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Direct linkAndrew Gelman; Matthijs Vákár – Grantee Submission, 2021
It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased…
Descriptors: Bayesian Statistics, Statistical Analysis, Efficiency, Statistical Inference
Van den Noortgate, Wim; Moeyaert, Mariola; Ugille, Maaike; Beretvas, Tasha; Ferron, John – Society for Research on Educational Effectiveness, 2014
Due to an increasing interest in the use of single-subject experimental designs (SSEDs), appropriate techniques are needed to analyze this type of data. The purpose of this paper proposal is to present four studies (Beretvas, Hembry, Van den Noortgate, & Ferron, 2013; Bunuan, Hembry & Beretvas, 2013; Moeyaert, Ugille, Ferron, Beretvas,…
Descriptors: Research Methodology, Simulation, Bias, Statistical Inference
Martin, Andrew J.; Wilson, Rachel; Liem, Gregory Arief D.; Ginns, Paul – Journal of Higher Education, 2014
In the context of "academic momentum," a longitudinal study of university students (N = 904) showed high school achievement and ongoing university achievement predicted subsequent achievement through university. However, the impact of high school achievement diminished, while additive effects of ongoing university achievement continued.…
Descriptors: Foreign Countries, College Students, Longitudinal Studies, Academic Achievement
