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Answering the career criminal debate: Comparing finite mixture modeling with growth mixture modeling

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Abstract:

I compare finite mixture modeling (FMM) with growth mixture modeling (GMM) and then apply both procedures to data from the Oregon Youth Study (OYS) to empirically test the Criminal Career Paradigm (CCP). Criminality was measured by the OYS boy’s annual self-report of serious crimes committed from age 12 to 20. FMM has been used to test the CCP because of its ability to account for unobserved heterogeneity within a population and its ability to handle non-normally distributed data. Nagin and colleagues have made substantial contributions to the CCP by providing a statistical modeling procedure that can handle non-normal distributions and heterogeneity within a population. Their FMM does not allow for within-class variation and therefore cannot test for heterogeneity. Muthén (2001) has combined structural equation modeling with mixture modeling creating GMM which can account for unobserved heterogeneity.
Both methods support CCP. GMM procedure resulted in fewer classes. The FMM procedure resulted in a five class solution: (1) zero intercept non-significant slope, (2) near zero intercept non-significant slope, (3) near zero intercept with significant linier growth, (4) high intercept with a positive slope and negative quadratic slope, and (5) high intercept with a steep slope and a negative quadratic slope. The GMM procedure resulted in a three class solution: (1) zero intercept with zero slope, (2) low intercept with non-significant slope, and (3) high intercept with a steep positive slope and negative quadratic slope.

Most Common Document Word Stems:

class (186), model (119), n (98), offend (82), fmm (52), slope (47), age (45), d (43), ggmm (42), crimin (40), offens (40), 3 (39), latent (38), use (37), serious (36), intercept (36), j (34), 1 (34), differ (34), trajectori (34), nagin (33),

Author's Keywords:

Criminal Careers, Growth modeling, unobserved heterogeneity, juvenile delinquency
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Name: American Sociological Association
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MLA Citation:

Burraston, Bert. "Answering the career criminal debate: Comparing finite mixture modeling with growth mixture modeling" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA, Aug 12, 2005 <Not Available>. 2017-10-10 <http://citation.allacademic.com/meta/p23019_index.html>

APA Citation:

Burraston, B. O. , 2005-08-12 "Answering the career criminal debate: Comparing finite mixture modeling with growth mixture modeling" Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA Online <PDF>. 2017-10-10 from http://citation.allacademic.com/meta/p23019_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Abstract: I compare finite mixture modeling (FMM) with growth mixture modeling (GMM) and then apply both procedures to data from the Oregon Youth Study (OYS) to empirically test the Criminal Career Paradigm (CCP). Criminality was measured by the OYS boy’s annual self-report of serious crimes committed from age 12 to 20. FMM has been used to test the CCP because of its ability to account for unobserved heterogeneity within a population and its ability to handle non-normally distributed data. Nagin and colleagues have made substantial contributions to the CCP by providing a statistical modeling procedure that can handle non-normal distributions and heterogeneity within a population. Their FMM does not allow for within-class variation and therefore cannot test for heterogeneity. Muthén (2001) has combined structural equation modeling with mixture modeling creating GMM which can account for unobserved heterogeneity.
Both methods support CCP. GMM procedure resulted in fewer classes. The FMM procedure resulted in a five class solution: (1) zero intercept non-significant slope, (2) near zero intercept non-significant slope, (3) near zero intercept with significant linier growth, (4) high intercept with a positive slope and negative quadratic slope, and (5) high intercept with a steep slope and a negative quadratic slope. The GMM procedure resulted in a three class solution: (1) zero intercept with zero slope, (2) low intercept with non-significant slope, and (3) high intercept with a steep positive slope and negative quadratic slope.


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Criminal careers of homicide offenders: do offenders of different types of homicides show different trajectories?

Latent Dynamic Trajectory Modeling of Criminal Careers: An Econometric Approach

Differences Within Juvenile Offenders: Using Latent Class Analysis to Predict Degree of Offending.


 
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