Meta Data Standards for Managing and Archiving Longitudinal Data: Achieving Best Practice Melanie...
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![Page 1: Meta Data Standards for Managing and Archiving Longitudinal Data: Achieving Best Practice Melanie Spallek*, Michele Haynes* & Mark Western* presented by.](https://reader034.fdocuments.in/reader034/viewer/2022051619/56649d4b5503460f94a28357/html5/thumbnails/1.jpg)
Meta Data Standards for Managing and Archiving Longitudinal Data:
Achieving Best Practice
Melanie Spallek*, Michele Haynes* & Mark Western*
presented by
Steven McEachern
*The Institute for Social Science Research (ISSR)
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Brisbane
Institute of Social Science Research at the University of Queensland
ASSDA – Queensland node
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WHY• Cross-sectional and longitudinal data
structure is different
• Current meta data standards not sufficient
• Great need for international standard in best practice for archiving longitudinal data
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Overview• Cross-sectional studies versus longitudinal
studies
>different types of longitudinal studies
• Major longitudinal studies archived with ASSDA
• Challenges with documenting longitudinal studies
• Compare meta data standards internationally
• Future plans at ASSDA
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Cross- sectional
• Multiple variables observed at a single point in time
• One- dimensional
Longitudinal
• Repeated observations over time
• Two or more dimensional• Change over time, cause-
effect, shifting attitudes
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Different types of longitudinal studies
• Repeated cross-sectional studies
> new sample at different points in time
> represents snapshot of population at each time point
> aspect of individual’s change not available
• Cohort studies> group of individuals at a similar state in the life
course, studied over time
> problems with drop-outs
• Household panels
> Household as a study unit
> Number of individuals can vary (move in, move out)
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Major longitudinal studies archived with ASSDA
• Negotiating the Life Course (NLC) > 1500 participants at wave 1 in 1996
> five waves archived so far
• Australian Longitudinal Study on Women's Health (ALSWH)
> three cohorts (younger, mid-aged, older)
> 40,000 participants at wave 1 in 1996
> four waves archived for the younger and older cohorts and five for the mid-aged cohort
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• Australian Longitudinal Survey of Ageing (ALSA)
> 2,087 participants at wave 1 in 1992
> seven waves archived so far
• Longitudinal Surveys of Australian Youth (LSAY)> 13,613 participants at wave one in 1995
> all four waves have been archived
• Longitudinal Survey of Immigrants to Australia (LSIA)>Phase 1 (three waves) and Phase 2 (two waves) have been archived
Professor Mary Luszcz with the oldest ALSA participant who is 108 years old.
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• DDI2 is used for describing cross-sec and longitudinal data
• coverage of DDI2 is focused on single studies, single data files, simple surveys and aggregated data files
• metadata requirements for longitudinal studies differ from that of cross-sectional studies and also across types of longitudinal studies
• DDI3.1 supports the description of longitudinal data, but few archives have facilitated DDI3.1 yet
Meta data standards used at ASSDA
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Challenges
• Combining Data on Same Individuals from Repeated Surveys
– How do longitudinal studies name comparable variables at different surveys?
– What tools are in place to easily identify variables and their comparability?
– What makes a variable incomparable?
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surveyvariable
namevalues question variable relates to
1 m1q30b n/anon existent
2 m2q30b 1,2,3,4,5,6,.
Over the last 12 months, how stressed have you felt about the following
areas of your life: Health of other family members.
1 n/a, 2 not at all stressed, 3 somewhat stressed,
4 moderately stressed, 5 very stressed, 6 extremely stressed
3 m3q30b 0,1,.
Some women have experienced difficulties in becoming pregnant. Have you
ever had any of the following problems with fertility: You were diagnosed
as infertile by a doctor?
1 yes, 0 no
4 m4q30b n/anon existent
5 m5q30b 1,2,3,4,5,6,.
Thinking about your own health care, how would you rate the following:
Access to hospital if you need it.
1 excellent, 2 very good, 3 good, 4 fair, 5 poor, 6 don't know
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Agree Disagree
Strongly Agree
StronglyDisagree
Survey 1: Marriage improves your health
Survey 2: Marriage improves your health
Incomparability
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Challenges
• Combining data on same individuals from repeated surveys– How do longitudinal studies name comparable
variables at different surveys?– What tools are in place to easily identify variables and
their comparability?– What makes a variable incomparable?
• Updating longitudinal surveys
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Updating Longitudinal Surveys
• Additional logic check within a study participant between surveys across time
• S1 S2 S3
• S1 Osteoporosis
S2 Osteoporosis
S3 Osteoporosis
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Comparisons among International Archives
• UK Data Archive’s Survey Question Bank http://surveynet.ac.uk/sqb/introduction.asp
• CentERdata uses some DDI3.1 http://www.lissdata.nl/dataarchive/concepts
• Other archives have not been found to address issues relating meta data for longitudinal data archiving
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Future Plans at ASSDA
• Website for longitudinal data archiving
• Provide guidelines for data dictionary and variable map development
• Require data dictionary and variable map with deposit of longitudinal data
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Website/ Contact
Australian Social Science Data Archive18 Balmain CrescentThe Australian National UniversityACTON ACT 0200
Email: [email protected], [email protected]: www.assda.edu.auPhone: +61 2 6125 4400 Fax: +61 2 6125 0627