Effect of Missing Data on Classification Error in Panel Surveys

Allison, P.D. 2001. “Missing Data.” In Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-136. Thousand Oaks, CA: Sage.Search in Google Scholar

Allison, P.D. 2012. “Handling Missing Data by Maximum Likelihood.” In Proceedings of SAS Global Forum 2012, Statistics and Data Analysis, April 22–25, 2012. 312. Haverford, PA: SAS Institute. Available at: http://www.statisticalhorizons.com/wp-content/uploads/MissingDataByML.pdf (accessed August 2016).Search in Google Scholar

Bartolucci, F., A. Farcomeni, and F. Pennoni. 2013. Latent Markov Models for Longitudinal Data. Boca Raton, FL: CRC Press.10.1201/b13246Search in Google Scholar

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.Search in Google Scholar

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.10.1002/9781119041702.ch18Search in Google Scholar

Biemer, P.P. 2004. “An Analysis of Classification Error for the Revised Current Population Survey Employment Questions.” Survey Methodology 30(2): 127–140.Search in Google Scholar

Biemer, P.P. 2011. Latent Class Analysis of Survey Error. Hoboken, NJ: Wiley.10.1002/9780470891155Search in Google Scholar

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/10.1080/10705511.2016.1191015.10.1080/10705511.2016.1191015Search in Google Scholar

Dias, J.G., J.K. Vermunt, and S. Ramos. 2008. “Heterogeneous Hidden Markov Models.” In Compstat 2008 Proceedings, August, 2008. City, State: Compstat. Available at: http://members.home.nl/jeroenvermunt/dias2008.pdf (accessed March 2015).Search in Google Scholar

Enders, C.K. 2010. Applied Missing Data Analysis. New York: Guilford Press.Search in Google Scholar

Fay, R.E. 1986. “Causal Models for Patterns of Nonresponse.” Journal of the American Statistical Association 81(394): 354–365. Doi: http://dx.doi.org/10.1080/01621459.1986.10478279.10.1080/01621459.1986.10478279Search in Google Scholar

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/10.2307/2287230.10.2307/2287230Search in Google Scholar

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/10.2307/2281999.10.2307/2281999Search in Google Scholar

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/10.2307/2334920.10.2307/2334920Search in Google Scholar

Graham, J.W. 2009. “Missing Data Analysis: Making It Work in the Real World.” Annual Review of Psychology 60: 549–576. Doi: http://dx.doi.org/10.1146/annurev.psych.58.110405.085530.10.1146/annurev.psych.58.110405.08553018652544Search in Google Scholar

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/s10940-005-4275-4.10.1007/s10940-005-4275-4Search in Google Scholar

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).Search in Google Scholar

Iannacchione, V. 1982. “Weighted Sequential Hot Deck Imputation Macros.” In Proceedings of the SAS Users Group International Conference, February 14–17, 1982. 759–763. San Francisco, CA. Available at: http://www.sascommunity.org/sugi/SUGI82/Sugi-82-139%20Iannacchione.pdf (accessed March 2015).Search in Google Scholar

Langton, L. and J. Truman. 2015. Criminal Victimization, 2014. Washington, DC: Bureau of Justice Statistics. (NCJ 248973).Search in Google Scholar

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.Search in Google Scholar

Little, R.J. and D.B. Rubin. 2002. Wiley Series in Probability and Statistics: Statistical Analysis with Missing Data. 2nd ed. Somerset, NJ: Wiley.10.1002/9781119013563Search in Google Scholar

Poulsen, C.A. 1982. Latent Structures Analysis with Choice Modeling Applications. Aarhus, Denmark: Aarhus School of Business Administration and Economics.Search in Google Scholar

Rand, M. and S. Catalano. 2007. Criminal Victimization, 2006. Washington, DC: U.S. Department of Justice, Office of Justice Programs. (NCJ 219413).Search in Google Scholar

Rubin, D.B. 1976. “Inference and Missing Data.” Biometrika 63(3): 581–592. Doi: http://dx.doi.org/10.1093/biomet/63.3.581.10.1093/biomet/63.3.581Search in Google Scholar

Schafer, J.L. and J.W. Graham. 2002. “Missing Data: Our View of the State of the Art.” Psychological Methods 7(2): 147–177. Doi: http://dx.doi.org/10.1037//1082-989x.7.2.147.10.1037//1082-989X.7.2.147Search in Google Scholar

Truman, J.L. and R.E. Morgan. 2016. Criminal Victimization, 2015. Washington, DC: Bureau of Justice Statistics. (NCJ 250180).Search in Google Scholar

U.S. Census Bureau. 2014. National Crime Victimization Survey: Technical Documentation. Washington, DC: U.S. Census Bureau. (NCJ 247252).Search in Google Scholar

U.S. Department of Justice. 2015. Bureau of Justice Statistics. National Crime Victimization Survey, 2014. Ann Arbor, MI: Inter-university Consortium for Political and Social Research.Search in Google Scholar

Van de Pol, F. and J. de Leeuw. 1986. “A Latent Markov Model to Correct for Measurement Error.” Sociological Methods & Research 15: 118–141. Doi: http://dx.doi.org/10.1177/0049124186015001009.10.1177/0049124186015001009Search in Google Scholar

Van de Pol, F. and R. Langeheine. 1990. “Mixed Markov Latent Class Models.” In Sociological Methodology, edited by C.C. Clogg, 213–247. Oxford: Blackwell.10.2307/271087Search in Google Scholar

Vermunt, J.K. 1997. Log-Linear Models for Event Histories. London: Sage.Search in Google Scholar

Vermunt, J.K. and J. Magidson. 2013. Technical Guide to Latent Gold 5.0: Basic, Advanced, and Syntax. Belmont, MA: Statistical Innovations.Search in Google Scholar

Wiggins, L.M. 1973. Panel Analysis, Latent Probability Models For Attitude And Behavior Processing. Amsterdam: Elsevier SPC.Search in Google Scholar