Abstract
In this chapter, we cover two further options in the statistical analysis of social interactions between occupations. One concerns the extent to which distributional parameters about the wider social structure can be used to provide alternative summary statistics concerned with social distance and occupational inequalities (Sect. 9.2). The other concerns scenarios where it can be useful to use ‘random effects’ models to explore occupation-to-occupation variations in relevant outcomes (Sect. 9.3).
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Notes
- 1.
We don’t give examples, but we could also construct ‘post-hoc’ summary statistics about the social interaction patterns for a similar purpose (for instance, the correlation between the derived SID scale and a direct measure of, say, income).
- 2.
Data from https://www.cia.gov/library/publications/the-world-factbook/rankorder/2004rank.html (Accessed 1 August 2013).
- 3.
Generally speaking, ‘fixed effects’ models are best applied to scenarios where interest focusses clearly upon determinants of variations in an outcome given the categories of the higher-level unit (e.g. Allison 2009), whereas random effects models are better suited to describing social processes that feature a combination of higher-level and lower-level mechanisms (e.g. Rabe-Hesketh and Skrondal 2012, p. 92).
- 4.
For completeness, the formulation also shows how the intercept term is allowed to vary in its impact from cluster to cluster. This term features in the simpler ‘random intercepts’ model (9.1) but is not normally written explicitly.
- 5.
It is also feasible to fit a ‘cross-classified’ model with random effects for both own job and spouse’s job, but this is not shown in the table.
- 6.
This pattern might also arise if the occupation requires physical fitness as an entry criteron. However, there are few barriers to entry to this relatively disadvantaged occupation, so we suspect that it is plausible that this occupation is genuinely good for people’s health (net of other factors).
- 7.
A convenience of these statistics, which will also apply to the intra-cluster correlation statistics for occupations in the right panel of Fig. 9.5, is a natural comparability in scaling for the influence of linear measures and random effects terms for occupations. This contrast with the challenges of presenting comparable statistics based upon non-linear occupation-based class measure (cf. Smits 2003; Smits et al. 1999; Luijkx 1994, c6).
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Lambert, P., Griffiths, D. (2018). Occupational-Level Residuals and Distributional Parameters. In: Social Inequalities and Occupational Stratification. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-02253-0_9
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