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A Comparison of Three Approaches to Handling Incomplete State-Level Data

Published online by Cambridge University Press:  25 January 2021

J. Scott Granberg-Rademacker
J. Scott Granberg-Rademacker*
Affiliation:
Minnesota State University, Mankato
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Abstract

This article compares three approaches to handling missing data at the state level under three distinct conditions. Using Monte Carlo simulation experiments, I compare the results from a linear model using listwise deletion (LD), Markov Chain Monte Carlo with the Gibbs sampler algorithm (MCMC), and multiple imputation by chained equations (MICE) as approaches to dealing with different severity levels of missing data: missing completely at random (MCAR), missing at random (MAR), and nonignorable missingness (NI). I compare the results from each of these approaches under each condition for missing data to the results from the fully observed dataset. I conclude that the MICE algorithm performs best under most missing data conditions, MCMC provides the most stable parameter estimates across the missing data conditions (but often produced estimates that were moderately biased), and LD performs worst under most missing data conditions. I conclude with recommendations for handling missing data in state-level analysis.

Type
The Practical Researcher
Information
State Politics & Policy Quarterly , Volume 7 , Issue 3 , Fall 2007 , pp. 325 - 338
Copyright
Copyright © The American Political Science Association, 2007

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References

Alt, James E., and Lowry, Robert C.. 1994. “Divided Government, Fiscal Institutions, and Budget Deficits: Evidence from the States.” American Political Science Review 88:811–28.CrossRefGoogle Scholar
Alt, James E., and Lowry, Robert C.. 2000. “A Dynamic Model of State Budget Outcomes under Divided Partisan Government.” Journal of Politics 62:1035–69.CrossRefGoogle Scholar
Arvanites, Thomas M., and Asher, Martin A.. 1998. “State and County Incarceration Rates: The Direct and Indirect Effects of Race and Inequality.” American Journal of Economics and Sociology 57:207–21.CrossRefGoogle Scholar
Dempster, A. P., Laird, N. M., and Rubin, D. B.. 1997. “Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society (Methodological Series B) 39:1–38.Google Scholar
Fiorina, Morris P. 1994. “Divided Government in American States: A Byproduct of Legislative Professionalism?” American Political Science Review 88:304–16.CrossRefGoogle Scholar
Gelman, Andrew, Carlin, John B., Stern, Hal S., and Rubin, Donald B.. 1995. Bayesian Data Analysis. New York, NY: Chapman and Hall.CrossRefGoogle Scholar
Gill, Jeff. 2001. “Whose Variance Is It Anyway? Interpreting Empirical Models with State-Level Data.” State Politics and Policy Quarterly 1:318–39.CrossRefGoogle Scholar
Gill, Jeff. 2002. Bayesian Methods: A Social and Behavioral Sciences Approach. New York, NY: Chapman and Hall/CRC Press.CrossRefGoogle Scholar
Greene, William H. 2003. Econometric Analysis. 5th ed. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
King, Gary, Honaker, James, Joseph, Anne, and Scheve, Kenneth. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review 95:49–69.CrossRefGoogle Scholar
Little, Roderick J. A., and Rubin, Donald B.. 2002. Statistical Analysis with Missing Data. 2nd ed. New York, NY: Wiley-Interscience.CrossRefGoogle Scholar
McLachlan, Geoffrey J., and Krishan, Thriyambakam. 1997. The EM Algorithm and Extensions. New York, NY: Wiley.Google Scholar
Millard, Steven P., and Neerchal, Nagaraj K.. 2001. Environmental Statistics with S-Plus. Boca Raton, FL: CRC Press.Google Scholar
Nagel, Jack. 1982. “The Relationship Between Crime and Incarceration Among the American States.” Policy Studies Review 2:193–200.CrossRefGoogle Scholar
Neter, John, Kutner, Michael H., Nachtsheim, Christopher J., and Wasserman, William. 1996. Applied Linear Statistical Models. New York, NY: McGraw Hill.Google Scholar
Orchard, T., and Woodbury, M.A. 1972. “A Missing Information Principle: Theory and Applications.” In Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and Probability. Eds., Le Cam, Lucien M., Neyman, Jerzy, and Scott, Elizabeth L.. Berkeley, CA: University of California Press.Google Scholar
Roeder, Philip W. 1976. “Classifying the American States: Temporal Stability and Analytic Utility.” Western Political Quarterly 29:563–74.CrossRefGoogle Scholar
Royston, Patrick. 2004. “Multiple Imputation of Missing Values.” The Stata Journal 4:227–41.CrossRefGoogle Scholar
Rubin, Donald. 1976. “Inference and Missing Data.” Biometrika 87:113–24.Google Scholar
Schafer, Joseph L. 1997. Analysis of Incomplete Multivariate Data. London, UK: Chapman and Hall.CrossRefGoogle Scholar
Smith, Kevin B. 2004. “The Politics of Punishment: Evaluating Political Explanations of Incarceration Rates.” Journal of Politics 66:925–38.CrossRefGoogle Scholar
Spiegelhalter, David, Thomas, Andrew, Best, Nicky, and Lunn, Dave. 2003. WinBUGS User Manual: Version 1.4. MRC Biostatistics Unit. http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/manual14.pdf (April 7, 2007).Google Scholar
Taggart, William A., and Winn, Russell G.. 1993. “Imprisonment in the American States.” Social Science Quarterly 74:736–49.Google Scholar
Tanner, M.A., and Wong, W. H.. 1987. “The Calculation of Posterior Distributions by Data Augmentation.” Journal of the American Statistical Association 82:528–50.Google Scholar
van Buuren, S., Boshuizen, H. C., and Knook, D. L.. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18:681–94.3.0.CO;2-R>CrossRefGoogle ScholarPubMed
van Buuren, S., and Oudshoorn, K.. 1999. Flexible Multivariate Imputation by MICE. Leiden, The Netherlands: TNO Prevention and Health.Google Scholar
Western, Bruce, Kleykamp, Meredith, and Rosenfeld, Jake. 2006. “Did Falling Wages and Employment Increase U.S. Imprisonment?” Social Forces 84:2291–311.CrossRefGoogle Scholar