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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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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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Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin – Society for Research on Educational Effectiveness, 2015
Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…
Descriptors: Pretests Posttests, Statistical Bias, Accuracy, Regression (Statistics)
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Gokalp, Muhammed Sait – International Journal of Environmental and Science Education, 2013
The use of the web in teaching and learning and research studies on this issue are increasingly common in science education. In most of these studies, teachers' and students' perceptions of and their attitudes toward the specific web-assisted/based learning activities and the effects of these activities on their achievement and attitudes have been…
Descriptors: Foreign Countries, Internet, Educational Resources, Physics
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Dong, Nianbo – American Journal of Evaluation, 2015
Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to…
Descriptors: Probability, Statistical Analysis, Research Design, Causal Models
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Wing, Coady; Cook, Thomas D. – Journal of Policy Analysis and Management, 2013
The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…
Descriptors: Regression (Statistics), Research Design, Statistical Analysis, Research Problems
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Love, Edwin; Stelling, Pete – Marketing Education Review, 2012
The reaction that occurs when Mentos are added to bottled soft drinks has become a staple demonstration in earth science courses to explain how volcanoes erupt. This paper presents how this engaging exercise can be used in a marketing research course to provide hands-on experience with problem formation, hypothesis testing, and causal research. A…
Descriptors: Marketing, Research, Comparative Analysis, Experiments
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Wong, Manyee; Cook, Thomas D.; Steiner, Peter M. – Journal of Research on Educational Effectiveness, 2015
Some form of a short interrupted time series (ITS) is often used to evaluate state and national programs. An ITS design with a single treatment group assumes that the pretest functional form can be validly estimated and extrapolated into the postintervention period where it provides a valid counterfactual. This assumption is problematic. Ambiguous…
Descriptors: Evaluation Methods, Time, Federal Legislation, Educational Legislation
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Shadish, William R. – Psychological Methods, 2010
This article compares Donald Campbell's and Donald Rubin's work on causal inference in field settings on issues of epistemology, theories of cause and effect, methodology, statistics, generalization, and terminology. The two approaches are quite different but compatible, differing mostly in matters of bandwidth versus fidelity. Campbell's work…
Descriptors: Inferences, Generalization, Epistemology, Causal Models
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Powers, William T. – American Behavioral Scientist, 1990
Contrasts modeling methods in control theory to the methods of statistical generalizations in empirical studies of human or animal behavior. Presents a computer simulation that predicts behavior based on variables (effort and rewards) determined by the invariable (desired reward). Argues that control theory methods better reflect relationships to…
Descriptors: Behavior, Causal Models, Comparative Analysis, Computer Simulation
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Stuart, Elizabeth A. – Educational Researcher, 2007
Education researchers, practitioners, and policymakers alike are committed to identifying interventions that teach students more effectively. Increased emphasis on evaluation and accountability has increased desire for sound evaluations of these interventions; and at the same time, school-level data have become increasingly available. This article…
Descriptors: Research Methodology, Computation, Causal Models, Intervention
Helberg, Clay – 1996
Abuses and misuses of statistics are frequent. This digest attempts to warn against these in three broad classes of pitfalls: sources of bias, errors of methodology, and misinterpretation of results. Sources of bias are conditions or circumstances that affect the external validity of statistical results. In order for a researcher to make…
Descriptors: Causal Models, Comparative Analysis, Data Analysis, Error of Measurement