Gendered Responses to Serious Strain: The Argument for a General Strain Theory of Deviance

Abstract

This paper expands and builds on newer avenues in research on gender and general strain theory (GST). I accomplish this by focusing on serious strains that are relevant for males and females, including externalizing and internalizing forms of negative emotions, and including multiple gendered deviant outcomes. Using the Add Health dataset, I find strong support for the impact of serious strains on both types of negative emotions and different forms of deviance for males and females. However, the experience of serious strain, emotionally and behaviorally, is gendered. Depressive symptoms are particularly important for all types of deviance by females. Including multiple types of deviant outcomes offers a fuller understanding of both similarities and differences by gender. These results support the utility of GST as a theory of deviance in general and support greater connections between GST, feminist theorizing, and the sociology of mental health.

Keywords:

• general strain theory
• gender and deviance
• gendered responses
• serious strain

Notes

1. Although there are other items in Add Health that may be forms of strain (pubertal timing, problems at school), I am primarily interested in very serious strains that meet Agnew's (2001) criteria for encouraging crime. Because many forms of strain may not clearly lead to deviant behaviors (see Agnew, 2001), I focus on the most serious types.

2. Add Health has very limited and problematic coverage of rape and sexual assault. Only females were asked about rape/sexual assault in a poorly worded question that elicited very few positive responses, suggesting serious under‐reporting due to the validity of the question.

3. Although I cannot rule out the possibility that the serious strain may have been experienced during the week before the interview and co‐occur with the measure of depressive symptoms, it is unlikely to be the case for most individuals in the sample.

4. Because 15% of the parent data is missing due to non‐participation, I imputed the missing data using a stochastic imputation technique that assigns a 0 or 1 to each missing value using random numbers and predicted values from a logistic regression with all the independent variables (Landerman, Land, & Pieper, 1997). The variable with imputed data produced similar results to the variable without the imputed data. I included the imputed data to preserve cases.

5. Add Health interviewers recorded if someone was present at the interview in addition to the respondent (parent, sibling, cousin, etc.). If any other person was present, the respondents reported lower levels of deviant behaviors compared to respondents with no one else present. Thus, I have kept this as a dichotomous measure to capture that issue.

6. While I use OLS for modeling violence, I also re‐ran all equations as negative binomial regressions and the results were virtually identical to the OLS.

7. I re‐ran all equations with depressive symptoms as a dependent variable as tobit regressions to allow for both right and left censoring of the dependent variable. These results were essentially identical to those with OLS. To be consistent with other literature, I have presented the OLS results.

8. The measure of bad temper in Add Health is only asked of the parent at Time 1 and there is no time frame specified making causal ordering problematic. A measure of situational anger would be preferable theoretically and allow for greater specification of causal order.