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Table 27: Estimated cumulative odds ratio and 95% CI in cumulative ordinal logit regression for subjective perception of socioeconomic status by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category OR 95% CI

Intercept Cut 1 0.71 (-0.34, 1.77)

Cut 2 3.73 (2.68, 4.78)

Disability type Physical 0.71 (0.57, 0.87)

Cognitive 1.01 (0.79, 1.30)

Age Continuous 1.07 (1.03, 1.10)

Gender Female 1.06 (0.97, 1.17)

Race/Ethnicity Black non-Hispanic 0.82 (0.71, 0.95)

Hispanic 1.03 (0.86, 1.24)

Others 0.88 (0.69, 1.11)

Parental education High School graduate/GED 1.25 (1.03, 1.52) Some education beyond High School 1.60 (1.30, 1.96) College graduate or higher 2.96 (2.39, 3.67)

Family structure Other 2-parents 0.64 (0.57, 0.73)

Other 0.65 (1.06, 1.87)

Language of survey Non-English interview 1.51 (1.00, 2.30) Recent immigrant status Immigrated within the last 5 years 1.40 (1.06, 1.87)

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: Highest position on the socioeconomic ladder, Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date). OR= odds ratio; CI= confidence interval.

Table 28: Estimated cumulative odds ratio and 95% CI in cumulative ordinal logit regression for highest level of education attained by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category OR 95% CI

Intercept Cut 1 0.82 (-1.29, 1.46)

Cut 2 1.53 (0.17, 2.89)

Cut 3 3.76 (2.40, 5.12)

Disability type Physical 0.69 (0.57, 0.85)

Cognitive 0.41 (0.33, 0.52)

Age Continuous 1.04 (1.00, 1.09)

Gender Female 1.74 (1.56, 1.94)

Race/Ethnicity Black non-Hispanic 0.94 (0.75, 1.18)

Hispanic 1.01 (0.86, 1.19)

Others 1.28 (0.92, 1.78)

Parental education High School graduate/GED 2.06 (1.75, 2.43) Some education beyond High School 3.86 (3.19, 4.67) College graduate or higher 11.48 (9.02, 14.60)

Family structure Other 2-parents 0.48 (0.42, 0.55)

Other 0.48 (0.43, 0.54)

Language of survey Non-English interview 10.5 (0.76, 1.45) Recent immigrant status Immigrated within the last 5 years 1.92 (1.24, 2.96)

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: College graduate or higher, Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date). OR= odds ratio; CI= confidence interval.

Table 29: Estimates of adjusted odds ratio and 95% CI in logistic regression model for employment status by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category OR 95% CI

Disability type Physical 0.75 (0.54, 1.03)

Cognitive 0.77 (0.54, 1.08)

Age Continuous 1.02 (0.98, 1.06)

Gender Female 0.54 (0.46, 0.63)

Race/Ethnicity Black non-Hispanic 0.98 (0.79, 1.23)

Hispanic 1.49 (1.15, 1.93)

Others 0.89 (0.63, 1.26)

Parental education High School graduate/GED 1.52 (1.20, 1.93) Some education beyond High School 1.62 (1.27, 2.06) College graduate or higher 1.78 (1.36, 2.33)

Family structure Other 2-parents 0.74 (0.63, 0.87)

Other 0.67 (0.56, 0.80)

Language of survey Non-English interview 1.23 (0.66, 2.29) Recent immigrant status Immigrated within the last 5 years 1.68 (1.00, 2.83)

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: Not employed, Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date). OR= odds ratio; CI= confidence interval.

Table 30: Estimates of adjusted odds ratio and 95% CI in logistic regression model for occupation type by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category OR 95% CI

Disability type Physical 0.84 (0.66, 1.08)

Cognitive 0.50 (0.39, 0.64)

Age Continuous 1.05 (1.01, 1.09)

Gender Female 1.73 (1.56, 1.92)

Race/Ethnicity Black non-Hispanic 0.77 (0.62, 0.94)

Hispanic 1.10 (0.91, 1.33)

Others 1.43 (1.13, 1.82)

Parental education High School graduate/GED 1.51 (1.29, 1.79) Some education beyond High School 2.02 (1.72, 2.37) College graduate or higher 4.52 (3.67, 5.57)

Family structure Other 2-parents 0.60 (0.52, 0.70)

Other 0.66 (0.59, 0.74)

Language of survey Non-English interview 1.08 (0.77, 1.51) Recent immigrant status Immigrated within the last 5 years 2.19 (1.38, 3.48)

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: Managerial, Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date).

Table 31: Beta coefficients and 95% CI in generalized linear model for annual income by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category Beta 95% CI

Disability type Physical -.0535 -.1593 .0522

Cognitive -.2747 -.3948 -.1547

Age Continuous .0604 .0442 .0766

Gender Female -.2680 -.3179 -.2182

Race/Ethnicity Black non-Hispanic -.1548 -.2432 -.0664

Hispanic -.0099 -.0777 .0974

Others .1593 .0396 .2791

Parental education High School graduate/GED .1346 .0082 .2611 Some education beyond High School .2237 .0852 .3622 College graduate or higher .4137 .2562 .5713 Family structure Other 2-parents -.0830 -.1695 .0034

Other -.1667 -.2170 -.1162

Language of survey Non-English interview .1401 -.0187 .2989 Recent immigrant status Immigrated within the last 5 years .0902 -.0334 .2138

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date).

Sub-population of workers (n=10,801). OR= odds ratio; CI= confidence interval.

Table 32: Beta coefficients and 95% CI in generalized linear model for wage rate by disability status, age, gender, race/ethnicity, parent education, family structure, language of survey administration, and recent immigration status among young adults.

Predictor Category Beta 95% CI

Disability type Physical -.0585 -.1634 .0464

Cognitive -.2902 -.4108 -.1698

Age Continuous .0573 .0412 .0733

Gender Female -.1485 -.2080 -.0890

Race/Ethnicity Black non-Hispanic -.1478 -.2312 -.0644

Hispanic .0009 -.1115 .1133

Others .1611 .0529 .2693

Parental education High School graduate/GED .0764 -.1376 .2904 Some education beyond High School .1544 -.0706 .3793 College graduate or higher .3374 .1040 .5707

Family structure Other 2-parents .0039 -.1464 .1543

Other -.1470 -.1949 -.0991

Language of survey Non-English interview .0758 -.0788 .2303 Recent immigrant status Immigrated within the last 5 years .0991 -.0306 .2289

Table presents results of ordered logistic regression models comparing outcomes between young adults with a disability and those without a disability (reference categories: Male, White non-Hispanic, Less than High School, Two biological parents, English interview, Nonimmigrant or immigrated more than 5 years before the interview date).

Sub-population of workers (n=10,801). OR= odds ratio; CI= confidence interval.

REFERENCES

1. Centers for Disease Control and Prevention. National center on birth defects and developmental disabilities. http://www.cdc.gov/ncbddd/features/birthdefects-dd-keyfindings.html. Updated 2012. Accessed October,08, 2012.

2. Bronfenbrenner U. The ecology of human development: Experiments by nature and design. Cambridge, Mass.: Harvard University Press; 1979..

3. Groce NE. Adolescents and youth with disability: Issues and challenges. Asia Pacific Disability

Rehabilitation Journal. 2004;15(2).

4. World Health Organization. World report on disability. Geneva: WHO Press; 2011.

5. Boyle CA, Boulet S, Schieve LA, et al. Trends in the prevalence of developmental disabilities in US children, 1997-2008. Pediatrics (Evanston). 2011;127(6):1034-1042.

6. Institute of Medicine. The future of disability in America. Washington, DC: The National Academies Press; 2007.

7. Centers for Disease Control and Prevention. Prevalence of disabilities and associated health conditions among adults--United States, 1999. MMWR Morb Mortal Wkly Rep. 2001;50(7):120-125. 8. Blomquist KB, Brown G, Peersen A, Presler EP. Transitioning to independence: Challenges for young people with disabilities and their caregivers. Orthop Nurs. 1998;17(3):27-35.

9. Kaye HS, LaPlante MP, Carlson D, Wenger BL. Trends in disability rates in the United States, 1970-1994. Disability Statistics Abstract. 1996;17..

10. Centers for Disease Control and Prevention. National health interview survey.

http://www.cdc.gov/nchs/nhis/about_nhis.htm. Updated 2009. Accessed November, 7, 2010. 11. Disability Statistics Center, University of California, San Francisco (UCSF). Finding disability data on the web. http://dsc.ucsf.edu/main.php?name=finding_data. Updated 2011. Accessed 10,5, 2011.

12. Lindsay S. Employment status and work characteristics among adolescents with disabilities.

Disabil Rehabil. 2011; 33(10):845-54.

13. Janus AL. Disability and the transition to adulthood. Social Forces. 2009;88(1):99-120. 14. Wells TDP, Sandefur GD, Hogan DP. What happens after the high school years among young persons with disabilities? Social forces. 2003;82(2):803-832.

15. World Health Organization. Disabilities. http://www.who.int/topics/disabilities/en/. Updated 2012. Accessed September,30, 2012.

16. Patel DR, Greydanus DE, Calles JL,Jr, Pratt HD. Developmental disabilities across the lifespan.

17. Decoufle P, Autry A. Increased mortality in children and adolescents with developmental disabilities. Paediatr Perinat Epidemiol. 2002;16(4):375-382.

18. Boyle CA, Decoufle P, Yeargin-Allsopp M. Prevalence and health impact of developmental disabilities in US children. Pediatrics. 1994;93(3):399-403.

19. Fremstad S. Half in ten: Why taking disability into account is essential to reducing income

poverty and expanding economic inclusion. Center for Economic and Policy Research (CEPR). 2009. 20. Wehby GL, Ohsfeldt RL. The impact of having A young child with disabilities on maternal labor supply by race and marital status. J Health Hum Serv Adm. 2007:327.

21. Sen A. The idea of justice. Cambridge, Mass.: Belknap Press of Harvard University Press; 2009. 22. Marjoribanks K. Family background, academic achievement, and educational aspirations as predictors of australian young adults' educational attainment. Psychol Rep. 2005;96(3 Pt 1):751-754. 23. American Association on Health and Disability. Depressive symptoms and clinical depression and people with disabilities: Prevalence.

http://www.aahd.us/page.php?pname=publications/fact_sheets/prevelance. Updated 2009. Accessed

12,5, 2010.

24. Sen A. Capability and well-being. In: Nussbaum MC, Sen A, World Institute for Development Economics Research., eds. The quality of life [electronic resource]. Oxford: Clarendon; 1993:1-32. 25. Becker GS. Human capital: A theoretical and empirical analysis, with special reference to

education. Chicago: The University of Chicago Press; 1993.

26. Schultz TW. Investment in human capital. Am Econ Rev. 1961;LI(1):1.

27. Becker GS. Investment in human capital: A theoretical analysis. Journal of Political Economy. 1962;70(5):9.

28. Becker GS. Health as human capital: Synthesis and extensions. Oxford economic papers. 2007;59(3):379-410.

29. Bleakley H. Health, human capital, and development. Annu Rev Econ. 2010;2(1):283-310. 30. Becker GS. Child endowments and the quantity and quality of children. Journal of Political

Economy. 1976;84(4):143.

31. Carter EW, Austin D, Trainor AA. Predictors of postschool employment outcomes for young adults with severe disabilities. Journal of Disability Policy Studies. 2012;23(1):50-63.

32. Solberg VS, Howard K, Gresham S, Carter E. Quality learning experiences, self-determination, and academic success: A path analytic study among youth with disabilities. Career Development and

Transition for Exceptional Individuals. 2012;35(2):85-96.

33. Donohue JJ, Stein MA, Griffin CL, Becker S. Assessing post-ADA employment: Some econometric evidence and policy considerations. J Empir Leg Stud. 2011;8(3):477-503.

34. Blomquist KB. Health, education, work, and independence of young adults with disabilities.

Orthopaedic Nursing. 2006;25(3):168.

35. Yamaki K, Fujiura GT. Employment and income status of adults with developmental disabilities living in the community. Ment Retard. 2002;40(2):132-141.

36. Baldwin ML, Johnson WG. Labor market discrimination against women with disabilities.

Industrial relations (Berkeley). 1995;34(4):555-577.

37. Stapleton D, Livermore G. Costs, cuts, and consequences: Charting a new course for working-age people with disabilities. Disability Policy Issue Brief. 2011;11(03).

38. Brault MW. American with disabilities: 2005. Current Population Reports. Washington, DC: US Census Bureau; 2008.

39. Ferrans CE, Powers MJ. Quality of life index: Development and psychometric properties. ANS

Adv Nurs Sci. 1985;8(1):15-24.

40. Contreras D, Ruiz-Tagle J, Garcés P, Azócar I, eds. Socio-economic impact of disability in Latin

America: Chile and Uruguay ; 2006.

41. DeLeire T. The wage and employment effects of the americans with disabilities act. J Hum

Resour. 2000;35(4):693-715.

42. U.S. Department of Labor. Persons with a disability: Labor force characteristics - 2011. News

Release - Office of Disability Employment Policy. 2011;12-1125.

43. Furstenberg F. The sociology of adolescence and youth in the 1990s: A critical commentary.

Journal of Marriage and Family. 2000;62(4):896-910.

44. White J, Weiner JS. Influence of least restrictive environment and community based training on integrated employment outcomes for transitioning students with severe disabilities. Journal of

vocational rehabilitation. 2004;21(3):149.

45. Harris KM. Design features of add health.

http://www.cpc.unc.edu/projects/addhealth/data/guides/design%20paper%20WI-IV.pdf. Updated

2011. Accessed October,25, 2012.

46. Harris KM, Halpern CT, Whitsel E, et al. The national longitudinal study of adolescent health: Research design http://www.cpc.unc.edu/projects/addhealth/design. Updated 2009. Accessed November, 11, 2010.

47. World Health Organization. International classification of functioning, disability and health: ICF. Geneva: World Health Organization; 2001.

48. World Health Organization. International classification of impairments, disabilities, and

handicaps: A manual of classification relating to the consequences of disease. Geneva: World Health

49. Cheng MM, Udry JR. Sexual behaviors of physically disabled adolescents in the United States. J

Adolesc Health. 2002;31(1):48-58.

50. Dunn LM, Dunn LM. Peabody picture vocabulary test-revised: Manual for forms L and M. Circle Pines, NM: American Guidance Service; 1981.

51. Halpern CT, Joyner K, Udry JR, Suchindran C. Smart teens don’t have sex (or kiss much either).

J Adolesc Health. 2000;26(3):213-225.

52. Haydon AA, McRee AL, Halpern CT. Unwanted sex among young adults in the United States: The role of physical disability and cognitive performance. J Interpers Violence. 2011;26(17):3476- 3493.

53. Cheng MM, Udry JR. Sexual experiences of adolescents with low cognitive abilities in the U.S.

Journal of developmental and Physical Disabilities. 2005;17(2):155-172.

54. Groth-Marnat G. Handbook of psychological assessment. Hoboken, NJ: John Wiley & Sons; 2009.

55. Halpern CT, Oslak SG, Young ML, Martin SL, Kupper LL. Partner violence among adolescents in opposite-sex romantic relationships: Findings from the national longitudinal study of adolescent health. Am J Public Health. 2001;91(10):1679-1685.

56. U.S. Bureau of Labor Statistics. Standard occupational classification and coding structure. 2010

SOC User Guide. Washington, DC; 2010.

57. Kirchhoff AC, Krull KR, Ness KK, et al. Occupational outcomes of adult childhood cancer survivors: A report from the childhood cancer survivor study. BMC Cancer. 2011;117(13):3033- 3044.

58. Ostrove JM, Adler NE, Kuppermann M, Washington AE. Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women. Health Psychol. 2000;19(6):613-618.

59. Adler NE, Epel ES, Castellazzo G, Ickovics JR. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy white women.

Health Psychol. 2000;19(6):586-592.

60. Halpern CT, Waller MW, Spriggs A, Hallfors DD. Adolescent predictors of emerging adult sexual patterns. J Adolesc Health. 2006;39(6):926.e1-926.e10.

61. Heeringa S. Applied survey data analysis. Boca Raton, FL: Chapman & Hall/CRC; 2010. 62. Feliciano C, Ashtiani M. Low-income young adults continue to face barriers to college entry and degree completion. UC/ACCORD Research Brief. 2012.

63. Furney KS, Hasazi SB, Destefano L. Transition policies, practices, and promises: Lessons from three states. Except Child. 1997;63:343.

64. U.S. Department of Labor. The American with Disabilities Act Public Law 101-336 [electronic resource]. http://nara-wayback-

001.us.archive.org/peth04/20041108022736/http://www.dol.gov/odep/pubs/fact/ada92fs.htm.

Updated 2001. Accessed 05,18, 2012.

65. Alderson PP. Theories in health care and research: The importance of theories in health care. Br

Med J. 1998;317(7164):1007-1010.

66. Smith SM. The role of social cognitive career theory in information technology based academic performance performance. J Inform Technol. 2002;20(2):1-10.

67. Lent RW, Brown SD, Hackett G. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. J Vocat Behav. 1994;45(1):79.

68. Bandura A, 1925-. Social foundations of thought and action : A social cognitive theory. Englewood Cliffs, N.J.: Prentice-Hall; 1986.

69. Ehrenberg MF, Cox DN, Koopman RF. The relationship between self-efficacy and depression in adolescents. Adolescence. 1991;26(102):361-374.

70. Rottinghaus PPJ. Relation of depression and affectivity to career decision status and self-efficacy in college students. J CareerAcssess. 2009;17(3):271-285.

71. Fletcher JM. The effects of adolescent health on educational outcomes: Causal evidence using ‘Genetic lotteries’ between siblings. Forum for Health Economics & Policy. 2009;12(2):8. 72. Migliore A, Domin D. Data note: Setting higher employment expectations for youth with intellectual disabilities. http://scholarworks.umb.edu/ici_datanote/5. Updated 2011. Accessed October,10, 2012.

73. Benz MR, Lindstrom L, Yovanoff P. Improving graduation and employment outcomes of students with disabilities: Predictive factors and student perspectives. Except Child. 2000;66(4):509- 529.

74. Blalock G, Kochhar-Bryant CA, Test DW, et al. The need for comprehensive personnel preparation in transition and career development: A position statement of the division on career development and transition. Career Development for Exceptional Individuals. 2003;26(2):207-226. 75. Carr RV, Wright JD, Brody CJ. Effects of high school work experience a decade later: Evidence from the national longitudinal survey. Sociology of Education. 1996;69(1):66-81.

76. LaPlante MP, Carlson D. Disability in the united states: Prevalence and causes, 1992. Disability

Statistics Report. 1996;7.

77. U.S. Social Security Administration. Measures of central tendency for wage data.

http://www.ssa.gov/oact/cola/central.html. Updated 2012. Accessed September, 25, 2012.

78. U.S. Department of Labor. Wage and hour division (WHD) - changes in basic minimum wages in non-farm employment under state law: Selected years 1968 to 2012.

79. Radloff LS. The CES-D scale: A self report depression scale for research in the general. Applied

Psychological Measurement. 1977;1(3):385.

80. Khan MR, Kaufman JS, Pence BW, et al. Depression, sexually transmitted infection, and sexual risk behavior among young adults in the united states. Arch Pediatr Adolesc Med. 2009;163(7):644- 652.

81. Spriggs AL, Halpern CT. Sexual debut timing and depressive symptoms in emerging adulthood. J

Youth Adolesc. 2008;37(9):1085-1096.

82. World Health Organization. Adolescent health. http://www.who.int/topics/adolescent_health/en/. Updated 2011. Accessed November, 09, 2011.

83. Baron RM, Kenny DA. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173- 1182.

84. Sobel M. Asymptotic confidence intervals for indirect effects in structural equation models, S. leinhardt, editor. In: Leinhardt S, ed. Sociological Methodology. Washington, D. C.: American Sociological Association; 1982:290-312.

85. Zhao X, Lynch JG, Jr, Chen Q. Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research. 2010;37(2):197-206.

86. MacKinnon DP. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. 2002;7(1):83-104.

87. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Rresearch Methods. 2008;40(3):879-891. 88. Campbell-Whatley GD. Mentoring students with mild disabilities: The "nuts and bolts" of program development. Intervention in School and Clinic. 2001;36(4):211-216.

89. Katsiyannis A, Zhang D, Woodruff N. Transition supports to students with mental retardation: An examination of data from the national longitudinal transition study 2. Education & Training in

Developmental Disabilities. 2005;40(2):109.

90. Williams R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata journal. 2006;6(1):58.

91. Williams R. Gologit2/oglm troubleshooting. http://www.nd.edu/~rwilliam/gologit2/tsfaq.html. Updated 2012. Accessed August,30, 2012.

92. Archer KJ, Lemeshow S. Goodness-of-fit test for a logistic regression model fitted using survey sample data. The Stata Journal. 2006;6(1):97.

93. Breslow NE. Generalized linear models: Checking assumptions and strengthening conclusions.

94. Basu A. Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. The Stata Journal. 2005;5(4):501.

95. Nichols A. Stata conference - regression for nonnegative skewed dependent variables.

http://www.stata.com/meeting/boston10/boston10_nichols.pdf. Updated 2010. Accessed

September,03, 2012.

96. Norton EC, Han E. Genetic information, obesity, and labor market outcomes. Health Econ. 2008;17(9):1089-1104.

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