General Strain Theory, Depression, and Substance Use: Results From a Nationally Representative, Longitudinal Sample of White, African-American, and Hispanic Adolescents and Young Adults

ABSTRACT

Based on the interpretations of Agnew's general strain theory (GST), White, African-American, and Hispanic adolescents and young adults were examined longitudinally to identify the intersection between strain and depression that could produce a long-term effect on substance use. Results from full sample and group-based path models indicate that some support was found for the general tenets of GST. African-Americans were significantly different from Whites and Hispanics in regard to experiencing certain types of strain; however, the results were not always in the anticipated direction. Possible theoretical reasons for the findings and directions for future research are discussed.

KEYWORDS:

• depression
• race/ethnicity
• strain
• substance use

Acknowledgments

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

Notes

1 Coping is a multifaceted construct that is conceptualized in various ways according to different theoretical perspectives. In regard to GST, Agnew (2015) states that coping is a way to relieve pressure in the form of corrective action. This action will end or reduce the strain.

2 Cyberbullying victimization predicted higher depressive symptoms and problematic Internet usage, while substance use and cyberbully victimization did not have a significant association (Gámez-Guadix, Orue, Smith, & Calvete, 2013).

3 Where chi-square difference tests compare model fit for a constrained model versus an unconstrained model, coefficient comparison tests focus strictly on unstandardized regression coefficients (b) and standard errors (SE) between two variables across two groups/samples. We are unable to conclude if the two approaches (chi-square test of model fit and z-scores) produce similar results because they are examining different components of a model. The group-based path model focuses on differences within groups for each group, while the coefficient comparison tests examine differences across groups.

4 As a diagnostic check, we also performed a group-based path analysis that constrained the path coefficients to be equal across the groups. Several model fit indices provided support for rejecting the constrained model and analyzing an unconstrained (i.e., path coefficients are free to vary across the groups) model. For the constrained model, χ² (75) = 115.70, p < .01; RMSEA was 0.08, CFI and TLI were less than 0.90. Unfortunately, due to the large sample size, chi-square difference testing was not an informative test statistic to compare models. However, all of the model fit indices clearly support the unconstrained model.