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Fansher, Madison; Adkins, Tyler J.; Shah, Priti – Grantee Submission, 2022
Media articles often communicate the latest scientific findings, and readers must evaluate the evidence and consider its potential implications. Prior work has found that the inclusion of graphs makes messages about scientific data more persuasive (Tal & Wansink, 2016). One explanation for this finding is that such visualizations evoke the…
Descriptors: Graphs, Correlation, Visual Aids, News Reporting
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Cummiskey, Kevin; Adams, Bryan; Pleuss, James; Turner, Dusty; Clark, Nicholas; Watts, Krista – Journal of Statistics Education, 2020
Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable…
Descriptors: Statistics, Teaching Methods, Inferences, Guidelines
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York, Richard – International Journal of Social Research Methodology, 2018
A common motivation for adding control variables to statistical models is to reduce the potential for spurious findings when analyzing non-experimental data and to thereby allow for more reliable causal inferences. However, as I show here, unless "all" potential confounding factors are included in an analysis (which is unlikely to be…
Descriptors: Inferences, Control Groups, Correlation, Experimental Groups
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Halpern, Joseph Y.; Hitchcock, Christopher – Cognitive Science, 2013
Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of "normality." In Halpern and Hitchcock (2011), we offer a definition of actual causation…
Descriptors: Causal Models, Cognitive Science, Definitions, Correlation
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Goedert, Kelly M.; Ellefson, Michelle R.; Rehder, Bob – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2014
Individuals have difficulty changing their causal beliefs in light of contradictory evidence. We hypothesized that this difficulty arises because people facing implausible causes give greater consideration to causal alternatives, which, because of their use of a positive test strategy, leads to differential weighting of contingency evidence.…
Descriptors: Causal Models, Inferences, Beliefs, Attitude Change
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Wu, Amery D.; Zumbo, Bruno D. – Social Indicators Research, 2008
Mediation and moderation are two theories for refining and understanding a causal relationship. Empirical investigation of mediators and moderators requires an integrated research design rather than the data analyses driven approach often seen in the literature. This paper described the conceptual foundation, research design, data analysis, as…
Descriptors: Research Design, Investigations, Structural Equation Models, Data Analysis
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Humphreys, Lloyd G. – Intelligence, 1991
Cross-lagged methodology (CLM), which is virtually ignored by psychological researchers, is suggested for studies of causal relations in which controlled experimentation is unfeasible. The longitudinal facet in the design of CLM is highlighted. Advantages and limitations of the CLM are described. (SLD)
Descriptors: Causal Models, Correlation, Etiology, Inferences
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Beishuizen, Jos; van der Schalk, Janine; Le Grand, Julie – Learning and Instruction, 1999
The causal network model of P. Van den Broeck and T. Trabasso (1986) was used to analyze the inferencing processes induced from verbal protocols from reading and thinking-aloud tasks. Results from 66 Dutch 10- and 11-year olds show no correlation between reading and think-aloud performance and comprehensive scores of a closed comprehension test.…
Descriptors: Causal Models, Correlation, Elementary School Students, Foreign Countries
Thompson, Bruce – 1994
The present paper suggests that multivariate methods ought to be used more frequently in behavioral research and explores the potential consequences of failing to use multivariate methods when these methods are appropriate. The paper explores in detail two reasons why multivariate methods are usually vital. The first is that they limit the…
Descriptors: Analysis of Covariance, Behavioral Science Research, Causal Models, Correlation