Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will perform an article critique on correlation and bivariate regression. Be sure and remember that the goal is to obtain constructive feedback to improve the research and its interpretation, so please view this as an opportunity to learn from one another.
To prepare for this Discussion:
- Review the Learning Resources and the media programs related to correlation and regression.
- Search for and select a quantitative article specific to your discipline and related to correlation or regression. Help with this task may be found in the Course guide and assignment help linked in this week’s Learning Resources. Also, you can use as guide the Research Design Alignment Table located in this week’s Learning Resources.
By 1/20/2021
Write a 3- to 5-paragraph critique of the article. In your critique, include responses to the following:
- What is the research design used by the authors?
- Why did the authors use correlation or bivariate regression?
- Do you think it’s the most appropriate choice? Why or why not?
- Did the authors display the data?
- Do the results stand alone? Why or why not?
- Did the authors report effect size? If yes, is this meaningful?
Learning Resources
Required Readings
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 12, “Regression and Correlation” (pp. 401-457)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 8, “Correlation and Regression Analysis”
Walden University Library. (n.d.). Course Guide and Assignment Help for RSCH 8210. Retrieved from http://academicguides.waldenu.edu/rsch8210
For help with this week’s research, see this Course Guide and related weekly assignment resources.
Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html
As you review this web blog, select New d3.js visualization: Interpreting Correlations link, once you select the link, follow the instructions to view the interactive for interpreting correlations. This interactive will help you to visualize and understand correlations between two variables.
Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.
Required Media
Laureate Education (Producer). (2016b). Correlation and bivariate regression [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones demonstrates correlation and bivariate regression using the SPSS software.
Accessible player –Downloads–Download Video w/CCDownload AudioDownload Transcript
Optional Resources
Correlation
Klingenberg, B. (2016). Correlation game. Retrieved from https://istats.shinyapps.io/guesscorr/
Use the following app/weblink for an interactive visual display of correlation slopes.
Regression
Klingenberg, B. (2016). Explore linear regression. Retrieved from https://istats.shinyapps.io/ExploreLinReg/
Use the following app/weblink for an interactive visual display of regression slopes.
APA REQUIRED
TURN IT IN REQUIRED
SUBHEADING REQUIRED
FOLLOW THE DIRECTIONS
MUST RECEIVE A PASSING GRADE OF A (b) OR BETTER.
Student 1
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In this analysis, the author of A Comparative Analysis of Illinois, Ohio, Colorado and South Dakota Park Districts and Parks and Recreation Departments to Wisconsin, Iowa, Missouri, Kansas, Indiana, and Michigan Parks and Recreation Departments used quantitative method design with comparative means-testing. The study evaluates park district services efficiencies and effectiveness in comparison to services provided by municipal governments. Emanuelson (2008) used bivariate and multivariate linear regression to test the relationships between service levels and efficiency levels.
This design and test are appropriate. The difference between correlation or bivariate regression measurements is that “correlation measures the degree of a relationship between two variables, whereas regression is how one variable affects another” (Calvello, 2020). The author used bivariate regression analysis because it analyzes two variables to establish the strength between the service levels and efficiency levels and how one affected the other (Emanuelson, 2008). Bivariate regression analysis is “useful for examining the ability of the independent variable or predictor variable to predict the dependent of criterion variable” (Rockinson-Szapkiw, 2013).
The author displayed all of the data through multiple tables and figures and included his questionnaire in the Appendix. Tables 12 and 13 are two examples of data provided.
Table (12) shows the Linear Regression with the Structure of Government as the Independent Variable with predictor, or constant as the department of the city, the independent variable is not statistically significant. The article states a weak relationship between parks and recreation and budgetary efficiency, as seen in R-Square .008. Coefficients – dependent variable: Budgetary efficiency
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .087 (a) | .008 | .003 | 1.0565985 |
| Mode1 | Unstandardized Coefficients | standardized Coefficients | t | Sig. | |||
| B | Std. Error | Beta | |||||
| 1 (constant) department of city | .812.184 | .101.144 | .087 | 8.0251.275 | .000.204 | ||
In another table (13) the Linear Regression with Structure of Government as the Independent Variable with the predictor or constant as the park district. The data is “neither meaningful nor statistically significant as a predictor of staff efficiency” (Emanuelson, 2008). R value is .039. Coefficients – dependent variable: Budgetary efficiency
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .039 (a) | .002 | -.003 | 42.89480 |
| Mode1 | Unstandardized Coefficients | standardized Coefficients | t | Sig. | |||
| B | Std. Error | Beta | |||||
| 1 (constant) Park district | 25.196-3.464 | 3.5876.038 | -.039 | 7.024-.574 | .000.567 | ||
This study provides meaningful data and affects current literature, and provides useful data for social change. “The data regarding differences in means of levels of service and efficiency support the theories contained in the current literature of public choice and metropolitan ecology theory” (Emanuelson, 2008).
While not every variable that was run is considered meaningful, the data’s conclusion indicates information obtained and can be applied. Emanuelson (2008) concluded that park districts provide greater effectiveness in services than parks and recreation departments. In contrast, per capita, spending on parks and recreation services is a good predictor of services, and training is essential (Emanuelson, 2008).
References:
Calvello, M. (2020, January 2). Correlation vs. Regression Made Easy: Which to Use + Why. Retrieved January 21, 2021, from https://learn.g2.com/correlation-vs-regression
Emanuelson, D. N. (2008). A Comparative Analysis of Illinois, Ohio, Colorado and South Dakota Park Districts and Parks and Recreation Departments to Wisconsin, Iowa, Missouri, Kansas, Indiana, and Michigan Parks and Recreation Departments. Conference Papers — Midwestern Political Science Association, 1–42.
Rockinson-Szapkiw, A. J. (2013, October 4). SPSS Tutorial: Bivariate Regression. Retrieved from https://www.youtube.com/watch?v=qfilvBlg0iI
