Applied researchers have employed chi-square tests for more than one hundred years. This paper addresses the question of how one should follow a statistically significant chi-square test result in order to determine the source of that result. Four approaches were evaluated: calculating residuals, comparing cells, ransacking, and partitioning. Data…

Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…

In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…

There have been many discussions of how Type I errors should be controlled when many hypotheses are tested (e.g., all possible comparisons of means, correlations, proportions, the coefficients in hierarchical models, etc.). By and large, researchers have adopted familywise (FWER) control, though this practice certainly is not universal. Familywise…

This paper examines strategies for interpreting and reporting estimates of intervention effects for subgroups of a study sample. Specifically, the paper considers: why and how subgroup findings are important for applied research, the importance of pre-specifying sub- groups before analyses are conducted, the importance of using existing theory and…

This study is an evaluation of the behavior of the Liu-Agresti estimator of the cumulative common odds ratio when identifying differential item functioning (DIF) with polytomously scored test items using small samples. The Liu-Agresti estimator has been proposed by Penfield and Algina as a promising approach for the study of polytomous DIF but no…

This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus…

This paper presents an updated WWC (What Works Clearinghouse) Review of the Article "Culture and the Interaction of Student Ethnicity with Reward Structure in Group Learning". The study examined the effects of different reward systems used in group learning situations on the math skills of African-American and White students. The…

"Culture and the Interaction of Student Ethnicity with Reward Structure in Group Learning" examined the effects of different reward systems used in group learning situations on the math skills of African-American and white students. The study analyzed data on 75 African-American and 57 white fourth- and fifth-grade students from urban…

This NCEE Technical Methods report examines how to address the problem of missing data in the analysis of data in Randomized Controlled Trials (RCTs) of educational interventions, with a particular focus on the common educational situation in which groups of students such as entire classrooms or schools are randomized. Missing outcome data are a…

A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This…

This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…

In mean and covariance structure analysis, the chi-square difference test is often applied to evaluate the number of factors, cross-group constraints, and other nested model comparisons. Let model M[a] be the base model within which model M[b] is nested. In practice, this test is commonly used to justify M[b] even when M[a] is misspecified. The…

The issue of lower than expected mathematics achievement is a concern to education leaders and policymakers at all levels of the U.S. PK-12 education system. The purpose of this quantitative, quasi-experimental study was to determine if there was a measurable difference in achievement on the mathematics section of the state test for students (n =…