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

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

Abstract

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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    Cited by:

    1. Roe, R.A., 2005. "Studying time in organizational behavior," Research Memorandum 046, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    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. Dang, Hai-Anh & Carletto, Calogero & Gourlay, Sydney & Abanokova, Kseniya, 2024. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," GLO Discussion Paper Series 1445, Global Labor Organization (GLO).
    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. Andrea Leiter, 2011. "Age effects in monetary valuation of reduced mortality risks: the relevance of age-specific hazard rates," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 12(4), pages 331-344, August.
    6. Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar & 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.
    7. 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.
    8. 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.
    9. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    10. Hai‐Anh Dang & Dean Jolliffe & Calogero Carletto, 2019. "Data Gaps, Data Incomparability, And Data Imputation: A Review Of Poverty Measurement Methods For Data‐Scarce Environments," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 757-797, July.
    11. Hamid Heidarian Miri & Jafar Hassanzadeh & Abdolreza Rajaeefard & Majid Mirmohammadkhani & Kambiz Ahmadi Angali, 2016. "Multiple Imputation to Correct for Nonresponse Bias: Application in Non-communicable Disease Risk Factors Survey," Global Journal of Health Science, Canadian Center of Science and Education, vol. 8(1), pages 133-133, January.
    12. Ting Dai & Adam Davey, 2023. "Determining Dimensionality with Dichotomous Variables: A Monte Carlo Simulation Study and Applications to Missing Data in Longitudinal Research," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    13. 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.
    14. 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.
    15. 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.
    16. Lili Yu & Yichuan Zhao, 2022. "A Bootstrap Method for a Multiple-Imputation Variance Estimator in Survey Sampling," Stats, MDPI, vol. 5(4), pages 1-11, November.
    17. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.

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