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Working with Longitudinal Data: Attrition and retention, data quality, measures of change and other analytical issues
Anne Young
Jennifer Powers
Virginia Wheway
Abstract
Longitudinal studies are important because they can help provide answers to questions about cause and effect, although their complexity leads to a number of challenges for the researcher.
By their very nature longitudinal studies may continue over a long period of time and/or have many data points and therefore good documentation of procedures is essential. In addition, it is important to develop dynamic databases that reflect the current status of participants in the project and to develop protocols for dealing with inconsistent or missing responses over time.
This paper provides some guidance about these issues as well as information about longitudinal data structure and ways to summarise and display the information obtained from longitudinal studies.
Keywords
longitudinal studies, panel studies, research methods
References
Adamson L and Graves A (2007) Cohort management: Developing and maintaining participant databases in longitudinal studies, International Journal of Multiple Research Approaches.
Anstey KJ and Hofer SM (2004) Longitudinal designs, methods and analysis in psychiatric research, Australian and New Zealand Journal of Psychiatry 38: 93-104.
Bowe S, Young AF, Sibbritt D and Furuya H (2006) Transforming the SF-36 to account for death in longitudinal studies with three-year follow-up, Medical Care 44: 956-959.
Brown WJ, Bryson L, Byles JE, Dobson AJ, Lee C, Mishra G and Schofield M (1998) Women's Health Australia: Recruitment for a national longitudinal cohort study, Women and Health 28: 23-40.
Chojenta C, Byles J, Loxton D and Mooney R (2007) Communication and dissemination of longitudinal study findings, International Journal of Multiple Research Approaches.
Deeg DJ (2002) Attrition in longitudinal population studies: does it affect the generalizability of the findings? Journal of Clinical Epidemiology 55: 213-215.
Diehr P, Patrick DL, Spertus J, Kiefe CI, McDonell M and Fihn SD (2001) Transforming self-rated health and the SF-36 scales to include death and improve interpretability, Medical Care 39: 670-80.
Diggle PJ, Heagerty P, Liang K-Y and Zeger SL (2002) Analysis of Longitudinal Data. Oxford University Press.
Graves A, Ball J and Fraser E (2007) Data management: the building blocks of clean, accurate and reliable longitudinal datasets, International Journal of Multiple Research Approaches.
Hedeker D and Gibbons RD (1999) Sample size Estimation for Longitudinal Designs with Attrition: Comparing Time-related Contrasts Between Two Groups, Journal of Educational and Behavioural Statistics 24: 70-93.
Little RJA and Rubin DB (2002) Statistical analysis with missing data. Second edition. Wiley-Interscience.
Powers J, Ball J, Adamson L and Dobson A (2000) Effectiveness of the National Death Index for establishing the vital status of older women in the Australian Longitudinal Study on Women's Health, Australian and New Zealand Journal of Public Health 24: 526-8.
Schafer JL and Graham JW (2002) Missing data: our view of the state of the art, Psychological Methods 7: 147-77.
Singer JD and Willett JB (2003) Applied longitudinal data analysis: Modelling change and event occurrence. Oxford University Press.
Tooth L, Ware R, Bain C, Purdie DM and Dobson A (2005) Quality of reporting of observational longitudinal research, American Journal of Epidemiology 161: 280-8.
Twisk JWR (2003) Applied Longitudinal Data Analysis for Epidemiology. Cambridge Press.
Warner-Smith P, Loxton D and Brown W (2007) Human resources for longitudinal studies: Matching people to skills and tasks, International Journal of Multiple Research Approaches.
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