1 What is… The Analysis of Longitudinal Survey Data Paul Lambert University of Stirling Prepared for: National Centre for Research Methods, Research Methods Festival, St Catherine’s College, Oxford, 7 July 2010 Also see: www.longitudinal.stir.ac.uk / www.dames.org.uk
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1 What is… The Analysis of Longitudinal Survey Data Paul Lambert University of Stirling Prepared for: National Centre for Research Methods, Research Methods.
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What is… The Analysis of Longitudinal Survey Data
Paul Lambert University of Stirling
Prepared for: National Centre for Research Methods, Research Methods Festival, St Catherine’s College, Oxford, 7 July 2010
Also see: www.longitudinal.stir.ac.uk / www.dames.org.uk
Temporal effects in single cross-sectional surveys
• Temporal effects are (a) present and (b) of interest in most social science studies
• We can measure differences between people in terms of their age / year of birth
• These matter empirically & are interesting substantively
• But we can’t tell if differences are due to age or period or cohort (or other things that are collinear with these, e.g. life course stage or major events)
July 2010: LDA 5
[Data type: 1/6]
Longitudinal statements from cross-sectional data are common...
• We typically fit linear/curvilinear trend lines for time effects
• Treiman (2009: 162): nonlinear specifications of time and age effects
– Year of birth effect on literacy in China: discontinuity at 1955; curve 1955-1967; knot at 1967
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gamma = 0.2626 ASE = 0.010 Cramér's V = 0.1385 Pearson chi2(12) = 700.6095 Pr = 0.000
– Intuitive type of repeated contact data – e.g. ‘7-up’ series
− Often contributes to cross-cohort comparisons − e.g. UK Birth cohort studies in 1946, 1958, 1970 and 2000
Information on a group of cases which share a common circumstance, collected repeatedly
as they progress through a life course
[Data type: 4/6]
July 2010: LDA 20
Cohort data and analysis in the social sciences
• Many circumstances parallel other panel types: Large scale studies ambitious & expensive Small scale cohorts still quite common…
Attrition problems often more severe Considerable study duration limits
Glenn (2005) argues that ‘cohort analysis’ should be specifically directed to understanding effects of ageing/progression over time• Other uses of cohort data are just = panel data• It remains hard - even with extensive cohort data - to
authoritatively understand ageing effects (age = period – cohort)
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Event history data analysis[esp. Blossfeld et al 2007]
• Data sources are panel / cohort studies, or retrospective interviews (…recall errors..)Analysis of event durations: ‘Event history analysis’;
‘Survival data analysis’; ‘Failure time analysis’; ‘hazards’; ‘risks’; ..
Key to event histories is ‘state space’ Episodes within state space : Lifetime work histories for 3 adults born 1935 State space Person 1 FT work PT work Not in work Person 2 FT work PT work Not in work Person 3 FT work PT work Not in work 1950 1960 1970 1980 1990 2000
July 2010: LDA 23
Example: Cox regression (SPSS example at www.longitudinal.stir.ac.uk)
Cox regression estimates: risks of quicker exit from firstemployment state of BHPS adults
.194 .081 .017
-.617 .179 .001
-.062 .003 .000
.000 .000 .000
-.013 .001 .000
.214 .109 .049
-.003 .002 .061
.000 .004 .897
.006 .001 .000
Female
Self-employed
Age in 1990
Age in 1990 squared
Hope-Goldthorpe scale
Female*self-employed
Female* HG scale
Self-employed*HG scale
Female*Age in 1990
B SE Sig.
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Time series data
Examples:• Unemployment rates by year in UK• University entrance rates by year by country
Comments: – Panel = many variables few time points
= ‘cross-sectional time series’ to economists– Time series = few variables, many time points– Descriptive analyses – e.g. charts of statistics over time– Advanced modelling analyses typically involve including ‘autoregressive’
terms (e.g. lag effects) amongst explanatory factors
Statistical summary of one particular concept, collected at repeated time points from one or
more subjects
[Data type: 6/6]
July 2010: LDA 25
….Six types of data/analysis…!0. Temporal effects in cross-sectional data
1. Repeated cross-sections
2. Panel datasets 3. Cohort studies
4. Event history datasets 5. Time series analyses
2. Data management issues • Working with longitudinal survey data is made
more challenging by important issues of ‘data management’ Variable operationalisations for comparisons
e.g. strategies for standardisation, harmonisation
Linking datasets internally to a study Linking with other datasets to enhance analysis
[Value of organising your data and files – e.g. Long, 2009]
Recognising data structure in analysis e.g. missing data; survey effects; modelling specifications
[..and then there’s another thing..]
Dealing with complex dataIn the UK we host many projects and centres which
contribute to enabling the analysis of complex longitudinal data for social science research
– Specifying suitably complex statistical models • Examples at the Centre for Multilevel Modelling (‘E-Stat’ a
generic tool for specifying advanced models; Realcom – for analysing longitudinal missing data); Lancaster-Warwick-Stirling NCRM Node; ULSC (Essex) on survey design effects
– Resources on accessing and handling complex data• e.g. ESDS; ADMIN Node; Obesity e-lab; DAMES Node
• ..Session 17 in yesterday’s programme..
July 2010: LDA 27
My own pet project concerns comparability of variables over time..(see www.dames.org.uk)
July 2010: LDA 28
Unskilled
Skilled manual
Petty-bourg.
Non-manual
Salariat
Source: Females from LFS/GHS, using data from Li and Heath (2008)
percent of year category
Goldthorpe class scheme harmonised over time
July 2010: LDA 29
Managers and Administrators
Professional
Associate professional and technical
Clerical and secretarial
Craft and related
Personal and protective servicesSales
Plant and machine operativesOther occupations
.
higher degree
first degree
teaching qf
other higher qf
nursing qf
gce a levels
gce o levels or equiv
commercial qf, no o levels
cse grade 2-5,scot grade 4-5apprenticeship
other qf
no qf
.white
black-carib
black-african
black-other
indianpakistani
bangladeshi
chinese
other ethnic grp
2030
4050
0 1 2 3Source: British Household Panel Survey 2007, adults aged 18+ and father's Cambridge Scale score.Points at 1-3 show category mean. Points at 0 show individual values (scaled mean=28, sd=6; pop. mean=28, sd=18).
…‘Effect proportional scaling’ using parents’ occupational advantage
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3. Some closing comments on the analysis of longitudinal survey data
Why bother with all this..?– Focus on change / stability
– Focus on the life course Distinguish age, period and cohort effects Career trajectories / life course sequences
– Focus on time / durations Substantive role of durations (e.g. Unemployment)
– Getting the ‘full picture’ Causality and residual heterogeneity Examining multivariate relationships Representative conclusions
[e.g. Abbott 2006; Mayer 2005; Menard 2002; Baltagi 2001; Rose 2000; Dale and Davies 1994; Hannan and Tuma 1979; Moser 1958]
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Research traditions• ‘geographers study space and economists study time’
[adage quoted in Fotheringham et al. 2000:245] Vast economics literature using techniques for temporal analysis Other social science disciplines to some degree catching up Though methodological research on longitudinal models, and data quality,
cross-cuts disciplines [e.g. Dale and Davies, 1994]
• Data expansions c1990 -> more encompassing models; new substantive applications areas – For example: – [Platt 2005] - ethnic minorities’ social mobility 1971-2001– [Pahl & Pevalin 2005] – Friendship patterns over time– [Verbakel & de Graaf 2008] – spouses effect on careers 1941-2003
• …One challenge is getting used to talking about time in a more disciplined way: e.g. traditional sociological characterisations of ‘the past’ and ‘social change’ may not be empirically satisfactory
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What’s exciting in the analysis of longitudinal social survey data?
• A personal view:
By and large, the core analytical & methodological issues have been recognised for some time
What is exciting is the rapid expansion of secondary quantitative longitudinal data, its quality, its volume and its accessibility
(a) - new data
(b) - new tools for accessing, handling and
modelling large and complex data
References• Abbott, A. (2006). 'Mobility: What? When? How?' in Morgan, S.L., Grusky, D.B. and Fields, G.S. (eds.) Mobility and
Inequality. Stanford: Stanford University Press.• Baltagi, B.H. (2001). Econometric Analysis of Panel Data. New York: Wiley.• Blossfeld, H.P. and Rohwer, G. (2002). Techniques of Event History Modelling: New Approaches to Causal Analysis,
2nd Edition. Mawah, NJ: Lawrence Erlbaum Associates.• Blossfeld, H. P., Grolsch, K., & Rohwer, G. (2007). Event History Analysis with Stata. New York: Lawrence Erlbaum • Davies, R.B. (1994). 'From Cross-Sectional to Longitudinal Analysis' in Dale, A. and Davies, R.B. (eds.) Analysing
Social and Political Change : A casebook of methods. London: Sage.• Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative Geography: Perspectives on Spatial Data
Analysis. London: Sage.• Glenn, N. D. (2005). Cohort Analysis, 2nd Edition. London: Sage.• Hannan, M. T., & Tuma, N. B. (1979). Methods for Temporal Analysis. Annual Review of Sociology, 5, 303-328.• Li, Y., & Heath, A. F. (2008). Socio-Economic Position and Political Support of Black and Ethnic Minority Groups in
the United Kingdom, 1972-2005 [computer file]. 2nd Ed. Colchester, Essex: UK Data Archive [distributor], SN: 5666.• Long, J.S. (2009). The Workflow of Data Analysis using Stata. Boca Raton, Texas: • Martin, J., Bynner, J., Kalton, G., Boyle, P., Goldstein, H., Gayle, V., Parsons, S. and Piesse, A. 2006. Strategic Review
of Panel and Cohort Studies. London: Longview, and www.longviewuk.com/• Mayer, K.U. 2005. 'Life courses and life chances in a comparative perspective' in Svallfors, S. (ed.) Analyzing
Inequality: Life Chances and Social Mobility in Comparative Perspective. Stanford: Stanford University Press.• Menard, S. 2002. Longitudinal Research, 2nd Edition. London: Sage, Number 76 in Quantitative Applications in the
Social Sciences Series.• Moser, C. A. (1958). Survey Methods in Social Investigation. London: Heinemann.• Pahl, R., & Pevalin, D. (2005). Between family and friends: a longitudinal study of friendship choice. British Journal of
Sociology, 56(3), 433-450.• Platt, L. (2005). Migration and Social Mobility: The Life Chances of Britain's Minority Ethnic Communities . Bristol:
The Policy Press.• Rose, D. (2000). Researching Social and Economic Change: The Uses of Household Panel Studies. London: Routledge.• Taris, T.W. (2000). A Primer in Longitudinal Data Analysis. London: Sage.• Treiman, D.J. (2009). Quantitative Data Analysis: Doing Social Research to Test Ideas. New York: Josey Bass. • Verbakel, E., & de Graaf, P. M. (2008). Resources of the Partner: Support or Restriction in the Occupational Career
Developments in the Netherlands Between 1940 and 2003. European Sociological Review, 24(1), 81-95.33