<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p><p>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.</p></abstract>

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.

Effect of Missing Data on Classification Error in Panel Surveys

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Effect of Missing Data on Classification Error in Panel Surveys"}

Sensitive outcomes of surveys are plagued by wave nonresponse and measurement error (classification error for categorical outcomes). These types of error can...

Effect of Missing Data on Classification Error in Panel Surveys

Online veröffentlicht: 12. Juni 2017

Seitenbereich: 551 - 570

Eingereicht: 01. Jan. 2016

Akzeptiert: 01. Apr. 2017

DOI: https://doi.org/10.1515/jos-2017-0026

Schlüsselwörter
Survey error, full information maximum likelihood, measurement error, Markov latent class analysis, national crime victimization

© 2017 Susan L. Edwards et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Effect of Missing Data on Classification Error in Panel Surveys

Online veröffentlicht: 12. Juni 2017

Seitenbereich: 551 - 570

Eingereicht: 01. Jan. 2016

Akzeptiert: 01. Apr. 2017

DOI: https://doi.org/10.1515/jos-2017-0026

SchlüsselwörterSurvey error, full information maximum likelihood, measurement error, Markov latent class analysis, national crime victimization

© 2017 Susan L. Edwards et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Survey error, full information maximum likelihood, measurement error, Markov latent class analysis, national crime victimization