Given the growing reliance on longitudinal self-report data for making causal inferences about crime, it is essential to investigate whether the within-individual change in criminal involvement exists and is not a measurement artifact driven by attrition or survey fatigue—a very real possibility first identified by Lauritsen (Soc Forces 77(1):127–154, 1998) using the National Youth Survey (NYS). The current study examines whether the same threats to the validity of within-individual change in criminal involvement exist in the National Longitudinal Survey of Youth 1997 cohort (NLSY97).
We first estimate cohort-specific growth curve models of general crime, arrest, and substance use, and then test the difference between the age–crime curves of adjacent cohorts. We take a general approach to test cohort differences in the growth curve models, which advances the existing method separately modeling for each pair of adjacent cohorts. To explore the sources of cohort differences, we also estimate separate growth curve models by individual crime item and by demographic group.
We document non-standard cohort differences between the age–crime curves of adjacent cohort pairs that are consistent with the findings of Lauritsen (1998) on measures of self-reported offending. However, the size of the cohort effects in the NLSY97 is substantially smaller than those in the NYS. We also found that the cohort effects were only evident in some of the survey items. Moreover, we did not identify any similar cohort issues in the longitudinal measure of arrest.
The findings of cohort effects localized in a certain crime items and demographic groups may mitigate concerns over the limited validity of longitudinal self-report data. We discuss how the survey techniques used in the NLSY97 might explain our findings and suggest an area of future study to explicate remaining cohort differences.
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Demographers readily assume that individuals in adjacent years will follow similar patterns. For example, O'Brien et al. (1999) argue that “cohorts should be based on the grouping of individuals born in more than a single year” (p. 1068). This is because characteristics of individuals born in certain years are shared with those born in similar years. Particular events, such as economic depressions or natural disasters, influence not only individuals who were born in the year of the event, but also those born in neighboring years. Lauritsen used this logic to her advantage. Adjacent birth year cohorts that share similar social circumstances should report similar levels of offending at the same ages. Any differences found in the comparison of given age are more likely to be the data artifact (e.g. attrition, testing effect, etc.) rather than the evidence of real differences in behavior. We adopt her analytic strategy in this paper and we also examine the possibility of a real cohort effect as the potential explanation by using cross-sectional data. Details of method and findings are discussed in the result section.
A Google Scholar citation search on May 26, 2016 found 58 citations to the Brame et al. (2014) article.
Given that the main focus of the current study is within-individual change in criminal involvement, which is more prevalent behavior among racial minorities, we argue that the inclusion of oversample is not necessary. Also, we use the first seven waves of the information because, starting from wave 8, criminal involvement questions are limited to those who have ever been arrested before wave 8 and a control group.
Screener questions at wave 1 ask a respondent on his/her lifetime experience of criminal involvement, but are followed by the question asking whether it happened in last 12 months. Using the follow-up question on the timing of involvement, for the first wave, we exclude any involvement that happened more than a year ago.
Our model included random components for the intercept and for the linear term only and did not include random components for the age-quadratic and the age-cubic terms. This is because most of the models with more than two random effects did not converge due to model complexity.
In the results and conclusion sections, we discuss the possibility of the violation of this assumption: true cohort changes in the age-crime relationship.
We thank an anonymous reviewer for this suggestion.
ICC = Variance of random effects/(Variance of random effects + Variance of fixed effects).
The NLSY97 differentiates race/ethnicity group into “Black or Hispanic” group and “non-Black and non-Hispanic” group. To minimize confusion, we refer to the “non-Black and non-Hispanic” group as the “White” group. Among the sample eligible for initial interview, majority (82%) of the non-Black and non-Hispanic groups were Whites (Moore et al. 2000). Also, we exclude those who are mixed races (n = 81) in our analysis to examine race and sex differences.
We used the UCR to get the crime rate during the survey period. We thank one of our anonymous reviewer for this suggestion.
We thank one of our anonymous reviewers who pointed out telescoping as the possible explanations of the unexpected downward trend in the growth curve of the general crime scale and cohort effect.
Number of follow-up questions: property crime = 1, other property offense = 2, attacking others = 1, selling drugs = 4, smoking cigarettes = 4, alcohol consumption = 4, marijuana use = 3.
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Kim, J., Bushway, S.D. Using Longitudinal Self-Report Data to Study the Age–Crime Relationship. J Quant Criminol 34, 367–396 (2018). https://doi.org/10.1007/s10940-017-9338-9
- Longitudinal self-report
- Panel survey
- Growth curve model
- Cohort effect