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Jie Fang; Zhonglin Wen; Kit-Tai Hau – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e.,…
Descriptors: Structural Equation Models, Mediation Theory, Data Analysis, Longitudinal Studies
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
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
Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying…
Descriptors: Structural Equation Models, Statistical Analysis, Educational Research, Causal Models
Yanping Pei; Adam Sales; Johann Gagnon-Bartsch – Grantee Submission, 2024
Randomized A/B tests within online learning platforms enable us to draw unbiased causal estimators. However, precise estimates of treatment effects can be challenging due to minimal participation, resulting in underpowered A/B tests. Recent advancements indicate that leveraging auxiliary information from detailed logs and employing design-based…
Descriptors: Randomized Controlled Trials, Learning Management Systems, Causal Models, Learning Analytics
Elaine Chiu – Society for Research on Educational Effectiveness, 2024
Background: Observation Studies, Unmeasured Confounding, and Sensitivity Analysis: An important part of educational research is identifying important, potentially causal, factors that influence children's learning from observational studies. However, it is well-known that discovering such factors from observational studies can be biased due to…
Descriptors: Educational Research, Research Methodology, Attribution Theory, Learning Processes