Missing data? Plan on it!

Journal of the American Geriatrics Society

Volumn 58, Issue SUPPL. 2, 2010, Pages

  Palmer, Raymond F ^a   Royall, Donald R ^b  

^a UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

^b SOUTH TEXAS VETERANS HEALTH CARE SYSTEM   (United States)

Author keywords

longitudinal aging research; missing data methods; statistical measurement

Indexed keywords

AGING; CLINICAL RESEARCH; DATA ANALYSIS; GROWTH CURVE; HUMAN; LONGITUDINAL STUDY; METHODOLOGY; REVIEW; STATISTICAL ANALYSIS; STATISTICAL MODEL;

AGE FACTORS; AGED; AGING; HUMANS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 77957358302     PISSN: 00028614     EISSN: 15325415     Source Type: Journal    
DOI: 10.1111/j.1532-5415.2010.03053.x     Document Type: Review

References (44)

1. Cronbach LJ, Furby L. How we should measure "change" - or should we? Psychol Bull 1970 74 : 68 80.
2. Singer J, Willett J. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York : Oxford University Press, 2003.
3. Collins L, Sayer M, editors. New Methods for the Analysis of Change. Washington, DC : American Psychological Association, 2001.
4. Raudenbush SW, Bryk AS. Hierarchical Linear Models, 2nd Ed. Thousand Oaks, CA : Sage Publications, Inc., 2002.
5. Goldstein H. Multilevel Statistical Models, 2nd Ed. New York : Halstead Press, 1995.
6. Laird NM, Ware JH. Random effects models for longitudinal data. Biometrics 1982 38 : 963 974.
7. McArdle JJ, Epstein D. Latent growth curves within developmental structural equation models. Child Devel 1987 58 : 110 133.
8. Willet J, Sayer A. Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychol Bull 1994 116 : 363 381. (Pubitemid 24976776)
9. Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol Methods 2001 6 : 330 351.
10. Schafer JL, Graham JW. Missing data: Our view of the state of the art. Psychol Methods 2002 7 : 147 177.
11. Little RJA, Rubin DB. Statistical Analysis with Missing Data, 2nd Ed. Hoboken, NJ : Wiley & Sons, 2002.
12. Ferraro K, Kelley-Moore J. A half-century of longitudinal methods in social gerontology: Evidence of change in the journal. J Gerontol B Psychol Sci Soc Sci 2003 58 : BS264 S270.
13. Martin M, Hofer SM. Intraindividual variability, change, and aging: Conceptual and analytical issues. Gerontology 2004 50 : 7 11.
14. Ghisletta P, Lindenberger U. Static and dynamic longitudinal structural analysis of cognitive changes in old age. Gerontology 2004 50 : 12 16.
15. Pugh MJ, Palmer RF, Parchman ML et al. The association of suboptimal prescribing and change in lower extremity physical function over time. Gerontology 2007 53 : 445 453.
16. Royall DR, Palmer R, Chiodo LK et al. Decline in learning ability best predicts future dementia type: The Freedom House Study. Exp Aging Res 2003 29 : 385 406.
17. Royall DR, Palmer R, Chiodo LK et al. Executive control mediates memory's association with change in IADL: The Freedom House Study. J Am Geriatr Soc 2005 53 : 1 7.
18. Hardy SE, Allore H, Studenski SA. Missing data: A special challenge in aging research. J Am Geriatr Soc 2009 57 : 722 729.
19. Graham JW. Missing data analysis: Making it work in the real world. Annu Rev Psychol 2009 6 : 549 576.
20. Nunally J, Berstein I. Psychometric Theory, 3rd Ed. New York : McGraw Hill, 1994.
21. Bollen KA. Structural Equations with Latent Variables. New York : John Wiley & Sons, Inc., 1989.
22. Dwyer J. Statistical Models for the Social and Behavioral Sciences. Oxford, UK : University Press, 1983.
23. Liu K. Measurement error and its impact on partial correlation and multiple linear regression analysis. Am J Epidemiol 1988 127 : 864 874.
24. Bollen KA, Long JS. Testing Structural Equation Models. Newbery Park, CA : Sage Publications, Inc., 1993.
25. McArdle J, Small B, Backman L et al. Longitudinal models of growth and survival applied to the early detection of Alzheimer's disease. J Geriatr Psychiatry Neurol 2005 18 : 234 241.
26. Insel KC, Palmer RF, Stroup-Benham CA et al. Association between change in systolic blood pressure and cognitive decline among elderly Mexican Americans: Data from the Hispanic established population for epidemiology study of the elderly. Exp Aging Res 2005 31 : 35 54.
27. Royall DR, Palmer R, Chiodo LK et al. Declining executive control in normal aging predicts change in functional status: The Freedom House Study. J Am Geriatr Soc 2004 52 : 346 352.
28. Royall DR, Palmer R, Chiodo LK et al. Normal rates of cognitive change in successful aging: The Freedom House Study. J Int Neuropsychol Soc 2005 11 : 1 11.
29. McArdle JJ, Hamagami F. Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. Exp Aging Res 1992 18 : 145 166.
30. Allison PD. Missing Data. Thousand Oaks, CA : Sage, 2002.
31. Arbuckle JL. Full information estimation in the presence of incomplete data. In : Marcoulides GA, Schumacker RE, editors. Advanced Structural Equation Modeling. Mahwah, NJ : Lawrence Erlbaum Associates, Publishers, 1996, pp 243 277.
32. Schafer JL. Analysis of Incomplete Multivariate Data. New York : Chapman & Hall, 1997.
33. Graham J, Hofer S, Piccinin A. Analysis with missing data in drug prevention research. In: Collins L, Seitz L, editors. Advances in Data Analysis for Prevention Intervention Research. NIDA Research Monograph. U.S Dept. of Health and Human Services. 1994,142:13-63.
34. Graham JW, Taylor BJ, Cumsille PE. Planned missing data designs in analysis of change. In : Collins L, Sayers M, editors. New Methods for the Analysis of Change. Washington, DC : American Psychological Association, 2001, pp 335 353.
35. Graham J, Hofer S. Multiple imputation in multivariate research. In : Little T, Schnabel K, Baumert J, editors. Modeling Longitudinal and Multilevel Data: Practical Issues, Applied Approaches, and Specific Examples. Mahwah, NJ : Lawrence Erlbaum Associates, Publishers, 2000, pp 203.
36. Graham JW, Hofer SM, MacKinnon DP. Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behav Res 1996 31 : 197 218.
37. Weiss DJ, Kingsbury GG. Application of computerized adaptive testing to educational problems. J Educat Measure 1984 21 : 361 375.
38. Graham JW, Taylor BJ, Olchowski AE et al. Planned missing data designs in psychological research. Psychol Methods 2006 11 : 323 343.
39. Palmer R, Dwyer J, Semmer N. A measurement model of adolescent smoking. Addict Behav 1994 19 : 477 489.
40. Palmer R, Graham J, Taylor B et al. Construct validity in health behavior research: Interpreting latent variable models involving self-report and objective measures. J Behav Med 2002 25 : 525 550.
41. Graham JW, Collins NL. Controlling correlational bias via confirmatory factor analysis of MTMM data. Multivariate Behav Res 1995 26 : 607 629.
42. McArdle JJ, Hamagami F. Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. See Collins & Sayer 2001, pp 137-175.
43. Bell RQ. Convergence: An accelerated longitudinal approach. Child Dev 1953 24 : 145 152.
44. Enders C. Applied Missing Data Analysis. New York, NY : Guilford Press, 2010.