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Correcting for Selective Nonresponse in the National Longitudinal Survey of Youth Using Multiple Imputation


  • Adam Davey
  • Michael J. Shanahan
  • Joseph L. Schafer


Survey attrition and nonresponse, particularly when selective, present unique challenges to researchers interested in studying developmental processes and longitudinal change. Four distinct patterns of nonresponse on children's psychosocial adjustment and lifetime poverty experiences and family histories are identified using principal components analysis. In turn, membership in these four groups is significantly predicted by the child's demographic characteristics, family experiences, and previous values on adjustment variables, indicating selective nonresponse and raising the possibility of biased estimates based on listwise deletion of missing data. We then examine a set of latent growth curve models that interrelate children's family experiences and psychosocial adjustment using listwise deletion (LD) and multiple imputation (MI) procedures. Implications for treatment of nonresponse in national longitudinal surveys are discussed.

Suggested Citation

  • Adam Davey & Michael J. Shanahan & Joseph L. Schafer, 2001. "Correcting for Selective Nonresponse in the National Longitudinal Survey of Youth Using Multiple Imputation," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 500-519.
  • Handle: RePEc:uwp:jhriss:v:36:y:2001:i:3:p:500-519

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    References listed on IDEAS

    1. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
    2. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
    3. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-473, March.
    4. Atkinson, Anthony B., 1970. "On the measurement of inequality," Journal of Economic Theory, Elsevier, vol. 2(3), pages 244-263, September.
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    Cited by:

    1. Dang, Hai-Anh & Jolliffe, Dean & Carletto, Calogero, 2018. "Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data-Scarce Environments," GLO Discussion Paper Series 179, Global Labor Organization (GLO).
    2. Roe Robert A., 2005. "Studying time in organizational behavior," Research Memorandum 048, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    3. Andrea Leiter & Gerald Pruckner, 2009. "Proportionality of Willingness to Pay to Small Changes in Risk: The Impact of Attitudinal Factors in Scope Tests," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(2), pages 169-186, February.
    4. Yongwei Chen & Dahai Fu, 2015. "Measuring income inequality using survey data: the case of China," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(2), pages 299-307, June.
    5. Jolliffe,Dean Mitchell & Dang,Hai-Anh H. & Carletto,Calogero & Dang,Hai-Anh H. & Jolliffe,Dean Mitchell & Carletto,Calogero, 2017. "Data gaps, data incomparability, and data imputation : a review of poverty measurement methods for data-scarce environments," Policy Research Working Paper Series 8282, The World Bank.
    6. David R. Mann & Todd Honeycutt, 2016. "Understanding the Disability Dynamics of Youth: Health Condition and Limitation Changes for Youth and Their Influence on Longitudinal Survey Attrition," Demography, Springer;Population Association of America (PAA), vol. 53(3), pages 749-776, June.
    7. David A. Penn, 2005. "Determinants of Self-Reported Financial Security for Oklahoma County Households – An Application of Multiple Imputation," Working Papers 200504, Middle Tennessee State University, Department of Economics and Finance.
    8. Dang, Hai-Anh H. & Lanjouw, Peter F. & Serajuddin, Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    9. David A. Penn, 2005. "Financial Well-Being in an Urban Setting: An Application of Multiple Imputation," Working Papers 200506, Middle Tennessee State University, Department of Economics and Finance.
    10. Luis Ayala & Carolina Navarro & Mercedes Sastre, 2011. "Cross-country income mobility comparisons under panel attrition: the relevance of weighting schemes," Applied Economics, Taylor & Francis Journals, vol. 43(25), pages 3495-3521.

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