procedural misunderstandings about the nature of MANOVA and its…

Traditions of statistical significance testing in second language (L2) quantitative research are strongly entrenched in how researchers design studies, select analyses, and interpret results. However, statistical significance tests using "p" values are commonly misinterpreted by researchers, reviewers, readers, and others, leading to…

The effective evaluation of correctional programs is critically important. However, research in corrections rarely allows for the randomization of offenders to conditions of the study. This limitation compromises internal validity, and thus, causal conclusions can rarely be drawn. Increasingly, researchers are employing propensity score matching…

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 paper challenges Hanushek and Woessmann's (2008) contention that the quality and not the quantity of schooling determines a nation's rate of economic growth. I first show that their statistical analysis is flawed. I then show that when a nation's average test scores and average schooling attainment are included in a national income model,…

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…

Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions…

This paper proposes a new typology for understanding common research errors that expands on the four types of error commonly discussed in the research literature. Examples are presented to illustrate Type I and Type II errors, errors related to the interpretation of statistically significant and nonsignificant results respectively, with attention…

Communication researchers have recently expressed concern with the lack of statistical power in their literature. Authors propose a method for increasing statistical power: the partitioning of the decision region in three parts. This procedure results in an unambiguous interpretation of nonsignificant results and leads to increased power. (PD)

A dialog between two fictional teachers provides some basic examples of how research that uses approved methodology may provide results that are significant statistically but not significant practically. (PGD)

The second half of a dialogue between two fictional teachers examines the significance of statistical significance in research and considers the factors affecting the extent to which research results provide important or useful information. (PGD)

Reading researchers should be encouraged to include assessments of practical significance in all their journal articles, reviews of research, technical reports, and dissertations. The tendency has been to base the integration of data entirely on the results of tests for the significance of differences, overlooking the assessment of practical…

Although the primary responsibility rests with the authors of articles reporting nonsignificant results to demonstrate the worth of the results by discussing the power of the tests, consumers should be prepared to conduct their own power analyses. This article demonstrates the use of power analysis for the interpretation of nonsignificant…