A key job of education policymakers is to make decisions under uncertainty. They must weigh the risks, rewards, and costs of different interventions, policies, and mixes of resources, and make decisions even when the likely outcome is uncertain. How policymakers think about and deal with uncertainty has important implications for resource…

In their essay, "Why Assessment Will Never Work...," Bacon and Stewart (2016) recommend that instead of carrying out the expensive process of experimenting themselves, many business schools would get a bigger bang for their buck if they used "published pedagogical studies that use direct measures of learning with sufficient…

Most education reporters from time to time will tread into the world of education research, whether to gauge charter school achievement, the impact of teacher quality, or the effects of a reading program, among myriad possibilities. But making sense of the research, with its often-impenetrable prose, dizzying figures, and mathematical formulas,…

The purpose of this quantitative study was to identify the instructional leadership practices that develop a growth mindset in teachers and to determine if the evaluation model will lead to enhanced pedagogical practices. Schools were separated into schools of affluence and poverty. Schools of affluence had less than a 35% free and reduced lunch…

Identifies the common supposition in research by business educators that a research outcome that is statistically significant is necessarily practically significant. Describes the use and interpretation of a simple objective measure of practical significance, called Effect Size. (Author)

Practical significance is concerned with whether a research result is useful in the real world. The use of procedures to supplement the null hypothesis significance test in four journals of the American Psychological Association is examined, and an approach to assessing practical significance is presented. (SLD)

Review states that the book gives an examination of the null-hypothesis significance test procedure as an integral component of data analysis of quantitative research studies in the social sciences. It is designed for the nonmathematics student who will be doing empirical studies involving the testing of substantive hypotheses. (SLD)

This paper reviews criticism on misinterpretation and overuse of significance testing in the social sciences and examines use of effect size measures to enhance interpretation of significance testing. Review of typical effect size measures and their application is followed by analysis of use of effect size measures in studies reported over 10…

Statistical significance does not necessarily make research meaningful. The problems and implications must be significant and valuable, and the research process should not be emphasized more than product. Qualitative methods and better reporting of conclusions would enhance significance. (SK)

Effect Sizes (ES) are an increasingly important index used to quantify the degree of practical significance of study results. This paper gives an introduction to the computation and interpretation of effect sizes from the perspective of the consumer of the research literature. The key points made are: (1) "ES" is a useful indicator of the…

Knowledge of the magnitude of effect(s) of an experimental study in science education should be of utmost concern to researchers in the field, but is often not reported. This document describes the concept of "explained variance" in analysis of variance designs and then explains how it can be calculated and reported. Reporting the magnitude of…

Discusses the potential for increasing library effectiveness through the appropriate design and utilization of research and describes fundamental research and statistical techniques. Topics examined include areas amenable to data collection, data collection methods amenable to statistical analysis, measurement techniques and levels of…

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…