Limited research has investigated dissertation methodology choice and the factors that contribute to this choice. Quantitative research is based in mathematics and scientific positivism, and qualitative research is based in constructivism. These underlying philosophical differences posit the question if certain factors predict dissertation…

This paper presents a non-parametric imputation technique, named random forest, from the machine learning field. The random forest procedure has two main tuning parameters: the number of trees grown in the prediction and the number of predictors used. Fifty experimental conditions were created in the imputation procedure, with different…

The School Composition and the Black-White Achievement Gap study was undertaken by the National Center for Education Statistics to present both descriptive and associative information on the relationships among the percentage of students in a school who were Black (referred to as "Black student density" or "density"), the…

With its critical assessments of scientific evidence on the effectiveness of education programs, policies, and practices (referred to as "interventions"), and a range of products summarizing this evidence, the What Works Clearinghouse (WWC) is an important part of the Institute of Education Sciences' strategy to use rigorous and relevant…

Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…

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…

Contrary to popular opinion, significance testing does not inform the researcher of the likelihood of the replication of results from current research findings. Result replicability has been ignored by researchers because of an overreliance on significance testing. Several alternatives have been offered to provide the researcher with more…

Available approaches for determing significant differences between various groups are considered for use when the homogeneity of slopes precludes standard analysis of covariance. The advantages and statistical procedures for relating to a reference group are presented and criteria are suggested for choosing the best groups. (Author)

An analytic procedure is presented which casts sequential moderator analysis in the role of a multivariate test of parallelism of regressions. The procedure addresses a test for comparing predictor-criterion relationships for one set of measurements or multiple predictors and repeated measurements on a criterion. (Author/PN)

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…

Practical formulas for several analysis of variance (ANOVA) designs and models are presented which make it possible for readers to compute strength of association measures without the use of complete ANOVA tables. (Author/PN)

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

Selected methods for evaluating the stability of research results empirically are described, especially with regard to multivariate analysis. It is critically important to evaluate the influences of sampling error on obtained results; statistical significance testing does not inform judgment regarding the probable replicability or the sampling…

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Methods for examining the viability of assumptions underlying generalized linear models are considered. By appealing to the likelihood, a natural generalization of the raw residual plot for normal theory models is derived and is applied to investigating potential misspecification of the linear predictor. A smooth version of the plot is also…