Reducing selection bias in quasi-experimental educational studies. Brooks, C., Chavez, O., Tritz, J., & Teasley, S. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, of LAK '15, pages 295–299, Poughkeepsie, NY, USA, March, 2015. Association for Computing Machinery. ![Reducing selection bias in quasi-experimental educational studies [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@inproceedings{brooks_reducing_2015,
address = {Poughkeepsie, NY, USA},
series = {{LAK} '15},
title = {Reducing selection bias in quasi-experimental educational studies},
isbn = {978-1-4503-3417-4},
url = {http://doi.org/10.1145/2723576.2723614},
doi = {10.1145/2723576.2723614},
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.},
urldate = {2020-09-23},
booktitle = {Proceedings of the {Fifth} {International} {Conference} on {Learning} {Analytics} and {Knowledge}},
publisher = {Association for Computing Machinery},
author = {Brooks, Christopher and Chavez, Omar and Tritz, Jared and Teasley, Stephanie},
month = mar,
year = {2015},
pages = {295--299}
}
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