RUNNING HEAD: PROBLEM IDENTIFICATION Johnston-Taylor
Problem Identification and Model Planning
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.
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).
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).