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Li, Tenglong; Frank, Ken – Sociological Methods & Research, 2022
The internal validity of observational study is often subject to debate. In this study, we define the counterfactuals as the unobserved sample and intend to quantify its relationship with the null hypothesis statistical testing (NHST). We propose the probability of a robust inference for internal validity, that is, the PIV, as a robustness index…
Descriptors: Probability, Inferences, Validity, Correlation
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Lloyd, Kevin; Sanborn, Adam; Leslie, David; Lewandowsky, Stephan – Cognitive Science, 2019
Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the…
Descriptors: Short Term Memory, Bayesian Statistics, Cognitive Ability, Individual Differences
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Loftus, Mary; Madden, Michael G. – Teaching in Higher Education, 2020
How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher…
Descriptors: Teaching Methods, Data Analysis, Literacy, Learning Processes
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Winchell, Adam; Mozer, Michael; Lan, Andrew; Grimaldi, Phillip; Pashler, Harold – International Educational Data Mining Society, 2018
When engaging with a textbook, students are inclined to highlight key content. Although students believe that highlighting and subsequent review of the highlights will further their educational goals, the psychological literature provides no evidence of benefits. Nonetheless, a student's choice of text for highlighting may serve as a window into…
Descriptors: Textbooks, Biology, Documentation, Science Instruction
Martori, Francesc; Cuadros, Jordi; González-Sabaté, Lucinio – International Educational Data Mining Society, 2015
Student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. In this context, Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its predictive accuracy, interpretability and ability to infer student knowledge. However,…
Descriptors: Bayesian Statistics, Inferences, Prediction, Accuracy
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Stamey, James D.; Beavers, Daniel P.; Sherr, Michael E. – Sociological Methods & Research, 2017
Survey data are often subject to various types of errors such as misclassification. In this article, we consider a model where interest is simultaneously in two correlated response variables and one is potentially subject to misclassification. A motivating example of a recent study of the impact of a sexual education course for adolescents is…
Descriptors: Bayesian Statistics, Classification, Models, Correlation
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Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Reading Comprehension, Reading Achievement, Elementary School Students, Secondary School Students
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Petscher, Yaacov; Kershaw, Sarah; Koon, Sharon; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
Districts and schools use progress monitoring to assess student progress, to identify students who fail to respond to intervention, and to further adapt instruction to student needs. Researchers and practitioners often use progress monitoring data to estimate student achievement growth (slope) and evaluate changes in performance over time for…
Descriptors: Response to Intervention, Achievement Gains, High Stakes Tests, Prediction
Zhu, Shizhuo – ProQuest LLC, 2010
Clinical decision-making is challenging mainly because of two factors: (1) patient conditions are often complicated with partial and changing information; (2) people have cognitive biases in their decision-making and information-seeking. Consequentially, misdiagnoses and ineffective use of resources may happen. To better support clinical…
Descriptors: Medical Evaluation, Clinical Diagnosis, Decision Making, Bayesian Statistics
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Rakow, Tim; Newell, Ben R.; Fayers, Kathryn; Hersby, Mette – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2005
The authors identify and provide an integration of 3 criteria for establishing cue-search hierarchies in inferential judgment. Cues can be ranked by information value according to expected information gain (Bayesian criterion), cue-outcome correlation (correlational criterion), or ecological validity (accuracy criterion). All criteria…
Descriptors: Cues, Inferences, Criteria, Bayesian Statistics