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Weidlich, Joshua; Gaševic, Dragan; Drachsler, Hendrik – Journal of Learning Analytics, 2022
As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…
Descriptors: Causal Models, Inferences, Learning Analytics, Comparative Analysis
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Mikkelsen, Kim Sass – Sociological Methods & Research, 2017
Contemporary case studies rely on verbal arguments and set theory to build or evaluate theoretical claims. While existing procedures excel in the use of qualitative information (information about kind), they ignore quantitative information (information about degree) at central points of the analysis. Effectively, contemporary case studies rely on…
Descriptors: Case Studies, Mathematical Models, Theories, Causal Models
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Wing, Coady; Bello-Gomez, Ricardo A. – American Journal of Evaluation, 2018
Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…
Descriptors: Regression (Statistics), Research Design, Validity, Evaluation Methods
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Lebow, Richard Ned – History Teacher, 2007
Counterfactuals are routinely used in physical and biological sciences to develop and evaluate sophisticated, non-linear models. They have been used with telling effect in the study of economic history and American politics. For some historians, counterfactual arguments have no scholarly standing. They consider them flights of fancy, fun over a…
Descriptors: Research Tools, Historians, Research Methodology, History