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Ken Frank; Guan Saw; Qinyun Lin; Ran Xu; Joshua Rosenberg; Spiro Maroulis; Bret Staudt Willet – Grantee Submission, 2025
This is a practical guide for applying the Impact Threshold for a Confounding Variable and the Robustness of Inference to Replacement using the konfound packages in Stata and R as well as the R-shiny app. It includes motivation worked examples, and tutorials.
Descriptors: Robustness (Statistics), Statistical Inference, Programming Languages, Computer Software
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Lucy D'Agostino McGowan; Travis Gerke; Malcolm Barrett – Journal of Statistics and Data Science Education, 2024
This article introduces a collection of four datasets, similar to Anscombe's quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the…
Descriptors: Statistics Education, Programming Languages, Statistical Inference, Causal Models
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Troy, Jesse D.; Neely, Megan L.; Pomann, Gina-Maria; Grambow, Steven C.; Samsa, Gregory P. – Journal of Curriculum and Teaching, 2022
Student evaluation is a key consideration for educational program administrators because program success depends on students' ability to demonstrate successful development of core competencies. Student evaluations must therefore be aligned with learning objectives and overall program goals. Graduate level educational programs typically incorporate…
Descriptors: Student Evaluation, Evaluation Methods, Statistics Education, Alignment (Education)