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Rosa W. Runhardt – Sociological Methods & Research, 2024
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…
Descriptors: Causal Models, Statistical Inference, Intervention, Investigations
Peter Z. Schochet – Journal of Educational and Behavioral Statistics, 2025
Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
Cody Ding – Educational Psychology Review, 2024
In the article "It's Just an Observation," Robinson and Wainer (Educational Psychology Review 35, Robinson, D., & Wainer, H. (2023). It's just an observation. Educational Psychology Review, 35(83), Published online: 14 August, 2023) lamented that educational psychology is moving toward the dark side of the quality continuum, with…
Descriptors: Journal Articles, Educational Psychology, Quality Assurance, Barriers
Heining Cham; Hyunjung Lee; Igor Migunov – Asia Pacific Education Review, 2024
The randomized control trial (RCT) is the primary experimental design in education research due to its strong internal validity for causal inference. However, in situations where RCTs are not feasible or ethical, quasi-experiments are alternatives to establish causal inference. This paper serves as an introduction to several quasi-experimental…
Descriptors: Causal Models, Educational Research, Quasiexperimental Design, Research Design
Nicholas D. Myers; Ahnalee M. Brincks; Seungmin Lee – Measurement in Physical Education and Exercise Science, 2024
Physical activity promotion is a best buy for public health because it has the potential to help individuals feel better, sleep better, and perform daily tasks more easily, in addition to providing disease prevention benefits. There is strong evidence that individual-level theory-based behavioral interventions are effective for increasing physical…
Descriptors: Physical Activity Level, Health Behavior, Health Promotion, Public Health
Adlof, Lauren; Kim, Minkyoung; Crawley, William – TechTrends: Linking Research and Practice to Improve Learning, 2023
Undergraduate student retention is considered a critical issue in higher education, due to its impact on student success, degree completion, and the financial health of universities (Cataldi et al., 2018; Cornelius & Cavanaugh, 2016; Hermes, "Community College Journal," 82(4), 26, 2012; Tinto, "NACADA Journal," 19(2), 5-9,…
Descriptors: Undergraduate Students, School Holding Power, Performance Technology, Academic Achievement
Nicholas D. Myers; Ahnalee M. Brincks; Seungmin Lee – Measurement in Physical Education and Exercise Science, 2025
Physical activity (PA) promotion is an ideal intervention target for public health because it has the potential to help individuals feel better, sleep better, and perform daily tasks more easily, in addition to providing disease prevention benefits. There is strong evidence that individual-level theory-based behavioral interventions are effective…
Descriptors: Physical Activity Level, Intervention, Program Effectiveness, Adults
Vidushi Adlakha; Eric Kuo – Physical Review Physics Education Research, 2023
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…
Descriptors: Physics, Science Education, Statistical Inference, Causal Models
Nianbo Dong; Keith Herman; Benjamin Kelcey; Sirui Ren; Wendy Reinke; Jessaca Spybrook – Grantee Submission, 2025
Contextual, identity, and cultural factors are not only associated with student outcomes but can also serve to moderate the effects of interventions. However, the conventional analysis of moderation commonly used in school psychology is subject to the selection bias potentially introducing bias into estimated moderator effects. This article…
Descriptors: Causal Models, Statistical Analysis, Context Effect, Intervention
Manolov, Rumen; Tanious, René; Fernández-Castilla, Belén – Journal of Applied Behavior Analysis, 2022
In science in general and in the context of single-case experimental designs, replication of the effects of the intervention within and/or across participants or experiments is crucial for establishing causality and for assessing the generality of the intervention effect. Specific developments and proposals for assessing whether an effect has been…
Descriptors: Intervention, Behavioral Science Research, Replication (Evaluation), Research Design
Deborah L. Hall; Yasin N. Silva; Brittany Wheeler; Lu Cheng; Katie Baumel – International Journal of Bullying Prevention, 2022
Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection…
Descriptors: Bullying, Computer Mediated Communication, Social Media, Research
Joshua B. Gilbert; Luke W. Miratrix; Mridul Joshi; Benjamin W. Domingue – Journal of Educational and Behavioral Statistics, 2025
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.…
Descriptors: Causal Models, Item Response Theory, Statistical Inference, Psychometrics
Kraft, Matthew A. – Educational Researcher, 2020
Researchers commonly interpret effect sizes by applying benchmarks proposed by Jacob 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.…
Descriptors: Effect Size, Benchmarking, Educational Research, Intervention
Cohausz, Lea – Journal of Educational Data Mining, 2022
Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…
Descriptors: Guidelines, Academic Achievement, Dropouts, Prediction
Wiedermann, Wolfgang; Reinke, Wendy M.; Herman, Keith C. – Developmental Psychology, 2020
Direction dependence analysis (DDA) is a recently developed method that addresses the need for more sophisticated tools to evaluate causal mechanisms of developmental processes and interventions. The present study applied DDA to evaluate the hypothesized mediators of a classroom behavior management training program on student academic competence.…
Descriptors: Prosocial Behavior, Interpersonal Competence, Classroom Techniques, Causal Models