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Sample Size and Robustness of Inferences from Logistic Regression in the Presence of Nonlinearity and Multicollinearity

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  • Bergtold, Jason S.
  • Yeager, Elizabeth A.
  • Featherstone, Allen M.

Abstract

The logistic regression models has been widely used in the social and natural sciences and results from studies using this model can have significant impact. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this study is to examine the impact of sample size on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model. A number of simulations are conducted examining the impact of sample size, nonlinear predictors, and multicollinearity on substantive inferences (e.g. odds ratios, marginal effects) and goodness of fit (e.g. pseudo-R2, predictability) of logistic regression models. Findings suggest that sample size can affect parameter estimates and inferences in the presence of multicollinearity and nonlinear predictor functions, but marginal effects estimates are relatively robust to sample size.

Suggested Citation

  • Bergtold, Jason S. & Yeager, Elizabeth A. & Featherstone, Allen M., 2011. "Sample Size and Robustness of Inferences from Logistic Regression in the Presence of Nonlinearity and Multicollinearity," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103771, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea11:103771
    DOI: 10.22004/ag.econ.103771
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    Cited by:

    1. Dwivedi Alok Kumar & Mallawaarachchi Indika & Figueroa-Casas Juan B. & Morales Angel M. & Tarwater Patrick, 2015. "Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 203-222, June.
    2. Angel M. Morales & Patrick Tarwater & Indika Mallawaarachchi & Alok Kumar Dwivedi & Juan B. Figueroa-Casas, 2015. "Multinomial logistic regression approach for the evaluation of binary diagnostic test in medical research," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(2), pages 203-222, June.
    3. Alok Kumar Dwivedi & Indika Mallawaarachchi & Juan B. Figueroa-Casas & Angel M. Morales & Patrick Tarwater, 2015. "Multinomial Logistic Regression Approach For The Evaluation Of Binary Diagnostic Test In Medical Research," Statistics in Transition New Series, Polish Statistical Association, vol. 16(2), pages 203-222, June.

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    Research Methods/ Statistical Methods;

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