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The effects of sampling strategies on the small sample properties of the logit estimator

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  • Jason Dietrich

Abstract

Empirical researchers face a trade-off between the lower resource costs associated with smaller samples and the increased confidence in the results gained from larger samples. Choice of sampling strategy is one tool researchers can use to reduce costs yet still attain desired confidence levels. This study uses Monte Carlo simulation to examine the impact of nine sampling strategies on the finite sample performance of the maximum likelihood logit estimator. The results show stratified random sampling with balanced strata sizes and a bias correction for choice-based sampling outperforms all other sampling strategies with respect to four small-sample performance measures.

Suggested Citation

  • Jason Dietrich, 2005. "The effects of sampling strategies on the small sample properties of the logit estimator," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 543-554.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:543-554
    DOI: 10.1080/02664760500078888
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    References listed on IDEAS

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    1. Judith A. Giles & Marsha J. Courchane, 2000. "Stratified Sample Design for Fair Lending Binary Logit Models," Econometrics Working Papers 0007, Department of Economics, University of Victoria.
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    6. Mitchell Stengel & Dennis Glennon, 1999. "Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank‐Specific Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(2), pages 299-334, June.
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    Cited by:

    1. Gyanendra Singh Sisodia & Isabel Soares & Paula Ferreira, 2016. "The effect of sample size on European Union’s renewable energy investment drivers," Applied Economics, Taylor & Francis Journals, vol. 48(53), pages 5129-5137, November.
    2. Judith A. Clarke & Nilanjana Roy & Marsha J. Courchane, 2006. "On the Robustness of Racial Disrcimination Findings in Motgage Lending Studies," Econometrics Working Papers 0604, Department of Economics, University of Victoria.
    3. Jason S. Bergtold & Elizabeth A. Yeager & Allen M. Featherstone, 2018. "Inferences from logistic regression models in the presence of small samples, rare events, nonlinearity, and multicollinearity with observational data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 528-546, February.

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    Keywords

    Sampling; Logit; Monte Carlo;
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