Immigration is sometimes associated with crime and delinquency in the mind of the public, and it is often assumed that individuals not born in the U.S. engage in more crime and delinquency than do the native born. However, not enough research to date has looked at offending across the life-course and compared trajectories between U.S. born and non-U.S. born populations. Guided by life-course perspectives and utilizing group-based trajectory modeling (GBTM), this article uses four waves of National Longitudinal Study of Adolescent to Adult Health (Add Health) data to describe and contrast longitudinal variations in offending behavior between US born and non-US born individuals. The analyses show that there are fewer offending trajectory groups among the non-U.S. born, and that they generally offend at lower levels across the life-course than do the U.S. born. Further analysis also shows differences between the two groups in factors that shape trajectory group membership.
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For all analyses, we utilized the appropriate Add Health-provided longitudinal sample weight variable to correct for sample attrition and maintain the generalizability of the results.
For the purposes of the trajectory models there needs to be the same number of items in this index at each wave, and for all four waves, Cronbach’s alpha cannot be meaningfully improved by removing items from the index.
The average age at Wave II was 16.18, 21.62 at Wave III, and 28.12 at Wave IV.
These theoretical variables of interest are not measured at all waves in the Add Health data, and in most cases only at Waves I and II. Therefore, although some of these constructs could have time-varying impacts on offending behavior, we include them as risk factors measured at Wave I only.
For all measures except those found in the relationship with mom scale, missing data was handled through mean substitution because there was consistently less than 1% missing data, and with so little missing data mean substitution was as appropriate and effective an option as any of the more advanced methods for dealing with missing data (Schafer & Graham, 2002). For the parenting measures, there was consistently about 5% missing data, and therefore multiple imputation in Stata was utilized to handle missing data for these variables.
Nagin (2005) recommends that time-stable covariates come from a wave of data collection prior to the start of trajectories, but that was not an option here. However, most of the time-stable covariates represent ascriptive categories (i.e., age, race/ethnicity) and therefore causal ordering is not an issue. For the measures of theoretical import, much theory and research suggest they generally come before offending (Perrone et al., 2004).
Due to rounding, the percentages in the figures may not total 100%.
Looking at the y-axis, the range of values the trajectories fall in for the US born is 0 to about 4, while the range of values the trajectories fall in for the non-US born is 0 to approximately 1.5.
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Items in the Self-Control Scale:
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Watts, S.J., Evans, S.Z. Trajectories of Offending: Comparing US Born and Non-US Born Respondents in the Add Health. Am J Crim Just (2022). https://doi.org/10.1007/s12103-022-09693-6
- Trajectory modeling
- Race and ethnicity