Abstract
We begin with a conceptual overview of time-varying effect modeling (TVEM), focusing on definitions of “time” and how this modeling framework extends the commonly used linear regression framework. In this first chapter, we describe the concept and promise of TVEM and convey the utility of TVEM findings for researchers (e.g., epidemiologists, prevention scientists), administrators (e.g., policy-makers, public school administrators), and practitioners (e.g., clinicians). This discussion presents a wide range of research questions TVEM could be used to address. To help more concretely illustrate the conceptual approach of TVEM, we walk the reader through a recently published empirical example assessing the risk for suicidal behavior across ages 18–60 for sexual minority versus heterosexual adults. We conclude with a brief roadmap of the subsequent chapters of the book.
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References
Bollen, K. A. (1989). Structural equation modeling with latent constructs. Wiley-Interscience.
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective (Vol. 467). Hoboken, NJ: John Wiley & Sons.
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. John Wiley & Sons.
Fish, J. N., Rice, C. E., Lanza, S. T., & Russell, S. T. (2019). Is young adulthood a critical period for suicidal behavior among sexual minorities? Results from a US national sample. Prevention Science, 20(3), 353–365.
Fosco, G. M., & Lydon‐Staley, D. M. (2019). A within‐family examination of interparental conflict, cognitive appraisals, and adolescent mood and well‐being. Child Development, 90(4), e421–e436.
Grant, B. F., Chu, A., Sigman, R., Amsbary, M., Kali, J., Sugawara, Y., Jiao, R., Ren, W., & Goldstein, R. (2014). Source and accuracy statement: National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) (pp. 1–125). National Institute on Alcohol Abuse and Alcoholism.
Harris, K. M. (2013). The Add Health study: Design and accomplishments. Carolina Population Center, University of North Carolina at Chapel Hill.
Harris, K. M., Halpern, C. T., Whitsel, E. A., Hussey, J. M., Killeya-Jones, L. A., Tabor, J., & Dean, S. C. (2019). Cohort profile: The national longitudinal study of adolescent to adult health (Add Health). International Journal of Epidemiology, 48(5), 1415–1415.
Johnston, L. D., O’Malley, P. M., Bachman, J. G., Schulenberg, J. E., & Miech, R. A. (2014). Monitoring the Future: National survey results on drug use, 1975–2013: Volume I, secondary school students. Institute for Social Research, University of Michigan.
Kuhfeld, M., Gershoff, E., & Paschall, K. (2018). The development of racial/ethnic and socioeconomic achievement gaps during the school years. Journal of Applied Developmental Psychology, 57, 62–73.
Lanza, S. T., Russell, M. A., & Braymiller, J. L. (2017). Emergence of electronic cigarette use in US adolescents and the link to traditional cigarette use. Addictive Behaviors, 67, 38–43.
Lanza, S. T., Vasilenko, S. A., Dziak, J. J., & Butera, N. M. (2015). Trends among US high school seniors in recent marijuana use and associations with other substances: 1976–2013. Journal of Adolescent Health, 57(2), 198–204.
Lanza, S. T., Vasilenko, S. A., & Russell, M. A. (2016). Time-varying effect modeling to address new questions in behavioral research: Examples in marijuana use. Psychology of Addictive Behaviors, 30(8), 939–954.
Linden-Carmichael, A. N., Vasilenko, S. A., Lanza, S. T., & Maggs, J. L. (2017). High-intensity drinking versus heavy episodic drinking: Prevalence rates and relative odds of alcohol use disorder across adulthood. Alcoholism: Clinical and Experimental Research, 41(10), 1754–1759.
Little, T. D. (2013). Longitudinal structural equation modeling. Guilford Press.
MacKinnon, D. (2008). Intoduction to statistical mediation analysis. Taylor &Francis Group.
Maher, J. P., & Dunton, G. F. (2020). Within-day time-varying associations between motivation and movement-related behaviors in older adults. Psychology of Sport and Exercise, 47, 101522.
Mason, M., Mennis, J., Way, T., Lanza, S., Russell, M., & Zaharakis, N. (2015). Time-varying effects of a text-based smoking cessation intervention for urban adolescents. Drug and Alcohol Dependence, 157, 99–105.
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107–122.
Miech, R., Johnston, L., O’Malley, P., Bachman, J., Schulenberg, J., & Patrick, M. (2020). Monitoring the future national survey results on drug use, 1975–2019: Volume I, Secondary school students. Institute for Social Research, The University of Michigan.
Nagin, D. S. (2005). Group-based modeling of development. Harvard University Press.
Nagin, D. S., Jones, B. L., Passos, V. L., & Tremblay, R. E. (2018). Group-based multi-trajectory modeling. Statistical Methods in Medical Research, 27(7), 2015–2023.
Shiyko, M. P., Burkhalter, J., Li, R., & Park, B. J. (2014). Modeling nonlinear time-dependent treatment effects: An application of the generalized time-varying effect model (TVEM). Journal of Consulting and Clinical Psychology, 82(5), 760–772.
Terry-McElrath, Y. M., O’Malley, P. M., Patrick, M. E., & Miech, R. A. (2017). Risk is still relevant: Time-varying associations between perceived risk and marijuana use among US 12th grade students from 1991 to 2016. Addictive Behaviors, 74, 13–19.
Vasilenko, S. A. (2017). Age-varying associations between nonmarital sexual behavior and depressive symptoms across adolescence and young adulthood. Developmental Psychology, 53(2), 366–378.
Vasilenko, S. A., Evans-Polce, R. J., & Lanza, S. T. (2017). Age trends in rates of substance use disorders across ages 18–90: Differences by gender and race/ethnicity. Drug and Alcohol Dependence, 180, 260–264.
Vasilenko, S. A., Piper, M. E., Lanza, S. T., Liu, X., Yang, J., & Li, R. (2014). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine & Tobacco Research, 16(Suppl_2), S135–S143.
Wright, A. G., Hallquist, M. N., Swartz, H. A., Frank, E., & Cyranowski, J. M. (2014). Treating co-occurring depression and anxiety: Modeling the dynamics of psychopathology and psychotherapy using the time-varying effect model. Journal of Consulting and Clinical Psychology, 82(5), 839–853.
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Lanza, S.T., Linden-Carmichael, A.N. (2021). A Conceptual Introduction to Time-Varying Effect Modeling. In: Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-70944-0_1
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DOI: https://doi.org/10.1007/978-3-030-70944-0_1
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