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The War on Crime in California: Exploring Drug Use and Age Among California Rehabilitation Centers for Answers. Nick Chadwick Sociology 680 Fall 2012.

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Presentation on theme: "The War on Crime in California: Exploring Drug Use and Age Among California Rehabilitation Centers for Answers. Nick Chadwick Sociology 680 Fall 2012."— Presentation transcript:

1 The War on Crime in California: Exploring Drug Use and Age Among California Rehabilitation Centers for Answers. Nick Chadwick Sociology 680 Fall 2012

2 Drug use, Age, and Crime in the United States Purpose: To see how drug use and age play a role in our criminal justice system by analyzing a respondents age, and whether or not they have ever used methamphetamine and how this affects whether or not they have ever been arrested. Method: In order to predict a persons arrest record through their age and methamphetamine use I used logistic analysis. Logistic Regression: The classification of individuals into groups in order to predict values on a DV that is categorical.

3 Literature Review: Age In Relation to Crime “Most adult criminals begin their criminal careers as juveniles. Delinquents… take a heavy toll both financially and emotionally, on victims and taxpayers, who must share the cost.” (Greenwood 2008:186) “Saving youth from delinquency saves them from wasted lives.” (Greenwood 2008:185) The younger generation are often the first to be looked at when criminal activity takes place.

4 Literature Review: Methamphetamine Use and Crime “Despite substantial efforts to reduce the supply of, and demand for, illicit drugs, use of certain drugs has continued to grow. Methamphetamine is of particular concern due to the rapid increase in its use and the belief that it causes substantial amounts of crime.” (Dobkin and Nicosia 2009:324) “In the early 1990’s methamphetamine use was concentrated among white males in California and nearby Western states. Since then it has spread both demographically and geographically.” (Dobkin and Nicosia 2009: 325) I decided to look specifically at methamphetamine use because historically this has been such an epidemic in California, and now that it is being used across the nation I felt it is important to analyze California’s data to predict what could happen elsewhere.

5 How does this affect you? We live in a city where both meth use and crime are happening all over. In order to protect yourself from crime it is important to know what groups you are protecting yourself from. It is important to feel safe in your community for your own well being, and having insight into crime is always a good thing.

6 Hypotheses H 1 : The younger the reported age of the respondent at the intake of their drug and alcohol rehabilitation it is more likely they will report also having been arrested in their lifetime. H 2 : If a respondent reported that they used methamphetamines in the last thirty days it is more likely they will also report having been arrested in their lifetime. H 0 : Neither the reported younger age of a person at the intake into drug and alcohol rehab, nor if a respondent reported they used methamphetamines in the last thirty days will have any affect on whether or not they report being arrested in their lifetime.

7 Dataset Used California Drug and Alcohol Treatment Assessment (CALDATA), Data collected through survey form. Clients asked questions before rehab and after as a follow-up to their treatment. Questions were based extensively on how their drug use has effected their lives before and after treatment. (Many clients court ordered). Funded by The California Department of Alcohol and Drug Programs. Given to clients of California based treatment providers who received any type of public funding or are required to report to California Alcohol and Drug Data System as a condition of state licensing during the year ,826 After every survey was collected they used 1,826 in their final analysis.

8 Methodology Population: 1,826 Drug and Alcohol rehabilitation clients. Dependent Variable Dependent Variable (DV): Have you ever been arrested? Independent Variables Independent Variables (IV’s): (2 of them) 1) Age at admission into rehab (in years), categorical, including; 17 and under, 18-20, 21-24, 25-29, 35-39, 40-44, 45-49, 50-54, and ) Used Methamphetamines in the last 30 days? With “yes” and “no” as answers. Test Used Test Used: Binary Logistic Regression was conducted to determine which independent variables (age at admission into rehab, methamphetamine use in past 30 days) were predictors of a respondent having reported they had been arrested in their lifetime.

9 Logistic Regression Output Model Summary Step-2 Log likelihood Cox & Snell R Square Nagelkerke R Square a a. Estimation terminated at iteration number 5 because parameter estimates changed by less than.001.  In the model summary the -2 Log likelihood provides us with an index of model fit. A perfect model would have a score of 0.  In this analysis a score of shows that this model does not fit the data as strongly as it possibly could.  Looking at the Cox & Snell R Square and the Nagelkerke R Square we get two different estimates of the amount of variance in the DV accounted for by the model.  Both.090 and.140 show normal amounts of variance.

10 Logistic Regression Output, Continued… Omnibus Tests of Model Coefficients Chi-squaredfSig. Step 1Step Block Model In the Omnibus Tests of Model Coefficients table the model runs for chi-square statistics with levels of significance in the step, block, and model. However. It must be noted that a large sample size can skew the likelihood of finding significance when a poor fitting model may have been generated. Our sample size is reasonably large (1,826 respondents) so we don’t have much emphasis placed on the chi-square

11 Logistic Regression Output, Continued… Classification Table a Observed Predicted EVER ARRESTED Percentage Correct YESNO Step 1 EVER ARRESTED YES NO Overall Percentage 80.1 a. The cut value is.500 The classification table, which applies to the generated regression model in predicting group membership shows us that 80.1% of cases are correctly predicted.

12 Logistic Regression Output, Continued… Variables in the Equation BS.E.WalddfSig.Exp(B) Step 1 a Q Q43B Constant a. Variable(s) entered on step 1: Q10, Q43B. Q10: Age of respondent at intake? Q43B: Used methamphetamine in last 30 days?  Looking at the Sig. box we see that for reported age of respondent there is a (.000) significance level and Having used methamphetamines in the last 30 days yields a (.036) significance level, which means that both of these variables are significant for predicting whether or not a respondent reports having ever been arrested.  When looking at the Exp(B) (calculated odds ratio) the first variable, Age of respondent at the time of their intake into treatment, has an Exp(B) of.617 which shows a negative effect which means that (in combination with a negative B value) for every one unit change decrease in age(IV) there is significantly smaller ratio of a respondent having reported ever been to jail(DV).  When looking at the Exp(B) (calculated odds ratio) for the second variable we get a score of which means that for every one unit increase in positive responses to having used methamphetamines in the last 30 days, we are drastically more likely to have a respondent answer yes to having been arrested in their lifetime.

13 Discussion FAIL TO REJECT We FAIL TO REJECT the null hypothesis that the younger the age of a respondent upon entering treatment not having an effect on their response of having ever been arrested in their lifetime. As age decreased so did the likelihood of the respondent having been arrested in their lifetime. REJECT We REJECT the null hypothesis on whether or not a respondent reported having used methamphetamine in the last 30 days not having an effect on whether or not they had been arrested in their lifetime. The more a respondent answered yes to having used methamphetamine in the last 30 days, there was a drastically higher chance they had been arrested in their lifetime.

14 Limitations  Only used by California rehabilitation centers, which have drastically higher numbers of methamphetamine users (1992).  Dataset was from 1992, so it is possible these results would be much different today.  Arrest record and crime could be separate.  Arrest record could be a result of something completely separate from drug or alcohol use.  Respondents could be dishonest in their for fear of punishment in the rehabilitation, or trying to conceal addictions from counselors.  Procedurally it was difficult working with the data because so many respondents didn’t respond to certain answers, particularly around their recovery.  There were so many respondent’s who had been arrested that it was hard to tell if methamphetamine use or age were major components in why.

15 Suggestions For Future Research Possibly reward patients for their work on surveys, through things like gift cards or vouchers. This may improve likelihood of respondents completing survey all the way through. Attempt to have equal users of all drugs that are mentioned on survey, low numbers made for data that was unusable. Possibly survey law enforcement to see the correlation between court ordered rehabilitation treatment, and success lower or higher rates of re offending. If a rehabilitation survey is used try to include a more representative sample of people from all socioeconomic classes.

16 References Dobkin, Carlos and Nancy Nicosia “The War on Drugs: Methamphetamine, Public Health and Crime.” The American Economic Review 99(1): Furstenberg, Frank F. and Mary Elizabeth Hughes “Social Capital and Successful Development Among At-Risk Youth.” Journal of Marriage and Family 57(3): Greenwood, Peter “Prevetion and Intervention Programs for Juvenile Offenders.” Juvenile Justice 18(2):


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