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.
N1 - Funding Information:
Funding This work was supported by the national Institutes of Health including the national Institute on Aging, the Division of Behavioral and social sciences Research for the national Health, social Life, and Aging project (nsHAp) (grant numbers R01 Ag021487, R37 Ag030481), and the nsHAp Wave 2 partner project (R01 Ag033903) and by nORC, which was responsible for the data collection. Coauthor, J. Wong was supported by a national Institute on Aging predoctoral Traineeship (T32 Ag00243).
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
C2 - 24809854
AN - SCOPUS:84922481154
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 -