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Bhatia, Jaydeep-Singh; Oaksford, Mike – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
When directed to ignore evidence of a witness's previous bad character because of a violation of the rules of evidence, are jurors' beliefs still affected? The intuition is that they will be because in everyday argumentation, fallacies, like the ad hominem, are effective argumentative strategies. An ad hominem argument (against the person)…
Descriptors: Persuasive Discourse, Logical Thinking, Court Litigation, Bayesian Statistics
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Ahern, David C.; Bridges, Ana J.; Faust, David – Journal of Child Sexual Abuse, 2012
Our series of three chapters (Faust, Bridges, & Ahern, 2009a, 2009b; Bridges, Faust, & Ahern, 2009) on the methodology of identifying sexually abused children elicited a number of comments, both supportive and critical. The criticisms appear related to three primary issues or apparent misconceptions of our work, perhaps due in part to incomplete…
Descriptors: Child Abuse, Misconceptions, Sexual Abuse, Identification
Levy, Roy – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2014
Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…
Descriptors: Video Games, Educational Games, Bayesian Statistics, Observation
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Maraun, Michael; Gabriel, Stephanie – Psychological Methods, 2010
In his article, "An Alternative to Null-Hypothesis Significance Tests," Killeen (2005) urged the discipline to abandon the practice of "p[subscript obs]"-based null hypothesis testing and to quantify the signal-to-noise characteristics of experimental outcomes with replication probabilities. He described the coefficient that he…
Descriptors: Hypothesis Testing, Statistical Inference, Probability, Statistical Significance
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Killeen, Peter R. – Psychological Methods, 2010
Lecoutre, Lecoutre, and Poitevineau (2010) have provided sophisticated grounding for "p[subscript rep]." Computing it precisely appears, fortunately, no more difficult than doing so approximately. Their analysis will help move predictive inference into the mainstream. Iverson, Wagenmakers, and Lee (2010) have also validated…
Descriptors: Replication (Evaluation), Measurement Techniques, Research Design, Research Methodology
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Lecoutre, Bruno; Lecoutre, Marie-Paule; Poitevineau, Jacques – Psychological Methods, 2010
P. R. Killeen's (2005a) probability of replication ("p[subscript rep]") of an experimental result is the fiducial Bayesian predictive probability of finding a same-sign effect in a replication of an experiment. "p[subscript rep]" is now routinely reported in "Psychological Science" and has also begun to appear in…
Descriptors: Research Methodology, Guidelines, Probability, Computation
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Iverson, Geoffrey J.; Wagenmakers, Eric-Jan; Lee, Michael D. – Psychological Methods, 2010
The purpose of the recently proposed "p[subscript rep]" statistic is to estimate the probability of concurrence, that is, the probability that a replicate experiment yields an effect of the same sign (Killeen, 2005a). The influential journal "Psychological Science" endorses "p[subscript rep]" and recommends its use…
Descriptors: Effect Size, Evaluation Methods, Probability, Experiments
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Cumming, Geoff – Psychological Methods, 2010
This comment offers three descriptions of "p[subscript rep]" that start with a frequentist account of confidence intervals, draw on R. A. Fisher's fiducial argument, and do not make Bayesian assumptions. Links are described among "p[subscript rep]," "p" values, and the probability a confidence interval will capture…
Descriptors: Replication (Evaluation), Measurement Techniques, Research Methodology, Validity
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use