Effective Use of Analytics Human Services
Model Building
Introduction
In data analytics, model building refers to assembling the needed data and analyzing it to address your identified problem. For this second course project assignment, you will complete one statistical analysis for your quantitative data and one content analysis for your qualitative data from the previous project assignment.
Statistical and Content Analyses
Work with your group on developing the following analyses based on the data from your previous project assignment. You can schedule a time to work together in real time, or work on the analyses individually and provide each other with feedback.
- Statistical analysis:
- Identify the specific analysis type (such as an independent samples t test, paired sample t test, or one-way ANOVA) and explain why it is appropriate.
- Assume that you have a sufficient sample size for your analysis (normally, you would need to conduct a power analysis to ensure this).
- Conduct the analysis in Microsoft Excel or comparable software. Note: If you use software other than Excel, make sure the files that the program generates can be read in the courseroom (PDF, JPEG, XLS, and PNG formats should be acceptable).
- Present the results and explain if they are statistically significant or not.
- Content analysis:
- Explain the purpose of content analysis.
- Identify the qualitative data that you are analyzing and present the analysis.
- Identify at least one theme from your data, present quotes from the transcripts as examples, and explain the theme and how it helps you better understand the experiences of the teens.
PLACE YOUR ORDER NOW
Instructions
Once you have completed your group analyses, prepare this assignment individually.
While most of this assignment should be written in paragraph format, it is appropriate to present some information (such as the results of the analyses) in tables. You should also present exploratory analyses and descriptive data (for example, a description of the group included in your analysis by race, gender, etcetera) in visual formats such as charts, graphs, and tables.
This paper should include the following sections:
- Identification of the Problem: This should just be a brief recap from the first assignment.
- Quantitative Analyses:
- Identify the data and variable type (continuous or categorical).
- Identify the specific type of statistical analysis.
- Present your results.
- Present the significance testing.
- Discuss possible implications of these findings.
- Qualitative Analyses:
- Identify the data.
- Present the results of your content analysis.
- Identify theme(s).
- Provide illustrative quotations for the theme(s).
- Discuss possible implications of these findings.
This assignment should be 3–5 pages long. It should be written in narrative format with tables, charts, graphs, and so forth included as well. Writing should be well organized, free of mechanical errors (in grammar and punctuation), and in correct APA format.
Examine the assignment scoring guide to be sure you have addressed all of the evaluation criteria.
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.
PLACE YOUR ORDER NOW
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 Dev 43, 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.
PLACE YOUR ORDER NOW
