The difference in differences design is widely used to assess treatment effects in natural experiments or other situations where random assignment cannot, or is not, used (see, e.g., Angrist & Pischke, 2009). The researcher must make important decisions about which comparisons to make, the measurements to make, and perhaps the number of…

Current research on intergenerational mobility (IGM) is informed by "statistical" approaches based on log-level regressions, whose "economic" interpretations remain largely unknown. We reveal the subjective value-judgments in them: they are represented by weighted-sums (or aggregators) over heterogeneous groups, with…

School start regulations allocate children born immediately either side of a given date to different life paths: those slightly older starting school a full year earlier. School effectiveness literature exploits this to estimate causal effects described as 'the absolute effect of schooling' or 'the effect of an additional year's schooling', using…

Background: Educational programs often use student test scores to determine access to some treatment, such as remedial support or graduation (Jacob & Lefgren, 2004; Martorell, 2004; Matsudaira, 2008; Papay et al., 2011, 2014). In these cases, treatment assignment is based on the student's score from one or more subjects. For example, students…

Fuzzy regression-discontinuity evaluations of college remediation often find negative and null estimates of local average treatments effects (LATEs), but with substantial heterogeneity. We find that a remedial quantitative skills course at Wellesley College has a modestly positive LATE on participation in mathematically intensive fields of…

Prior research has suggested that clustered regression discontinuity designs are a formidable alternative to cluster randomized designs because they provide targeted treatment assignment while maintaining a high-quality basis for inferences on local treatment effects. However, methods for the design and analysis of clustered regression…

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant…

This study compared Stepwise Logistic Regression (Stepwise-LR) and three machine learning (ML) methods--Classification and Regression Trees (CART), Random Forest (RF), and Generalized Boosted Modeling (GBM) for estimating propensity scores (PS) applied in causal inference. A simulation study was conducted considering factors of the sample size,…

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of…

We study the external validity of instrumental variable estimation. The key assumption we impose for external validity is conditional external unconfoundedness among compliers, which means that the treatment effect and target selection are independent among compliers conditional on covariates. We study this assumption with a case study about the…

Causal asymmetry is a situation where the causal factors under study are more suitable for explaining the outcome than its absence (or vice versa); they do not explain both equally well. In such a situation, presence of a cause leads to presence of the effect, but absence of the cause may not lead to absence of the effect. A conceptual discussion…

Although Cattaneo et al. (2019) provided a data-driven framework for power computations for Regression Discontinuity Designs in line with rdrobust Stata and R commands, which allows higher-order functional forms for the score variable when using the non-parametric local polynomial estimation, analogous advancements in their parametric estimation…

Despite the availability and potential usefulness of demographic and contextual data in many quantitative studies within engineering education, the preference for ANOVA over regression models remains prevalent, often without clear justification. A mapping review of literature from the EJEE and JEE spanning 2012-2022 identified 98 studies using…

In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Factor score regression has recently received growing interest as an alternative for structural equation modeling. However, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. We perform a simulation study to examine how a selection of factor scoring methods compare when estimating…