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Journal of Urban Health

, Volume 93, Issue 1, pp 213–232 | Cite as

Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Census Tracts

  • Jeremy N. Miles
  • Margaret M. WedenEmail author
  • Diana Lavery
  • José J. Escarce
  • Kathleen A. Cagney
  • Regina A. Shih
Article

Abstract

Contextual research on time and place requires a consistent measurement instrument for neighborhood conditions in order to make unbiased inferences about neighborhood change. We develop such a time-invariant measure of neighborhood socio-economic status (NSES) using exploratory and confirmatory factor analyses fit to census data at the tract level from the 1990 and 2000 U.S. Censuses and the 2008–2012 American Community Survey. A single factor model fit the data well at all three time periods, and factor loadings—but not indicator intercepts—could be constrained to equality over time without decrement to fit. After addressing remaining longitudinal measurement bias, we found that NSES increased from 1990 to 2000, and then—consistent with the timing of the “Great Recession”—declined in 2008–2012 to a level approaching that of 1990. Our approach for evaluating and adjusting for time-invariance is not only instructive for studies of NSES but also more generally for longitudinal studies in which the variable of interest is a latent construct.

Keywords

Neighborhood socio-economic status Neighborhood disadvantage Neighborhood change Confirmatory factor analysis Measurement bias Invariance 

References

  1. 1.
    Yen IH, Syme SL. The social environment and health: a discussion of the epidemiologic literature. Annu Rev Public Health. 1999; 20: 287–308.CrossRefPubMedGoogle Scholar
  2. 2.
    Robert SA. Socioeconomic position and health: The independent contribution of community socioeconomic context. Annu Rev Sociol. 1999; 25: 489–516.CrossRefGoogle Scholar
  3. 3.
    Obradović J, Pardini DA, Long JD, Loeber R. Measuring interpersonal callousness in boys from childhood to adolescence: An examination of longitudinal invariance and temporal stability. J Clin Child Adolesc Psychol. 2007; 36(3): 276–292.CrossRefPubMedGoogle Scholar
  4. 4.
    Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001; 55(2): 111–122.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Galea S, Vlahov D, Tracy M, Hoover DR, Resnick H, Kilpatrick D. Hispanic ethnicity and post-traumatic stress disorder after a disaster: Evidence from a general population survey after September 11, 2001. Ann Epidemiol. 2004; 14(8): 520–531.CrossRefPubMedGoogle Scholar
  6. 6.
    Sampson RJ, Morenoff JD, Gannon-Rowley T. Assessing neighborhood effects: social processes and new directions in research. Annu Rev Sociol. 2002; 28(1): 443–478.CrossRefGoogle Scholar
  7. 7.
    Diez Roux AV. Investigating neighborhood and area effects on health. Am J Public Health. 2001; 91(11): 1783–1789.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Morenoff JD, House JS, Hansen BB, Williams DR, Kaplan GA, Hunte HE. Understanding social disparities in hypertension prevalence, awareness, treatment, and control: the role of neighborhood context. Soc Sci Med. 2007; 65(9): 1853–1866.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Bird CE, Seeman T, Escarce JJ, et al. Neighbourhood socioeconomic status and biological 'wear and tear' in a nationally representative sample of US adults. J Epidemiol Community Health. 2010; 64(10): 860–865.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Freedman VA, Grafova IB, Schoeni RF, Rogowski J. Neighborhoods and disability in later life. Soc Sci Med. 2008; 66(11): 2253–2267.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ross CE. Neighborhood disadvantage and adult depression. J Health Soc Behav. 2000; 41(2): 177–187.CrossRefGoogle Scholar
  12. 12.
    Robert SA, Cagney KA, Weden MM. A life-course approach to the study of neighborhoods and health. In: Chloe E, Bird PC, Fremont AM, Timmermans S, eds. Handbook of Medical Sociology. 6th ed. Nashville: Vanderbilt University Press; 2010.Google Scholar
  13. 13.
    Janson CG. Factorial social ecology - an attempt at summary and evaluation. Annu Rev Sociol. 1980; 6: 433–456.CrossRefGoogle Scholar
  14. 14.
    Schwirian KP. Models of neighborhood change. Annu Rev Sociol. 1983; 9: 83–102.CrossRefGoogle Scholar
  15. 15.
    Browning CR, Cagney KA, Morris K. Early Chicago School theory. In: G. Bruinsma DW, ed. Encyclopedia of Criminology and Criminal Justice. New York: Springer Science and Business Media; 2014: 1233–1242.CrossRefGoogle Scholar
  16. 16.
    Hogan DP, Kitagawa EM. The Impact of social status, family structure, and neighborhood on the fertility of Black adolescents. Am J Sociol. 1985; 90(4): 825–855.CrossRefGoogle Scholar
  17. 17.
    Diez Roux AV, Nieto FJ, Muntaner C, et al. Neighborhood environments and coronary heart disease: A multilevel analysis. Am J Epidemiol. 1997; 146(1): 48–63.CrossRefPubMedGoogle Scholar
  18. 18.
    Duncan GJ, Aber L. Neighborhood models and measures. Neighborhood Poverty: Context and Consequences for Children. New York: Russell Sage Foundation; 1997: 62–78.Google Scholar
  19. 19.
    Ross CE, Mirowsky J. Neighborhood disadvantage, disorder, and health. J Health Soc Behav. 2001; 42(3): 258–276.CrossRefPubMedGoogle Scholar
  20. 20.
    Boardman JD, Finch BK, Ellison CG, Williams DR, Jackson JS. Neighborhood disadvantage, stress, and drug use among adults. J Health Soc Behav. 2001; 42(2): 151–165.CrossRefPubMedGoogle Scholar
  21. 21.
    Browning CR, Cagney KA. Neighborhood structural disadvantage, collective efficacy, and self-rated physical health in an urban setting. J Health Soc Behav. 2002; 43(4): 383–399.CrossRefPubMedGoogle Scholar
  22. 22.
    Weden MA, Carpiano RA, Robert SA. Subjective and objective neighborhood characteristics and adult health. Soc Sci Med. 2008; 66(6): 1256–1270.CrossRefPubMedGoogle Scholar
  23. 23.
    Messer LC, Laraia BA, Kaufman JS, et al. The development of a standardized neighborhood deprivation index. J Urban Health-Bull New York Acad Med. 2006; 83(6): 1041–1062.Google Scholar
  24. 24.
    Wen M, Hawkley LC, Cacioppo JT. Objective and perceived neighborhood environment, individual SES and psychosocial factors, and self-rated health: an analysis of older adults in Cook County, Illinois. Soc Sci Med. 2006; 63(10): 2575–2590.CrossRefPubMedGoogle Scholar
  25. 25.
    Winkleby M, Cubbin C, Ahn D. Effect of cross-level interaction between individual and neighborhood socioeconomic status on adult mortality rates. Am J Public Health. 2006; 96(12): 2145–2153.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Dubowitz T, Heron M, Bird CE, et al. Neighborhood socioeconomic status and fruit and vegetable intake among whites, blacks, and Mexican Americans in the United States. Am J Clin Nutr. 2008; 87(6): 1883–1891.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Clarke P, Morenoff J, Debbink M, Golberstein E, Elliott MR, Lantz PM. Cumulative exposure to neighborhood context: consequences for health transitions over the adult life course. Research on Aging. 2014; 36(1): 115–142.CrossRefPubMedGoogle Scholar
  28. 28.
    Wodtke GT, Harding DJ, Elwert F. Neighborhood effects in temporal perspective: The impact of long-term exposure to concentrated disadvantage on high school graduation. Am Sociol Rev. 2011; 76(5): 713–736.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Little TD. Longitudinal structural equation modeling. New York: Guilford Press; 2013.Google Scholar
  30. 30.
    Borsboom D. When does measurement invariance matter? Med Care. 2006; 44(11): S176–S181.CrossRefPubMedGoogle Scholar
  31. 31.
    Bollen KA. Latent variables in psychology and the social sciences. Annu Rev Psychol. 2002; 53: 605–634.CrossRefPubMedGoogle Scholar
  32. 32.
    Marshall GN. Posttraumatic Stress Disorder Symptom Checklist: Factor structure and English-Spanish measurement invariance. J Trauma Stress. 2004; 17(3): 223–230.CrossRefPubMedGoogle Scholar
  33. 33.
    McDonald SD, Beckham JC, Morey R, Marx C, Tupler LA, Calhoun PS. Factorial invariance of posttraumatic stress disorder symptoms across three veteran samples. J Trauma Stress. 2008; 21(3): 309–317.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Miles JNV, Shih RA, Tucker JS, Zhou A, D’Amico EJ. Assessing measurement invariance of familism and parental respect across race/ethnicity in adolescents. BMC Med Res Methodol. 2012; 12(1): 61.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Shevlin M, Brunsden V, Miles JNV. Satisfaction With Life Scale: analysis of factorial invariance, mean structures and reliability. Personal Individ Differ. 1998; 25: 569–574.CrossRefGoogle Scholar
  36. 36.
    Herrero J, Gracia E. Measuring perceived community support: Factorial structure, longitudinal invariance, and predictive validity of the PCSQ (Perceived Community Support Questionnaire). J Comm Psychol. 2007; 35(2): 197–217.CrossRefGoogle Scholar
  37. 37.
    Motl RW, DiStefano C. Longitudinal invariance of self-esteem and method effects associated with negatively worded items. Struct Equ Model. 2002; 9(4): 562–578.CrossRefGoogle Scholar
  38. 38.
    Pentz M, Chou C. Measurement invariance in longitudinal clinical research assuming change from development and intervention. J Consult Clin Psychol. 1994; 62(3): 450–462.CrossRefPubMedGoogle Scholar
  39. 39.
    Schaie KW, Maitland SB, Willis SL, Intrieri RC. Longitudinal invariance of adult psychometric ability factor structures across 7 years. Psychol Aging. 1998; 13(1): 8.CrossRefPubMedGoogle Scholar
  40. 40.
    Millsap RE. Statistical approaches to measurement invariance. New York: Routledge; 2011.Google Scholar
  41. 41.
    US Census Bureau. 1990 Census. http://www.census.gov/main/www/cen1990.html. Accessed 9/09/2014.
  42. 42.
    US Census Bureau. 2000 Census. www.census.gov/main/www/cen2000.html. Accessed 9/09/2014.
  43. 43.
    US Census Bureau. 2008–2012 American Community Survey. www2.census.gov/acs2012_5yr/summaryfile Accessed 9/092014.
  44. 44.
    Logan JR, Xu Z, Stults B. Interpolating US decennial census tract data from as early as 1970 to 2010: a longitudinal tract database. Prof Geogr. 2014; 66(3): 412–420.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Tatian PA. Neighborhood Change Database (NCDB) 1970-2000 Tract Data: Data Users Guide. Washington: Urban Institute; 2003.Google Scholar
  46. 46.
    US Bureau of Labor Statistics. Updated Consumer Price Index Research Series Using Current Methods (CPI-U-RS), All items. 1978–2013. http://www.bls.gov/cpi/cpiurs.htm. Accessed 9/10/2014.
  47. 47.
    Posey K, Welniak E, Nelson C. Income in the American Community Survey: comparisons to Census 2000. Paper presented at: Annual Meeting of the American Statistical Association, San Francisco, California, 2003; http://www2.census.gov/programs-surveys/acs/methodology/ASA_nelson.pdf.
  48. 48.
    US Census Bureau. A Compass for Understanding and Using the American Community Survey: What Researchers Need to Know. Washington, DC: U.S. Government Printing Office; 2009.Google Scholar
  49. 49.
    US Census Bureau. Comparing 2012 American Community Survey Data. 2014; https://www.census.gov/acs/www/guidance_for_data_users/comparing_2012/. Accessed 9/1/2014, 2014.
  50. 50.
    Scopp T. The relationship between the 1990 Census and Census 2000 industry and occupation classification systems. US Census Bureau Technical Paper #65. 2003; http://beta.census.gov/people/io/files/techpaper2000.pdf. Accessed 9/23/2014.
  51. 51.
    US Bureau of Labor Statistics. What's New in the 2010 SOC. 2010; http://www.bls.gov/soc/soc_2010_whats_new.pdf. Accessed 9/23/2014.
  52. 52.
    Muthén BO, Muthén L. Mplus Version 7.11. Los Angeles: Muthen and Muthen; 2013.Google Scholar
  53. 53.
    Yuan K, Bentler PM. Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociol Methodol. 2000; 30: 167–202.Google Scholar
  54. 54.
    Satorra A, Bentler PM. Corrections to test statistics and standard errors in covariance structure analysis. In: von Eye A, Clogg CC, eds. Latent Variables Analysis: Applications for Developmental Research. Thousand Oaks: Sage; 1994.Google Scholar
  55. 55.
    Curran PJ, West SG, Finch JF. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods. 1996; 1(1): 16–29.CrossRefGoogle Scholar
  56. 56.
    Browne MW. An overview of analytic rotation in exploratory factor analysis. Multivariate Behav Res. 2001; 36(1): 111–150.CrossRefGoogle Scholar
  57. 57.
    Millsap RE, Meredith. Factorial invariance: historical perspectives and new problems. In: Cudeck R, MacCallum R, eds. Factor Analysis at 100: Historical Developments and Future Directions. Hillsdale: Erlbaum; 2007.Google Scholar
  58. 58.
    Steiger JH, Lind JC. Statistically based tests for the number of factors. Iowa City: Psychometric Society; 1980.Google Scholar
  59. 59.
    Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull. 1980; 88: 588–606.CrossRefGoogle Scholar
  60. 60.
    Hu L, Bentler P. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999; 6: 1–55.CrossRefGoogle Scholar
  61. 61.
    Cheung GW, Rensvold RB. Evaluation goodness-of-fit indexes for testing measurement invariance. Struct Equ Model. 2002; 9: 235–255.Google Scholar
  62. 62.
    Kaplan D. On the modification and predictive validity of covariance structure models. Qual Quant. 1991; 25: 307–314.CrossRefGoogle Scholar
  63. 63.
    Osterlind SJ, Everson HT. Differential Item Functioning. Thousand Oaks: Sage; 2009.CrossRefGoogle Scholar
  64. 64.
    Embretson SE. The new rules of measurement. Psychol Assess. 1996; 8(4): 341–349.CrossRefGoogle Scholar
  65. 65.
    Hurd MD, Rohwedder S. Effects of the financial crisis and Great Recession on American households. Washington: National Bureau of Economic Research; 2010.CrossRefGoogle Scholar
  66. 66.
    Harknett K, Kuperberg A. Education, labor markets and the retreat from marriage. Social Forces. 2011; 90(1): 41–63.CrossRefGoogle Scholar
  67. 67.
    Livingston G, Parker K. Since the start of the Great Recession, more children raised by grandparents. Pew Research Center. 2010; http://www.pewsocialtrends.org/2010/09/09/since-the-start-of-the-greatrecession-more-children-raised-by-grandparents/.
  68. 68.
    Grogger J, Karoly LA. Welfare reform: effects of a decade of change. Cambridge: Harvard University Press; 2009.Google Scholar
  69. 69.
    Weeden KA, Grusky DB. Are there any big classes at all? Res Soc Stratif Mobil. 2004; 22: 3–56.CrossRefGoogle Scholar
  70. 70.
    Weeden KA, Grusky DB. The case for a new class map. Am J Sociol. 2005; 111(1): 141–212.CrossRefGoogle Scholar
  71. 71.
    Hauser RM, Warren JR. Socioeconomic indexes for occupations: A review, update, and critique. Sociol Methodol. 1997; 27(1): 177–298.CrossRefGoogle Scholar
  72. 72.
    Weeden KA, Kim Y-M, Di Carlo M, Grusky DB. Social class and earnings inequality. Am Behav Sci. 2007; 50(5): 702–736.CrossRefGoogle Scholar
  73. 73.
    Moench E, Ng S. A hierarchical factor analysis of U.S. housing market dynamics. Econometrics J. 2011; 14(1): C1–C24.CrossRefGoogle Scholar
  74. 74.
    Ryan CL, Siebens J. Educational attainment in the United States: 2009. Current Population Reports. U.S. Census Bureau. 2012; February 2012.Google Scholar
  75. 75.
    Heckman JJ, Lafontaine PA. The American high school graduation rate: trends and levels. Rev Econ Stat. 2010; 92(2): 244–262.CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Duncan GJ, Brooks-Gunn J, Aber L. Neighborhood Poverty: Context and Consequences for Children. New York: Russell Sage Foundation; 1997.Google Scholar
  77. 77.
    Beaghen M, Weidman L. Statistical issues of interpretation of the American Community Survey's one-, three-, and five-year period estimates. 2008; http://www.census.gov/acs/www/Downloads/library/2008/2008_Beaghen_01.pdf. Accessed 9/23/2014.
  78. 78.
    Weden MM, Peterson C, Shih RA. Evaluating tract-level intercensal estimates of neighborhood demographics and socioeconomics for U.S. counties 2001-2009. Popul Res Policy Rev. 2015; 34(4): 541–559.CrossRefPubMedGoogle Scholar
  79. 79.
    Steiger JH. Factor indeterminancy in the 1930s and the1970s: Some interesting parallels. Psychometrika. 1979; 44(2): 157–167.CrossRefGoogle Scholar
  80. 80.
    Sokal RR, Rohlf FJ, Zang E, Osness W. Reification in factor analysis: A plasmode based on human physiology-of-exercise variables. Multivar Behav Res. 1980; 15(2): 181–202.CrossRefGoogle Scholar
  81. 81.
    Galbraith SJ, Bowden J, Mander A. Accelerated longitudinal designs: an overview of modelling, power, costs and handling missing data. Statistical Methods in Medical Research. 2014;0962280214547150.Google Scholar
  82. 82.
    Curran PJ, Hussong AM. Integrative data analysis: the simultaneous analysis of multiple data sets. Psychol Methods. 2009; 14(2): 81–100.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The New York Academy of Medicine 2015

Authors and Affiliations

  • Jeremy N. Miles
    • 1
  • Margaret M. Weden
    • 1
    Email author
  • Diana Lavery
    • 1
  • José J. Escarce
    • 1
    • 2
  • Kathleen A. Cagney
    • 3
  • Regina A. Shih
    • 1
  1. 1.RAND CorporationSanta MonicaUSA
  2. 2.University of CaliforniaLos AngelesUSA
  3. 3.University of ChicagoChicagoUSA

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