19 research outputs found

    Causal Inference with Group-Based Trajectories and Propensity Score Matching: Is High School Dropout a Turning Point?

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    Life course criminology focuses on trajectories of deviant or criminal behavior punctuated by turning point events that redirect trajectories onto a different path. There is no consensus in the field on how to measure turning points. In this study I ask: Is high school dropout a turning point in offending trajectories? I utilize two kinds of matching methods to answer this question: matching based on semi-parametric group-based trajectory models, and propensity score matching. These methods are ideally suited to measure turning points because they explicitly model counterfactual outcomes which can be used to estimate the effect of turning point events over time. It has been suggested that dropout is the end result of a process of disengagement from school. In order to assess the effect of the event of dropout, it is necessary to separate dropout from the processes that lead to it. The extent to which this is accomplished by matching is assessed by comparing dropouts to matched non-dropouts on numerous background characteristics. As such, it is desirable to use a wide range of measures to compare the two groups. I use the National Longitudinal Survey of Youth 1997 to address this question. Delinquency is measured in two ways: a six-item variety scale and a scale based on a graded-response model. Dropout is based on self-reports of educational attainment supplemented with official transcripts provided by high schools. Because of the breadth of topics covered in this survey, it is very well-suited to matching methods. The richness of these data allows comparisons on over 300 characteristics to assess whether the assumptions of matching methods are plausible. I find that matching based on trajectory models is unable to achieve balance in pre-dropout characteristics between dropouts and non-dropouts. Propensity score matching successfully achieves balance, but dropout effects are indistinguishable from zero. I conclude that first-time dropout between the ages of 16 and 18 is not a turning point in offending trajectories. Implications for life course criminology and dropout research are discussed

    School Dropout and Subsequent Offending: Distinguishing Selection From Causation

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    Past research on the relationship between school dropout and offending is inconclusive. In explaining findings, researchers have focused on strain and control theories, and have been unable to rule out selection effects. A key advance in understanding the effect of high school dropout is disaggregation by reason for dropout. Waves one through five of the National Longitudinal Survey of Youth 1997 is used to answer the question: Does dropout have a causal impact on offending? Dropouts are divided into four groups depending on reason given for dropout: personal, school, economic and other. Estimation of a random effects model indicates that dropout for school reasons and "other" reasons causes a small temporary increase in the frequency of offending whereas dropout for personal or economic reasons does not affect frequency of offending. It also shows that youths who drop out for school reasons have higher rates of offending across all five waves

    The Boston Special Youth Project Affiliation Dataset

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    Expectations in ministry

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    https://place.asburyseminary.edu/ecommonsatschapelservices/1803/thumbnail.jp

    A Life-Course Analysis of Offense Specialization Across Age: Introducing a New Method for Studying Individual Specialization Over the Life Course

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    Much of the knowledge base on offense specialization indicates that, although there is some (short-term) specialization, it exists amidst much versatility in offending. Yet this general conclusion is drawn on studies using very different conceptualizations of specialization and emerges with data primarily through the first two to three decades of life. Using data on a sample of Dutch offenders through age 72 years, this article introduces and applies a new method for studying individual offender specialization over the life course. The results indicate that although, in general, individual offending patterns over the life course are diverse, there is also evidence of an age—diversity curve. Linking offense frequency trajectories to the estimated diversity index, the authors also examine distinct specialization patterns across unique trajectory groups. Implications for theory and research are outlined
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