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Tennekoon V, Rosenman R. SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES. Commun Stat Theory Methods. 2014;45(9):2538-2555doi: 10.1080/03610926.2014.887105.
Tennekoon, V., & Rosenman, R. (2016). SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES. Communications in statistics: theory and methods, 45(9), 2538-2555. https://doi.org/10.1080/03610926.2014.887105
Tennekoon, Vidhura, and Rosenman, Robert. "SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES." Communications in statistics: theory and methods vol. 45,9 (2016): 2538-2555. doi: https://doi.org/10.1080/03610926.2014.887105
Tennekoon V, Rosenman R. SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES. Commun Stat Theory Methods. 2016;45(9):2538-2555. doi: 10.1080/03610926.2014.887105. Epub 2014 May 16. PMID: 27293307; PMCID: PMC4896402.
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Commun Stat Theory Methods. 2016;45(9):2538-2555. doi: 10.1080/03610926.2014.887105. Epub 2014 May 16.

SYSTEMATICALLY MISCLASSIFIED BINARY DEPENDENT VARIABLES.

Communications in statistics: theory and methods

Vidhura Tennekoon, Robert Rosenman

Affiliations

  1. Department of Economics, 308 Cate Center Drive, University of Oklahoma, Norman, OK 73019, USA. [email protected] . 1-405-325-3614. Fax 1-405-325-5842. Corresponding author.
  2. School of Economic Sciences, Washington State University, Pullman, WA 99164, USA. [email protected] . 1-509-335-1193. Fax 1-509-335-1173.

PMID: 27293307 PMCID: PMC4896402 DOI: 10.1080/03610926.2014.887105

Abstract

When a binary dependent variable is misclassified, that is, recorded in the category other than where it really belongs, probit and logit estimates are biased and inconsistent. In some cases the probability of misclassification may vary systematically with covariates, and thus be endogenous. In this paper we develop an estimation approach that corrects for endogenous misclassification, validate our approach using a simulation study, and apply it to the analysis of a treatment program designed to improve family dynamics. Our results show that endogenous misclassification could lead to potentially incorrect conclusions unless corrected using an appropriate technique.

Keywords: Likert scales; binary choice model; measurement error; misclassification; response shift bias

References

  1. Int J Behav Healthc Res. 2011 Oct;2(4):320-332 - PubMed

Publication Types

Grant support