# Applying Regression to Research and Evaluation

Applying Regression to Research and Evaluation
Applying Regression to Research and Evaluation This assignment is responding to the two post below. Requirements for each response is:
1. At least one paragraph
2. Contain intext citation where appropriate
3. At least one reference per paragraph (it could be the same reference for each) Do not connect the two posts. They are completely separate. One reference for each paragraph. No introduction and no summary.
ASSIGNMENT: Respond to the two posts by providing feedback on the regression analyses presented in their posts. Are these regression analyses appropriate? Why or why not? If not, offer suggestions and/or ideas about other analytical tools you think are more appropriate.
POST 1 Amonica Gibson: Discussion – Week 10 Applying Regression to Research and Evaluation Multiple linear regression refers to the statistical techniques used in identifying the relationship between several variables in a regression model (Yan et al., 2009).
It is applied in cases where there are different explanatory variables to predict the outcome of a response variable. Multiple linear regression is used to establish a direct relationship between the independent variables (explanatory variables) and the response variables (dependent variables). It is possible to make predictions about a variable based on available information using multiple or linear regression.
The model assumes the existence of a relationship between the dependent and independent variables and that the independent variables are not very closely correlated to each other (Ryan, 2009). The other assumptions by this model include the normalcy in distributing the residuals with a mean of zero and a variance of zero, and the observations in a sample are selected randomly to remove potential bias. In my final evaluation design, the linear regression model will be the most suitable in analyzing how different business factors affect the performance of the organization.
The approach will aim at understanding the organization’s industry of operation and help in making informed decisions. Variables such as marketing, product quality, pricing, and costs of production will be regressed against performance. Variables will be added and removed from the market, and changes in performance noted.
The Beta coefficient obtained in the model will help to determine the factors with the most significant effect on the organization’s performance. Multiple linear regression will also play a role in carrying out a SWOT analysis for the institution. Besides, regression will also be useful in making predictions about the factors that may affect the performance of the organization in the future.
References Ryan, T. P. (2009). Modern regression methods. New York: John Wiley & Sons. Yan, X., Su, X., & World Scientific (Firm). (2009).
Linear regression analysis: Theory and . Singapore: World Scientific Pub. Computing POST 2 Conrad RE: Discussion – Week 10 COLLAPSE Regression analysis of PSCC Data for Sick Leave
In assessing the Data to be collected for the PSCC it needs to be understood that the dependent variable is the amount of sick leave and that there are multiple actual independent variables. The independent variable in question is the sick leave policy and whether it has an impact on the amount of sick leave being taken. As mentioned in the previous discussion, (Westerson, 2020) the employees’ age, sex, years of service, race, education, or if the family has dependent children may have an impact.
These are all independent variables. The analysis will need to consider these variables and determine what if any impact they have on the correlation of the results and the question.
The best method to take these into account is the Binary Logistic Regression (BLR). It will consider the multiple variables impacting the outcome, and identify their specific relations using the binary impacts of most of these variables; (Male/Female, Yes/No for dependent children, education High School/University, etc.)
The r, r2, R and R2, correlation coefficient, multiple regression coefficients and the coefficient of multiple determinants will clearly identify the impacts of each of the different variable son the outcomes. Significant effort will be required to mathematically review the data and make a determination as to what the variables are and what their coefficients are and determine their Beta Weights to see which variables have the greatest impact on the outcome. (Johnson, 2014)
Statistical mathematics not being the author’s strongest suit, a researcher with experience in statistics will be employed to assist with the creation of these mathematical constructs to assist with the understanding of the complex relationships involved.
Johnson, G. (2014). Research methods for public administrators (3rd ed.). Armonk, NY: M. E. Sharpe. o Chapter 15, “Data Analysis: Regression” (pp. 216–229) ·
Garson, G. D. (2009, April 4). Logistic regression. Retrieved from http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm Media ·
Laureate Education (Producer). (2013a). Correlation and introduction to regression. [Multimedia file] Baltimore, MD: Author. “Correlation and introduction to regression” Transcript (PDF)
Below is additional information. The assignment is above This is the original assignment they were completing Discussion – Week 10 Discussion: Applying Regression to Research and Evaluation Because public and nonprofit administrators operate in a complex world, they often ask research questions that can be answered with multiple explanations.
Multiple explanations for an outcome require multiple independent variables. As a reminder, independent variables are those that influence the outcome or dependent variable. For example, consider research designed to explain why some students earn good grades in Applied Research and Evaluation, while others do not.
Perhaps one independent variable, such as time spent studying, explains good grades versus poor grades (dependent variable). However, it is more likely that many independent variables are involved. It could be that time spent studying, along with previous experience taking a research methods course, and the amount of time spent at a job, contributes to the explanation.
Simple associational research, such as the research you considered in Week 9, overlooks the complexity of multiple variables because it focuses only on one independent variable. Explanations that involve multiple independent variables are better served by approaches such as OLS regression or binary logistic regression analysis. The choice often depends on the nature of the dependent variable.
An interval dependent variable may lead you to choose OLS regression, while a nominal dependent variable may require binary logistic regression. OLS is a simple, yet limited approach to regression. You must assume that variables are related in a linear fashion, and the dependent variable must be somewhat interval in nature. Research does not always present interval-dependent variables.
There are situations, however, in which dependent variables are more nominal in nature. Assume that you want to predict the likelihood of an individual leaving the organization in the next six months. The dependent variable in this example would be nominal (either he/she leaves or he/she does not).
OLS regression would not work in this situation, so you would need another type of regression, known as binary logistic regression. There is no “perfect” approach to regression analysis. Rather, you must select the “most appropriate” type of analysis, depending on the purpose of your research and the types of variables you are examining.
Review the Learning Resources for this week. Consider how regression analysis (OLS and binary logistic) could apply to your Final Evaluation Design (Final Project) to help answer your research question.
Post by Day 4 an explanation of whether OLS regression or binary logistic regression is more appropriate to evaluate the program, problem, or policy you selected for your Evaluation Design. Explain how you would use the selected analysis, and justify why this type of regression analysis is most appropriate.
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