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Data Management: The building blocks of clean, accurate and reliable longitudinal datasets

Anna Graves

Jean Ball

Eliza Fraser

Abstract

Data management involves the planning, management and production of data in a format suitable for researchers to use. The products of longitudinal studies are the datasets.

Efficient and careful data management will result in datasets that are as accurate and as complete as possible. In addition, effective data management can reduce missing data and minimize data entry error. The final dataset must be in a form that is easy to understand and to use with a variety of statistical packages.

Most importantly, data management processes and manipulations must be reproducible and well documented. This paper aims to provide some insight into data management procedures, using the Australian Longitudinal Study on Women’s Health (ALSWH) as an example.

Keywords

longitudinal studies, data management, dataset, recoding, derived variables


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References

Adamson L and Graves A (2007) Cohort management: Developing and maintaining participant databases in longitudinal studies, International Journal of Multiple Research Approaches.

ALSWH Data Dictionary Supplement 2007 Section 1 The Data, accessed at http://www.alswh.org.au/InfoData/dictsupp.html on 9 October 2007.

ALSWH Data Dictionary Supplement 2007 Section 1 Scales in the ALSWH, accessed at http://www.alswh.org.au/InfoData/dictsupp.html on 9 October 2007.

Chojenta C Mooney R and Warner-Smith P (2007) Accessing and disseminating longitudinal data: Protocols and policies, International Journal of Multiple Research Approaches.

de Vaus D A (2002) Surveys in social research, 5th edn, Allen & Unwin, Australia

Loxton D and Young A (2007) Longitudinal survey development and design, International Journal of Multiple Research Approaches.

Microsoft Access 2003 [software package] (2003) The Microsoft Corporation.

National Health and Medical Research Council, 2007. National Statement on Ethical Conduct in Human Research 2007. Australia, accessed at http://www.nhmrc.gov.au/publications/synopses/_files/e72.pdf on 5 October 2007.

Young A Powers J Wheway V (2007) Working with longitudinal data: attrition and retention, data quality, measures of change and other analytical issues, International Journal of Multiple Research Approaches.



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