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Analyzing Social Mobility Through Latent Trajectory Group Analysis

This study by Patrick Sturgis from the University of Southampton explores social mobility via latent trajectory group analysis. Traditional models focus on static ‘origin’ and ‘destination’ points, potentially missing key insights about 'in-between' growth trajectories. By utilizing latent curves and case-by-case analyses, we illustrate individual growth patterns, allowing for a comprehensive understanding of social mobility dynamics. Our findings reveal various trajectory groups through latent class growth analysis, highlighting the interaction of different covariates. This approach provides valuable insights while suggesting further research into cohort modeling.

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Analyzing Social Mobility Through Latent Trajectory Group Analysis

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  1. Exploring social mobility with latent trajectory group analysis Patrick Sturgis, University of Southampton and National Centre for Research Methods From work co-authored with Louise Sullivan

  2. Motivation • Conventional focus on correspondence between ‘origin’ and ‘destination’ points • Does this overlook potentially interesting information about what goes on in-between? • Our approach aims to uncover latent mobility trajectories • And to model the antecedents of membership of different trajectory groups

  3. Latent curves

  4. Conceptual example • we have one child, size of vocabulary measured each year from age 1 to 5 • Plot vocabulary size against time

  5. Vocabulary size child 1, t=5

  6. Add line of best fit Can be expressed as regression equation: y = 0.79x + 1.39

  7. Vocabulary size child 2, t=5 Less rapid growth y = 0.24x + 1.94

  8. Case-by-Case approach • So each individual’s growth trajectory can be expressed as a linear equation: • If we have lots of individual growth equations… • We can find the average of the intercepts… • …and the average of the slopes • And the variances of intercepts and slopes • The averages tell us about initial status and rate of growth for sample as a whole • Variances tell us about individual variability around these averages

  9. Latent curves Extend model to examine variability between individuals in initial position and rate of change

  10. Latent Class Growth Analysis (LCGA) • Latent curve approach yields parameters for whole sample/population • But what if there are qualitatively different growth trajectories? • Use latent class analysis to find distinct groupings which possess similar trajectory parameters • Multinomial logistic regression of group membership on fixed covariates

  11. Data • 1970 British Cohort Study • Every child born in week in 1970 • n = • Direct Maximum Likelihood

  12. Registrar General’s Social Class I Professional etc occupations II Managerial and technical occupations IIIN Skilled non-manual occupations IIIM Skilled manual occupations IV Partly-skilled occupations V Unskilled occupations

  13. BCS70 latent curve model

  14. How many latent trajectory groups?

  15. BICs for conditional LCGA Models

  16. Posterior probability plot for 5 group LCGA

  17. Estimated parameters for the 5 latent groups

  18. Lower middle class stable (21%)

  19. Working class rising

  20. Covariate coefficient contrasts for trajectory group membership

  21. Predicted probability of trajectory group membership

  22. Predicted probability of trajectory group membership

  23. Mother interested in child’s education

  24. Father post-compulsory education

  25. Conclusions • Potentially useful approach • But this exercise hasn’t told us much new in substantive terms • Problem = endogeneity of predictors • Extension = modelling different cohorts simultaneously

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