Targeted Outreach - Governor's Office of Crime Control & Prevention ...

Appendices 67
Appendix D
Analytic Strategies
• Y2 = a + b1Y1 + b2X + b3T + b4P + e1
where: Y2 = the follow-up (12-month) value of the
variable of interest
Estimation of the effect of participation in GPTTO and
GITTO relied heavily on multi-variate analysis. In general,
the multi variate model used to estimate the effect of
GPTTO and GITTO on various outcome measures took
the following form:
• Y2 = a + b1Y1 + b2X + b3T + b4C + b4P + e1
Y1
X
T
= the baseline value of the variable of
interest
= a vector of explanatory variables
= whether the youth received
GPTTO or GITTO treatment
where: Y2 = the follow-up (12-month) value of the
variable of interest
Y1
X
T
C
P
= the baseline value of the variable of
interest
= a vector of explanatory variables
= whether the youth received
GPTTO or GITTO treatment
= whether the youth came to BGC
without being part of the
GPTTO or GITTO target group
= the level of participation at BGC
a, bi = coefficients
ei
= a stochastic disturbance term wit a
mean of zero and a constant variance
The explanatory variable (X) included in the model were
measures of: age, gender and race/ethnicity; gang risk factor
score; level of social support received from adults and
engagement in other after-school type activities; number of
stressful life events in the preceding year. For the intervention,
we included a control for Club site; we were not able
to include this control variable for prevention Clubs due
to the large number of Clubs.
This specification made it possible to estimate the effect of
GPTTO and GITTO more precisely by controlling for preexisting
differences among youth. The estimated effect of
GPTTO and GITTO is the coefficient on the dichotomous
variable T, b3.
P
= the level of participation at BGC
a, bi = coefficients
ei
= a stochastic disturbance term wit a
mean of zero and a constant variance
The estimated effect of participation at the BGC is the
variable P, b4.
In addition to estimating the overall effect of the program
using equation (1), a series of subgroup-treatment interaction
variables were used to estimate the effect of
GPTTO/GITTO on gender and age subgroups.
Algebraically, equation (1) was modified as follows:
• Y2= a + b1Y1 + b2X + b3T + b4P + c1PM + e2
• Y2= a + b1Y1 + b2X + b3T + b4P + c1PA1 + C2PA2 + e2
where:
M = a dummy variable that equals 1 for males
Ai = Age category dummy variables for age
13 to 15 and age 16 to 18
(These are the variables only for prevention,
with an omitted category of 9-12. For intervention,
the variable is 14-18 with an omitted category
of 9-13.)
Ci = coefficients
The use of ordinary least squares (OLS) was not warranted
when the dependent variable was dichotomous, such as in
the case of whether a participant initiated drug or alcohol
use or initiated gang behaviors. In such cases, logistic
regression analysis, using maximum likelihood estimation,
was used to estimate the treatment impact by specifying a
linear function for the logit (the logarithm of the odds) of
having a positive response (e.g., initiating drug use):