Meta Data Standards for Managing and Archiving Longitudinal Data: Achieving Best Practice Melanie...

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)

Brisbane

Institute of Social Science Research at the University of Queensland

ASSDA – Queensland node

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

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

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

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)

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

• 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 all four waves have been archived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Phase 1 (three waves) and Phase 2 (two waves) have been archived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.

• 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

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?

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

Agree Disagree

Strongly Agree

StronglyDisagree

Survey 1: Marriage improves your health

Survey 2: Marriage improves your health

Incomparability

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

Updating Longitudinal Surveys

• Additional logic check within a study participant between surveys across time

• S1 S2 S3

• S1 Osteoporosis

S2 Osteoporosis

S3 Osteoporosis

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

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

Website/ Contact

Australian Social Science Data Archive18 Balmain CrescentThe Australian National UniversityACTON ACT 0200

Email: assda@anu.edu.au, m.spallek@uq.edu.auWebsite: www.assda.edu.auPhone: +61 2 6125 4400 Fax: +61 2 6125 0627