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Dropping out of High School in the United States: An Observational Study

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  • Paul R. Rosenbaum

Abstract

Using data from a two-stage probability sample of U.S. high school students, an attempt is made to estimate the effect that dropping out has on cognitive achievement test scores. Each sampled dropout from a school is matched by a multivariate procedure to a student who remained in the same school. The matched pair differences are then adjusted using analysis of covariance. The possibility that important covariates have been omitted from the analysis is addressed through tests of ignorable treatment assignment and through sensitivity analyses.

Suggested Citation

  • Paul R. Rosenbaum, 1986. "Dropping out of High School in the United States: An Observational Study," Journal of Educational and Behavioral Statistics, , vol. 11(3), pages 207-224, September.
    • Handle: RePEc:sae:jedbes:v:11:y:1986:i:3:p:207-224
    DOI: 10.3102/10769986011003207
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    Cited by:

    1. Piotr Tarka, 2018. "An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 313-354, January.
    2. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    3. Jesse Y. Hsu & Dylan S. Small, 2013. "Calibrating Sensitivity Analyses to Observed Covariates in Observational Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 803-811, December.
    4. José M. Cordero & Víctor Cristóbal & Daniel Santín, 2018. "Causal Inference On Education Policies: A Survey Of Empirical Studies Using Pisa, Timss And Pirls," Journal of Economic Surveys, Wiley Blackwell, vol. 32(3), pages 878-915, July.

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