HRS Bibliography

Bibliography Search
Export 47 results:
Filters: Keyword is Survey Methodology  [Clear All Filters]

Forthcoming

Baidwan NKaur, Gerberich SGoodwin, Kim H, Ryan AD, Church T, Capistrant B. A marginal structural model approach to analyse work-related injuries: An example using data from the Health and Retirement Study. Injury Prevention. Forthcoming. doi:10.1136/injuryprev-2018-043124.
http://www.ncbi.nlm.nih.gov/pubmed/31018941?dopt=Abstract
Howrey BT, Hand CL. Measuring Social Participation in the Health and Retirement Study. Gerontologist. Forthcoming. doi:10.1093/geront/gny094.
http://www.ncbi.nlm.nih.gov/pubmed/30169644?dopt=Abstract

2019

Miller B, Arpawong TE, Jiao H, et al. Comparing the utility of mitochondrial and nuclear DNA to adjust for genetic ancestry in association studies. Cells. 2019;8(4). doi:10.3390/cells8040306.
http://www.ncbi.nlm.nih.gov/pubmed/30987182?dopt=Abstract
Gianattasio KZ, Wu Q, Glymour MM, Power MC. Comparison of methods for algorithmic classification of dementia status in the Health and Retirement Study. Epidemiology. 2019;30(2):291-302. doi:10.1097/EDE.0000000000000945.
http://www.ncbi.nlm.nih.gov/pubmed/30461528?dopt=Abstract
Jackson HM, Engelman M, Bandeen-Roche K. Robust Respondents and Lost Limitations: The Implications of Nonrandom Missingness for the Estimation of Health Trajectories. Journal of Aging and Health. 2019;31(4):685-708. doi:10.1177/0898264317747079.
http://www.ncbi.nlm.nih.gov/pubmed/29254422?dopt=Abstract

2018

Tan YVincent, Flannagan CAC, Pool L, Elliott MR. Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study. Ithaca: arXiv.org; 2018.
Wagner J, Olson K. An Analysis of Interviewer Travel and Field Outcomes in Two Field Surveys. Journal of Official Statistics. 2018;34(1):211-237. doi:10.1515/jos-2018-0010.
Kröger H, Hoffmann R. The association between CVD-related biomarkers and mortality in the Health and Retirement Survey. Demographic Research. 2018;38:1933-2002. doi:10.4054/DemRes.2018.38.62.
van Raalte AA, Sasson I, Martikainen P. The case for monitoring life-span inequality. Science. 2018;362(6418):1002-1004. doi:10.1126/science.aau5811.
Wu C, G. Geldhof J, Xue Q-L, Kim DHyun, Newman AB, Odden MC. Development, Construct Validity, and Predictive Validity of a Continuous Frailty Scale: Results from Two Large U.S. Cohorts. American Journal of Epidemiology. 2018;187(8):1752-1762. doi:10.1093/aje/kwy041.
http://www.ncbi.nlm.nih.gov/pubmed/29688247?dopt=Abstract
Chin A, De Bruin WBruine. Eliciting Stock Market Expectations: The Effects of Question Wording on Survey Experience and Response Validity. Journal of Behavioral Finance. 2018;19(1):101-110. doi:10.1080/15427560.2017.1373353.
McFall BHelppie, Sonnega A. Feasibility and reliability of automated coding of occupation in the Health and Retirement Study. Ann Arbor: Survey Research Center, Institute for Social Research, University of Michigan; 2018:1-19.
Ejima K, Pavela G, Li P, Allison DB. Generalized lambda distribution for flexibly testing differences beyond the mean in the distribution of a dependent variable such as body mass index. International Journal of Obesity. 2018;42(4):930-933. doi:10.1038/ijo.2017.262.
Shim H, Ailshire JA, Zelinski EM, Crimmins E. The Health and Retirement Study: Analysis of Associations Between Use of the Internet for Health Information and Use of Health Services at Multiple Time Points. Journal of Medical Internet Research. 2018;20(5):e200. doi:10.2196/jmir.8203.
Gosselin P. How We Measured Involuntary Job Losses Among Older Workers. ProPublica. https://www.propublica.org/article/how-we-measured-involuntary-job-losses-among-older-workers. Published 2018.
Edwards RD. If My Blood Pressure Is High, Do I Take It to Heart? Behavioral Effects of Biomarker Collection in the Health and Retirement Study. Demography. 2018;55(2):403-434. doi:10.1007/s13524-018-0650-2.
McClain CA, Ofstedal MB, Couper MP. Measuring Cognition in a Multi-mode Context. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan; 2018:1-48.PDF icon Download PDF (411.01 KB)
Barcelo H, Faul JD, Crimmins EM, Thyagarajan B. A Practical Cryopreservation and Staining Protocol for Immunophenotyping in Population Studies. Current Protocols in Cytometry. 2018;84(1):e35. doi:10.1002/cpcy.35.
http://www.ncbi.nlm.nih.gov/pubmed/30040214?dopt=Abstract
Wen L, Seaman SR. Semi-parametric methods of handling missing data in mortal cohorts under non-ignorable missingness. Biometrics. 2018;74(4):1427-1437. doi:10.1111/biom.12891.
http://www.ncbi.nlm.nih.gov/pubmed/29772074?dopt=Abstract
Giustinelli P, Manski CF, Molinari F. Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study. Cambridge, MA: National Bureau of Economic Research; 2018. doi:10.3386/w24559.
Bartolucci F, Nigro V, Pigini C. Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model. Econometric Reviews. 2018;37(1):61-88. doi:10.1080/07474938.2015.1060039.

2017

Cagnone S, Bartolucci F. Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models. Computational Economics. 2017;49(4):599 - 622. doi:10.1007/s10614-016-9573-4.
Sakib N, Sun X, Kong N, Meng H, Li M. Bi-level heterogeneity modeling of functional performance degradation for the aging population. IISE Transactions on Healthcare Systems Engineering. 2017;7(3):156-167. doi:10.1080/24725579.2017.1339147.
Dey R, Islam MA. A conditional count model for repeated count data and its application to GEE approach. Statistical Papers. 2017;58(2):485-504. doi:10.1007/s00362-015-0708-9.
Wilkinson LR, Ferraro KF, Kemp BR. Contextualization of Survey Data: What Do We Gain and Does It Matter?. Research in Human Development. 2017;14(3):234-252. doi:10.1080/15427609.2017.1340049.