title: Missing data in Wave 2 of NSHAP: prevalence, predictors, and recommended treatment.
abstract: OBJECTIVE
  This report seeks to inform National Social Life, Health, and Aging Project (NSHAP) data users of the prevalence and predictors of missing data in the in-person interview (CAPI) and leave-behind questionnaire (LBQ) in Wave 2 of NSHAP, and methods to handle missingness.
  
  
  METHODS
  Missingness is quantified at the unit and item levels separately for CAPI and LBQ data, and at the item level is assessed within domains of conceptually related variables. Logistic and negative binomial regression analyses are used to model predictors of unit- and item-level nonresponse, respectively.
  
  
  RESULTS
  Unit-level nonresponse on the CAPI was 10.6% of those who responded at Wave 1, and LBQ nonresponse was 11.37% of those who completed the Wave 2 CAPI component. CAPI item-level missingness was less than 1% of items for most domains but 7.1% in the Employment and Finances domain. LBQ item-level missingness was 5% across domains but 8.3% in the Attitudes domain. Missingness was predicted by characteristics of the sample and features of the study design.
  
  
  CONCLUSIONS
  Multiple imputation is recommended to handle unit- and item-level missingness and can be readily and flexibly conducted with multiple imputation by chained equations, inverse probability weighting, and in some instances, full-information maximum-likelihood methods.
authors.affiliation: Academic Research Centers, NORC at the University of Chicago, Illinois. Hawkley-louise@norc.org.
authors.forename: Louise C
authors.initials: LC
authors.lastname: Hawkley
authors.affiliation: Department of Health Studies and.
authors.forename: Masha
authors.initials: M
authors.lastname: Kocherginsky
authors.affiliation: Department of Sociology, University of Chicago, Illinois.
authors.forename: Jaclyn
authors.initials: J
authors.lastname: Wong
authors.affiliation: Department of Sociology, National University of Singapore.
authors.forename: Juyeon
authors.initials: J
authors.lastname: Kim
authors.affiliation: Department of Sociology, University of Chicago, Illinois.
authors.forename: Kathleen A
authors.initials: KA
authors.lastname: Cagney
mesh_terms: D000367:Age Factors; D000368:Aged / Q000706:statistics & numerical data; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology / Q000523:psychology; D003625:Data Collection / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005260:Female; D006304:Health Status; D006801:Humans; D007407:Interviews as Topic; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015995:Prevalence; D012959:Socioeconomic Factors; D011795:Surveys and Questionnaires; D014481:United States / Q000453:epidemiology
keywords: Full-information maximum-likelihood; Inverse probability weighting; Missing data; Multiple imputation by chained equations.