We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score…

Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past…

Research synthesis is the practice of systematically distilling and integrating data from many studies in order to draw more reliable conclusions about a given research issue. When the first edition of "The Handbook of Research Synthesis and Meta-Analysis" was published in 1994, it quickly became the definitive reference for conducting…

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

The purpose of this article is to inform researchers about and encourage the use of longitudinal designs to further understanding of human resource development and organizational theory. This article presents information about a variety of longitudinal research designs, related statistical procedures, and an overview of general data collecting…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

Nearly all quantitative analyses in higher education draw from incomplete datasets-a common problem with no universal solution. In the first part of this paper, we explain why missing data matter and outline the advantages and disadvantages of six common methods for handling missing data. Next, we analyze real-world data from 5,905 students across…

Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

Discusses problems researchers face when they want to describe relationship between several predictors and criterion variable. Considers ways of addressing problem of contribution of each predictor depending on which other predictors are in regression equation. Focuses on parallel information for each variable, examining initial and final…

It is generally believed that the correction formula will yield exact correlational values only when the regression of z on x is both linear and homoscedastic. The formula is shown to hold for nonlinear heteroscedastic relationships. A simple sufficient condition for formula validity and estimation predictions is demonstrated in a numerical…

An evaluation of open education was shown to produce misleading results due to probable regression phenomena. These questionable results are now spread throughout the literature of education, sociology, and psychology. Researchers are advised to review, not merely summarize, prior articles. (Author/PN)

Discusses problems in analyzing change in nonexperimental data. Tests three ordinary least-squares regression models to illustrate similarities/differences. Notes that model based on raw difference change scores applies best to studying change processes; model based on outcome scores applies best to assessing consequences of change; and model…

The data analysis problem posed by a repeated measures design that includes a single observation on a covariate for each subject is considered. The current paper discusses how best to capture a possible dependence of the effect of the within-subject factor on the level of the covariate. Procedures originally explicated by Rogosa (1980) for dealing…

Educational outcomes are affected by student level, classroom level, and school level characteristics. The fact that educational data are multilevel in nature poses serious analysis questions. Though strong arguments can be made for focusing on a single level of analysis, such studies have several basic limitations: the choice of analytic level…