Transcript Nickx

The War on Crime in California:
Exploring Drug Use and Age
Among California Rehabilitation
Centers for Answers.
Nick Chadwick
Sociology 680
Fall 2012
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.
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.
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.
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.
Hypotheses
H1: 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.
H2: 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.
H0: 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.
Dataset Used
• California Drug and Alcohol Treatment Assessment (CALDATA),
1991-1993.
• 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 1992.
• After every survey was collected they used 1,826 in their final
analysis.
Methodology
Population: 1,826 Drug and Alcohol rehabilitation clients.
Dependent Variable (DV): Have you ever been arrested?
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 50+.
2) Used Methamphetamines in the last 30 days? With “yes”
and “no” as answers.
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.
Logistic Regression Output
Model Summary
Step
1
-2 Log likelihood
209.889a
Cox & Snell R
Nagelkerke R
Square
Square
.090
.140
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 209.889 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.
Logistic Regression Output,
Continued…
Omnibus Tests of Model Coefficients
Chi-square
Step 1
df
Sig.
Step
21.195
2
.000
Block
21.195
2
.000
Model
21.195
2
.000
• 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
Logistic Regression Output,
Continued…
Classification Tablea
Predicted
EVER ARRESTED
Observed
Step 1
YES
Percentage
Correct
NO
EVER
YES
176
3
98.3
ARRESTED
NO
42
5
10.6
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.
Logistic Regression Output,
Continued…
Variables in the Equation
B
Step
1a
Q10
Q43B
Constant
S.E.
Wald
df
Sig.
Exp(B)
-.483
.118
16.857
1
.000
.617
.762
.363
4.407
1
.036
2.142
-.438
.627
.487
1
.485
.646
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 2.142 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.
Discussion
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.
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.
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.
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.
References
• Dobkin, Carlos and Nancy Nicosia. 2009. “The War on Drugs:
Methamphetamine, Public Health and Crime.” The American
Economic Review 99(1):324-349
• Furstenberg, Frank F. and Mary Elizabeth Hughes. 1995.
“Social Capital and Successful Development Among At-Risk
Youth.” Journal of Marriage and Family 57(3):580-592
• Greenwood, Peter. 2008. “Prevetion and Intervention
Programs for Juvenile Offenders.” Juvenile Justice 18(2):185210