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Xu Qin – Asia Pacific Education Review, 2024
Causal mediation analysis has gained increasing attention in recent years. This article guides empirical researchers through the concepts and challenges of causal mediation analysis. I first clarify the difference between traditional and causal mediation analysis and highlight the importance of adjusting for the treatment-by-mediator interaction…
Descriptors: Causal Models, Mediation Theory, Statistical Analysis, Computer Software
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Corrado Matta; Jannika Lindvall; Andreas Ryve – American Journal of Evaluation, 2024
In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a…
Descriptors: Data Analysis, Theories, Program Evaluation, Information Management
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Yi Feng – Asia Pacific Education Review, 2024
Causal inference is a central topic in education research, although oftentimes it relies on observational studies, which makes causal identification methodologically challenging. This manuscript introduces causal graphs as a powerful language for elucidating causal theories and an effective tool for causal identification analysis. It discusses…
Descriptors: Causal Models, Graphs, Educational Research, Educational Researchers
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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
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Stephen Porter – Asia Pacific Education Review, 2024
Instrumental variables is a popular approach for causal inference in education when randomization of treatment is not feasible. Using a first-year college program as a running example, this article reviews the five assumptions that must be met to successfully use instrumental variables to estimate a causal effect with observational data: SUTVA,…
Descriptors: Causal Models, Educational Research, College Freshmen, Observation
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Julian Schuessler; Peter Selb – Sociological Methods & Research, 2025
Directed acyclic graphs (DAGs) are now a popular tool to inform causal inferences. We discuss how DAGs can also be used to encode theoretical assumptions about nonprobability samples and survey nonresponse and to determine whether population quantities including conditional distributions and regressions can be identified. We describe sources of…
Descriptors: Data Collection, Graphs, Error of Measurement, Statistical Bias
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Alrik Thiem; Lusine Mkrtchyan – Field Methods, 2024
Qualitative comparative analysis (QCA) is an empirical research method that has gained some popularity in the social sciences. At the same time, the literature has long been convinced that QCA is prone to committing causal fallacies when confronted with non-causal data. More specifically, beyond a certain case-to-factor ratio, the method is…
Descriptors: Qualitative Research, Comparative Analysis, Research Methodology, Benchmarking
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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
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Jeroen D. Mulder; Kim Luijken; Bas B. L. Penning de Vries; Ellen L. Hamaker – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of…
Descriptors: Structural Equation Models, Time on Task, Time Management, Causal Models
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Sang-June Park; Youjae Yi – Journal of Educational and Behavioral Statistics, 2024
Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…
Descriptors: Interaction, Predictor Variables, Causal Models, Mathematical Models
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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
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David Byrne – International Journal of Social Research Methodology, 2024
Conventional approaches to causation in the social sciences draw on approaches in the Philosophy of Science in which a causal force acts on cases and generates change in the form of events. This relies on just one of the Aristotelian conceptions of cause - efficient cause - what brings the effect in to being. We should also pay attention to Final…
Descriptors: Personal Autonomy, Social Sciences, Causal Models, Social Change
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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
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Leslie Rutkowski; David Rutkowski – Journal of Creative Behavior, 2025
The Programme for International Student Assessment (PISA) introduced creative thinking as an innovative domain in 2022. This paper examines the unique methodological issues in international assessments and the implications of measuring creative thinking within PISA's framework, including stratified sampling, rotated form designs, and a distinct…
Descriptors: Creativity, Creative Thinking, Measurement, Sampling
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Jacob Pleasants – Science & Education, 2024
As part of a growing emphasis on "STEM," engineering has gained prominence in precollege education. In response to that trend, an emerging area of educational research focuses on the "Nature of Engineering" (NOE), a collection of ideas about what engineering is, what engineers do, and how engineering is related to science and…
Descriptors: Social Environment, STEM Education, Engineering, Engineering Education
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