Reducing selection bias in quasi-experimental educational studies
| Autor: | Stephanie D. Teasley, Jared Tritz, Omar Chavez, Christopher Brooks |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Selection bias
Measure (data warehouse) Higher education business.industry Computer science media_common.quotation_subject Matched Sampling Machine learning computer.software_genre Data science Data warehouse Propensity score matching Artificial intelligence Educational interventions business computer media_common |
| Zdroj: | LAK |
| DOI: | 10.1145/2723576.2723614 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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