QUANTITATIVE ANALYSIS Methodology

The study outlined in the article aims at calculating the present value
of cost saving benefits in relation to the participation in prison
programs. It aimed at answering questions pertaining to the impact that
the level of education has on prisoner recidivism, examining the effects
that racism had on recidivism in colored prisoners, as well as assessing
the effects that prisoners’ emotional implications had on recidivism.
The effectiveness of these programs is seen as being built on varied
factors. First, it increases the possibility that a former convict will
be employed after leaving the prison thereby reducing recidivism.
Second, it enhances the working habits and skills of the prisoners
thereby making them successful employees and increasing the time-period
within which they keep the jobs.
Low education levels have a positive relationship with participation in
crime and drug abuse. While education may reduce the levels of
recidivism, the research acknowledges that educational programs provided
in prisons may come with mind blocks in instances where an individual
leans in the same place where he is confined. In essence, the learning
environment should be changed so as to safeguard learning. The research
uses descriptive method to determine the details pertaining to the
study. This ensures systematic description using factual and accurate
information. On the same note, stratified sampling method is used as the
population is heterogeneous in nature. Random sampling is also employed
in the varied strata.
Data collection
The study employs data obtained from sources in prisons, as well as
interviewing prisoners in the U.S. Selected participants are categorized
according to time spent in prison, length of sentence, and age at
release, previous education level, as well as education received in
prison. There is an indication that these variables have a relationship
with the post-release employment status of prisoners.
Results and Discussion
Data
A regression analysis was carried out using data from Bureau of Justice
Statistics (2013), as well as studies by Fabelo et al (2000) and Nally
(2012). Fabelo et al (2000) and Nally (2000) provided data on employment
status after release (X2), Time that offenders spent in prison in months
(X6) and education that offenders received in prison.
Variables
Several variables were derived and defined from varied sources. These
included the following.
Dependent variables
Y- The research uses this variable as an average rate of the five
supporting variables designed by BJS namely Rearrested, Readjudicated,
Reconvicted, Reincarcerated, or Reimprisoned.
Independent variables
X1 – the variable represents the offender’s age upon their release
as measured in months. It importance lies in the fact that it has a
bearing on the amount of education an individual can have while in
prison (Nally et al 2002).
X2- the variable represents the time (in month) spent in prison. The
research assumes that the likelihood of recidivism reduces with increase
in X2.
X3- this variable represents the number of times that the offender has
previously been imprisoned, with the study assuming that education would
have little effect on such individuals.
X4- this variable measured in Education Achievement Score (EAS)
evaluates education level that the offender obtains while in prison. It
is an indirect measure of the offender’s IQ.
X5- this variable measures the offender’s educational attainment prior
to incarceration, with the research assuming that the higher the X5 the
lower the recidivism rate.
X6- this variable represents the offender’s employment status after
release, with the research assuming that the higher its rate the lower
the recidivism rate.
Recidivism analysis tool used by BJS and Microsoft Excel were used to
determine the relationship between the dependent and independent
variables. The Best Fit curves for every relationship were determined
through the determination of the Pearson Product Moment Correlation
Value (R2) for all trial functions including Polynomial Functions,
Logarithmic function, Exponential function, and Linear function. The
best fit curve was obtained by determining the trial function that had
the highest R2 function.
Analysis and findings
1: Correlation between likelihood of recidivism and age of release
The R2 value if 0.9088 shows a string correlation between the dependent
variable Y and X1. The researchers noted that there is a high likelihood
of recidivism for offenders released in their late 30s, with those below
20s and above 50s being least likely to recidivate.
X2: Correlation between potential for recidivism and time spent in
prison
R2= 0.7723, which shows a strong correlation between Y and X2 whose best
fit curve is linear. However, the negative slope value (-0.1244) shows a
decrease in the likelihood for recidivism with increase in the time
spent in prison.
3: Correlation between likelihood of recidivism and number of previous
imprisonment
The two variables have a strong correlation as shown by the high R2
value (R=0.9254). The positive slope of the linear curve indicates that
offenders with a history of multiple imprisonments have higher
recidivism potential. The high slope implies that Y would significantly
increase with a small increase in X3.
4: Correlation between potential for recidivism and level of education
received while in prison in terms education achievement score (EAS)
There is a high correlation between the two variables Y and X4 (R2=
0.9868). The researchers noted that the recidivism potential decreases
with increase in level of education that an offender obtains while in
prison.
5: Correlation between likelihood of recidivism and level of education
of offender prior to imprisonment measured in years
The research showed that an increase in the level of education that an
offender obtains while in prison would result in a decrease in
recidivism likelihood. The exception, in this case, is for individuals
who attained lower than 10 years of education as they have lower
recidivism tendencies. The figure affirms previous results in 4 and
could underline a relationship with age, where offenders with low
education levels from within and without prison may belong to younger
age-groups (Nally et al 2012).
6: Correlation between potential for recidivism and employment after
release
Y and X6 are strongly correlated as shown by the high R2 value (R2 =
0.944) and slope (-1.1136). The high slope value means that there would
be a significant decrease in likelihood for recidivism with a small
increase in employability. This shows that individuals who get education
in prison have a lower likelihood of getting back to prison as they
would have the capacity to fend for themselves.
Conclusion
This study indicates that education can result in a significant decrease
in the recidivism likelihood. It underlines the fact that prison
programs should emphasize more on enhancing employability. In addition,
it is imperative that attention is paid on individuals who have been
imprisoned multiple times, as well as individuals with low education
levels as they have high recidivism tendencies. This should inform
policy makers who seem to focus more on individuals who have yet to be
imprisoned.
Bibliography
Nally, John M, et al. “The Post-Release Employment and Recidivism
Among Different Types of Offenders With A Different Level of Education:
A 5-Year Follow-Up Study in Indiana”. Justice Policy Journal. Volume
9—No. 1 (2012).
Fabello, Tony. Impact of Educational Achievement of Inmates in Windham
School District on Recidivism. Criminal Justice Policy Council. (2000)
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