This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to…

Single-case experimental designs (SCEDs) have been used with increasing frequency to identify evidence-based interventions in education. The purpose of this study was to explore how several procedural characteristics, including within-phase variability (i.e., measurement error), number of baseline observations, and number of intervention…

Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer" in PER, this paper discusses some of the fundamental issues in statistical causal inference. In…

Teacher shortages and attrition are problems of international concern. One of the most frequent reasons for teachers leaving the profession is a lack of job satisfaction. Accordingly, in this study we have adopted a causal inference machine learning approach to identify practical interventions for improving overall levels of job satisfaction. We…

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…

This article reviews important statistical methods for estimating the impact of interventions on outcomes in education settings, particularly programs that are implemented in field, rather than laboratory, settings. We begin by describing the causal inference challenge for evaluating program effects. Then four research designs are discussed that…

BASIE is a framework for interpreting impact estimates from evaluations. It is an alternative to null hypothesis significance testing. This guide walks researchers through the key steps of applying BASIE, including selecting prior evidence, reporting impact estimates, interpreting impact estimates, and conducting sensitivity analyses. The guide…

Analyzing heterogeneous treatment effects (HTE) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and pre-intervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

Single-case design is a repeated-measures research approach for the study of the effect of an intervention, and its importance is increasingly being recognized in education and psychology. We propose a Bayesian approach for estimating intervention effects in SCD. A Bayesian inference does not rely on large sample theories and thus is particularly…

Much research in autism spectrum disorders (ASD) has focused on the development of efficacious interventions to address the core deficits of ASD. However, the heterogeneous nature of ASD complicates the development of such interventions. With great heterogeneity in the expression of ASD's core deficits, it is unlikely that there is a one size fits…

Teachers who engage primary school students in informal statistical inference (ISI) must themselves have good content knowledge of ISI (ISI-CK). However, little is known about how college education for pre-service teachers can contribute to the development of their ISI-CK. To address this shortcoming, we used a case study to investigate ISI-CK…

Propensity score matching techniques are becoming increasingly common as they afford applied practitioners the ability to account for systematic bias related to self-selection. However, "best practices" for implementing these techniques in applied settings is scattered throughout the literature. The current article aims to provide a…

Purpose: Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)-based approaches to analyzing speech-evoked neurophysiological responses. Method: Two categories of ML-based…

Multisite trials, which are being used with increasing frequency in education and evaluation research, provide an exciting opportunity for learning about how the effects of interventions or programs are distributed across sites. In particular, these studies can produce rigorous estimates of a cross-site mean effect of program assignment…