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Graphing Age Trajectories

Author

Listed:
  • John Mirowsky

    (The University of Texas at Austin, mirowsky@prc.utexas.edu)

  • Jinyoung Kim

    (University of Colorado at Denver and Health Sciences Center)

Abstract

Surveys often sample adults across a broad range of ages, measuring the same outcomes in several interviews spaced during a period of years and comparing the changes observed across segments of the adult life course. Put in sequence, those change vectors provide a composite image of the outcome's life course trajectory. To illustrate, the authors estimate depression vectors in a sample of U.S. adults ages 18 and older at baseline in 1995, with follow-up interviews in 1998 and 2001. They show the vector equations and their graphs and also their synthetic-cohort projection. The authors introduce the trend-function and virtual-cohort projection, showing how they provide tests of ``convergence'' and other hypotheses about trajectories and trends. Results show depression dropping and then rising across adulthood more steeply than suggested by cross-sectional differences among age groups. They also indicate a rise and fall in age-specific levels of depression across cohorts.

Suggested Citation

  • John Mirowsky & Jinyoung Kim, 2007. "Graphing Age Trajectories," Sociological Methods & Research, , vol. 35(4), pages 497-541, May.
    • Handle: RePEc:sae:somere:v:35:y:2007:i:4:p:497-541
    DOI: 10.1177/0049124106296015
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Steven A. Haas & Katsuya Oi & Zhangjun Zhou, 2017. "The Life Course, Cohort Dynamics, and International Differences in Aging Trajectories," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2043-2071, December.
    2. Umberson, Debra & Liu, Hui & Mirowsky, John & Reczek, Corinne, 2011. "Parenthood and trajectories of change in body weight over the life course," Social Science & Medicine, Elsevier, vol. 73(9), pages 1323-1331.
    3. Kim, Jinyoung & Miech, Richard, 2009. "The Black-White difference in age trajectories of functional health over the life course," Social Science & Medicine, Elsevier, vol. 68(4), pages 717-725, February.
    4. Diana Worts & Amanda Sacker & Peggy McDonough, 2010. "Re-Assessing Poverty Dynamics and State Protections in Britain and the US: The Role of Measurement Error," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 97(3), pages 419-438, July.
    5. Kim, Jinyoung & Durden, Emily, 2007. "Socioeconomic status and age trajectories of health," Social Science & Medicine, Elsevier, vol. 65(12), pages 2489-2502, December.
    6. Liliya Leopold, 2019. "Health Measurement and Health Inequality Over the Life Course: A Comparison of Self-rated Health, SF-12, and Grip Strength," Demography, Springer;Population Association of America (PAA), vol. 56(2), pages 763-784, April.
    7. Ross, Catherine E. & Mirowsky, John, 2011. "The interaction of personal and parental education on health," Social Science & Medicine, Elsevier, vol. 72(4), pages 591-599, February.
    8. Ang, Shannon, 2019. "Intersectional cohort change: Disparities in mobility limitations among older Singaporeans," Social Science & Medicine, Elsevier, vol. 228(C), pages 223-231.
    9. Liliya Leopold, 2018. "Education and Physical Health Trajectories in Later Life: A Comparative Study," Demography, Springer;Population Association of America (PAA), vol. 55(3), pages 901-927, June.
    10. Asakawa, Keiko & Senthilselvan, Ambikaipakan & Feeny, David & Johnson, Jeffrey & Rolfson, Darryl, 2012. "Trajectories of health-related quality of life differ by age among adults: Results from an eight-year longitudinal study," Journal of Health Economics, Elsevier, vol. 31(1), pages 207-218.

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