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Reducing selection bias in quasi-experimental educational studies

Published: 16 March 2015 Publication History

Abstract

In this paper we examine the issue of selection bias in quasi-experimental (non-randomly controlled) educational studies. We provide background about common sources of selection bias and the issues involved in evaluating the outcomes of quasi-experimental studies. We describe two methods, matched sampling and propensity score matching, that can be used to overcome this bias. Using these methods, we describe their application through one case study that leverages large educational datasets drawn from higher education institutional data warehouses. The contribution of this work is the recommendation of a methodology and case study that educational researchers can use to understand, measure, and reduce selection bias in real-world educational interventions.

References

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B. Hansen. The prognostic analogue of the propensity score. Biometrika, pages 1--17, 2008.
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P. Rosenbaum and D. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1): 41--55, 1983.
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D. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 1974.
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W. R. Shadish, M. H. Clark, and P. M. Steiner. Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments. Journal of the American Statistical Association, 103(484): 1334--1344, Dec. 2008.
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P. M. Steiner, T. D. Cook, W. R. Shadish, and M. H. Clark. The importance of covariate selection in controlling for selection bias in observational studies. Psychological methods, 15(3): 250--67, Sept. 2010.

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LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
March 2015
448 pages
ISBN:9781450334174
DOI:10.1145/2723576
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 March 2015

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LAK '15

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LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;
Overall Acceptance Rate 236 of 782 submissions, 30%

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  • (2022)Thinking with causal models: A visual formalism for collaboratively crafting assumptionsLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506899(250-259)Online publication date: 21-Mar-2022
  • (2021)Complexity and Difficulty of Items in Learning SystemsInternational Journal of Artificial Intelligence in Education10.1007/s40593-021-00252-432:1(196-232)Online publication date: 4-May-2021
  • (2018)Classifying and visualizing students' cognitive engagement in course readingsProceedings of the Fifth Annual ACM Conference on Learning at Scale10.1145/3231644.3231648(1-10)Online publication date: 26-Jun-2018
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