Missing data in wave 2 of NSHAP

Prevalence, predictors, and recommended treatment

Louise C. Hawkley*, Masha Kocherginsky, Jaclyn Wong, Juyeon Kim, Kathleen A. Cagney

*Corresponding author for this work

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Objectives. 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. Method. 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. Discussion. 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.

Original languageEnglish (US)
Pages (from-to)S38-S50
JournalJournals of Gerontology - Series B Psychological Sciences and Social Sciences
Volume69
DOIs
StatePublished - Nov 1 2014

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response behavior
questionnaire
Health
health
weighting
finance
logistics
Regression Analysis
Interviews
regression
human being
Surveys and Questionnaires
interview

Keywords

  • Full-information maximum-likelihood
  • Inverse probability weighting
  • Missing data
  • Multiple imputation by chained equations

ASJC Scopus subject areas

  • Health(social science)
  • Sociology and Political Science
  • Life-span and Life-course Studies

Cite this

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abstract = "Objectives. 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. Method. 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. Discussion. 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.",
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Missing data in wave 2 of NSHAP : Prevalence, predictors, and recommended treatment. / Hawkley, Louise C.; Kocherginsky, Masha; Wong, Jaclyn; Kim, Juyeon; Cagney, Kathleen A.

In: Journals of Gerontology - Series B Psychological Sciences and Social Sciences, Vol. 69, 01.11.2014, p. S38-S50.

Research output: Contribution to journalArticle

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T1 - Missing data in wave 2 of NSHAP

T2 - Prevalence, predictors, and recommended treatment

AU - Hawkley, Louise C.

AU - Kocherginsky, Masha

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AU - Kim, Juyeon

AU - Cagney, Kathleen A.

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