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Measuring SES/SEP Notes

Measuring SES/SEP Notes. Why SES? a ‘down-n-dirty’ review. The strong relationship between SES and health has been documented for centuries , dating back to ancient Greece, Egypt, and China

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Measuring SES/SEP Notes

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  1. Measuring SES/SEP Notes

  2. Why SES? • a ‘down-n-dirty’ review

  3. The strong relationship between SES and health has been documented for centuries, dating back to ancient Greece, Egypt, and China A better understanding of the relationship between SES and disease may reveal important new points for intervention and epi screening

  4. The socioeconomic structure in the US, and elsewhere, is rapidly changing (eg, outsourcing, career?, women, elderly. Racial/ethnic disparities in health may be construed as signs of genetic differences or behavioral choices rather than powerful clues about how forms of racial discrimination and structural constraints, past and present, harm health

  5. No consensus on a nominal definition of SES; it’s more than income and/or educational attainment , it’s a latent variable A widely accepted SES instrument does not exist Appears to be among the more difficult and controversial subjects in all of social research Prominent scholars have debated the theory, operationalization, and usefulness of SES constructs for about 125 years Basically, we got nothing…

  6. Gap between “SES Measurement” & “SES and Health Studies”

  7. Chris Hamlin (2007) “Social class as species”

  8. 2) Historical Review Even more ‘down-n-dirty’ and American-focused

  9. Americans are aware of social stratification and have fairly firm views about their social standing and that of others… think of Britney! But social scientists have not made much progress in measuring SES Progress retarded due to lack of conceptual clarity about social stratification… and it’s all about social hyper-dimensional stratification

  10. Early empirical sociological research mostly were studies of single small communities Status was assigned to households through an unarticulated theory of stratification mainly based on individuals’ reputation Underlying this approach is the assumption that everyone in a small community knows its status hierarchy and can place most individuals in it

  11. In 1947, NORC conducted a national sample survey asking respondents to rate on a 5 point scale "the social standing" of 90 occupations The average social standing given to each occupation can be regarded as the societal consensus (circa 1947) concerning the status of each occupation These social standing averages (also known as prestige scores) were the first measures of the national consensus on occupational status.

  12. Problem was NORC prestige scores were known for only 90 out of the thousands of occupations. OD Duncan wanted status scores for all Census occupations. Regressed known NORC occupational scores on the median occupational educational and incomes Predicted values were called Duncan’s SEI... a continuous variable that could be calculated for almost all occupational titles recognized in the Census

  13. Nam and Powers didn’t like “subjective” ratings in SEI and thought an “objective” approach was better Education as dues, Occupation as reward Occupational status score (OSS) was a simple function of educational attainment and income derived from a given occupation.

  14. In 1974, Rossi produced a Household Prestige Score Factorial survey: Husband’s occupation and education, along with wife’s occupation were randomly varied in vignettes Regressed ratings on characteristics of vignette examples to infer the relative influence of the social characteristics of households Predictive equation gives HHP scores to households based on the occupations, educational levels and ethnicities of spouses Worked pretty well… but ignored!

  15. But despite, SEI, OCC, HHP, we still have two main problems: • (1) Lack of consensus on a nominal definition • Empirical researchers must either adapt vague theories and develop idiosyncratic indicators or use whatever vaguely related data elements exist to construct ad hoc measures of SES (2) Absence of sound measurement theory Psychometrics has not been exploited in the development, testing, and validation of SES measures Routinely done in latent constructs, such as depression Early efforts of Lundberg (1940) and Gordon (1952), and the empirical efforts of Rossi (1951) have been overlooked

  16. Oakes & Rossi’s effort Oakes, JM & Rossi, PH. 2003. The measurement of SES in health research: current practice and steps toward a new approach. Social Science & Medicine, 56(4), 769-784.

  17. Define SES as differential access (realized and potential) to desired resources • Use existing theory (Jim Coleman’s) which aims to understand and explain the functioning and organization of the social system • Two kinds of elements and two ways in which they are related: The elements are (1) actors and (2) resources, related through (3) interests and (4) control. • Components of the theory have been increasingly subjected to theoretical and empirical scrutiny, with a few pleasing results

  18. Resources may take the form of • (1) Material and monetary goods • (2) Skills and capabilities • (3) The strengths of social relationships & resources of others

  19. SES = f (Material Capital, Human Capital, Social Capital) CAPSES Scale

  20. CAPSES Ratings of Subject SES Visual Analogue Scale (VAS) The rating scale ranges from lowest possible socioeconomic status on the left to highest possible socioeconomic status on the right. Place an “X” on each bar to indicate your rating of the subject’s status.

  21. Subject Self-Rated SES Self-reported scale (LADDER) Self-reported social class (CLASS)

  22. But… • Our own (pilot) survey work showed no contribution of social capital to “SES” beyond income and education. • Psychometrics were “inconclusive”

  23. Forget SES, use Poverty! Sit tight… more coming…

  24. Forget SES, use Income! Not very good… Income changes year-to-year Retired 20-somethings Trust funds

  25. Forget SES, use Wealth! Good luck!

  26. Forget SES, use Education! Good, but… Continuous or discrete, Cohort effects, “foreigners”, Trade-workers

  27. SES = Neighborhood? My new approach

  28. “Facts” • People are sorted in demarcated geographic areas, called neighborhoods; some good, some not so good • Thus, persons from same area are more alike than persons from other areas… a clustering phenomena • Early work showed SES was poor proxy for individual SES, but I think this is backwards!

  29. Oakes, JM. 2004. "The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology." Soc Sci Med 58:1929-52. (with discussion) • Oakes, JM. 2006. “Invited Commentary: Advancing neighbourhood-effects research--selection, inferential support, and structural confounding." Int J Epidemiol 35:643-7. • Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods for social epidemiology." Pp. 370-392 in Methods in Social Epidemiology, edited by JM Oakes and JS Kaufman. San Francisco: Jossey-Bass • Major talks – 2007 SER; 2006 JSM; 2005 ALR

  30. 3) Analysis with SES measures?

  31. a) Mismeasure SES and you’ve got trouble! What is impact of measurement error in confounder?

  32. b) The trouble with Neighborhood SES Hmmm… an insurmountable comparison problem! Compare: Boulder v. Mobile

  33. ‘We can only evaluate sharply distinct treatments that could happen to anyone.’ Paul Rosenbaum (2002) ‘If the differences between groups is large, the average value applied to each group with adjustment may represent “no man’s land”, a place where no actual observations exist. Given this scenario, the interpretation of the estimate becomes speculative rather than soundly based. Heroic modeling assumptions are required.’ William Cochran (1957)

  34. 1.0 Actually Exposed 0.5 Estimated Probability of Exposure Actually Unexposed 0.0 100 50 0 50 100 Number of Observed Subjects Comparative inference is off-support of data and thus requires “heroic” modeling assumptions. Source: Oakes, JM and PJ Johnson. 2006. "Propensity score matching methods for social epidemiology.“ Pp. 370-392 in Methods in Social Epidemiology, edited by Oakes and Kaufman. San Francisco: Jossey-Bass See also – Johnson PJ. 2004. "The Effect of Neighborhood Poverty on American Indian Infant Death." PhD Dissertation, UMN Hearst MO. 2007. "The Effect of Racial Residential Segregation on Infant Death in the US.“ PhD Dissertation, UMN

  35. What is the effect of neighborhood poverty on American Indian infant death in Minnesota? Compare AI IMR in poor vs. not-so-poor neighborhood

  36. But neighborhood effects are “independent”, which means we must rule out (ie, adjust out) individual-level confounders. The trouble is, there are few AI living in low poverty areas who are like (ie, exchangeable) to those AI living in poverty areas. More technically, AI with a high probability of living in poverty rarely reside in low poverty neighborhoods,but some must if a meaningful (ie, empirically based) counterfactual comparison is to be made.

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