Despite calls to expand early childhood education (ECE) in the United States, questions remain regarding its medium- and long-term impacts on educational outcomes. We use meta-analysis of 22 high-quality experimental and quasi-experimental studies conducted between 1960 and 2016 to find that on average, participation in ECE leads to statistically…

Outcome-reporting bias occurs when primary studies do not include information about all outcomes measured in a study. When studies omit findings on important measures, efforts to synthesize the research using systematic review techniques will be biased and interpretations of individual studies will be incomplete. Outcome-reporting bias has been…

Gender differences in human capital investments made outside of the traditional school day suggest that males and females consume, respond to, and form habits relating to education differently. We document robust, statistically significant one-hour weekly gender gaps in secondary students' non-school study time using time diary data from the…

Given the increasing costs associated with commercial textbooks and decreasing financial support of public schools, it is important to better understand the impacts of open educational resources on student outcomes. The purpose of this quantitative study is to analyze whether the adoption of open science textbooks significantly affects science…

The authors analyze national data on recent college matriculants to investigate gender and racial/ethnic disparities in STEM fields, with an eye toward the role of academic preparation and attitudes in shaping such disparities. Results indicate that physical science/engineering (PS/E) majors are dominated by men, but not, however,…

Correlation matrices and standard deviations are the building blocks of many of the commonly conducted analyses in published research, and AERA and APA reporting standards recommend their inclusion when reporting research results. The authors argue that the inclusion of correlation/covariance matrices, standard deviations, and means can enhance…

In the educational research literature, alpha and p are often conflated. Paradoxically, alpha retains a prominent place in textbook discussions, but it is often supplanted by p in the results sections of journal articles. Because alpha and p have unique uses, researchers should continue to employ both conventions in summarizing the outcomes of…

Criticizes an article in which S. A. Cohen and J. S. Hyman describe as "suspicious" the high proportion of statistically significant findings reported in published educational research journals and offers alternative explanations for the high statistical power reported. (GC)

The authors contend that most research in education lacks statistical power. They feel that the poor use of statistics as a tool for enhancing internal validity must be remediated. The adoption of a new convention is proposed in order to put statistical certainty into reasonable perspective. (RLV)

Shows why the two-step approach proposed by D. Robinson and J. Levine (1997) is inappropriate for the evaluation of empirical results and reiterates the preferred approach of increased sample size and the computation of confidence intervals. (SLD)

Supports a two-step approach to the estimation and discussion of effect sizes, making a distinction between single-study decision-oriented research and multiple-study synthesis. Introduces and illustrates the concept of "conclusion coherence." (Author/SLD)

Discusses criticisms of null hypothesis significance testing (NHST), suggesting that historical use of NHST was reasonable, and current users should read Sir Ronald Fisher's applied work. Notes that modifications to NHST and interpretations of its outcomes might better suit the needs of modern science. Concludes that NHST is most often useful as…

Reviews practices regarding tests of statistical significance and policies of the American Educational Research Association (AERA). Decades of misuse of statistical significance testing are described, and revised editorial policies to improve practice are highlighted. Correct interpretation of statistical tests, interpretation of effect sizes, and…

Surveyed AERA members regarding their perceptions of: statistical issues and statistical significance testing; the general linear model; stepwise methods; score reliability; type I and II errors; sample size; statistical probabilities as exclusive measures of effect size; p values as direct measures of result value; and p values evaluating…

Argues that describing results as "significant" rather than "statistically significant" is confusing to the very people most apt to misinterpret this telegraphic wording. The importance of reporting the effect size and the value of both internal and external replicability analyses are stressed. (SLD)