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…

Purpose: This article provides a tutorial introduction to ordinal pattern analysis, a statistical analysis method designed to quantify the extent to which hypotheses of relative change across experimental conditions match observed data at the level of individuals. This method may be a useful addition to familiar parametric statistical methods…

Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…

Takers of educational tests often receive proficiency levels instead of or in addition to scaled scores. For example, proficiency levels are reported for the Advanced Placement (AP®) and U.S. Medical Licensing examinations. Technical difficulties and other unforeseen events occasionally lead to missing item scores and hence to incomplete data on…

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…

Data transformations have been promoted as a popular and easy-to-implement remedy to address the assumption of normally distributed errors (in the population) in linear regression. However, the application of data transformations introduces non-ignorable complexities which should be fully appreciated before their implementation. This paper adds to…

We study a popular dual enrollment program in Washington State, "Running Start" using a new administrative database that links high school and postsecondary data. Conditional on prior high school performance, we find that students participating in Running Start are more likely to attend any college but less likely to attend four-year…

We study a popular dual enrollment program in Washington State, "Running Start" using a new administrative database that links high school and postsecondary data. Conditional on prior high school performance, we find that students participating in Running Start are more likely to attend any college but less likely to attend four-year…

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…

The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…

In this study, data envelopment analysis (DEA) is used to compute efficiency scores for a sample of Italian schools by employing OECD-PISA2006 data aggregated at school level. Efficiency has been defined as the ability to transform inputs (resources, student background, etc.) into outputs (student achievement). Different versions of the DEA models…

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…

The purpose of this study is to look closely at how aspects of instructional leadership, and organizational learning affect the quality of madrasah in improving the quality of graduate the state madrasah aliyah. The experiment was conducted using a quantitative approach with descriptive and inferential methods, in inferential methods used…

This paper illustrates how information collected through interviews can develop a richer understanding of the assignment mechanism, which can result in more plausible causal effect estimates from observational studies and provides a roadmap for sensitivity analysis. Focusing on the issue of assignment to algebra in 8th grade, the author shows how…

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…