1RUNNING HEAD PROBLEM IDENTIFICATIONJohnston-Taylor.docx

1
RUNNING HEAD: PROBLEM IDENTIFICATION Johnston-
Taylor
Problem Identification and Model Planning
Capella University
HMSV5316: Effective Use of Analytics Human Services

In this paper, this writer, along with this writer’s project group,
has identify a specific issue to focus on for our project and has
plan how to use the data to examine it. We choose a problem
from the scenario of the Homeless Teen Program run by the
Riverbend City Community Action Center (CAC) and imply the
identify problem to the linear regression model. We have
decided to focus on the question/problem #6: “Is there a
relationship between participation in individual mental health
treatment and family tension?” (Riverbend City, 2020). It is
important to learn more about teen mental health and family
tension because mental health is important at every stage of life,
from childhood to old age. But mental health treatment in young
adult is extremely important and it can be examined as a very
sensitive subject. Edidin et. al (2012) stated, youth
homelessness is a growing concern in the United States. Despite
the difficulty studying this particular population due to the
inconsistent definitions of what it means to be homeless and a
youth, the current body of research indicates disruptive family
relationships, family breakdown, and abuse are all common
contributing factors to youth homelessness.
According to EMC Educational Services (2015) stated data
analytics lifestyle is the process used for incorporating data. It

is also organized process that provides arrangement to the
whole process of data analytics, which starts before the actual
data is analyzed and connected. The data analytics lifestyle
assist individuals to ensure there is an identified reason for
collecting data, which data is available, and muse about the
model using the data before collecting and analyzing the data.
The lifecycle has six phases, and the project work can occur in
several phases at once. The six phases are discovery, data
preparation, model planning, model building, communicate
results, and operationalize. Phase three of the data analytics is
model planning, where the team has to determine the methods,
techniques, and the workflow that tends to follow the
subsequent model building phase (EMC Education Services,
2015). The best model chosen imply the identify problem is the
linear regression model.
The linear regression model assumes that there is an immediate
relationship between the outcome and input variables. As a
group, we imply that an individual’s homelessness is can be
expressed by two variables, which are family tension and mental
health. Mental health and family tension is the input variables
while homelessness is the outcome variable. We are focusing on
the possible issue between family tension and mental health
treatment, and analyze the data provided from the Homeless
Teen Program scenario. This model is appropriate for this
specific problem due to it is trying to tie in what is the
possibility causing homelessness and if the need for family
tension and mental health services can be worked on to change
the outcome of homelessness specially teens.
The identification of data needed is both quantitative and
qualitative data. Both data are used for research and statistical
analysis. Although, they have different approaches, they can
both be used for the same thing. In order to collect the
appropriate information for quantitative data, I will use possible
data from the needs in the community. How many community
members are currently receiving mental health services, family
services, and how many community members are on a

community service waiting list for those services. By collecting
data this way it can determine the demand of services
community members are currently lacking. On the other hand,
to collect data using the qualitative data, I can pass out surveys
the program participants to gather data on their opinions on
whether they had family issues linked into their mental health
services what they believed their outcome were.
In the Homeless Teen Program scenario, a case manager name
Heather stated, they gather enough basic information but gather
specific background information that digs deeper into each
participant family situation. The data can provide a better
understanding of what the home was like for the teenagers
(Riverbend City, 2020). Heather’s method of collecting data is
another way of collecting qualitative data. However, once all of
the data is gathered, as a group we can determine how many
participants utilizes mental health service if the reason why
teens are utilizing mental health services due to family issues.
Or, if there a mental health diagnosis that is hereditary.

References:
Edidin, J.P., Ganim, Z., Hunter, S.J. et al. The Mental and
Physical Health of Homeless Youth:
A Literature Review. Child Psychiatry Hum Dev43, 354–375
(2012).
https://doi.org/10.1007/s10578-011-0270-1
EMC Education Services (Eds.). (2015). Data science and big
data analytics: Discovering,
analyzing, visualizing and presenting data. Indianapolis, IN:
Wiley.
Riverbend City: Data Analytics Internship Introduction (2020).
https//:medic.capella.edu/CourseMedia/HMSV5316.

Rubic_Print_FormatCourse CodeClass CodeAssignment
TitleTotal PointsSOC-505SOC-505-O500Benchmark -
Theoretical Perspectives on the
Family90.0CriteriaPercentageUnsatisfactory (0.00%)Less than
Satisfactory (74.00%)Satisfactory (79.00%)Good
(87.00%)Excellent (100.00%)CommentsPoints
EarnedContent70.0%Select an issue that has a major effect on
families, such as divorce, for example. In an essay of 1,000-
1,250 words, explain the cause and effect of the issue from each
of the following perspectives, 1. Functionalism (C
4.2)25.0%Analysis of the cause and effect on an issue affecting
families from the functionalist perspective is absent.Essay
contains a weak analysis of the cause and effect on an issue
affecting families from the functionalist perspective. The
approach is not based on sound reasoning or is poorly
described. Demonstrates poor understanding of topic.Essay
contains an analysis of the cause and effect on an issue
affecting families from the functionalist perspective with some
level of depth; explanation is somewhat limited but approach is
based on sound reasoning. Demonstrates a minimal
understanding of the topicEssay contains an analysis of the
cause and effect on an issue affecting families from the
functionalist perspective with accurate details; description
shows sound analysis of a clear and valid approach.Essay
contains an analysis of the cause and effect on an issue
affecting families from the functionalist perspective with
quality details; description is comprehensive and insightful,
showing an exceptional approach.2. conflict15.0%Analysis of
the cause and effect on an issue affecting families from the
conflict perspective is absent.Essay contains a weak analysis of
the cause and effect on an issue affecting families from the
conflict perspective. The approach is not based on sound
reasoning or is poorly described. Demonstrates poor
understanding of topic.Essay contains an analysis of the cause

and effect on an issue affecting families from the conflict
perspective with some level of depth; explanation is somewhat
limited but approach is based on sound reasoning. Demonstrates
a minimal understanding of the topicEssay contains an analysis
of the cause and effect on an issue affecting families from the
conflict perspective with accurate details; description shows
sound analysis of a clear and valid approach.Essay contains an
analysis of the cause and effect on an issue affecting families
from the conflict perspective with quality details; description is
comprehensive and insightful, showing an exceptional
approach.3. Symbolic-interactionism15.0%Analysis of the cause
and effect on an issue affecting families from the symbolic-
interactionism perspective is absent.Essay contains a weak
from the symbolic-interactionism perspective. The approach is
not based on sound reasoning or is poorly described.
Demonstrates poor understanding of topic.Essay contains an
from the symbolic-interactionism perspective with some level of
depth; explanation is somewhat limited but approach is based on
sound reasoning. Demonstrates a minimal understanding of the
topicEssay contains an analysis of the cause and effect on an
issue affecting families from the symbolic-interactionism
perspective with accurate details; description shows sound
analysis of a clear and valid approach.Essay contains an
from the symbolic-interactionism perspective with quality
details; description is comprehensive and insightful, showing an
exceptional approach.Utilize the GCU Library to locate at least
three relevant, scholarly sources in support of the
content.15.0%No outside sources are cited.Some sources may be
cited but they are not scholarly and/or relevant.At least three
relevant, scholarly sources are cited in a loosely connected,
vague way.At least three relevant, scholarly sources are cited in
a well-connected way and elaborated on.At least three relevant,
scholarly sources are cited and are flawlessly integrated into the

essay to support the claims made therein.Organization and
Effectiveness20.0%Paragraph Development and
Transitions10.0%Paragraphs and transitions consistently lack
unity and coherence. No apparent connections between
paragraphs are established. Transitions are inappropriate to
purpose and scope. Organization is disjointed.Some paragraphs
and transitions may lack logical progression of ideas, unity,
coherence, and/or cohesiveness. Some degree of organization is
evident.Paragraphs are generally competent, but ideas may show
some inconsistency in organization and/or in their relationships
to each other.A logical progression of ideas between paragraphs
is apparent. Paragraphs exhibit a unity, coherence, and
cohesiveness. Topic sentences and concluding remarks are
appropriate to purpose.There is a sophisticated construction of
paragraphs and transitions. Ideas progress and relate to each
other. Paragraph and transition construction guide the reader.
Paragraph structure is seamless.Mechanics of Writing (includes
spelling, punctuation, grammar, language use)10.0%Surface
errors are pervasive enough that they impede communication of
meaning. Inappropriate word choice and/or sentence
construction are used.Frequent and repetitive mechanical errors
distract the reader. Inconsistencies in language choice (register)
and/or word choice are present. Sentence structure is correct but
not varied.Some mechanical errors or typos are present, but are
not overly distracting to the reader. Correct and varied sentence
structure and audience-appropriate language are employed.Prose
is largely free of mechanical errors, although a few may be
present. The writer uses a variety of effective sentence
structures and figures of speech.Writer is clearly in command of
standard, written, academic English.Format10.0%Paper Format
(use of appropriate style for the major and
assignment)5.0%Template is not used appropriately, or
documentation format is rarely followed correctly.Appropriate
template is used, but some elements are missing or mistaken. A
lack of control with formatting is apparent.Appropriate template
is used. Formatting is correct, although some minor errors may

be present.Appropriate template is fully used. There are
virtually no errors in formatting style.All format elements are
correct.Documentation of Sources (citations, footnotes,
references, bibliography, etc., as appropriate to assignment and
style)5.0%Sources are not documented.Documentation of
sources is inconsistent and/or incorrect, as appropriate to
assignment and style, with numerous formatting errors.Sources
are documented, as appropriate to assignment and style,
although some formatting errors may be present.Sources are
documented, as appropriate to assignment and style, and format
is mostly correct.Sources are completely and correctly
documented, as appropriate to assignment and style, and format
is free of error.Total Weightage100%
Sheet1Pre-MHPost-
MH863888849156796186710.0000000229885674398187793294
81897983849184828286699472927996957936947191839678846
181498781693779297634514781567659928696799389
5/30/2020 Riverbend City: Conducting Data Analysis
https://media.capella.edu/CourseMedia/HMSV5316/conducting-
data-analysis/transcript.asp 1/17
Riverbend City ® Activity
Conducting Data Analysis
Introduction
Mentor Talk
Check Your Email
Conclusion
Introduction

Welcome back to your virtual internship at the Riverbend
Community Action Center! So far, you have been introduced to
your overarching project of using data analytics to help RCAC
evaluate the effectiveness of their Ruby Lake Teen
Homelessness
Task Force and figured out how to store the data using the
appropriate data models. Now it’s time to learn how to actually
analyze the data that’s been stored.
It's time for another meeting with your mentor, Brenda.
Mentor Talk
Riverbend City Community Action
Center: Mentor's Office
Check in with with your mentor, Brenda.
All right, welcome back! I hope you’re finding our talks useful.
Today’s
session definitely should be… we’re going to be talking nuts
and bolts
here.
At this point, we’ve got the data stored in an organized way so
that we
can find what we need. Now it’s time to do some actual data
analysis
using statistics. The results of the statistical analysis are what
we need to

understand what’s happening, based on the data, and make some
recommendations.
Today we’re going to walk through how to do one statistical
analysis, an
Independent Samples T-Test, using MicroSoft Excel. You can
do these
types of analyses with programs like SPSS and SAS, but since
we already
invested in buying the MS Office suite, and Excel can run these
calculations too, we just use Excel. Using Excel takes some
practice, but
keep at it! It’s not that complicated once you get used to the
program.
You know one of the main reasons teens can end up homeless is
because
they’re having a lot of problems with their parents. So when the
teens first
come to us for services, one of the measures we give them in
their intake
packet is a Parental Rejection Scale. The final score on the
scale is a
number that can range from 0 (no sense of rejection from
parents) to 100
(total rejection by parents). This gives us a sense of how much
of an issue
this may be for the teenager.
I’m sure you also know that there’s research out there that says
LGBT
teens are more likely to run away from home—or get kicked out
of their
homes—because they feel rejected by their parents. We want to
see if
that’s the case here at the RCAC or not. So we randomly

selected the
Parental Rejection Scale scores of 30 LGBT teens and 30
heterosexual
teens who’ve come for services over the past year.
Remember, an Independent Samples T-Test compares the means
of two
different groups. Here, we have two independent groups: one
group of
LGBT teens and one group of heterosexual teens. We have the
score for
each group. Once we tell Excel to run the t-test, it will do all
the rest for
us.
You can see that there are two columns here: one labeled LGBT
and one
labeled Heterosexual. In each column you can see there are lists
of
numbers. Those numbers are each teen’s score on the Parental
Rejection
Scale. So the first LGBT teen provided a score of 79; 79 out of
100
suggests this teen felt pretty rejected by their parent. For the
first
Heterosexual teen, that person reported a score of 76; 76 out of
100
suggests that teen also felt pretty rejected by their parent.
Looking down the columns of scores, they don’t look all that
different, do
they? Well, this is why we do statistical analyses: we don’t
know if there’s
a statistically significant difference until we do the analysis!
We can’t just
trust our first impressions.

Now, we’re going to start telling Excel to run an Independent
Samples T-
Test. Remember that you don’t have to calculate the means of
the group
yourself: Excel will do all of that. All you have to give Excel is
the raw data
(in this case, the scores on the test) in the correct columns.
Here’s something to remember: if you tried opening up your
Excel and
don’t have the Data Analysis options available, then you need to
install
the Analysis ToolPak. This is available as a free add-in in
Excel: I’ll give
you some instructions later on how to do that.
Now, let’s run this t-test!
Go up to the top menu and click on “Data.”
Look to the left under the Data menu and click on Data
Analysis.
You’ll see that we have two options for an Independent Samples
T-Test:
t-test assuming equal variances, and
t-test assuming unequal variances.

We have to know up front if the variances in our two groups
(LGBT teens
and heterosexual teens) are the same or different. In other
words, is the
range in scores (in other words, the variance) for the LGBT
teens similar to
the range in scores of the heterosexual teens or not?
Luckily, Excel will calculate that for us, too!
If you scroll up through the list of data analyses, there’s an
option for “F-
test Two Sample for Variances.” Select that option and click
OK.
A new box comes up, asking you for input. Variable 1 range is
going to
be the scores for the LGBT teens. You don’t have to input each
and every
value for all thirty teens! Instead, just highlight the first value
in the LGBT
column (row #2), hold the Shift key, and scroll down to the
final value in
the same column (row #31).
Make sure you DO NOT highlight the first row, with the LGBT
in it: that’s
not a numerical value!
The column should now be highlighted. Now MS Excel inputs
those
values. You should see the Variable 1 box now has values in it.
Go to the Variable 2 box and input the values from the
Heterosexual

column, just like you did for the LGBT teens.
Then click OK.
On the Excel spreadsheet (it might go to a different sheet or put
the
values in the original sheet, depending on your version of
Excel), you’ll
see the results of the F-Test. If the results come up and the
columns go
missing, look at the bottom of the Excel sheet and see if you’re
on Sheet
1 (where the scores are) or on Sheet 2. If you’re on Sheet 2,
your scores
are on Sheet 1.
Look at the row with the “P(F<=f).” If the value for that is less
than .05,
then we assume that variances are NOT equal. This means we
would run
an Independent Samples T-Test assuming unequal variances.
However, our p = 0.49. This is more than p = .05, so we can
assume our
variances are equal.
We will run the Independent Samples T-Test assuming equal
variances.

Now that we have that settled, let’s run the t-test.
Go back to the sheet with our LGBT and Heterosexual columns
(look at
the sheets at the bottom of the screen and go back to Sheet 1).
Select Data from the menu.
Select Data Analysis.
Select Independent Samples T-Test assuming equal variances.
Just like you did for the F-test, highlight the LGBT column
values for
Variable 1 Range.
Do the same thing for Variable 2 with the Heterosexual column.
Click OK.
OK, then! Let’s take a look at the results.
The mean tells you the average of each group. For LGBT teens,
the mean
parental rejection score was 83.37. For heterosexual teens, the
mean
parental rejection score was 79.53. This means that both groups
felt
rejected by their parents, but the LGBT teens felt more rejected
compared to their heterosexual counterparts.
The t-value is 2.47. What does that mean? What you need to
know is
whether or not that t-value is statistically significant (i.e., is the
difference

between the means of the two groups statistically significant?).
This is where significance testing is key. That is determined by
the p-
value; this is significance testing. Look at the P (two-tailed)
value. Typically
values below .05 are typically considered significant (.10 and
.01 are other
common significant levels). In this case, the p-value is 0.02,
which is
below .05, so the difference between the two groups is
statistically
significant.
OK. I hope that made sense. I know it was a lot of steps to
absorb, but I
promise, this gets easy the more you do it. And it’s a very, very
powerful
tool to have in your toolbox.
Data for Analysis
LGBT Heterosexual
79 76
83 72
91 77

88 71
84 68
72 81
81 93
72 77
80 71
84 82
93 74
84 79
87 82
90 86
82 81
85 83
77 74
86 81
80 91
84 79
79 80

73 78
95 82
86 78
91 84
82 81
LGBT Heterosexual
78 77
84 89
92 87
79 72
Check Your Email
You have an email from your CAC
Mentor, Brenda.
Subject: Content Analysis
From: Brenda Campbell, CAC Mentor

I hope the t-test discussion made sense! There’s another type of
analysis
that I wanted to run by you… My meeting schedule’s too crazy
for us to
talk about it face to face, but I tried to put it together as a step-
by-step
guide.
These days, people are very concerned with statistical analysis,
like the t-
test, because the data is considered more objective. And data
can tell us
a lot about what is happening generally for a large group of
people, so
there’s no question it’s useful. We know from our t-test results
that LGBT
teens perceive significantly higher levels of parental rejection
than
heterosexual teens, but we also know that both groups of teens
perceive
high levels of parental rejection. That’s general information that
tells us
this is a problem.
But when you’re designing programs, you need to know more
about the
details of people’s experiences. We can’t get that with
statistical analyses.
We can get that with content analyses. “Content analyses”
simply refers
to the analysis of data that is not numerical. Qualitative
researchers at the
doctoral level do detailed content analyses: we’re not going to
do
anything as detailed as what they do.

In addition to giving measures for the teens to complete, we had
a focus
group of teens where we asked them specifically about their
experiences
with their parents.
Let’s look at part of a transcript of a focus group of teen clients
who were
involved in sex trafficking. The two questions we have here
asked them
about how they got along with their parents and how they ended
up
leaving home. (You can download the transcript if you’d like.)
Now, what
most people do with this type of data is simply summarize it.
That’s not
making good use of this data. We want to analyze it more
systematically,
which is what a content analysis is.
We’re going to do the content analysis in five basic steps:
1. First, we’re going to identify key words and phrases that
seem
important.
2. Second, we’ll just make a list of the highlighted phrases. At
this
point, we’re moving away from summary to actually treating

what
was said as data.
3. The third step is to consider which phrases seem to mean the
same
thing: refer to the same concept.
4. The fourth step is identifying themes. “Themes” is another
word for
“concept”: we say the concepts are themes.
5. Finally, let’s analyze those themes and see what deeper
information
we can extract.
OK! Let’s walk through this: Qualitative Analysis of Focus
Group
The purpose of a content analysis is to go beyond simply
summarizing
what the respondents said. The point of a content analysis is to
find
deeper meaning through a systematic analysis: that deeper
meaning is
typically discussed in terms of identifying themes. Here are the
steps to a
basic content analysis.
STEP ONE:
Identify key words/phrases that seem
important.
Some words are highlighted.
Transcript of Focus Group of Teen Clients Involved in
Sex Trafficking

FIRST QUESTION: How do you get along with your parents?
Heterosexual female (age
15):
My mother and me- we nearly
kill each other if we’re around
each other for too long. Like,
she’s always gettin’ on me
about somethin’. ‘Do your
homework. Why you failin’
everything? You need to get a
job to pay for all that s--- you
want.’ (Shrugs) So I got a job-
on the streets. Turnin’ tricks
pays a lot better than frying
fries.
Transgender female (age 16):
My mother don’t know what to
say to me. My dad- when I see
him. (Shakes her head) He says
I’m confused. My mom says
I’m just messed up. It don’t
matter what I do- I make good
grades, don’t get into trouble,
follow my mom’s rules, but- as

long as I like girls, they aren’t
gonna be happy. It got to the
point- I couldn’t stay there; it
was just too tense.
Lesbian female teen (age 16):
Same here: my mom don’t
know what to do with me. My
dad- he’s just- he’s disgusted.
When he found that skirt in my
room, he lost his s---, physically
pushed me down the stairs
and out the door. I fell out the
door on my back ‘cause I
didn’t want to hit him- he’s my
dad, but-he was through,
through with me.
Gay male teen (age 15):
I was hanging on with my
friend- well, my parents
thought he was just my boy,
but he was more than that- and
we were fooling around one
day after school- nothing
serious, just kissing and stuff-
but my mom came home early

and caught us. I’ve never seen
her like that. She was hollering
and her eyes were about to
bulge out of her head. She
told me to get out of her
house. My dad- I went to
spend the night at my friend’s
house, but I saw my parents
the next day- he said that my
little brothers might get
confused if they saw me, so it
was better for me not to be
around there, like, all the time.
Transgender male (age 17):
(Nods in gay teen’s direction)
My mom thought the same
thing: she said my little sister
and little brothers would get
confused being around me
with me wearing baggy pants
and getting a buzzcut and
stuff. She said I was almost
grown but they were still little,
so she- she had to- they have
to be her priority. (Eyes look
teary).

Heterosexual male (age 15):
I think my parents are, like-
they want to be prison guards
or something, they’re just so
strict. They want me to do
chores all the time, and then
the studying- they act like I’m
supposed to be studying 24/7.
I’m not ever supposed to play
games or watch TV or text or
do anything except chores and
schoolwork. I don’t get into
trouble and my grades are
okay, but they won’t get off
me. I just couldn’t take it
anymore.
SECOND QUESTION; How did you end up leaving home?
Heterosexual male:
I left. I was walking home from
the bus stop and ran into this
guy- this guy that I knew, I’d
seen him around, and he told
me that he knew how I could
make some serious cash, easy.
My parents won’t even give me
an allowance. I figured, why
not?
Lesbian female teen:
(Nodded) I left too. The
tension was just too much. I

knew someone who knew this
lady who helped kids make
money, and even let us stay at
her place. I didn’t realize what
she’d make me do to stay
there, but I ran away on my
own.
Gay male teen:
My parents told me I had to
go.
Heterosexual female:
I was up out of there. I
couldn’t take it no more.
Transgender male:
My mom said it would be too-
too confusing for my little
sister and brothers if I was
around, so I had to stay
somewhere else.
Transgender female:
My dad kicked me out.

Literally.
STEP TWO:
List the highlighted phrases.
Like I said above, here we’re moving away from summary to
actually
treating what was said as data.
We nearly kill each other.
She’s always getting on me.
Mother don’t know what to say to me.
He says I’m confused.
Mom says I’m just messed up.
They aren’t gonna be happy.
I couldn’t stay there.
Too tense.
He’s disgusted.
He lost his s---.
Physically pushed me.
He was through…with me.
She was hollering.
She told me to get out.
It was better for me not to be around there.
They (the younger siblings) have to be her priority.
They’re just so strict.
They won’t get off me.
I just couldn’t take it.
I left.

I left.
Parents told me I had to go.
I was up out of there.
I had to stay somewhere else.
My dad kicked me out.
STEP THREE:
Consider which phrases seem to refer to
the same concept.
For example, in terms of their parents’ reactions to them, some
teens
indicated they rejected their parents’ authority, while other kids
indicated
their parents rejected them. Some teens described running away;
others
described being kicked out. Making a chart with relevant
phrases (you
don’t need to use every phrase as long as you know that each
phrase
you’ve identified fits into one of these concepts) can help to see
this:
Concept: Phrases:
We nearly
kill each
other.
She’s
always
getting on
me.
They
aren’t
gonna be
happy.

Concept: Phrases:
Mother
don’t
know
what to
say to me.
[Dad] says
I’m
confused.
Mom says
I’m just
messed
up.
I couldn’t
stay there.
Too tense.
I left.
I was up
out of
there.
My dad
kicked me
out.
I had to
stay
somewhere

else.
STEP FOUR:
Identify themes.
In a content analysis, the concepts are usually referred to as
“themes.”
We made a chart where we grouped phrases that seemed to go
together;
now I’m going to go in and identify the concepts that seem
relevant. For
instance, in the chart we put together, it looks like some teens
referred to
problems between the parents and the teens. I’ve labeled that
theme
“Mutual tension between parents and teens.”
Concept: Phrases:
Concept: Phrases:
Mutual tension
between
parents and
teens.
We nearly
kill each
other.
She’s

always
getting on
me.
They
aren’t
gonna be
happy.
Parents’
negative
reaction
towards teens.
Mother
don’t
know
what to
say to me.
[Dad] says
I’m
confused.
Mom says
I’m just
messed
up.
Teens left
home.
I couldn’t
stay there.
Too tense.
I left.
I was up
out of
there.

Teens were put
out.
My dad
kicked me
out.
I had to
stay
somewhere
else.
Other teens seemed to feel that it was their parents who rejected
them.
I’ve labeled that theme “Parents’ negative reactions towards
teens.” The
next major theme appeared to be that some teens voluntarily left
home
while the final theme refers to teens whose parents put them out
of their
home. The themes above suggests that there were two main
types of
tension within these families (according to the teens): tension
between
the parents and teens and tension that primarily came from the
parents in
response to the teen. In terms of how they ended up leaving
home, there
were the teens who ran away and the teens who were put out of

the
house.
STEP FIVE:
Analyze concepts for deeper meaning.
At this point, we’ve distilled some repeating elements from the
teens’
responses to the questions. This allows us to draw larger
conclusions, and
to do so with more rigor than if we’d just given the transcript a
quick
read. We can say, for instance, that many teens fell into a
trafficking
situation after leaving home, but that twice as many left of their
own
accord than as left because their parents forced them out.
Similarly, we
can conclude that among these teens, tension in the home is a
repeating
factor, with rough equivalence in the tension between parents
and teens
being either mutual or predominantly from the parents’ side.
Do you see how this analysis goes beyond simply summarizing
what the
teens said (like a journalist might do) to analyzing what they
said to find
the themes? This is what separates a content analysis from a
summary.
This provides information about the experiences that these teens
have
had that cannot be obtained from a standardized questionnaire.
This is
the type of content analysis that you need to conduct for your
project.

Thanks for reading! I hope you find this useful.
Best,
Brenda
Transcript of Focus Group of Teen Clients
Involved in Sex Trafficking
FIRST QUESTION: How do you get along
with your parents?
Heterosexual female (age 15):
My mother and me- we nearly kill each other if we’re around
each other
for too long. Like, she’s always gettin’ on me about somethin’.
‘Do your
homework. Why you failin’ everything? You need to get a job
to pay for
all that s--- you want.’ (Shrugs) So I got a job- on the streets.
Turnin’ tricks
pays a lot better than frying fries.
Lesbian female teen (age 16):
My mother don’t know what to say to me. My dad- when I see
him.
(Shakes her head) He says I’m confused. My mom says I’m just
messed
up. It don’t matter what I do- I make good grades, don’t get into

trouble,
follow my mom’s rules, but- as long as I like girls, they aren’t
gonna be
happy. It got to the point- I couldn’t stay there; it was just too
tense.
Transgender female (age 16):
Same here: my mom don’t know what to do with me. My dad-
he’s just-
he’s disgusted. When he found that skirt in my room, he lost his
s---,
physically pushed me down the stairs and out the door. I fell out
the door
on my back ‘cause I didn’t want to hit him- he’s my dad, but- he
was
through, through with me.
Gay male teen (age 15):
I was hanging on with my friend- well, my parents thought he
was just my
boy, but he was more than that- and we were fooling around one
day
after school- nothing serious, just kissing and stuff- but my
mom came
home early and caught us. I’ve never seen her like that. She was
hollering
and her eyes were about to bulge out of her head. She told me to
get
out of her house. My dad- I went to spend the night at my
friend’s house,
but I saw my parents the next day- he said that my little
brothers might
get confused if they saw me, so it was better for me not to be
around
there, like, all the time.

Transgender male (age 17):
(Nods in gay teen’s direction) My mom thought the same thing:
she said
my little sister and little brothers would get confused being
around me
with me wearing baggy pants and getting a buzzcut and stuff.
She said I
was almost grown but they were still little, so she- she had to-
they have
to be her priority. (Eyes look teary)
Heterosexual male (age 15):
I think my parents are, like- they want to be prison guards or
something,
they’re just so strict. They want me to do chores all the time,
and then the
studying- they act like I’m supposed to be studying 24/7. I’m
not ever
supposed to play games or watch TV or text or do anything
except
chores and schoolwork. I don’t get into trouble and my grades
are okay,
but they won’t get off me. I just couldn’t take it anymore.
SECOND QUESTION: How did you end
up leaving home?
Heterosexual male:
I left. I was walking home from the bus stop and ran into this
guy- this guy

that I knew, I’d seen him around, and he told me that he knew
how I
could make some serious cash, easy. My parents won’t even
give me an
allowance. I figured, why not?
Lesbian female teen:
I left. The tension was just too much. I knew someone who
knew this lady
who helped kids make money, and even let us stay at her place.
I didn’t
realize what she’d make me do to stay there, but I ran away on
my own.
Gay male teen:
My parents told me I had to go.
Heterosexual female:
I was up out of there. I couldn’t take it no more.
Transgender male:
My mom said it would be too- too confusing for my little sister
and
brothers if I was around.
Transgender female:
My dad kicked me out. Literally.
Download this Transcript (documents/Focus-Group-Teen-
Clients-
transcript.pdf)
Conclusion
You have completed the Riverbend City: Conducting Data
Analysis
activity.

Licensed under a Creative Commons Attribution 3.0 License
(https://creativecommons.org/licenses/by-nc-nd/3.0/)
data-analysis/documents/Focus-Group-Teen-Clients-
transcript.pdf
https://creativecommons.org/licenses/by-nc-nd/3.0/
5/30/2020 Model Building Scoring Guide
https://courseroomc.capella.edu/bbcswebdav/institution/HMSV/
HMSV5316/191000/Scoring_Guides/u07a1_scoring_guide.html
1/1
Model Building Scoring Guide
Due Date: Unit 7
Percentage of Course Grade: 15%.
CRITERIA NON-PERFORMANCE BASIC PROFICIENT
DISTINGUISHED
Present data in a
descriptive format
(for example, tables,
charts and graphs).
20%
Does not present data
in any descriptive
formats.
Presents some data in a

descriptive format, but not
all of it.
Presents data
in a descriptive
format (for
example,
tables, charts
and graphs).
Presents data in a
descriptive format and
provides explanation of
the table, chart, graph,
or other format.
Identify analyses
conducted on both
quantitative and
qualitative data.
20%
Does not identify any
analyses conducted on
data.
Identifies one type of
analysis conducted on
data (either qualitative or
quantitative), but not both.
Identifies
analyses
conducted on
both
qualitative and

quantitative
data.
Identifies analyses
conducted on both
qualitative and
quantitative data and
explains why the
analyses were selected
for both.
Present content
analyses of
qualitative data.
20%
Does not discuss
content analyses of
qualitative data.
Discusses content
analyses of qualitative data
but does not present the
analyses in a clear or well-
organized manner.
Presents
content
analyses of
qualitative
data.
Presents content
analyses of qualitative
data, and explains the
analyses and possible

implications of the
results of the analyses.
Present statistical
analyses of
quantitative data.
20%
Does not discuss
statistical analyses of
quantitative data.
Discusses statistical
analyses of quantitative
data, but the results are
not clear.
Presents
statistical
analyses of
quantitative
data.
Presents statistical
analyses of
quantitative data,
interprets the results of
the statistical analyses,
and discusses the
implications of the
results.
Communicate in a
manner that is clear
and well organized.
20%

Communicates in a
manner that is so
disorganized and/or has
so many errors in
writing mechanics that
the ideas cannot be
understood.
Communicates in a
manner that is not well-
organized or has enough
errors in writing mechanics
that it interferes with
understanding the ideas
presented.
Communicates
in a manner
that is clear
and well
organized.
Communicates in a
clear, well-organized
manner that is free of
errors in writing
mechanics and APA
format.
5/30/2020 Riverbend City: Data Modeling
https://media.capella.edu/CourseMedia/HMSV5316/data-

modeling/transcript.asp 1/3
Riverbend City ® Activity
Data Modeling
Introduction
Mentor Talk
Conclusion
Introduction
Welcome back to your virtual internship at the Riverbend
Community Action Center! So far, you have been introduced to
your overarching project of using data analytics to help RCAC
evaluate the effectiveness of their Ruby Lake Teen
Homelessness
Task Force, and then talked to staff members to get a sense of
the types of questions you should be trying to answer with data.
The next step will be to take a closer look at data modeling.
It's time for another meeting with your mentor, Brenda.
Mentor Talk
Riverbend City Community Action
Center: Mentor's Office
Check in with your CAC Mentor, Brenda.
Come on in! I hope you’re finding your internship interesting
and
challenging so far.
So, since the last time we talked, you should have a more solid
sense of
what the overall plan is here, and what kind of questions we
need to be

answering through data analytics. That’s the first step for any
project like
this, and now it’s time to move forward.
The next thing I’d like to do is to talk to you a little about data
modeling.
it’s really important; it’s a foundational thing, and we have to
make sure
everything’s straight before we can proceed. Like, if you get
your
modeling done correctly, subsequent steps are that much easier
and
more logical. Get it wrong, and the whole thing is liable to blow
up on
your face when you’re halfway through the process and you
realize you
can’t actually answer the questions you’re trying to answer.
First off, what is this data modeling thing? It’s a little abstract.
A data
model is a conceptual representation of the structure that’s
going to
guide your database. One way to think of it is that it’s an
attempt to
properly represent reality through data. Maybe “conceptual
blueprint” is
a good way to think of it.

Your data model needs to account for the nature of the data
you’re
gathering, the institutional rules at play in using it, and the
organization of
the data itself. Think tables, columns, relationships, constraints,
and that
sort of thing.
There are three types of data models we’re going to think about:
relational, statistical, and predictive. These are all different
approaches to
structuring and handling data, depending on what kind of
information
you’re collecting and what you want to do with it.
If you have experience using databases, a relational data model
might
seem like the most intuitive and familiar approach. In this
setup, data is –
of course- stored in a relational database. Basically, your
classic database
setup: a series of indexed tables with one-to-one and one-to-
many
relations set between them, governed by keys. You can
manipulate the
data and report on it using something like SQL.
Next, statistical data modeling. It lives up to its name, more or
less-
you’re amassing and storing large amounts of data along some
pre-
identified variables, with the idea that you can aggregate these
datapoints and subject them to statistical analysis. This allows
you to
identify patterns and correlations, and possibly identify trends
that may

continue into the future.
And finally, I’d like to talk about predictive data modelling.
You can think
of it as a modelling approach that’s kind of a means to an end…
where
the end is being able to predict future outcomes based on the
data
you’re gathering. In this case, you structure your data model
around a
series of predictors that you’ve identified; in other words,
variables that
are likely to have an effect on the outcomes that you’re
concerned with.
There’s an interplay here between predictive and statistical data
modelling in that one informs the other; as things move
forward, for
instance, you might use a statistical data model to evaluate the
effectiveness of your predictive model.
I hope this helps! Next, we’ll be talking about putting some of
this stuff
into practice.
Conclusion
You have completed the Riverbend City: Data Modeling
activity.

Licensed under a Creative Commons Attribution 3.0 License
(https://creativecommons.org/licenses/by-nc-nd/3.0/)
https://creativecommons.org/licenses/by-nc-nd/3.0/

1RUNNING HEAD PROBLEM IDENTIFICATIONJohnston-Taylor.docx

Recommended

Recommended

More Related Content

More from RAJU852744

More from RAJU852744 (20)

Recently uploaded

Recently uploaded (20)

1RUNNING HEAD PROBLEM IDENTIFICATIONJohnston-Taylor.docx