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  1. What is… Quantitative Longitudinal Analysis? Paul Lambert and Vernon GayleUniversity of StirlingPrepared for: National Centre for Research Methods, Research Methods Festival, St Catherine’s College, Oxford, 2 July 2008 July 2008: LDA

  2. So what is quantitative longitudinal analysis? You already know.. • Working with (survey) datasets with longitudinal information (data about time) and the specialist techniques of statistical analysis that are appropriate You maybe don’t realise.. • Complex data and data management components • Groups of techniques and data types • Reasons why longitudinal analysis is advocated July 2008: LDA

  3. Quantitative longitudinal research in the social sciences • Survey resources • Longitudinal • Data concerned with more than one time point • [e.g. Taris 2000; Blossfeld and Rohwer 2002] • Repeated measures over time • [e.g. Menard 2002; Martin et al 2006] Data analysis is used to give a parsimonious summary of patterns of relations between variables in the survey dataset July 2008: LDA

  4. Motivations for QnLA • 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] July 2008: LDA

  5. What’s exciting about quantitative longitudinal analysis? • A personal view: July 2008: LDA

  6. Some comments on quantitative longitudinal analysis • Working with secondary surveys • Expense of long-term data collection • Complex data files, need good habits in data management (using syntax) • In practical terms – lots of gruelling computer programming • Resources for supporting researchers Easy access to data, e.g. • • Training in relevant analytical methods and data management, e.g. • • Distinctive research traditions and research centres.. July 2008: LDA

  7. Research traditions (methodology) • Statistical methods for quantitative longitudinal data • [esp. Dale & Davies 1994] • Research on data quality • Variable constructions in longitudinal research • • Harmonisation, standardisation, comparability • Missing data and attrition July 2008: LDA

  8. Research traditions (applications) • ‘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 are mostly catching up • ..we’ll come back to geography later • Data expansions c1990 -> 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 • Here, one critical 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 July 2008: LDA

  9. Some detail: Five traditions in Quantitative Longitudinal Analysis cf. July 2008: LDA

  10. Repeated cross-sectional data: in soc. sci., the most widely used longitudinal analysis

  11. Repeated cross sections • Easy to communicate & appealing: how things have changed between certain time points • Partially distinguishes age / period / cohort • Easier to analyse – less data management However.. • Don’t get other QnLR attractions (nature of changers; residual heterogeneity; causality; durations) • Hidden complications: are sampling methods, variable operationalisations really comparable? • cf. => measures are more often robust than not... July 2008: LDA

  12. Example 1.1: UK Census • Directly access aggregate statistics from census reports, books or web, e.g.: • Census v’s Surveys: larger scale surveys often have more data points and more reliable measures July 2008: LDA

  13. Example 1.2i: Labour Force Survey yearly stats July 2008: LDA

  14. Example 1.2ii: LFS and time July 2008: LDA

  15. Five traditions in Quantitative Longitudinal Analysis July 2008: LDA

  16. Panel Datasets Information collected on the same cases at more than one point in time • ‘classic’ longitudinal design • incorporates ‘follow-up’, ‘repeated measures’, and ‘cohort’ • Large and small scale panels are common July 2008: LDA

  17. Illustration: Unbalanced panel

  18. Panel data advantages • Study ‘changers’ – how many of them, what are they like, what caused change • Control for individuals’ unknown characteristics (‘residual heterogeneity’) • Develop a full and reliable life history • e.g. family formation, employment patterns July 2008: LDA

  19. Challenges for Panel data analysis • Complex data analysis and data management • need for training & good habits (syntax programming) • Data issues • confidential data; time lag until most useful data • variable constructions and comparability • Unbalanced panels and attrition • Balanced data is still required for many analytical techniques • transition tables; dynamic effects; trajectory profiles • Unbalanced cases and attrition as missing data • Complete case analysis = ‘MCAR’ • Ad hoc methods and imputatin • Missing data models, e.g. July 2008: LDA

  20. Example 2.1: Panel transitions July 2008: LDA

  21. Analytical approaches Panel data models: Yit = ΒXit + … + Є

  22. Panel data model types • Fixed and random effects • Ways of estimating panel regressions • Growth curves • Time effect in panel regression (cf. multilevel models) • Dynamic Lag-effects models • Theoretically appealing... Analytically complex and often need advanced or specialist software • Econometrics literature • Stata / GLLAMM; R; S-PLUS; SABRE / GLIM; LIMDEP; MLwiN; MPLUS; … July 2008: LDA

  23. Example 2.2: Panel model July 2008: LDA

  24. Five traditions in Quantitative Longitudinal Analysis July 2008: LDA

  25. Cohort Datasets Information on a group of cases which share a common circumstance, collected repeatedly as they progress through a life course • Intuitive type of repeated contact data • e.g. ‘7-up’ series • Cohort comparisons • e.g. UK Birth cohort studies in 1946, 1958, 1970 and 2000 July 2008: LDA

  26. 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) July 2008: LDA

  27. Five traditions in Quantitative Longitudinal Analysis July 2008: LDA

  28. Event history data analysis[esp. Blossfeld et al 2007] Focus shifts to length of time in a ‘state’ - analyses determinants of time in state • Alternative data sources: • Panel / cohort (more reliable) • Retrospective (cheaper, but recall errors) • Aka: ‘Survival data analysis’; ‘Failure time analysis’; ‘hazards’; ‘risks’; .. July 2008: LDA

  29. Event histories differ: • In form of dataset (cases are spells of time in a state) • Raises data management challenges • Comment: data analysis techniques are not well suited to complex variates; some argue than many Event History applications are artificially simplistic in their variables • In types of analytical method • Many techniques are new (and/or not well known), and specialist software may be needed • Time to labour market transitions • Time to recidivism • Time to end of cohabitation July 2008: LDA

  30. Key to event histories is ‘state space’ July 2008: LDA

  31. July 2008: LDA

  32. July 2008: LDA

  33. Event history analysis software SPSS – limited analysis options Stata – wide range of pre-prepared methods SAS – as Stata S-Plus/R – vast capacity but non-introductory GLIM / SABRE – some unique options TDA – simple but powerful freeware MLwiN; lEM; {others} – small packages targeted at specific analysis situations July 2008: LDA

  34. Eg 4.1 : Kaplan-Meir survival July 2008: LDA

  35. Eg 4.2: Cox’s regression July 2008: LDA

  36. Sequences / Trajectories: characterise event history progression through states into clusters / sequences / frameworks • Growing recent social science interest Optimal matching analysis / Correspondence analysis / log-linear models / Latent growth curves • Often analyse membership of cluster as an outcome (Problem – neutrality of data, e.g., cluster 1= Men in full time employment) July 2008: LDA

  37. Five traditions in Quantitative Longitudinal Analysis July 2008: LDA

  38. Time series data Statistical summary of one particular concept, collected at repeated time points from one or more subjects Examples: • Unemployment rates by year in UK • University entrance rates by year by country Comment: • Panel = many variables few time points = ‘cross-sectional time series’ to economists • Time series = few variables, many time points July 2008: LDA

  39. Time Series Analysis • Descriptive analyses • charts / text commentaries on values by time periods and different groups • Widely used (=Repeated X-Sectional analysis) ii) Time Series statistical models Advanced methods of modelling data are possible, require specialist stats functions • Autoregressive functions: Yt = Yt-1 + Xt + e • Widely employed in business / economics, but limited use in other disciplines July 2008: LDA

  40. ….Phew! July 2008: LDA

  41. Summary: Quantitative Longitudinal Analysis 1)Pro’s and cons to QnL research:: • Appealing analytical possibilities:e.g. analysis of change, controls for residual heterogeneity • Pragmatic constraints: data access, management, & analytical methods; practical applications often over-simplify topics • Uneven penetration of applications between research fields at present July 2008: LDA

  42. Summary: Quantitative Longitudinal Analysis 2)Undertaking QnL research: • Needs a bit of effort:learn software syntax, data management routines – workshops and training facilities available; exploit UK networks • Remain substantively driven: dangers of ’methodolatry’ (applications ‘forced’ into favourite complex techniques) and simplification (simpler techniques in the more popular & influential reports) • Learn by doing (..with Stata syntax examples..!) July 2008: LDA

  43. Summary: Quantitative Longitudinal Analysis 3)Some speculation on the future • Process of mainstreaming QLA into social science discourses (so we all need to know ‘what is’!!) • Complex multi-process models:new data & software for complex longitudinal statistical models • More new longitudinal data resources • More and more micro-data (e.g. UKHLS) • Data linking (e.g. administrative datasets) • Geographical data over time July 2008: LDA

  44. 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. • Lambert, P.S., Prandy, K. and Bottero, W. 2007. 'By Slow Degrees: Two Centuries of Social Reproduction and Mobility in Britain'. Sociological Research Online 12. • 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 • 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. • 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.