Sensitive outcomes of surveys are plagued by wave nonresponse and measurement error (classification error for categorical outcomes). These types of error can lead to biased estimates and erroneous conclusions if they are not understood and addressed. The National Crime Victimization Survey (NCVS) is a nationally representative rotating panel survey with seven waves measuring property and violent crime victimization. Because not all crime is reported to the police, there is no gold standard measure of whether a respondent was victimized. For panel data, Markov Latent Class Analysis (MLCA) is a model-based approach that uses response patterns across interview waves to estimate false positive and false negative classification probabilities typically applied to complete data.
This article uses Full Information Maximum Likelihood (FIML) to include respondents with partial information in MLCA. The impact of including partial respondents in the MLCA is assessed for reduction of bias in the estimates, model specification differences, and variability in classification error estimates by comparing results from complete case and FIML MLCA models. The goal is to determine the potential of FIML to improve MLCA estimates of classification error. While we apply this process to the NCVS, the approach developed is general and can be applied to any panel survey.
Bartolucci, F., A. Farcomeni, and F. Pennoni. 2013. Latent Markov Models for Longitudinal Data. Boca Raton, FL: CRC Press.
Berzofsky, M.E., P.P. Biemer, and S.L. Edwards. 2015. “Latent Class Analysis with Missing Data under Complex Sampling: Results of a Simulation Study.” Presented at 60th World Statistics Conference, July 26–31, 2015. Rio de Janeiro, Brazil: World Statistics Conference.
Berzofsky, M. and P.B. Biemer. 2017. “Classification Error in Crime Victimization Surveys: A Markov Latent Class Analysis.” In Total Survey Error in Practice, edited by P.P. Biemer, E. de Leeuw, S. Eckman, B. Edwards, F. Kreuter, L.E. Lyberg, N.C. Tucker, and B.T. West, 387–412. Hoboken, NJ: Wiley.
Biemer, P.P. 2004. “An Analysis of Classification Error for the Revised Current Population Survey Employment Questions.” Survey Methodology 30(2): 127–140.
Biemer, P.P. 2011. Latent Class Analysis of Survey Error. Hoboken, NJ: Wiley.
Di Mari, R., D.L. Oberski, and J.K. Vermunt. 2016. “Bias-Adjusted Three-Step Latent Markov Modeling with Covariates, Structural Equation Modeling.” Structural Equation Modeling 23(5): 649–660. Doi: http://dx.doi.org/10.1080/10705511.2016.1191015.
Fuchs, C. 1982. “Maximum Likelihood Estimation and Model Selection in Contingency Tables with Missing Data.” Journal of the American Statistical Association 77(378): 270–278. Doi: http://dx.doi.org/10.2307/2287230.
Goodman, L.A. 1961. “Statistical Methods for the Mover-Stayer Model.” Journal of the American Statistical Association 56(296): 841–868. Doi: http://dx.doi.org/10.2307/2281999.
Goodman, L.A. 1973. “The Analysis of Multidimensional Contingency Tables when Some Variables are Posterior to Others: A Modified Path Analysis Approach.” Biometrika 60(1): 179–192. Doi: http://dx.doi.org/10.2307/2334920.
Hart, T.C., C.M. Rennison, and C. Gibson. 2005. “Revisiting Respondent ‘Fatigue Bias’ in the National Crime Victimization Survey.” Journal of Quantitative Criminology 21(3): 345–363. Doi: http://dx.doi.org/10.1007/s10940-005-4275-4.
Hess, S., N. Sanko, J. Dumont, and A. Daly. 2013. “A Latent Variable Approach to Dealing with Missing or Inaccurately Measured Variables: The Case of Income.” In Proceedings of the Third International Choice Modelling Conference, July 3–5, 2013. Sydney, Australia: ICM Conference. Available at: http://www.icmconference.org.uk/index.php/icmc/ICMC2013/paper/viewFile/744/233 (accessed August 2015).
Langton, L. and J. Truman. 2015. Criminal Victimization, 2014. Washington, DC: Bureau of Justice Statistics. (NCJ 248973).
Lazarsfeld, P.F. 1950. “The Logical and Mathematical Foundation of Latent Structure Analysis.” In Studies on Social Psychology in World War II, Vol. 4, Measurement and Prediction, edited by S. Stauffer, E.A. Suchman, P.F. Lazarsfeld, S.A. Starr, and J. Clausen. Princeton, NJ: Princeton University Press.
Little, R.J. and D.B. Rubin. 2002. Wiley Series in Probability and Statistics: Statistical Analysis with Missing Data. 2nd ed. Somerset, NJ: Wiley.
Poulsen, C.A. 1982. Latent Structures Analysis with Choice Modeling Applications. Aarhus, Denmark: Aarhus School of Business Administration and Economics.
Rand, M. and S. Catalano. 2007. Criminal Victimization, 2006. Washington, DC: U.S. Department of Justice, Office of Justice Programs. (NCJ 219413).