What I need help on the most would be the following sections: 1. data management, 2. data analysis, 3. discussion. Just those parts would be only about 3 to 4 pages double space of writing. The document titled treatment satisfaction final draft has all the work I have done so far. I have highlighted and sort of color-coordinated the sections of the paper that you will need to do.  Here are some specifics you need to know: I had to pick 4 variable (3 independent 1 Dependent variables) which was pulled from the same Data which is called Cathy's data ( is attached to the homeworkmarket post ) My picks were: · Treatment Satisfaction (TS) · Desire for Help(DH) · Treatment Readiness(TR) · Treatment participation(TP)  Each indicator had there own data  sets which were: TS Cest 007 Time schedule is convenient Cest 0011 Program expects responsibility/self Cest 020 Program organized /run well Cest 030 Satisfied with program Cest 080 Staff efficient with Job Cest 115 Personal Counseling Cest 112 Location is convenient DH Cest 003 Cest 032 Cest 039 Cest 065 Cest 86 Cest 116 TR Cest006  TR Need to stay in treatment Cest 013  TR Solve Problems in treatment Cest 014 TR Treatment is not helping  ( this one had to be removed because it failed reliability test) Cest 054 TR Treatment gives you hope Cest 056 TR Want to be in drug treatment TP Cest 019 Cest 026 Cest 031 Cest 035 Cest 037 Cest 066 Cest 067 Cest 077 Cest 083 Cest 104 Cest 127 The example paper is exactly how the paper needs to read so really you would need to just copy and paste most of it and plug in my variables and maybe change some words around so that it will fit my data set. when it comes to interpreting the data for the finding section my professor has a specific way which she wants it to be written. I will give you some homework examples that we have done to give you an idea for the univariate and bivariate analysis but for the Mutlvariates you have to go off the final paper example because i do not have HW examples for them. Data Management: For the Data management just simply give a detailed summary of what methods/steps i used. Use Appendix A for your reference. To make it easy for you i made a table of contents  page 6: Ran Frequencies Page 23: Computations page 30: Factor Analysis Page 44: Reliability Analysis Page 53: Computation  Try to word it like it is for the example paper  Data Analysis: For Data Analysis use Appendix B info I highlighted or colored the different tables so you could easily find them. Lastly, I have attached Cathy's Data set for you in case you wanted to go in replicate my steps so you could get a better understanding of the data Discussion section is the conclusion Final ProjectComment by Davis-Ganao, Jessica S: Better title needed Institution Dr. Ganao Introduction The reason for this study is to understand counselor relationships based on key elements. The study seeks to find how well a counselor rapport hel ...

1. What I need help on the most would be the following sections:
1. data management,
2. data analysis,
3. discussion.
Just those parts would be only about 3 to 4 pages double space
of writing. The document titled treatment satisfaction final draft
has all the work I have done so far. I have highlighted and sort
of color-coordinated the sections of the paper that you will need
to do.
Here are some specifics you need to know:
I had to pick 4 variable (3 independent 1 Dependent variables)
which was pulled from the same Data which is called Cathy's
data ( is attached to the homeworkmarket post )
My picks were:
· Treatment Satisfaction (TS)
· Desire for Help(DH)
· Treatment Readiness(TR)
· Treatment participation(TP)
Each indicator had there own data sets which were:
TS
Cest 007 Time schedule is convenient
Cest 0011 Program expects responsibility/self
Cest 020 Program organized /run well
Cest 030 Satisfied with program
Cest 080 Staff efficient with Job
Cest 115 Personal Counseling
Cest 112 Location is convenient
DH
Cest 003
Cest 032

2. Cest 039
Cest 065
Cest 86
Cest 116
TR
Cest006 TR Need to stay in treatment
Cest 013 TR Solve Problems in treatment
Cest 014 TR Treatment is not helping ( this one had to be
removed because it failed reliability test)
Cest 054 TR Treatment gives you hope
Cest 056 TR Want to be in drug treatment
TP
Cest 019
Cest 026
Cest 031
Cest 035
Cest 037
Cest 066
Cest 067
Cest 077
Cest 083
Cest 104
Cest 127
The example paper is exactly how the paper needs to read so
really you would need to just copy and paste most of it and plug
in my variables and maybe change some words around so that it
will fit my data set.
when it comes to interpreting the data for the finding section my
professor has a specific way which she wants it to be written. I
will give you some homework examples that we have done to
give you an idea for the univariate and bivariate analysis but for
the Mutlvariates you have to go off the final paper example
because i do not have HW examples for them.

3. Data Management:
For the Data management just simply give a detailed summary
of what methods/steps i used. Use Appendix A for your
reference. To make it easy for you i made a table of contents
page 6: Ran Frequencies
Page 23: Computations
page 30: Factor Analysis
Page 44: Reliability Analysis
Page 53: Computation
Try to word it like it is for the example paper
Data Analysis:
For Data Analysis use Appendix B info I highlighted or colored
the different tables so you could easily find them.
Lastly, I have attached Cathy's Data set for you in case you
wanted to go in replicate my steps so you could get a better
understanding of the data
Discussion section is the conclusion
Final ProjectComment by Davis-Ganao, Jessica S: Better title
needed

4. Institution
Dr. Ganao
Introduction
The reason for this study is to understand counselor
relationships based on key elements. The study seeks to find
how well a counselor rapport helps with critical issues such as
drug abuse. The main variables are treatment readiness, desire
to help and personal irresponsibility. These variables are
centered on drug abuse and building a rapport with a counselor
to become sober. Important phases that are common across these
models include recognition of problems caused by drug use, an

5. interest and desire for help in making changes, readiness to
enter a formal process to guide change and action steps that will
help carry out the plan for change (Simpson & Joe, 1993).
It is important to understand all aspects of the above variables
and how building a rapport will help drug addicts feel
comfortable enough to share information with the counselor. In
this paper it will demonstrate different aspects of information
through text and a formation of statistics.
LITERATURE REVIEW
This section will examine relationships that are built with a
counselor for certain situations. Counselors Rapport will
discuss how well the counselor works with an individual to
build a relationship. Desire for help will discuss different types
of drugs an how far you will go to get help. Treatment readiness
gives you incite on treatment programs and how the counselor
will determine if you are ready for treatment. Lastly personal
irresponsibility will examine how drug addicts are quick to
blame others and get help from a counselor so that they will
learn personal responsibility.
Counselor Rapport
Counselors believe that genuine therapeutic work can occur
only when clients feel safe to explore personal and intimate
aspects of their lives within a confidential relationship (Glosoff,
2000). Building a rapport with your client letting them know
that everything is confidential allows the client to become more
vulnerable to opening up. Some clients are particular of what
type of counselor they would like categorizes them by their
demographics. Researchers found that, “Contrary to
expectation, however, client-counselor gender and ethnic
congruence were not consistently associated with higher levels
of treatment engagement and abstinence for all gender, ethnic,
and age groups (Firoentine, 1999)” In addition to the intensity

6. and duration of participation in counseling, the client-counselor
relationship has been found to be an important influence of
treatment outcomes (Firoentine, 1999).
Desire for Help
Having a drug addiction can take a toll on an individual’s life
mentally, emotionally and physically. Drug addiction is a
chronic, relapsing disorder in which compulsive drug-seeking
and drug-taking behavior persists despite serious negative
consequences (Cami, 2003). Some go to drugs because they use
it as stress reliever to take all of their worries away. According
to The New England Journal of Medicine, “Theories of
addiction have mainly been developed from neurobiological
evidence and data from studies of learning behavior and
memory mechanisms.”
There are several types of drugs that are abused; marijuana,
heroin, cocaine, opioids, zanax, molly, pcp, and bath salts just
to name a few. Drugs are mostly used by older people but as
society has changed the age of users are getting younger. The
article states that, “patients were also more likely to be in the
younger age groups, with the highest management rate recorded
for men aged 25-44 years (Charles, 2010).”
When someone is on drugs they get to a point when they are
tired and desire help from someone to become clean. Most drug
addicts enter a drug rehabilitation clinic, with several different
counselors to get them to express themselves and become sober
longterm. Building a relationship with your client is best way to
get them to open up. The counseling relationship or therapeutic
alliance is perceived to be central to achieving a positive
outcome in all mental health counseling (Gelso & Fretz, 1992),
and it is especially important that a positive relationship or
therapeutic alliance be formed early in addictions counseling
before the more difficult or challenging times (e.g., withdrawal
symptoms, relapse) occur (Merta, 2001).
Treatment Readiness

7. Drug treatment programs are approaches that help with
substance abuse (Sindelar, 2001). The variable treatment
readiness discusses ways that will show if you’re prepared to
receive treatment based on a scale. Treatment reduces drug use
and crime and increases individuals' functioning (Sindelar,
2001). Based on the variable treatment readiness one has to
need more medical care. When a person is on drugs they are at a
high risk for diseases and other medical issues. When you are in
the process of becoming sober, your whole body has to get
readjusted to a healthy lifestyle.
Treatment readiness helps improve treatment programs. Higher
treatment readiness also was significantly related to early
therapeutic engagement in each modality (Sindelar, 2001).
When it comes to treatment readiness you have to be forth
coming for help. In previous research with another data set
(Simpson et al., 1997e), we demonstrated the importance of
treatment engagement- defined in terms of counseling session
attendance and mutual ratings of the therapeutic relationship
between counselor and client-for retention in a sample of
methadone maintenance clients (Sindelar, 2001).
Personal Irresponsibility
When you are in school, you are taught to be responsible for
your own actions and not blame others. Being on drugs you
become out of touch with reality. You become personally
irresponsible by blaming others for your actions. One will put
the responsibility on their environment, demographics,
socioeconomic status just to name a few. A counselor tries to
find other methods to get the addict to take responsibility for
their actions.
Most drug addicts have families who won’t to be involved in
their treatment, but the drug addict has to take responsibility.
Substance abuse affects entire families, yet only recently has
attention been focused on the needs of children with parents
who abuse drugs and/or alcohol (Greenburg, 2000). Drugs can
also affect one’s life most of the time criminally. According to

8. article, “As conceptualized by Paul Goldstein of the University
of Illinois at Chicago, drug-related violence is of three types:
the systemic violence of drug-dealing organizations; the
economic-compulsive violence that results from securing money
to purchase drugs; and psychopharmacological violence, which
is caused by the excitability, irritability, aggression, or paranoia
associated with the physiological action of drugs
(Belenko,1998).”
Creating this type of violence will land you in jail or prison
majority of the time where you will receive in house treatment.
Therapeutic community is one type of treatment that is used
during counseling while in prison to help you once released.
TCs provide a very structured environment focusing on
resocialization, intensive therapy, behavior modification, and
gradually increasing responsibilities (Belenko, 1998). Under
this care you will receive a case manage, educational and
vocational training as well as medical and psychological
treatment. Psychological counseling is also important, because
substance abuse and mental disorders often go hand in hand
(Belenko, 1998).
MethodologyComment by Davis-Ganao, Jessica S: Missing
transition statement
Model
Comment by Davis-Ganao, Jessica S: Missing discussion of the
model
Figure 1. Building Relationships
Personal Irresponsibility

9. Counselor Rapport
Desire for Help
Treatment Readiness
Hypotheses
H1: There is an association between Personal Irresponsibility
and Counselors Rapport.
H2: There is an association between Desire for Help and
Counselors Rapport.
H3: There is an association between Treatment Readiness and
Counselors Rapport.
H4: There is an association between Desire for Help and
Personal Irresponsible.
H5: There is an association between Treatment Readiness and
Desire for Help
H6: There is an association between Treatment Readiness and
Personal Irresponsible
Data Management
In this study I computed a dependent variable Counselors

10. Rapport and three independent variables Desire for Help,
Personal Irresponsibility and Treatment Readiness.
I ran frequencies for variables CEST015, CEST021, CEST038,
CEST042, CEST043, CEST050, CEST052, CEST063, CEST084,
CEST110 and CEST128. There are three reasons why you run
frequencies. The first reason is to see if all of the indicators are
measured on the same scale. The second reason is to see if there
are any responses outside of the valid or legitimate responses.
The third reason is to see if everything is moving in the same
direction. As a result of running frequencies and looking at our
legitimate responses of all variables, we now know that the
variables are all nominal. With all of the variables having the
same value and label, we can tell that all of the variables are
moving in the same direction.
Variables CEST015, CEST021, CEST038, CEST042, CEST043,
CEST050, CEST052, CEST063, CEST084, CEST110 and
CEST128 were factored into a factor analysis. A factor analysis
is SPSS’s way of telling if the indicators are going to group
well together, based on the responses. According to Extraction
Sums of Squared Loadings under Total Variance Extraction, the
number of total values listed tells if the indicators group well
together. Because only one number appears under total, the
factor analysis for these indicators has determined that the
indicators are all grouping together under the same heading.
Another sign of the indicators grouping well together is having
no value for the rotated component matrix. The component
matrix showed a negative value, so I did a reverse code with the
variable CEST050 to CEST050r. Once the reverse code was
done I ran a frequency to make sure the negative value wasn’t
there anymore. The indicators are grouping well together and all
fit into one idea of “RN”.
The Eigen Values under Component Matrix represents how good
the actual indicators work with the rest. Any Eigen Value above
.4 is good value for these indicators. All indicators have Eigen
Values above .4 now. The recoded variable was run through a
reliability analysis. Cronbach's Alpha tells how well the

11. indicators group together. It gives an actual value to determine
how got a fit they are. Typically a Cronbach's Alpha of .75 or
higher is ideal. The Cronbach's Alpha for these indicators is
.937, which is above the ideal value for the Cronbach’s Alpha.
The variable CR was created using CEST015, CEST021,
CEST038, CEST042, CEST043, CEST052, CEST063, CEST084,
CEST110, CEST128 and CEST050r.
I then ran frequencies CEST003, CEST032, CEST039,
CEST065, CEST086, and CEST116. There are three reasons
why you run frequencies. The first reason is to see if all of the
indicators are measured on the same scale. The second reason is
to see if there are any responses outside of the valid or
legitimate responses. The third reason is to see if everything is
moving in the same direction. As a result of running frequencies
and looking at our legitimate responses of all variables, we now
know that the variables are all nominal. With all of the
variables having the same value and label, we can tell that all of
the variables are moving in the same direction.
I didn’t have to recode any variables. All of these variables
CEST003, CEST032, CEST039, CEST065, CEST086, and
CEST116 were factored to create a factor analysis. A factor
analysis is SPSS’s way of telling if the indicators are going to
group well together, based on the responses. According to
Extraction Sums of Squared Loadings under Total Variance
Extraction, the number of total values listed tells if the
indicators group well together. Because only one number
appears under total, the factor analysis for these indicators has
determined that the indicators are all grouping together under
the same heading. Another sign of the indicators grouping well
together is having no value for the rotated component matrix.
These six chosen indicators all fit into one idea of “DH”.
The Eigen Values under Component Matrix represents how good
the actual indicators work with the rest. Any Eigen Value above
.4 is good value for these indicators. All indicators have Eigen
Values above .4. The variables were ran through a reliability
analysis. Cronbach's Alpha tells how well the indicators group

12. together. It gives an actual value to determine how got a fit they
are. Typically a Cronbach's Alpha of .75 or higher is ideal. The
Cronbach's Alpha for these indicators is .715. This is above the
ideal value for the Cronbach’s Alpha. The variable DH was
created using six indicators: CEST003, CEST032, CEST039,
CEST065, CEST086, and CEST116.
Frequencies were run for variables CEST024, CEST047,
CEST055, CEST068, and CEST118. There are three reasons
why you run frequencies. The first reason is to see if all of the
indicators are measured on the same scale. The second reason is
to see if there are any responses outside of the valid or
legitimate responses. The third reason is to see if everything is
moving in the same direction. As a result of running frequencies
and looking at our legitimate responses of all variables, we now
know that the variables are all nominal. With all of the
variables having the same value and label, we can tell that all of
the variables are moving in the same direction.
I didn’t have to recode any variables. All of these variables
were factored into a factor analysis CEST024, CEST047,
CEST055, CEST068, and CEST118. A factor analysis is SPSS’s
way of telling if the indicators are going to group well together,
based on the responses. According to Extraction Sums of
Squared Loadings under Total Variance Extraction, the number
of total values listed tells if the indicators group well together.
Because only one number appears under total, the factor
analysis for these indicators has determined that the indicators
are all grouping together under the same heading. Another sign
of the indicators grouping well together is having no value for
the rotated component matrix. The five chosen indicators all fit
into one idea of “TN”.
The Eigen Values under Component Matrix represents how
good the actual indicators work with the rest. Any Eigen Value
above .4 is good value for these indicators. The only indicator
with an Eigen Value less than .4 is CEST118. In terms of
frequency it says “need more medical care”.
The variables were run through a reliability analysis.

13. Cronbach's Alpha tells how well the indicators group together.
It gives an actual value to determine how got a fit they are.
Typically a Cronbach's Alpha of .75 or higher is ideal. The
Cronbach's Alpha for these indicators is .599. This is below the
ideal value for the Cronbach’s Alpha. If CEST118 was removed
the value of Cronbach’s Alpha would increase to .634. The
decision was made to keep the indicator because more medical
care is needed. The variable TN was created using 5 indicators:
CEST024, CEST047, CEST055, CEST068, and CEST118.
The last set of frequencies I ran were CEST2003, CEST2004,
CEST2029, CEST2039, CEST2045 and CEST2056. There are
three reasons why you run frequencies. The first reason is to see
if all of the indicators are measured on the same scale. The
second reason is to see if there are any responses outside of the
valid or legitimate responses. The third reason is to see if
everything is moving in the same direction. As a result of
running frequencies and looking at our legitimate responses of
all variables, we now know that the variables are all nominal.
With all of the variables having the same value and label, we
can tell that all of the variables are moving in the same
direction.
I didn’t have to recode any variables. All of these variables
were factored into a factor analysis CEST2003, CEST2004,
CEST2029, CEST2039, CEST2045 and CEST2056. A factor
analysis is SPSS’s way of telling if the indicators are going to
group well together, based on the responses. According to
Extraction Sums of Squared Loadings under Total Variance
Extraction, the number of total values listed tells if the
indicators group well together. Because only one number
appears under total, the factor analysis for these indicators has
determined that the indicators are all grouping together under
the same heading. Another sign of the indicators grouping well
together is having no value for the rotated component matrix.
The six chosen indicators all fit into one idea of “PI”.
The Eigen Values under Component Matrix represents how good
the actual indicators work with the rest. Any Eigen Value above

14. .4 is good value for these indicators. All the Eigen Value is
above .4. The variables were run through a reliability analysis.
Cronbach's Alpha tells how well the indicators group together.
It gives an actual value to determine how got a fit they are.
Typically a Cronbach's Alpha of .75 or higher is ideal. The
Cronbach's Alpha for these indicators is .603. The variable PI
was created using 6 indicators.
Data AnalysisComment by Davis-Ganao, Jessica S: More detail
is needed
In the aspect of the Univariate descriptive analysis a SPSS
frequency was run for the nominal and ordinal level measure.
The measure of central tendency was used to describe the data
by showing the mode, median and frequencies. A frequency was
run to show how often something occurs. Also another
Univariate descriptive analysis SPSS frequency was run for the
interval and ratio level measure and I used the measure of
dispersion to show the range of the minimum and maximum
level of the variables along with mean and standard deviation.
For the Correlations Matrix I ran a bivariate descriptive
Analysis to compare the independent variable to the
independent variable or the independent variable to the
dependent variable. This correlation matrix is run based off of
an interval level measure.
I ran a multivariate analysis to show the t-test, anova,
regression and partial correlation because they demonstrate 3 or
more variables in the distribution. The t-test and the anova are
interval/ ratio level measure because it reports the mean and
standard deviation. A regression was also run and the
components standardized beta and statistical significance are
examined. A SPSS partial correlation was the last analysis run
which pointed out the main effects of a coefficient correlation
and the coefficient partial correlation control variable grade.

15. Findings
The tables below represent the findings of the Univariate,
Bivariate and Multivariate Analysis in SPSS Output
form.Comment by Davis-Ganao, Jessica S: More detailed
transition
Univariate Findings
The first table represents the demographics of Counselor
Rapport such as Job and Sex. The table uses nominal and
ordinal level measures showed by the frequencies, percentages
and measure of central tendency.
Table 1. Univariate Statistics for Demographics
Study Variables
n
Percentages
Measure of Central Tendency
Job
Median=2
Full time
727
46%
Part Time
185
12%
Other
623
39%
1535

16. 97%
Sex
Mode=1
Male
891
56%
Female
695
44%
1586
100%
As it relates to job, the median category is 2, which represents a
part time job. Also there is a lot of fluctuation at the beginning
and the end of the distribution. There are less part time workers
than any job (n=185).As it relates to gender, males represent
56% (n=891) of the sample. The modal category is 1 which
represents males. Although males are the modal category in
terms of gender, females are almost equally represented in this
data set.
The second table below displays the range, mean and standard
deviation for the interval and ratio level measures in this
distribution.
Table 2. Univariate Descriptive Findings for Interval and Ratio
level Study Variables

17. Study Variables
Range
Mean
Standard Deviation
Counselor Rapporta
13-65
49.15
10.578
Desire to Helpb
6-30
28.83
4.267
Treatment Readinessc
5-25
16.59
3.902
Personal Irresponsibilityd
6-30
13.00
.936
Gradee
0-20
10.52
1.924
a. The lower the number means that they don’t want counseling.
b. The lower the number the less they have a desire for help.
c. The lower the value the less treatment ready they are.
d. The higher the number the less personally irresponsible
someone is.
e. The highest grade complete the lower the result.
As it relates to Counselor Rapport, the average counselor has a
rapport of 49.15, a standard deviation of 10.578. The standard
deviation shows that there is little fluctuation around the mean.
The minimum score of the distribution is 13 and the maximum
score is 65 which means the lower the number means that they

18. don’t want counseling. As it relates to desire for help, the
average is 28.83, with a standard deviation of 4.267. This means
that there aren’t a lot of people who don’t desire help. The
standard deviation shows that there is little fluctuation around
the mean. The minimum score of the distribution is 6 and the
maximum score is 30 which mean the lower the number the less
they have a desire for help.
As it relates to the treatment readiness scale, the average is
16.59, with a standard deviation of 3.902. This means that the
average respondent should average 16.59 on the scale. The
standard deviation shows that there is little fluctuation around
the mean. The minimum score of the distribution is 5 and the
maximum score is 25 which means that on the scale the lower
the number the less you are on the scale.
As it relates to personal irresponsibility scale, the average is
13.00, with a standard deviation of .936. This means that the
average respondent scored a 13 on the personal irresponsibility
scale. The standard deviation shows that there is little
fluctuation around the mean. The minimum score is 6 and the
maximum score is 30 which means the distribution on the scale
go from high to low. As it relates to grade, the average
respondent has a grade of 10.52, a standard deviation of 1.924.
Which means the average person is a C student.
Bivariate Findings
This table is a correlation matrix that shows the main effects as
well as the intercorrelations. The asterisks display the
confidence level and statistical significance of each.
Table 3. Correlations Matrix for Counselor Rapport and
Independent Variables
Study Variables
1
2
3
4

19. 1.Counselor Rapport
1
2.Desire to Help
.338**
1
3.Treatment Readiness
.118**
.450**
1
4. Personal Irresponsibility
-.253**
-.323**
-.069**
1
*p≤.05; **p≤=.01; ***p≤.001
The strongest relationship between the independent variable and
dependent variable is that between Desire for help and
Counselors Rapport (r=.338; p=.000). The more the respondent
desired for help the more the counselor was able to build a
rapport. The next strongest relationship personal
irresponsibility and counselors rapport (r=-.253; p=.000).
The more personally irresponsible the respondent is the more
it’ll help build a counselors relationship with the individual.
The weakest relationship is treatment readiness and counselor
rapport (r=.118; p=.000). Those who perceived to be treatment

20. ready need more of a counselor’s relationship. All variables are
significant. Overall the relationships between the independent
variables are moderate to weak or show no relationship at all.
Multivariate AnalysisComment by Davis-Ganao, Jessica S:
Transition needed here.
Partial Correlations
Test whether or not a control variable is an intervening,
spurious, or no effect at all on the relationship between 2
variables.
Table 4. Partial Correlations with Counselors Rapports
Study Variables
Coefficient Correlation
Coefficient Partial Correlation Control Variable Grade
Desire to Help
.338**
.315
Treatment
.118**
.095
Personal Irresponsibility
-.253**
-.250
*p≤.05; **p≤=.01; ***p≤.001
t-tests
This table shows t-test which, are used when you want to
determine whether there is a difference between two groups on
some given variables. In this table you will see the mean and
standard deviation of males and females for each main effect.
Table 5. Multivariate results of T-tests with Study Variables
Study Variables

21. Males
Females
t-statistic
Counselor Rapport
48.16 (SD=10.759)
50.42(SD=10.221)
-4.110***
Desire to Help
22.76(SD=4.377)
25.18(SD=3.707)
-11.387***
Treatment
15.92(SD=3.900)
17.45(SD=3.736)
-7.712***
Personal Irresponsibility
13.46(SD=4.088)
12.42(SD=3.681)
5.156***
*p≤.05; **p≤=.01; ***p≤.001
In terms of the t-tests results, the findings indicate that there is
a statistically significant difference between the means for sex
on counselor rapport (t=-4.110; p=.000) for males (mean=48.16;
SD=10.759) and females (mean=50.42; SD=10.221). There is a
statistically significant difference between the means for sex on
desire for help (t=-11.387; p=000) for males (mean=22.76;
SD=4.377) and females (mean= 25.18; SD=3.707). There is a
statistically significant difference for males and females on sex
and treatment (t=-7.712; p=.000) males (mean=15.92;
SD=3.900) and females (mean=17.45; SD=3.736). In addition,
there is a statistically significant difference between sex and
personal irresponsibility (t=5.156; p=.000) males (mean=13.46;
SD=4.088) and females (mean=12.42; SD=3.681). Overall you
can reject the null hypothesis for all of the variables.

22. ANOVA
This table discusses the results of an ANOVA; an ANOVA is
run when you have 3-5 categories. In this table you will see the
control variable Jobs broken down into three categories full
time, part time and other by way of the mean and standard
deviation and the F value.
Table 6. Results of the Anova with Study Variables
Study Variables
Jobs
F
Full Time
Part Time
Other
Counselor Rapport
49.15(SD=10.768)
49.90(SD=9.820)
48.89(SD=10.483)
.603
Desire to Help
23.59(SD=4.227)
24.33(SD=4.184)
23.99(SD=4.342)
2.687
Treatment Readiness
16.13(SD=3.876)
17.03(SD=3.842)
16.94(SD=3.844)
8.714
Personal Irresponsibility
12.75(SD=3.837
12.97(SD=3.520)
13.27(SD=4.144)
2.826

23. In terms of the ANOVA results, There is a statistically
significant difference between the means for job on treatment
readiness (F=8.714; p=.000). In contrast there is no statistically
significant difference between the means for job on counselor
rapport (F=.603; p=.547). There is no significant difference for
jobs on desire for help (F=2.687; p= .068) or on personal
irresponsibility (F=2.826; p=.060) Overall you can reject the
null hypothesis for treatment readiness and accept the null
hypothesis for counselor rapport, desire for help and personal
irresponsibility.
Regression
In terms of the model for counselor rapport with variables
desire for help, treatment readiness and personal irresponsibility
the findings show a weak (r2=.127) and a significant
relationship (f=67.361; p=.000). The strongest relationship in
the model is that between desire for help and counselor rapport
(std .beta=.283; p=.000). The next strongest relationship is that
between personal irresponsibility and counselor rapport (std.
beta=-.163; p= .000). The weakest relationship is between
treatment readiness and counselor rapport (std. beta= -.042;
p=.137), this relationship is not significant. Overall the findings
show that there is support for the entire hypothesis except for
treatment readiness and counselor rapport.
Table 7. Results of the OLS Regression Analysis with
Counselor Rapport as the Dependent
Variable
Independent Variables
Standardized Beta
t
Significance
Desire to Help
.283
9.602
.000***

24. Treatment Readiness
-.042
-1.487
.137
Personal Irresponsibility
-.163
-6.132
.000***
F-Statistic=67.361
R2.125
*p≤.05; **p≤=.01; ***p≤.001
DiscussionComment by Davis-Ganao, Jessica S: This should be
longer
All of the main effects projected in the hypothesis model are the
same in the final model. The original model stated that there is
an association between Desire for Help and Personal
Irresponsible. The research states that, “Substance abuse affects
entire families, yet only recently has attention been focused on
the needs of children with parents who abuse drugs and/or
alcohol (Greenburg, 2000).”
In previous research with another data set (Simpson et al.,
1997e), we demonstrated the importance of treatment
engagement- defined in terms of counseling session attendance
and mutual ratings of the therapeutic relationship between
counselor and client-for retention in a sample of methadone
maintenance clients (Sindelar, 2001). The research collaborates
with my hypothesis that there is an association between
Treatment Readiness and Desire for Help.
There is an association between Treatment Readiness and
Personal Irresponsible according to my research. Treatment
reduces drug use and crime and increases individuals'
functioning (Sindelar, 2001).

25. Model Revision
As predicted, desire for help, treatment readiness and personal
irresponsibility all had an effect on counselor’s rapport.
Reliable information stated that one’s desire for help will help
build a relationship with a counselor. A counselor will help the
respondent in need get help. The counseling relationship or
therapeutic alliance is perceived to be central to achieving a
positive outcome in all mental health counseling (Gelso &
Fretz, 1992), and it is especially important that a positive
relationship or therapeutic alliance be formed early in
addictions counseling before the more difficult or challenging
times (e.g., withdrawal symptoms, relapse) occur (Merta, 2001).
The findings were moderately consistent with the original
model. Based on my findings building a relationship with your
counselor will help you if you’re desiring for help, ready for
treatment and ready to be personally responsible for your
actions. Because the counselor will help you transition to where
you need to be.
Figure 2. Revised Model of Building Relationships
Personal Irresponsibility
Counselor Rapport

26. Desire for Help
Treatment Readiness
Reference Page:Comment by Davis-Ganao, Jessica S: These are
not APA
1). Camí, Jordi, MD, PhD, & Farré, Magí, MD, PhD. (2003).
Drug addiction. The New England Journal of Medicine,
349(10), 975-86. Retrieved from
http://search.proquest.com/docview/223927319?accountid=1271
3
2). Charles, J., Britt, H., & Fahridin, S. (2010). Drug abuse.
Australian Family Physician, 39(8), 539. Retrieved from
http://search.proquest.com/docview/742431484?accountid=1271
3
Merta, R. J. (2001). Addictions counseling. Counseling and
Human Development, 33(5), 1. Retrieved from

27. http://search.proquest.com/docview/206851023?accountid=1271
3
4). Sindelar, J. L., & Fiellin, D. A. (2001). Innovations in
treatment for drug abuse:
Solution
s to a public health problem. Annual Review of Public Health,
22, 249-72. Retrieved from
http://search.proquest.com/docview/235215615?accountid=1271
3
5). Greenberg, R. (2000). Substance abuse in families:
Educational issues. Childhood Education, 76(2), 66-69.
Retrieved from
http://search.proquest.com/docview/210389563?accountid=1271
3
6). Belenko, S., & Peugh, J. (1998). Fighting crime by treating
substance abuse. Issues in Science and Technology, 15(1), 53-
60. Retrieved from
http://search.proquest.com/docview/195910261?accountid=1271
3
7). Firoentine, R., & Hillhouse, M. P. (1999). Drug treatment
effectiveness and client-counselor empathy: Exploring the
effects of gender and ethnics congruency. Journal of Drug
Issues, 29(1), 59-74. Retrieved from
http://search.proquest.com/docview/208862322?accountid=1271

28. 3
8). Glosoff, H. L., Herlihy, B., & E, B. S. (2000). Privileged
communication in the counselor-client relationship. Journal of
Counseling and Development : JCD, 78(4), 454-462. Retrieved
from
http://search.proquest.com/docview/219020693?accountid=1271
3
Appendices
Appendix A
Data Mangement
Frequencies
Notes
Output Created
21-JUL-2015 11:33:13
Comments

29. Input
Data
C:Usersslindse3DownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on all cases with valid data.

30. Syntax
FREQUENCIES VARIABLES=Counselor DH TN PI JOBr
GRADEr CSEXr
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
MEDIAN MODE
/ORDER=ANALYSIS.
Resources
Processor Time
00:00:00.05
Elapsed Time
00:00:00.04
Statistics
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
PI

31. JOBr
GRADEr
CSEXr
N
Valid
1483
1509
1531
1517
1535
1579
1586
Missing
106
80
58
72
54
10
3
Mean
49.15
23.83
16.59

32. 13.00
1.93
10.52
1.44
Median
51.00
24.00
17.00
13.00
2.00
11.00
1.00
Mode
52
26
18
12
1
12
1
Std. Deviation
10.578
4.267
3.902
3.946

33. .936
1.924
.496
Minimum
13
6
5
6
1
0
1
Maximum
65
30
25
30
3
20
2
Frequency Table

34. Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
13
3
.2
.2
.2
15
5
.3
.3
.5
16
3
.2

35. .2
.7
17
3
.2
.2
.9
18
5
.3
.3
1.3
19
4
.3
.3
1.6
20
4
.3
.3

36. 1.8
21
3
.2
.2
2.0
22
14
.9
.9
3.0
23
9
.6
.6
3.6
24
9
.6
.6
4.2

37. 25
9
.6
.6
4.8
26
3
.2
.2
5.0
27
4
.3
.3
5.3
28
6
.4
.4
5.7

38. 29
8
.5
.5
6.2
30
8
.5
.5
6.7
31
6
.4
.4
7.1
32
10
.6
.7
7.8
33

39. 16
1.0
1.1
8.9
34
10
.6
.7
9.6
35
18
1.1
1.2
10.8
36
14
.9
.9
11.7
37
19

40. 1.2
1.3
13.0
38
19
1.2
1.3
14.3
39
33
2.1
2.2
16.5
40
28
1.8
1.9
18.4
41
23
1.4

41. 1.6
20.0
42
34
2.1
2.3
22.3
43
43
2.7
2.9
25.2
44
35
2.2
2.4
27.5
45
40
2.5
2.7

42. 30.2
46
45
2.8
3.0
33.2
47
39
2.5
2.6
35.9
48
42
2.6
2.8
38.7
49
45
2.8
3.0
41.7

43. 50
54
3.4
3.6
45.4
51
77
4.8
5.2
50.6
52
108
6.8
7.3
57.9
53
74
4.7
5.0
62.8

44. 54
68
4.3
4.6
67.4
55
60
3.8
4.0
71.5
56
55
3.5
3.7
75.2
57
56
3.5
3.8
79.0
58

45. 34
2.1
2.3
81.3
59
47
3.0
3.2
84.4
60
40
2.5
2.7
87.1
61
40
2.5
2.7
89.8
62
55

46. 3.5
3.7
93.5
63
24
1.5
1.6
95.1
64
40
2.5
2.7
97.8
65
32
2.0
2.2
100.0
Total
1483
93.3

47. 100.0
Missing
System
106
6.7
Total
1589
100.0
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
6
2

48. .1
.1
.1
8
1
.1
.1
.2
9
3
.2
.2
.4
10
6
.4
.4
.8
11
4
.3

49. .3
1.1
12
7
.4
.5
1.5
13
6
.4
.4
1.9
14
13
.8
.9
2.8
15
17
1.1
1.1

50. 3.9
16
40
2.5
2.7
6.6
17
35
2.2
2.3
8.9
18
53
3.3
3.5
12.4
19
52
3.3
3.4
15.8

51. 20
74
4.7
4.9
20.7
21
83
5.2
5.5
26.2
22
91
5.7
6.0
32.3
23
121
7.6
8.0
40.3

52. 24
147
9.3
9.7
50.0
25
148
9.3
9.8
59.8
26
160
10.1
10.6
70.4
27
137
8.6
9.1
79.5
28

53. 113
7.1
7.5
87.0
29
103
6.5
6.8
93.8
30
93
5.9
6.2
100.0
Total
1509
95.0
100.0
Missing
System
80

54. 5.0
Total
1589
100.0
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
5
10
.6
.7
.7
6

55. 6
.4
.4
1.0
7
5
.3
.3
1.4
8
11
.7
.7
2.1
9
39
2.5
2.5
4.6
10
38

56. 2.4
2.5
7.1
11
36
2.3
2.4
9.5
12
83
5.2
5.4
14.9
13
97
6.1
6.3
21.2
14
128
8.1

57. 8.4
29.6
15
132
8.3
8.6
38.2
16
136
8.6
8.9
47.1
17
151
9.5
9.9
57.0
18
152
9.6
9.9

58. 66.9
19
135
8.5
8.8
75.7
20
126
7.9
8.2
83.9
21
88
5.5
5.7
89.7
22
75
4.7
4.9
94.6

59. 23
44
2.8
2.9
97.5
24
19
1.2
1.2
98.7
25
20
1.3
1.3
100.0
Total
1531
96.3
100.0
Missing

60. System
58
3.7
Total
1589
100.0
PI
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
6
68
4.3
4.5
4.5

61. 7
59
3.7
3.9
8.4
8
75
4.7
4.9
13.3
9
90
5.7
5.9
19.2
10
116
7.3
7.6
26.9
11

62. 118
7.4
7.8
34.7
12
213
13.4
14.0
48.7
13
117
7.4
7.7
56.4
14
186
11.7
12.3
68.7
15
86

63. 5.4
5.7
74.4
16
116
7.3
7.6
82.0
17
75
4.7
4.9
86.9
18
73
4.6
4.8
91.8
19
45
2.8

64. 3.0
94.7
20
25
1.6
1.6
96.4
21
18
1.1
1.2
97.6
22
12
.8
.8
98.4
23
5
.3
.3

65. 98.7
24
12
.8
.8
99.5
26
5
.3
.3
99.8
28
2
.1
.1
99.9
30
1
.1
.1
100.0

66. Total
1517
95.5
100.0
Missing
System
72
4.5
Total
1589
100.0
JOBr
Frequency
Percent
Valid Percent
Cumulative Percent

67. Valid
1 full time
727
45.8
47.4
47.4
2 part time
185
11.6
12.1
59.4
3 other
623
39.2
40.6
100.0
Total
1535
96.6
100.0
Missing

68. System
54
3.4
Total
1589
100.0
GRADEr
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
1
.1
.1
.1

69. 1
1
.1
.1
.1
3
5
.3
.3
.4
4
1
.1
.1
.5
5
5
.3
.3
.8
6

70. 18
1.1
1.1
2.0
7
39
2.5
2.5
4.4
8
143
9.0
9.1
13.5
9
230
14.5
14.6
28.1
10
296

71. 18.6
18.7
46.8
11
322
20.3
20.4
67.2
12
408
25.7
25.8
93.0
13
34
2.1
2.2
95.2
14
39
2.5

72. 2.5
97.7
15
13
.8
.8
98.5
16
18
1.1
1.1
99.6
17
3
.2
.2
99.8
18
1
.1
.1

73. 99.9
19
1
.1
.1
99.9
20
1
.1
.1
100.0
Total
1579
99.4
100.0
Missing
System
10
.6

74. Total
1589
100.0
CSEXr
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
891
56.1
56.2
56.2
2
695
43.7
43.8
100.0

75. Total
1586
99.8
100.0
Missing
System
3
.2
Total
1589
100.0
Correlations
Notes
Output Created
21-JUL-2015 11:33:59

76. Comments
Input
Data
C:Usersslindse3DownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used

77. Statistics for each pair of variables are based on all the cases
with valid data for that pair.
Syntax
CORRELATIONS
/VARIABLES=Counselor DH TN PI
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
Resources
Processor Time
00:00:00.03
Elapsed Time
00:00:00.06
Correlations
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
PI

78. Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
Pearson Correlation
1
.338**
.118**
-.253**
Sig. (2-tailed)
.000
.000
.000
N
1483
1452
1456
1443
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
Pearson Correlation
.338**

79. 1
.450**
-.323**
Sig. (2-tailed)
.000
.000
.000
N
1452
1509
1479
1466
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
Pearson Correlation
.118**
.450**
1
-.069**
Sig. (2-tailed)
.000

80. .000
.007
N
1456
1479
1531
1482
PI
Pearson Correlation
-.253**
-.323**
-.069**
1
Sig. (2-tailed)
.000
.000
.007
N
1443
1466

81. 1482
1517
**. Correlation is significant at the 0.01 level (2-tailed).
Partial Correlation
Notes
Output Created
21-JUL-2015 11:34:45
Comments
Input
Data
C:Usersslindse3DownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>

82. Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics are based on cases with no missing data for any
variable listed.
Syntax
PARTIAL CORR
/VARIABLES=Counselor DH TN PI BY GRADEr
/SIGNIFICANCE=TWOTAIL
/MISSING=LISTWISE.
Resources
Processor Time
00:00:00.09

83. Elapsed Time
00:00:00.09
Correlations
Control Variables
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
PI
GRADEr
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +

84. CEST050r
Correlation
1.000
.315
.095
-.250
Significance (2-tailed)
.
.000
.000
.000
df
0
1384
1384
1384
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
Correlation
.315

85. 1.000
.436
-.326
Significance (2-tailed)
.000
.
.000
.000
df
1384
0
1384
1384
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
Correlation
.095
.436
1.000
-.077

86. Significance (2-tailed)
.000
.000
.
.004
df
1384
1384
0
1384
PI
Correlation
-.250
-.326
-.077
1.000
Significance (2-tailed)
.000

87. .000
.004
.
df
1384
1384
1384
0
T-Test
Notes
Output Created
21-JUL-2015 11:35:35
Comments
Input
Data

88. C:Usersslindse3DownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no
missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST GROUPS=CSEXr(1 2)

89. /MISSING=ANALYSIS
/VARIABLES=Counselor DH TN PI
/CRITERIA=CI(.95).
Resources
Processor Time
00:00:00.03
Elapsed Time
00:00:00.05
Group Statistics
CSEXr
N
Mean
Std. Deviation
Std. Error Mean
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +

90. CEST050r
1
825
48.16
10.759
.375
2
656
50.42
10.221
.399
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
1
838
22.76
4.377
.151
2
669
25.18
3.707
.143

91. TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
1
854
15.92
3.900
.133
2
674
17.45
3.736
.144
PI
1
837
13.46
4.088
.141
2
677
12.42
3.681
.141

92. Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper

93. Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
Equal variances assumed
2.943
.086
-4.110
1479
.000
-2.263
.551
-3.342
-1.183
Equal variances not assumed
-4.134
1433.244
.000
-2.263
.547
-3.336
-1.189

94. DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
Equal variances assumed
19.900
.000
-11.387
1505
.000
-2.417
.212
-2.833
-2.000
Equal variances not assumed
-11.599
1499.706
.000
-2.417
.208
-2.825
-2.008
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118

95. Equal variances assumed
.809
.369
-7.712
1526
.000
-1.521
.197
-1.908
-1.134
Equal variances not assumed
-7.751
1470.414
.000
-1.521
.196
-1.906
-1.136
PI
Equal variances assumed
5.407
.020

96. 5.156
1512
.000
1.042
.202
.646
1.439
Equal variances not assumed
5.213
1494.719
.000
1.042
.200
.650
1.434

97. Oneway
Notes
Output Created
21-JUL-2015 11:36:45
Comments
Input
Data
C:Usersslindse3DownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling

98. Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on cases with no missing
data for any variable in the analysis.
Syntax
ONEWAY Counselor DH TN PI BY JOBr
/STATISTICS DESCRIPTIVES
/MISSING ANALYSIS.
Resources
Processor Time
00:00:00.03
Elapsed Time
00:00:00.04
Descriptives
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval for Mean

99. Minimum
Maximum
Lower Bound
Upper Bound
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
1 full time
686
49.15
10.768
.411
48.34
49.96
13
65

100. 2 part time
170
49.90
9.820
.753
48.41
51.39
19
65
3 other
585
48.89
10.483
.433
48.04
49.74
13
65
Total
1441
49.13
10.542
.278

101. 48.59
49.68
13
65
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
1 full time
696
23.59
4.227
.160
23.28
23.91
6
30
2 part time
174
24.33
4.184
.317
23.70
24.95
10
30

102. 3 other
595
23.99
4.342
.178
23.65
24.34
8
30
Total
1465
23.84
4.274
.112
23.62
24.06
6
30
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
1 full time
713
16.13

103. 3.876
.145
15.84
16.41
5
25
2 part time
176
17.03
3.842
.290
16.46
17.61
8
25
3 other
593
16.94
3.844
.158
16.63
17.25
5

104. 25
Total
1482
16.56
3.879
.101
16.37
16.76
5
25
12.44
13.49
699
12.75
3.837
.145
12.47
13.04
6
28
2 part time
175
12.97

105. 3.520
.266
6
24
3 other
597
13.27
4.144
.170
12.94
13.60
6
30
Total
1471
12.99
3.934
.103
12.79
13.19
6

106. 30
ANOVA
Sum of Squares
df
Mean Square
F
Sig.
Counselor COMPUTE Counselor=CEST002 + CEST008 +
CEST015 + CEST021 + CEST038 + CEST042 + CEST043 +
CEST052 + CEST063 + CEST084 + CEST110 + CEST128 +
CEST050r
Between Groups
134.100
2
67.050
.603
.547
Within Groups
159903.050
1438

107. 111.198
Total
160037.151
1440
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
Between Groups
97.962
2
48.981
2.687
.068
Within Groups
26647.242
1462
18.227

108. Total
26745.204
1464
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
Between Groups
259.576
2
129.788
8.714
.000
Within Groups
22029.088
1479
14.895
Total
22288.664

109. 1481
PI
Between Groups
87.259
2
43.629
2.826
.060
Within Groups
22666.521
1468
15.440
Total
22753.780
1470

110. Regression
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
PI, TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118, DH COMPUTE DH=CEST003 +
CEST039 + CEST065 + CEST086 + CEST032 + CEST116b
.
Enter
a. Dependent Variable: Counselor COMPUTE
Counselor=CEST002 + CEST008 + CEST015 + CEST021 +
CEST038 + CEST042 + CEST043 + CEST052 + CEST063 +
CEST084 + CEST110 + CEST128 + CEST050r
Model Summary
Model

111. R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.356a
.127
.125
9.751
a. Predictors: (Constant), PI, TN COMPUTE TN=CEST024 +
CEST047 + CEST055 + CEST068 + CEST118, DH COMPUTE
DH=CEST003 + CEST039 + CEST065 + CEST086 + CEST032
+ CEST116
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
19215.875

112. 3
6405.292
67.361
.000b
Residual
132174.063
1390
95.089
Total
151389.938
1393
a. Dependent Variable: Counselor COMPUTE
Counselor=CEST002 + CEST008 + CEST015 + CEST021 +
CEST038 + CEST042 + CEST043 + CEST052 + CEST063 +
CEST084 + CEST110 + CEST128 + CEST050r
b. Predictors: (Constant), PI, TN COMPUTE TN=CEST024 +
CEST047 + CEST055 + CEST068 + CEST118, DH COMPUTE
DH=CEST003 + CEST039 + CEST065 + CEST086 + CEST032

113. + CEST116
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
39.958
2.109
18.949
.000
DH COMPUTE DH=CEST003 + CEST039 + CEST065 +
CEST086 + CEST032 + CEST116
.700
.073

114. .283
9.602
.000
TN COMPUTE TN=CEST024 + CEST047 + CEST055 +
CEST068 + CEST118
-.112
.076
-.042
-1.487
.137
PI
-.432
.070
-.163
-6.132
.000
a. Dependent Variable: Counselor COMPUTE
Counselor=CEST002 + CEST008 + CEST015 + CEST021 +
CEST038 + CEST042 + CEST043 + CEST052 + CEST063 +
CEST084 + CEST110 + CEST128 + CEST050r
Appendix B

115. Data Analysis
Insert Data Analysis output here
1
Frequencies
Statistics
gangassociation COMPUTE gangassociation=v23c + v40d
peerdelinquency COMPUTE peerdelinquency=v1a + v2a + v3a
+ v4a + v5a + v6a + v7a + v8a + v9a + v10a + v11a + v12a
defiance COMPUTE defiance=v13b + v15b + v16b + v17b +
v18b + v19b + v20b + v21b + v22b
gangaffiliation COMPUTE gangaffiliation=v42d + v48d + v49d
gangacivities COMPUTE gangacivities=v44d + v45d + v46d +
v47d
N
Valid
824
961
915
959

116. 962
Missing
175
38
84
40
37
Mean
.34
5.53
23.92
1.78
.37
Median
.00
5.00
23.00
2.00
.00
Mode
0
2
22
3

117. 0
Std. Deviation
.615
3.483
4.897
1.279
.934
Minimum
0
0
10
0
0
Maximum
2
12
36
3
4
Frequency Table
gangassociation COMPUTE gangassociation=v23c + v40d
Frequency
Percent

118. Valid Percent
Cumulative Percent
Valid
0 no association
607
60.8
73.7
73.7
1 thought about it but did not join
154
15.4
18.7
92.4
2 thought about it and joined
63
6.3
7.6
100.0
Total
824
82.5
100.0

119. Missing
System
175
17.5
Total
999
100.0
peerdelinquency COMPUTE peerdelinquency=v1a + v2a + v3a
+ v4a + v5a + v6a + v7a + v8a + v9a + v10a + v11a + v12a
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
59
5.9

120. 6.1
6.1
1
35
3.5
3.6
9.8
2
147
14.7
15.3
25.1
3
107
10.7
11.1
36.2
4
84
8.4
8.7

121. 45.0
5
80
8.0
8.3
53.3
6
72
7.2
7.5
60.8
7
71
7.1
7.4
68.2
8
69
6.9
7.2
75.3

122. 9
79
7.9
8.2
83.6
10
52
5.2
5.4
89.0
11
53
5.3
5.5
94.5
12
53
5.3
5.5
100.0

123. Total
961
96.2
100.0
Missing
System
38
3.8
Total
999
100.0
Higher the value on the distribution indicates more delinquency.
defiance COMPUTE defiance=v13b + v15b + v16b + v17b +
v18b + v19b + v20b + v21b + v22b
Frequency
Percent
Valid Percent
Cumulative Percent

124. Valid
10
1
.1
.1
.1
11
1
.1
.1
.2
12
5
.5
.5
.8
13
5
.5
.5
1.3

125. 14
7
.7
.8
2.1
15
11
1.1
1.2
3.3
16
18
1.8
2.0
5.2
17
20
2.0
2.2
7.4
18

126. 37
3.7
4.0
11.5
19
45
4.5
4.9
16.4
20
67
6.7
7.3
23.7
21
76
7.6
8.3
32.0
22
95

127. 9.5
10.4
42.4
23
81
8.1
8.9
51.3
24
74
7.4
8.1
59.3
25
61
6.1
6.7
66.0
26
48
4.8

128. 5.2
71.3
27
60
6.0
6.6
77.8
28
45
4.5
4.9
82.7
29
31
3.1
3.4
86.1
30
35
3.5
3.8

129. 89.9
31
19
1.9
2.1
92.0
32
19
1.9
2.1
94.1
33
15
1.5
1.6
95.7
34
11
1.1
1.2
96.9

130. 35
9
.9
1.0
97.9
36
19
1.9
2.1
100.0
Total
915
91.6
100.0
Missing
System
84
8.4
Total

131. 999
100.0
The higher the value on the distribution the more defiant the
respondent is.
gangaffiliation COMPUTE gangaffiliation=v42d + v48d + v49d
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
267
26.7
27.8
27.8
1
113

132. 11.3
11.8
39.6
2
146
14.6
15.2
54.8
3
433
43.3
45.2
100.0
Total
959
96.0
100.0
Missing
System
40
4.0

133. Total
999
100.0
The higher the values the more gang affiliation, which is
defined as knowing someone who is in a gang or hanging out
with gang members.
gangacivities COMPUTE gangacivities=v44d + v45d + v46d +
v47d
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
798
79.9
83.0
83.0

134. 1
63
6.3
6.5
89.5
2
47
4.7
4.9
94.4
3
21
2.1
2.2
96.6
4
33
3.3
3.4
100.0
Total

135. 962
96.3
100.0
Missing
System
37
3.7
Total
999
100.0
The higher the value the more gang activities associate with by
the respondent.
Correlations
Correlations
gangassociation
peerdelinquency
defiance

136. gangaffiliation
gangacivities
gangassociation
Pearson Correlation
1
Sig. (2-tailed)
N
824
peerdelinquency
Pearson Correlation

137. .372**
1
Sig. (2-tailed)
.000
N
797
961
defiance
Pearson Correlation
.396**
.230**
1

138. Sig. (2-tailed)
.000
.000
N
765
884
915
gangaffiliation
Pearson Correlation
.411**
.514**
.199**
1
Sig. (2-tailed)
.000

139. .000
.000
N
816
927
887
959
gangacivities
Pearson Correlation
.691**
.345**
.395**
.323**
1
Sig. (2-tailed)
.000
.000
.000
.000

140. N
819
928
887
950
962
**. Correlation is significant at the 0.01 level (2-tailed).
Main Effects
As it relates to the main effects, the strongest relationship is
that between gang activities and gang association (r=.691;
p=.000). This relationship indicates that higher values to related
to more gang activities the more likely the respondent thought
about and joined a gang. The next strongest relationship is that
between gang affiliation and gang association (r=.411; p=.000).
This relationship means the higher the gang affiliation, which is
hanging out with gang members is associated with those
respondents who thought about and joined a gang. The weakest
relationship is between peer delinquency and gang association
(r=.372; p=.000). This relationship indicates those respondents
whose peers were delinquent were more likely those who
thought about and joined a gang. All relationships between the
independent variables and the dependent variables are
significant. In addition, all these relationships show a moderate

141. to strong association.
Inter-correlations
As it relates to the inter-correlations, the strongest relationship
is between peer delinquency and gang affiliation (r=.514;
p=.000). This relationship indicates higher peer delinquency is
associated with high gang affiliation. The next strongest
relationship is between defiance and gang activities (r=.395;
p=.000). This relationship indicates that the more defiant a
respondent the more likely they participate in gang activities.
The weakest is between defiance and gang affiliation (r=.199;
p=.000). This relationship indicates high defiance is associated
with high gang affiliation. All relationships hypothesized are
found to be significant. However, the relationships between
peer delinquency and defiance was not hypothesized but it is
significant (r=230; p=.000). This relationship indicates that
high peer delinquency is associated with high defiance. Also,
the relationship between defiance and gang affiliation is
significant (r=.199; p=.000). Again this relationship indicates
high defiance is associated with high gang affiliation. Overall
the significant relationships for the inter-correlations show
weak to strong associations.
Hypotheses

142. H1: There is a relationship between peer delinquency and gang
association
H2: There is a relationship between defiance and gang
association
H3: There is a relationship between gang affiliation and gang
association
H4: There is a relationship between gang activities and gang
association
H5: There is a relationship between peer delinquency and gang
activities
H6: There is a relationship between defiance and gang activities
H7: There is a relationship between peer delinquency and gang
affiliation
H8: There is a relationship between gang affiliation and gang
activities
Methodology
Transition paragraph
Model
This model illustrates the relationships between the dependent
variable gang association and the independent variables. The
first relationship between peer delinquency effects gang

143. association in that those whose peers are gang associated will
most likely themselves be associated with a gang.
Figure 1. Model of Gang Association
Gang Activities
Defiance
Peer Delinquency
Gang Association
Gang Affiliation

144. Discussion

145. Figure 2. Revised Model of Gang Association
Gang Activities
Defiance
Peer Delinquency
Gang Association
Gang Affiliation

147. Department of Criminal Justice
CRJU 4060 – Criminal Justice Statistics
Univariate Interpretations – Practice Sheet
Directions: Review the SPSS print out and answer the questions
below each variable in the Frequency Table. Please note the
amount of space you are given under each of these tables is not
indicative of the amount you should write. You can manipulate

148. the charts up and down after you have finished writing in your
answers to save space and paper.
Frequencies
Statistics
v60xsex Sex
v61xethn Ethnicity
v59xgrad Grade
v58xgpa GPA
SENGAGE Degree of School Engagement
GANGEXPO Exposure to Gangs
COUNTY
N
Valid
995
999
957
429
944
913
998
Missing

149. 4
0
42
570
55
86
1
Mean
.5095
3.0000
8.8495
3.0559
2.2373
3.2015
.5551
Median
1.0000
3.0000
9.0000
3.0000
2.0000
3.0000
1.0000
Mode
1.00

150. 3.00
9.00
3.00
2.00
.00
1.00
Std. Deviation
.50016
.00000
1.93847
.66008
1.40852
2.97484
.49720
Minimum
.00
3.00
6.00
1.00
.00
.00
.00
Maximum
1.00
3.00

151. 12.00
4.60
7.00
12.00
1.00
Frequency Table
v60xsex Sex
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
.00 female
488
48.8
49.0
49.0
1.00 males
507
50.8
51.0

152. 100.0
Total
995
99.6
100.0
Missing
System
4
.4
Total
999
100.0
1. What level of measurement is this?
_______nominal____________________________
2. What is your evidence? ___________categorical and not
ranked__________________________________

153. 3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to sex, the mode is 1, which is the male category
Males are 51% (n=507) of the sample. Although males are
majority of the sample, females represent 49% (n=488).
v59xgrad Grade
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
6.00 6th grade

154. 142
14.2
14.8
14.8
7.00 7th grade
140
14.0
14.6
29.5
8.00 8th grade
152
15.2
15.9
45.4
9.00 9th grade
154
15.4
16.1
61.4
10.00 10th grade
134

155. 13.4
14.0
75.4
11.00 11th grade
125
12.5
13.1
88.5
12.00 12th grade
110
11.0
11.5
100.0
Total
957
95.8
100.0
Missing
System
42
4.2

156. Total
999
100.0
1. What level of measurement is this?
________ordinal___________________________
2. What is your evidence? _____________Categorical and
ranked________________________________
3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to grade in school, the median category is 9, which
represents the 9th grade. Looking at the valid percent
distribution, middle school represents about 45% (n=434) of the
sample. On the other hand, high schoolers represent majority of
the respondents (n=523).
v58xgpa GPA

157. Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1.00
2
.2
.5
.5
1.20
2
.2
.5
.9
1.30
3
.3
.7
1.6
1.40
1

158. .1
.2
1.9
1.50
3
.3
.7
2.6
1.56
1
.1
.2
2.8
1.60
2
.2
.5
3.3
1.70
1
.1

159. .2
3.5
1.80
4
.4
.9
4.4
1.90
2
.2
.5
4.9
2.00
20
2.0
4.7
9.6
2.10
3
.3
.7

160. 10.3
2.20
4
.4
.9
11.2
2.30
10
1.0
2.3
13.5
2.40
3
.3
.7
14.2
2.50
35
3.5
8.2
22.4

161. 2.60
13
1.3
3.0
25.4
2.70
8
.8
1.9
27.3
2.75
1
.1
.2
27.5
2.80
23
2.3
5.4
32.9

162. 2.90
19
1.9
4.4
37.3
3.00
60
6.0
14.0
51.3
3.08
1
.1
.2
51.5
3.10
18
1.8
4.2
55.7
3.20

163. 27
2.7
6.3
62.0
3.30
15
1.5
3.5
65.5
3.40
10
1.0
2.3
67.8
3.50
44
4.4
10.3
78.1
3.60
12

164. 1.2
2.8
80.9
3.70
15
1.5
3.5
84.4
3.80
22
2.2
5.1
89.5
3.90
13
1.3
3.0
92.5
4.00
17
1.7

165. 4.0
96.5
4.10
2
.2
.5
97.0
4.20
4
.4
.9
97.9
4.30
1
.1
.2
98.1
4.40
3
.3
.7

166. 98.8
4.50
4
.4
.9
99.8
4.60
1
.1
.2
100.0
Total
429
42.9
100.0
Missing
System
570
57.1

167. Total
999
100.0
1. What level of measurement is this?
_________interval__________________________
2. What is your evidence? _____the distribution is numeric with
no true 0_________________________________
3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to GPA, the mean is 3.05(SD+.660), this indicates
the average student has a B average. The minimum of the
distribution is 1.0, which represents a D and the maximum score
of the distribution is 4.60, which represents an A+. Looking at
the valid percent distribution, it shows that majority of the
respondents are between a 2.5 and 3.5 GPA.
SENGAGE Degree of School Engagement

168. Frequency
Percent
Valid Percent
Cumulative Percent
Valid
.00
104
10.4
11.0
11.0
1.00
194
19.4
20.6
31.6
2.00
262
26.2
27.8
59.3
3.00

169. 211
21.1
22.4
81.7
4.00
127
12.7
13.5
95.1
5.00
33
3.3
3.5
98.6
6.00
3
.3
.3
98.9
7.00
10

170. 1.0
1.1
100.0
Total
944
94.5
100.0
Missing
System
55
5.5
Total
999
100.0
Note: The lower the value the more the student is engaged in
school.
1. What level of measurement is this?
_________ratio__________________________

171. 2. What is your evidence? __________the distribution is
numeric with a true 0__________________________
3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to student engagement, the mean is 2.23
(SD=1.41), which indicates on average student engagement is
high. The minimum score on the distribution is 0, which
represents high student engagement. The maximum score on the
distribution is 7, which indicates very low student engagement.
Overall the valid percent distribution shows that majority of the
students in the sample are highly engaged.
GANGEXPO Exposure to Gangs
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
.00

172. 221
22.1
24.2
24.2
1.00
100
10.0
11.0
35.2
2.00
88
8.8
9.6
44.8
3.00
149
14.9
16.3
61.1
4.00
114

173. 11.4
12.5
73.6
5.00
75
7.5
8.2
81.8
6.00
43
4.3
4.7
86.5
7.00
29
2.9
3.2
89.7
8.00
28
2.8

174. 3.1
92.8
9.00
21
2.1
2.3
95.1
10.00
15
1.5
1.6
96.7
11.00
15
1.5
1.6
98.4
12.00
15
1.5
1.6

175. 100.0
Total
913
91.4
100.0
Missing
System
86
8.6
Total
999
100.0
Note: The higher the value the more exposed to gangs.
1. What level of measurement is this?
_________ratio__________________________
2. What is your evidence? __________the distribution is
numeric with a true 0__________________________

176. 3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to gang exposure, the mean is 3.2 (SD=2.97),
which indicates on average student have low exposure to gangs.
The minimum score on the distribution is 0, which represents
low gang exposure. The maximum score on the distribution is
12, which represents high exposure to gangs. Overall the valid
percent distribution shows that majority of the students in the
sample have low exposure to gangs.
COUNTY
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
.00
444
44.4
44.5

177. 44.5
1.00
554
55.5
55.5
100.0
Total
998
99.9
100.0
Missing
System
1
.1
Total
999
100.0
Note: 0= the respondent resides outside of the county and 1=

178. the respondents resides inside the county.
1. What level of measurement is this?
________nominal___________________________
2. What is your evidence? ___________categorical and not
ranked__________________________________
3. Interpret the data using all appropriate information (Hint:
look at your quick reference guide for interpretations to help
you).
As it relates to county, the mode is 1, which represents
individuals who live in the county. Those who live in the county
represent about 56% (n=554) of the sample. Overall majority of
the respondents live in the county with about 45% (n=444) who
live outside the county.
5
Running head: [Shortened Title up to 50 Characters]1
[Shortened Title up to 50 Characters]8Treatment Satisfaction
Student Name
Institution

179. Instructor
Treatment Satisfaction
[The body of your paper uses a half-inch first line indent and is
double-spaced. APA style provides for up to five heading
levels, shown in the paragraphs that follow. Note that the word
Introduction should not be used as an initial heading, as it’s
assumed that your paper begins with an introduction.]Literature
Review
[The first two heading levels get their own paragraph, as shown
here. Headings 3, 4, and 5 are run-in headings used at the
beginning of the paragraph.]Treatment Satisfaction
[To add a table of contents (TOC), apply the appropriate
heading style to just the heading text at the start of a paragraph
and it will show up in your TOC. To do this, select the text for
your heading. Then, on the Home tab, in the Styles gallery,
click the style you need.]
Desire to Help.
[Include a period at the end of a run-in heading. Note that you
can include consecutive paragraphs with their own headings,
where appropriate.]Treatment Readiness
[When using headings, don’t skip levels. If you need a heading
3, 4, or 5 with no text following it before the next heading, just
add a period at the end of the heading and then start a new
paragraph for the subheading and its text.] (Last Name,

180. Year)Treatment Participation
[Like all sections of your paper, references start on their own
page. The references page that follows is created using the
Citations & Bibliography feature, available on the References
tab. This feature includes a style option that formats your
references for APA 6th Edition. You can also use this feature
to add in-text citations that are linked to your source, such as
those shown at the end of this paragraph and the preceding
paragraph. To customize a citation, right-click it and then click
Edit Citation.] (Last Name, Year)
Methodology
Model
Figure 1. Building Relationships
Desire for Help
Treatment Satisfaction (DV)

181. Treatment Participation
Treatment Readiness
Hypotheses
H1: There is an association between Desire for help and
Treatment satisfaction
H2: There is an association between Treatment Participation and
Treatment satisfaction.
H3: There is an association between Treatment Readiness and
Treatment satisfaction.
H4: There is an association between Desire for Help and
Treatment Participation.
H5: There is an association between Treatment readiness and

182. Treatment participation
H6: There is an association between Desire for Help and
Treatment Readiness
Data Management
Data Analysis
Findings
Univariate Findings
Table 1: No table yet
Table 2: No tables yet
Bivariate Findings
Correlation Matrix
Multi Variate Findings
Table 4 t- Test
Study Variables
Gender
t
Males (M/SD)
Females(M/SD)
Treatment Satisfaction
21.83(SD=5.883)
22.41(SD=5.339)

183. -1.976
Desire to Help
22.76(SD=4.377)
25.18(SD=3.707)
-11.387
Treatment Readiness
13.46(SD=4.220)
15.04(SD=3.603)
-7.733
Treatment Participation
48.75(SD=6.977)
50.86(SD=5.659)
-6.257
Table 5: ANOVA
Study variables
Very
unhappy
(M/SD)
Somewhat Unhappy
(M/SD)
Not
Sure
(M/SD)

184. Somewhat Happy
(M/SD)
Very Happy
(M/SD)
F
Treatment Satisfaction
22.04(SD=6.140)
22.32(SD=5.774)
21.94(SD=5.540)
22.43(SD=5.141)
22.06(SD=6.047)
.385
Desire to Help
23.56(SD=4.855)
24.93(SD=3.978)
24.53(SD=4.347)
24.08(SD=3.994)
22.88(SD=4.367)
8.544
Treatment Readiness
13.78(SD=4.526)
14.85(SD3.797)
14.30(SD=3.844)
14.40(SD3.906)
13.74(SD4.139)

185. 2.822
Treatment Participation
49.37 (SD=8.096)
50.06(SD=7.002)
49.23(SD=5.963)
49.59(SD=6.039)
49.91(SD=6.699)
.515
Regression
Table 6 : Regression with Treatment Satisfaction as the
Dependent Variable
Study Variable
Standardized Beta
t
Significance
Desire to Help
-.173
5.892
.000
Treatment Readiness
.614
20.467
.000

186. Treatment Participation
.226
9.198
.000
Discussion
Revised Model
Figure 2. Revised Model of Building Relationships
Desire for Help
Treatment Satisfaction (DV)
Treatment Participation

187. Treatment Readiness
References
Last Name, F. M. (Year). Article Title. Journal Title, Pages
From - To.
Last Name, F. M. (Year). Book Title. City Name: Publisher
Name.
Appendix A
Data Management Output
1. Ran the Frequencies
Frequencies
Statistics
CEST006 TR need to stay in treatment
CEST013 TR solve problems in treatment
CEST014 TR treatment is not helping

188. CEST054 TR treatment gives you hope
CEST056 TR want to be in drug treatment
N
Valid
1567
1567
1564
1565
1562
Missing
22
22
25
24
27
Frequency Table
CEST006 TR need to stay in treatment
Frequency
Percent

189. Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
351
22.1
22.4
22.4
2 Disagree
350
22.0
22.3
44.7
3 Uncertain
198
12.5
12.6
57.4
4 Agree
450
28.3
28.7

190. 86.1
5 Agree Strongly
218
13.7
13.9
100.0
Total
1567
98.6
100.0
Missing
System
22
1.4
Total
1589
100.0

191. CEST013 TR solve problems in treatment
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
121
7.6
7.7
7.7
2 Disagree
121
7.6
7.7
15.4
3 Uncertain
138
8.7
8.8
24.3

192. 4 Agree
646
40.7
41.2
65.5
5 Agree Strongly
541
34.0
34.5
100.0
Total
1567
98.6
100.0
Missing
System
22
1.4
Total

193. 1589
100.0
CEST014 TR treatment is not helping
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
419
26.4
26.8
26.8
2 Disagree
548
34.5
35.0
61.8

194. 3 Uncertain
293
18.4
18.7
80.6
4 Agree
169
10.6
10.8
91.4
5 Agree Strongly
135
8.5
8.6
100.0
Total
1564
98.4
100.0
Missing
System

195. 25
1.6
Total
1589
100.0
CEST054 TR treatment gives you hope
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
95
6.0
6.1
6.1
2 Disagree

196. 96
6.0
6.1
12.2
3 Uncertain
177
11.1
11.3
23.5
4 Agree
601
37.8
38.4
61.9
5 Agree Strongly
596
37.5
38.1
100.0
Total
1565

197. 98.5
100.0
Missing
System
24
1.5
Total
1589
100.0
CEST056 TR want to be in drug treatment
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
238

198. 15.0
15.2
15.2
2 Disagree
220
13.8
14.1
29.3
3 Uncertain
189
11.9
12.1
41.4
4 Agree
549
34.6
35.1
76.6
5 Agree Strongly
366
23.0

199. 23.4
100.0
Total
1562
98.3
100.0
Missing
System
27
1.7
Total
1589
100.0
FREQUENCIES VARIABLES=CEST007 CEST011 CEST020
CEST030 CEST080 CEST115 CEST122
/ORDER=ANALYSIS.

200. Frequencies
Statistics
CEST007 TS time schedules convenient
CEST011 TS program expects responsibility/self-discipline
CEST020 TS program organized/run well
CEST030 TS satisfied with program
CEST080 TS staff efficient at job
CEST115 TS personal counseling at program
CEST122 TS location is convenient
N
Valid
1569
1578
1569
1572
1563
1561
1555
Missing
20
11
20

201. 17
26
28
34
Frequency Table
CEST007 TS time schedules convenient
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
196
12.3
12.5
12.5
2 Disagree
246
15.5

202. 15.7
28.2
3 Uncertain
221
13.9
14.1
42.3
4 Agree
716
45.1
45.6
87.9
5 Agree Strongly
190
12.0
12.1
100.0
Total
1569
98.7
100.0

203. Missing
System
20
1.3
Total
1589
100.0
CEST011 TS program expects responsibility/self-discipline
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
46
2.9
2.9

204. 2.9
2 Disagree
33
2.1
2.1
5.0
3 Uncertain
87
5.5
5.5
10.5
4 Agree
567
35.7
35.9
46.5
5 Agree Strongly
845
53.2
53.5
100.0

205. Total
1578
99.3
100.0
Missing
System
11
.7
Total
1589
100.0
CEST020 TS program organized/run well
Frequency
Percent
Valid Percent
Cumulative Percent

206. Valid
1 Disagree Strongly
333
21.0
21.2
21.2
2 Disagree
362
22.8
23.1
44.3
3 Uncertain
309
19.4
19.7
64.0
4 Agree
437
27.5
27.9
91.8

207. 5 Agree Strongly
128
8.1
8.2
100.0
Total
1569
98.7
100.0
Missing
System
20
1.3
Total
1589
100.0
CEST030 TS satisfied with program

208. Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
262
16.5
16.7
16.7
2 Disagree
254
16.0
16.2
32.8
3 Uncertain
327
20.6
20.8
53.6
4 Agree

209. 553
34.8
35.2
88.8
5 Agree Strongly
176
11.1
11.2
100.0
Total
1572
98.9
100.0
Missing
System
17
1.1
Total
1589
100.0

210. CEST080 TS staff efficient at job
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
191
12.0
12.2
12.2
2 Disagree
297
18.7
19.0
31.2
3 Uncertain
306

211. 19.3
19.6
50.8
4 Agree
610
38.4
39.0
89.8
5 Agree Strongly
159
10.0
10.2
100.0
Total
1563
98.4
100.0
Missing
System
26
1.6

212. Total
1589
100.0
CEST115 TS personal counseling at program
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
264
16.6
16.9
16.9
2 Disagree
502
31.6

213. 32.2
49.1
3 Uncertain
260
16.4
16.7
65.7
4 Agree
413
26.0
26.5
92.2
5 Agree Strongly
122
7.7
7.8
100.0
Total
1561
98.2
100.0

214. Missing
System
28
1.8
Total
1589
100.0
CEST122 TS location is convenient
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
497
31.3
32.0

215. 32.0
2 Disagree
361
22.7
23.2
55.2
3 Uncertain
160
10.1
10.3
65.5
4 Agree
387
24.4
24.9
90.4
5 Agree Strongly
150
9.4
9.6
100.0

216. Total
1555
97.9
100.0
Missing
System
34
2.1
Total
1589
100.0
FREQUENCIES VARIABLES=CEST003 CEST032 CEST039
CEST065 CEST087 CEST116
/ORDER=ANALYSIS.
Frequencies
Statistics

217. CEST003 DH need help with drug use
CEST032 DH urgent help needed
CEST039 DH will give up friends to solve drug problems
CEST065 DH life out of control
CEST087 DM think of ways to solve problems
CEST116 DH want life straightened out
N
Valid
1569
1565
1562
1574
1559
1558
Missing
20
24
27
15
30
31
Frequency Table

218. CEST003 DH need help with drug use
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
161
10.1
10.3
10.3
2 Disagree
149
9.4
9.5
19.8
3 Uncertain
103
6.5
6.6
26.3

219. 4 Agree
582
36.6
37.1
63.4
5 Agree Strongly
574
36.1
36.6
100.0
Total
1569
98.7
100.0
Missing
System
20
1.3
Total
1589

220. 100.0
CEST032 DH urgent help needed
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
178
11.2
11.4
11.4
2 Disagree
271
17.1
17.3
28.7
3 Uncertain

221. 157
9.9
10.0
38.7
4 Agree
571
35.9
36.5
75.2
5 Agree Strongly
388
24.4
24.8
100.0
Total
1565
98.5
100.0
Missing
System
24

222. 1.5
Total
1589
100.0
CEST039 DH will give up friends to solve drug problems
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
71
4.5
4.5
4.5
2 Disagree
59

223. 3.7
3.8
8.3
3 Uncertain
120
7.6
7.7
16.0
4 Agree
460
28.9
29.4
45.5
5 Agree Strongly
852
53.6
54.5
100.0
Total
1562
98.3

224. 100.0
Missing
System
27
1.7
Total
1589
100.0
CEST065 DH life out of control
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
222
14.0

225. 14.1
14.1
2 Disagree
412
25.9
26.2
40.3
3 Uncertain
152
9.6
9.7
49.9
4 Agree
529
33.3
33.6
83.5
5 Agree Strongly
259
16.3
16.5

226. 100.0
Total
1574
99.1
100.0
Missing
System
15
.9
Total
1589
100.0
CEST116 DH want life straightened out
Frequency
Percent
Valid Percent

227. Cumulative Percent
Valid
1 Disagree Strongly
9
.6
.6
.6
2 Disagree
7
.4
.4
1.0
3 Uncertain
38
2.4
2.4
3.5
4 Agree
387
24.4
24.8
28.3

228. 5 Agree Strongly
1117
70.3
71.7
100.0
Total
1558
98.0
100.0
Missing
System
31
2.0
Total
1589
100.0

229. FREQUENCIES VARIABLES=CEST019 CEST026 CEST031
CEST035 CEST037 CEST062 CEST066 CEST067 CEST077
CEST083 CEST104 CEST127
/ORDER=ANALYSIS.
Frequencies
Statistics
CEST019 TP willing to talk about feelings
CEST026 TP made progress with drug/ alcohol problems
CEST031 TP learned to solve problems
CEST035 TP made progress toward goals
CEST037 TP always attend scheduled counseling
CEST062 TP stopped or greatly reduced drug use
CEST066 TP participate in counseling
CEST067 TP progress with feelings and behavior
CEST077 TP improved relationships because of treatment
CEST083 TP progress with emotional/ psychological issues
CEST104 TP provide honest feedback
CEST127 TP following your counselors guidance
N
Valid
1572
1567
1578

230. 1566
1569
1555
1571
1572
1565
1565
1562
1566
Missing
17
22
11
23
20
34
18
17
24
24
27
23

231. Frequency Table
CEST019 TP willing to talk about feelings
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
66
4.2
4.2
4.2
2 Disagree
109
6.9
6.9
11.1

232. 3 Uncertain
164
10.3
10.4
21.6
4 Agree
675
42.5
42.9
64.5
5 Agree Strongly
558
35.1
35.5
100.0
Total
1572
98.9
100.0
Missing
System

233. 17
1.1
Total
1589
100.0
CEST026 TP made progress with drug/ alcohol problems
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
41
2.6
2.6
2.6
2 Disagree

234. 27
1.7
1.7
4.3
3 Uncertain
126
7.9
8.0
12.4
4 Agree
652
41.0
41.6
54.0
5 Agree Strongly
721
45.4
46.0
100.0
Total
1567

235. 98.6
100.0
Missing
System
22
1.4
Total
1589
100.0
CEST031 TP learned to solve problems
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
29

236. 1.8
1.8
1.8
2 Disagree
54
3.4
3.4
5.3
3 Uncertain
143
9.0
9.1
14.3
4 Agree
928
58.4
58.8
73.1
5 Agree Strongly
424
26.7

237. 26.9
100.0
Total
1578
99.3
100.0
Missing
System
11
.7
Total
1589
100.0
CEST035 TP made progress toward goals
Frequency
Percent

238. Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
26
1.6
1.7
1.7
2 Disagree
40
2.5
2.6
4.2
3 Uncertain
106
6.7
6.8
11.0
4 Agree
796
50.1
50.8

239. 61.8
5 Agree Strongly
598
37.6
38.2
100.0
Total
1566
98.6
100.0
Missing
System
23
1.4
Total
1589
100.0

240. CEST037 TP always attend scheduled counseling
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
27
1.7
1.7
1.7
2 Disagree
34
2.1
2.2
3.9
3 Uncertain
37
2.3
2.4
6.2

241. 4 Agree
638
40.2
40.7
46.9
5 Agree Strongly
833
52.4
53.1
100.0
Total
1569
98.7
100.0
Missing
System
20
1.3
Total

242. 1589
100.0
CEST062 TP stopped or greatly reduced drug use
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
56
3.5
3.6
3.6
2 Disagree
28
1.8
1.8
5.4

243. 3 Uncertain
65
4.1
4.2
9.6
4 Agree
307
19.3
19.7
29.3
5 Agree Strongly
1099
69.2
70.7
100.0
Total
1555
97.9
100.0
Missing
System

244. 34
2.1
Total
1589
100.0
CEST066 TP participate in counseling
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
22
1.4
1.4
1.4
2 Disagree

245. 92
5.8
5.9
7.3
3 Uncertain
98
6.2
6.2
13.5
4 Agree
837
52.7
53.3
66.8
5 Agree Strongly
522
32.9
33.2
100.0
Total
1571

246. 98.9
100.0
Missing
System
18
1.1
Total
1589
100.0
CEST067 TP progress with feelings and behavior
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
23

247. 1.4
1.5
1.5
2 Disagree
23
1.4
1.5
2.9
3 Uncertain
88
5.5
5.6
8.5
4 Agree
831
52.3
52.9
61.4
5 Agree Strongly
607
38.2

248. 38.6
100.0
Total
1572
98.9
100.0
Missing
System
17
1.1
Total
1589
100.0
CEST077 TP improved relationships because of treatment
Frequency
Percent

249. Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
94
5.9
6.0
6.0
2 Disagree
161
10.1
10.3
16.3
3 Uncertain
234
14.7
15.0
31.2
4 Agree
679
42.7
43.4

250. 74.6
5 Agree Strongly
397
25.0
25.4
100.0
Total
1565
98.5
100.0
Missing
System
24
1.5
Total
1589
100.0

251. CEST083 TP progress with emotional/ psychological issues
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
34
2.1
2.2
2.2
2 Disagree
70
4.4
4.5
6.6
3 Uncertain
172
10.8
11.0
17.6

252. 4 Agree
808
50.8
51.6
69.3
5 Agree Strongly
481
30.3
30.7
100.0
Total
1565
98.5
100.0
Missing
System
24
1.5
Total

253. 1589
100.0
CEST104 TP provide honest feedback
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
18
1.1
1.2
1.2
2 Disagree
52
3.3
3.3
4.5

254. 3 Uncertain
109
6.9
7.0
11.5
4 Agree
847
53.3
54.2
65.7
5 Agree Strongly
536
33.7
34.3
100.0
Total
1562
98.3
100.0
Missing
System

255. 27
1.7
Total
1589
100.0
CEST127 TP following your counselors guidance
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1 Disagree Strongly
57
3.6
3.6
3.6
2 Disagree

256. 96
6.0
6.1
9.8
3 Uncertain
228
14.3
14.6
24.3
4 Agree
863
54.3
55.1
79.4
5 Agree Strongly
322
20.3
20.6
100.0
Total
1566

257. 98.6
100.0
Missing
System
23
1.4
Total
1589
100.0
3. Computations of Variables
Frequencies
Statistics

258. TR_C
N
Valid
1513
Missing
76
TR_C
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
5
3
.2
.2
.2
6
1
.1

259. .1
.3
7
6
.4
.4
.7
8
10
.6
.7
1.3
9
51
3.2
3.4
4.7
10
30
1.9
2.0

260. 6.7
11
37
2.3
2.4
9.1
12
65
4.1
4.3
13.4
13
94
5.9
6.2
19.6
14
109
6.9
7.2
26.8

261. 15
110
6.9
7.3
34.1
16
164
10.3
10.8
44.9
17
156
9.8
10.3
55.3
18
184
11.6
12.2
67.4

262. 19
174
11.0
11.5
78.9
20
158
9.9
10.4
89.4
21
123
7.7
8.1
97.5
22
20
1.3
1.3
98.8
23

263. 9
.6
.6
99.4
24
4
.3
.3
99.7
25
5
.3
.3
100.0
Total
1513
95.2
100.0
Missing
System
76

264. 4.8
Total
1589
100.0
Frequencies
Statistics
TS_c
N
Valid
1502
Missing
87
TS_c
Frequency
Percent
Valid Percent

265. Cumulative Percent
Valid
7
7
.4
.5
.5
8
1
.1
.1
.5
9
15
.9
1.0
1.5
10
20
1.3
1.3
2.9

266. 11
25
1.6
1.7
4.5
12
22
1.4
1.5
6.0
13
32
2.0
2.1
8.1
14
44
2.8
2.9
11.1

267. 15
50
3.1
3.3
14.4
16
39
2.5
2.6
17.0
17
53
3.3
3.5
20.5
18
84
5.3
5.6
26.1
19

268. 76
4.8
5.1
31.2
20
90
5.7
6.0
37.2
21
91
5.7
6.1
43.2
22
88
5.5
5.9
49.1
23
119

269. 7.5
7.9
57.0
24
120
7.6
8.0
65.0
25
85
5.3
5.7
70.6
26
88
5.5
5.9
76.5
27
82
5.2

270. 5.5
82.0
28
88
5.5
5.9
87.8
29
69
4.3
4.6
92.4
30
38
2.4
2.5
94.9
31
23
1.4
1.5

271. 96.5
32
20
1.3
1.3
97.8
33
12
.8
.8
98.6
34
9
.6
.6
99.2
35
12
.8
.8
100.0

272. Total
1502
94.5
100.0
Missing
System
87
5.5
Total
1589
100.0
FREQUENCIES VARIABLES=DH_C
/ORDER=ANALYSIS.
Frequencies
Statistics
DH_C

273. N
Valid
1509
Missing
80
DH_C
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
6
2
.1
.1
.1
8
1
.1
.1

274. .2
9
3
.2
.2
.4
10
6
.4
.4
.8
11
4
.3
.3
1.1
12
7
.4
.5
1.5

275. 13
6
.4
.4
1.9
14
13
.8
.9
2.8
15
17
1.1
1.1
3.9
16
40
2.5
2.7
6.6

276. 17
35
2.2
2.3
8.9
18
53
3.3
3.5
12.4
19
52
3.3
3.4
15.8
20
74
4.7
4.9
20.7
21

277. 83
5.2
5.5
26.2
22
91
5.7
6.0
32.3
23
121
7.6
8.0
40.3
24
147
9.3
9.7
50.0
25
148

278. 9.3
9.8
59.8
26
160
10.1
10.6
70.4
27
137
8.6
9.1
79.5
28
113
7.1
7.5
87.0
29
103
6.5

279. 6.8
93.8
30
93
5.9
6.2
100.0
Total
1509
95.0
100.0
Missing
System
80
5.0
Total
1589
100.0

280. FREQUENCIES VARIABLES=TP_C
/ORDER=ANALYSIS.
Frequencies
Statistics
TP_C
N
Valid
1487
Missing
102
TP_C
Frequency

281. Percent
Valid Percent
Cumulative Percent
Valid
12
2
.1
.1
.1
13
1
.1
.1
.2
19
1
.1
.1
.3
22
1
.1

282. .1
.3
24
1
.1
.1
.4
25
2
.1
.1
.5
28
3
.2
.2
.7
29
2
.1
.1

283. .9
30
5
.3
.3
1.2
31
3
.2
.2
1.4
32
2
.1
.1
1.5
33
6
.4
.4
2.0

284. 34
9
.6
.6
2.6
35
6
.4
.4
3.0
36
12
.8
.8
3.8
37
14
.9
.9
4.7

285. 38
19
1.2
1.3
6.0
39
8
.5
.5
6.5
40
15
.9
1.0
7.5
41
20
1.3
1.3
8.9
42

286. 29
1.8
2.0
10.8
43
40
2.5
2.7
13.5
44
55
3.5
3.7
17.2
45
49
3.1
3.3
20.5
46
70

287. 4.4
4.7
25.2
47
92
5.8
6.2
31.4
48
124
7.8
8.3
39.7
49
118
7.4
7.9
47.7
50
96
6.0

288. 6.5
54.1
51
97
6.1
6.5
60.7
52
92
5.8
6.2
66.8
53
76
4.8
5.1
72.0
54
58
3.7
3.9

289. 75.9
55
63
4.0
4.2
80.1
56
63
4.0
4.2
84.3
57
66
4.2
4.4
88.8
58
62
3.9
4.2
92.9

290. 59
48
3.0
3.2
96.2
60
57
3.6
3.8
100.0
Total
1487
93.6
100.0
Missing
System
102
6.4
Total

291. 1589
100.0
FACTOR
2.Factor Analysis
Communalities
Initial
Extraction
CEST006 TR need to stay in treatment
1.000
.468
CEST013 TR solve problems in treatment
1.000
.676
CEST014 TR treatment is not helping
1.000
.583
CEST054 TR treatment gives you hope

292. 1.000
.672
CEST056 TR want to be in drug treatment
1.000
.625
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.025
60.495
60.495
3.025
60.495

293. 60.495
2
.812
16.237
76.732
3
.431
8.627
85.359
4
.390
7.791
93.150
5
.342
6.850

294. 100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
CEST006 TR need to stay in treatment
.684
CEST013 TR solve problems in treatment
.822
CEST014 TR treatment is not helping
-.764
CEST054 TR treatment gives you hope
.820
CEST056 TR want to be in drug treatment
.791
Extraction Method: Principal Component Analysis.
a. 1 components extracted.

295. Rotated Component Matrixa
a. Only one component was extracted. The solution cannot be
rotated.
Alter Type
Altered Types
Interview Date
A33
AMIN
Date started current treatment program
A33
AMIN
DATASET NAME DataSet1 WINDOW=FRONT.
Factor Analysis
Notes
Output Created
16-JUL-2019 10:51:50

296. Comments
Input
Data
C:UserspgibsonAppDataLocalPackagesMicrosoft.Microsoft
Edge_8wekyb3d8bbweTempStateDownloadsCathys data.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
1589
Missing Value Handling
Definition of Missing
MISSING=EXCLUDE: User-defined missing values are treated
as missing.

297. Cases Used
LISTWISE: Statistics are based on cases with no missing values
for any variable used.
Syntax
FACTOR
/VARIABLES CEST006 CEST013 CEST014 CEST054
CEST056
/MISSING LISTWISE
/ANALYSIS CEST006 CEST013 CEST014 CEST054
CEST056
/PRINT INITIAL EXTRACTION ROTATION
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/METHOD=CORRELATION.
Resources
Processor Time
00:00:00.02
Elapsed Time
00:00:00.02
Maximum Memory Required

298. 4248 (4.148K) bytes
[DataSet1]
C:UserspgibsonAppDataLocalPackagesMicrosoft.Microsoft
Edge_8wekyb3d8bbweTempStateDownloadsCathys data.sav
Communalities
Initial
Extraction
CEST006 TR need to stay in treatment
1.000
.468
CEST013 TR solve problems in treatment
1.000
.676
CEST014 TR treatment is not helping
1.000
.583
CEST054 TR treatment gives you hope
1.000
.672

299. CEST056 TR want to be in drug treatment
1.000
.625
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.025
60.495
60.495
3.025
60.495
60.495
2

300. .812
16.237
76.732
3
.431
8.627
85.359
4
.390
7.791
93.150
5
.342
6.850
100.000

301. Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
CEST006 TR need to stay in treatment
.684
CEST013 TR solve problems in treatment
.822
CEST014 TR treatment is not helping
-.764
CEST054 TR treatment gives you hope
.820
CEST056 TR want to be in drug treatment
.791
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
Rotated Component Matrixa

302. a. Only one component was extracted. The solution cannot be
rotated.
Factor Analysis
Communalities
Initial
Extraction
CEST007 TS time schedules convenient
1.000
.366
CEST011 TS program expects responsibility/self-discipline
1.000
.318
CEST020 TS program organized/run well
1.000
.696
CEST030 TS satisfied with program
1.000
.711
CEST080 TS staff efficient at job
1.000
.621

303. CEST115 TS personal counseling at program
1.000
.469
CEST122 TS location is convenient
1.000
.089
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
3.271
46.729
46.729
3.271

304. 46.729
46.729
2
.993
14.187
60.915
3
.758
10.827
71.742
4
.713
10.187
81.929
5
.562

305. 8.031
89.960
6
.425
6.068
96.028
7
.278
3.972
100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component

306. 1
CEST007 TS time schedules convenient
.605
CEST011 TS program expects responsibility/self-discipline
.564
CEST020 TS program organized/run well
.834
CEST030 TS satisfied with program
.843
CEST080 TS staff efficient at job
.788
CEST115 TS personal counseling at program
.685
CEST122 TS location is convenient
.298
Extraction Method: Principal Component Analysis.
a. 1 components extracted.
Rotated Component Matrixa
a. Only one component was extracted. The solution cannot be
rotated.

307. Factor Analysis
Communalities
Initial
Extraction
CEST003 DH need help with drug use
1.000
.673
CEST032 DH urgent help needed
1.000
.636
CEST039 DH will give up friends to solve drug problems
1.000
.559
CEST065 DH life out of control
1.000
.566
CEST086 DH tired of problems caused by drugs
1.000
.628
CEST116 DH want life straightened out
1.000
.656
Extraction Method: Principal Component Analysis.

308. Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
2.610
43.501
43.501
2.610
43.501
43.501
1.926
32.097

309. 32.097
2
1.108
18.459
61.960
1.108
18.459
61.960
1.792
29.862
61.960
3
.740
12.337
74.296
4
.584
9.734
84.031

310. 5
.536
8.928
92.958
6
.422
7.042
100.000

311. Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
2
CEST003 DH need help with drug use
.736
.363
CEST032 DH urgent help needed
.719
.346
CEST039 DH will give up friends to solve drug problems
.684
-.302
CEST065 DH life out of control
.470
.587
CEST086 DH tired of problems caused by drugs
.700
-.371

312. CEST116 DH want life straightened out
.611
-.532
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
Rotated Component Matrixa
Component
1
2
CEST003 DH need help with drug use
.298
.764
CEST032 DH urgent help needed
.297
.740
CEST039 DH will give up friends to solve drug problems
.709
.238
CEST065 DH life out of control
-.049
.750

313. CEST086 DH tired of problems caused by drugs
.767
.199
CEST116 DH want life straightened out
.810
.020
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
Component Transformation Matrix
Component
1
2
1
.738
.675
2
-.675
.738
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Factor Analysis

314. Communalities
Initial
Extraction
CEST019 TP willing to talk about feelings
1.000
.415
CEST026 TP made progress with drug/ alcohol problems
1.000
.459
CEST031 TP learned to solve problems
1.000
.500
CEST035 TP made progress toward goals
1.000
.566
CEST037 TP always attend scheduled counseling
1.000
.538
CEST062 TP stopped or greatly reduced drug use
1.000
.245
CEST066 TP participate in counseling
1.000
.572

315. CEST067 TP progress with feelings and behavior
1.000
.576
CEST077 TP improved relationships because of treatment
1.000
.435
CEST083 TP progress with emotional/ psychological issues
1.000
.496
CEST104 TP provide honest feedback
1.000
.514
CEST127 TP following your counselors guidance
1.000
.366
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total

316. % of Variance
Cumulative %
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
4.678
38.980
38.980
4.678
38.980
38.980
3.375
28.126
28.126
2
1.004
8.370
47.350
1.004
8.370
47.350

317. 2.307
19.224
47.350
3
.977
8.138
55.488
4
.803
6.688
62.175
5
.695

318. 5.796
67.971
6
.680
5.664
73.635
7
.627
5.228
78.862

319. 8
.580
4.829
83.692
9
.540
4.503
88.195
10
.526

320. 4.384
92.580
11
.476
3.970
96.550
12
.414
3.450
100.000

321. Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
2
CEST019 TP willing to talk about feelings
.644
-.017
CEST026 TP made progress with drug/ alcohol problems
.646
-.203
CEST031 TP learned to solve problems
.631
-.318
CEST035 TP made progress toward goals
.736
-.154
CEST037 TP always attend scheduled counseling

322. .494
.543
CEST062 TP stopped or greatly reduced drug use
.395
.298
CEST066 TP participate in counseling
.639
.404
CEST067 TP progress with feelings and behavior
.752
-.104
CEST077 TP improved relationships because of treatment
.590
-.294
CEST083 TP progress with emotional/ psychological issues
.645
-.282
CEST104 TP provide honest feedback
.635
.333
CEST127 TP following your counselors guidance
.603
.057
Extraction Method: Principal Component Analysis.
a. 2 components extracted.

323. Rotated Component Matrixa
Component
1
2
CEST019 TP willing to talk about feelings
.528
.370
CEST026 TP made progress with drug/ alcohol problems
.640
.222
CEST031 TP learned to solve problems
.696
.121
CEST035 TP made progress toward goals
.683
.315
CEST037 TP always attend scheduled counseling
.073
.730
CEST062 TP stopped or greatly reduced drug use
.140