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 language | English (US) |
|---|---|
| Pages (from-to) | S38-S50 |
| Journal | Journals of Gerontology - Series B Psychological Sciences and Social Sciences |
| Volume | 69 |
| DOIs | |
| State | Published - Nov 1 2014 |
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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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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 journal › Article
TY - JOUR
T1 - Missing data in wave 2 of NSHAP
T2 - Prevalence, predictors, and recommended treatment
AU - Hawkley, Louise C.
AU - Kocherginsky, Masha
AU - Wong, Jaclyn
AU - Kim, Juyeon
AU - Cagney, Kathleen A.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - 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.
AB - 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.
KW - Full-information maximum-likelihood
KW - Inverse probability weighting
KW - Missing data
KW - Multiple imputation by chained equations
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U2 - 10.1093/geronb/gbu044
DO - 10.1093/geronb/gbu044
M3 - Article
VL - 69
SP - S38-S50
JO - Journals of Gerontology - Series B Psychological Sciences and Social Sciences
JF - Journals of Gerontology - Series B Psychological Sciences and Social Sciences
SN - 1079-5014
ER -