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Vidushi Adlakha; Eric Kuo – Physical Review Physics Education Research, 2023
Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer" in PER, this paper discusses some of the fundamental issues in statistical causal inference. In…
Descriptors: Physics, Science Education, Statistical Inference, Causal Models
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Krauss, S.; Bruckmaier, G.; Lindl, A.; Hilbert, S.; Binder, K.; Steib, N.; Blum, W. – ZDM: The International Journal on Mathematics Education, 2020
Two different tools for assessing pedagogical content knowledge (PCK) of mathematics teachers used in the framework of the COACTIV study are systematically compared in this paper, namely the paper-and-pencil test consisting of items on the three facets "knowledge of explaining and representation," "knowledge of student thinking and…
Descriptors: Pedagogical Content Knowledge, Mathematics Teachers, Measures (Individuals), Teacher Competency Testing
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Keele, Luke – American Journal of Evaluation, 2015
In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…
Descriptors: Mediation Theory, Causal Models, Inferences, Path Analysis
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Wainer, Howard – Journal of Educational and Behavioral Statistics, 2010
In this essay, the author tries to look forward into the 21st century to divine three things: (i) What skills will researchers in the future need to solve the most pressing problems? (ii) What are some of the most likely candidates to be those problems? and (iii) What are some current areas of research that seem mined out and should not distract…
Descriptors: Research Skills, Researchers, Internet, Access to Information
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Rastegar, Mina – Indian Journal of Applied Linguistics, 2006
This article aims at discussing a new statistical trend in research in applied linguistics. This rather new statistical procedure is causal modeling--path analysis. The article demonstrates that causal modeling--path analysis is the best statistical option to use when the effects of a multitude of L2 learners' variables on language achievement are…
Descriptors: Causal Models, Applied Linguistics, Academic Persistence, Path Analysis