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David Rutkowski; Leslie Rutkowski; Greg Thompson; Yusuf Canbolat – Large-scale Assessments in Education, 2024
This paper scrutinizes the increasing trend of using international large-scale assessment (ILSA) data for causal inferences in educational research, arguing that such inferences are often tenuous. We explore the complexities of causality within ILSAs, highlighting the methodological constraints that challenge the validity of causal claims derived…
Descriptors: International Assessment, Data Use, Causal Models, Educational Research
Joshua Weidlich; Ben Hicks; Hendrik Drachsler – Educational Technology Research and Development, 2024
Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today,…
Descriptors: Educational Research, Educational Technology, Research Design, Structural Equation Models
Peter Schochet – Society for Research on Educational Effectiveness, 2024
Random encouragement designs are randomized controlled trials (RCTs) that test interventions aimed at increasing participation in a program or activity whose take up is not universal. In these RCTs, instead of randomizing individuals or clusters directly into treatment and control groups to participate in a program or activity, the randomization…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
Cuartas, Jorge; McCoy, Dana Charles – International Journal of Behavioral Development, 2021
Mediation has played a critical role in developmental theory and research. Yet, developmentalists rarely discuss the methodological challenges of establishing causality in mediation analysis or potential strategies to improve the identification of causal mediation effects. In this article, we discuss the potential outcomes framework from…
Descriptors: Mediation Theory, Behavior Development, Influences, Inferences

Kenneth A. Frank; Qinyun Lin; Spiro J. Maroulis – Grantee Submission, 2024
In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…
Descriptors: Causal Models, Statistical Inference, Observation, Educational Policy
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Educational Researcher, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
Kaplan, Avi; Cromley, Jennifer; Perez, Tony; Dai, Ting; Mara, Kyle; Balsai, Michael – Grantee Submission, 2020
In this commentary, we complement other constructive critiques of educational randomized control trials (RCTs) by calling attention to the commonly ignored role of context in causal mechanisms undergirding educational phenomena. We argue that evidence for the central role of context in causal mechanisms challenges the assumption that RCT findings…
Descriptors: Context Effect, Educational Research, Randomized Controlled Trials, Causal Models
Kraft, Matthew A. – Annenberg Institute for School Reform at Brown University, 2019
Researchers commonly interpret effect sizes by applying benchmarks proposed by Cohen over a half century ago. However, effects that are small by Cohen's standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In…
Descriptors: Data Interpretation, Effect Size, Intervention, Benchmarking
Motz, Benjamin A.; Carvalho, Paulo F.; de Leeuw, Joshua R.; Goldstone, Robert L. – Journal of Learning Analytics, 2018
To identify the ways teachers and educational systems can improve learning, researchers need to make causal inferences. Analyses of existing datasets play an important role in detecting causal patterns, but conducting experiments also plays an indispensable role in this research. In this article, we advocate for experiments to be embedded in real…
Descriptors: Causal Models, Statistical Inference, Inferences, Educational Experiments
Gelman, Andrew; Imbens, Guido – National Bureau of Economic Research, 2014
It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…
Descriptors: Regression (Statistics), Mathematical Models, Causal Models, Research Methodology
Marcus, Sue M.; Stuart, Elizabeth A.; Wang, Pei; Shadish, William R.; Steiner, Peter M. – Psychological Methods, 2012
Although randomized studies have high internal validity, generalizability of the estimated causal effect from randomized clinical trials to real-world clinical or educational practice may be limited. We consider the implication of randomized assignment to treatment, as compared with choice of preferred treatment as it occurs in real-world…
Descriptors: Educational Practices, Program Effectiveness, Validity, Causal Models
Prescriptive Statements and Educational Practice: What Can Structural Equation Modeling (SEM) Offer?
Martin, Andrew J. – Educational Psychology Review, 2011
Longitudinal structural equation modeling (SEM) can be a basis for making prescriptive statements on educational practice and offers yields over "traditional" statistical techniques under the general linear model. The extent to which prescriptive statements can be made will rely on the appropriate accommodation of key elements of research design,…
Descriptors: Research Design, Structural Equation Models, Educational Practices, Inferences
Reardon, Sean F.; Raudenbush, Stephen W. – Society for Research on Educational Effectiveness, 2011
The purpose of this paper is to clarify the assumptions that must be met if this--multiple site, multiple mediator--strategy, hereafter referred to as "MSMM," is to identify the average causal effects (ATE) in the populations of interest. The authors' investigation of the assumptions of the multiple-mediator, multiple-site IV model demonstrates…
Descriptors: Educational Research, Social Science Research, Research Design, Causal Models
Shadish, William R. – Psychological Methods, 2010
This article compares Donald Campbell's and Donald Rubin's work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell's work…
Descriptors: Inferences, Generalization, Epistemology, Causal Models
Cook, Thomas D.; Shadish, William R.; Wong, Vivian C. – Journal of Policy Analysis and Management, 2008
This paper analyzes 12 recent within-study comparisons contrasting causal estimates from a randomized experiment with those from an observational study sharing the same treatment group. The aim is to test whether different causal estimates result when a counterfactual group is formed, either with or without random assignment, and when statistical…
Descriptors: Causal Models, Experiments, Pretests Posttests, Job Training
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