Empirical Performance of Covariates in Education Observational Studies
| Autor: | Kate Miller-Bains, Jeffrey C. Valentine, Vivian C. Wong |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Matching (statistics)
business.industry 05 social sciences 050401 social sciences methods Standardized test Observational methods in psychology Outcome (probability) Education 0504 sociology Sample size determination 0502 economics and business Statistics Covariate Econometrics Observational study 050207 economics business Selection (genetic algorithm) Mathematics |
| Zdroj: | Journal of Research on Educational Effectiveness. 10:207-236 |
| ISSN: | 1934-5739 1934-5747 |
| DOI: | 10.1080/19345747.2016.1164781 |
| Popis: | This article summarizes results from 12 empirical evaluations of observational methods in education contexts. We look at the performance of three common covariate-types in observational studies where the outcome is a standardized reading or math test. They are: pretest measures, local geographic matching, and rich covariate sets with a strong theory of treatment selection. Overall, the review demonstrates that although the pretest often reduces bias in observational studies, it does not always eliminate it. Its performance depends on the pretest's correlation with treatment selection and the outcome, and whether preintervention trends are present. We also find that although local comparisons are prioritized for matching, its performance depends on whether comparable no-treatment cases are available. Otherwise, local comparisons may produce badly biased results. In cases where researchers have a strong theory of selection and rich covariate sets, observational methods perform well, but additional r... |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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