Meta Data Standards for Managing and
Archiving Longitudinal Data:
Achieving Best Practice
Melanie Spallek*, Michele Haynes* & Mark Western*
*The Institute for Social Science Research (ISSR)
presented by
Steven McEachern
ASSDA – Queensland node
Brisbane
Institute of Social Science Research
at the University of Queensland
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, causeeffect, 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)
Professor Mary
Luszcz with the
oldest ALSA
participant
who is 108 years
old.
> 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 all four waves have been archived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waves) have been archived
Meta data standards used at ASSDA
• 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
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?
variable
survey
name
values
question variable relates to
non existent
1
m1q30b
n/a
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,
2
m2q30b
1,2,3,4,5,6,.
4 moderately stressed, 5 very stressed, 6 extremely stressed
Some women have experienced difficulties in becoming pregnant. Have
you ever had any of the following problems with fertility: You were
3
m3q30b
0,1,.
diagnosed as infertile by a doctor?
1 yes, 0 no
non existent
4
m4q30b
n/a
Thinking about your own health care, how would you rate the following:
Access to hospital if you need it.
5
m5q30b
1,2,3,4,5,6,.
1 excellent, 2 very good, 3 good, 4 fair, 5 poor, 6 don't know
Incomparability
Survey 1: Marriage improves your health
Agree
Disagree
Survey 2: Marriage improves your health
Strongly
Agree
Strongly
Disagree
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
• S1 Osteoporosis
S2 Osteoporosis
S3 Osteoporosis
S3
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 Archive
18 Balmain Crescent
The Australian National University
ACTON ACT 0200
Email: assda@anu.edu.au, m.spallek@uq.edu.au
Website: www.assda.edu.au
Phone: +61 2 6125 4400
Fax: +61 2 6125 0627