Week 8: Correlation and Bivariate Regression
Correlation tests are some of the most widely used tests; unfortunately, they are also some of the most misinterpreted tests. The term correlation is frequently used in a colloquial sense, but has a very specific definition within the context of statistics. As a critical consumer of research, after this week you will be able to properly interpret the strengths and weaknesses of this specific test.
Perhaps the most exciting part of this week’s activities is the introduction to ordinary least squares regression. This form of linear regression is frequently referred to as the “workhorse” of the social sciences, and for good reason. It is one of the most widely used statistical tests.
In this week, you will examine correlation and bivariate regression. In your examination you will construct research questions, evaluate research design, and analyze results related to correlation and bivariate regression.
Learning Objectives
Students will:
- Construct research questions
- Evaluate research design through research questions
- Analyze correlation and bivariate regression
- Analyze measures for correlation and bivariate regression
- Analyze significance of correlation and bivariate regression
- Analyze results for correlation and bivariate regression testing
- Analyze assumptions of correlation and bivariate regression
- Analyze implications for social change
- Evaluate research related to correlation and bivariate regression
Learning Resources
Required Readings
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications.
- Chapter 12, “Regression and Correlation” (pp. 325-371)
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.
Document: Walden University: Research Design Alignment Table
Datasets
Document: Data Set 2014 General Social Survey (dataset file)
Use this dataset to complete this week’s Discussion.
Note: You will need the SPSS software to open this dataset.
Document: Data Set Afrobarometer (dataset file)
Use this dataset to complete this week’s Assignment.
Note: You will need the SPSS software to open this dataset.
Document: High School Longitudinal Study 2009 Dataset (dataset file)
Use this dataset to complete this week’s Assignment.
Note: You will need the SPSS software to open this dataset.
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.
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.
Skill Builder: Interpreting Correlation and Regression Coefficients
To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.
You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.
Assignment: Testing for Correlation and Bivariate Regression
You had the chance earlier in the week to perform an article critique on correlation and simple linear regression and obtain peer feedback. Hopefully you are excited about the potential these tests hold; equally important is that you recognize some of their weaknesses. Now, it is once again time to put all of that good brainstorming to use and answer a social research question with the correlation and simple linear regression. As you begin the Assignment, be sure and pay close attention to the assumptions of the test. Specifically, make sure that your variables are metric level variables that can easily be interpreted in these tests.
For this Assignment, you will examine correlation and bivariate regression testing.
To prepare for this Assignment:
- Review this week’s Learning Resources and media program related to regression and correlation.
- Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in the Learning Resources for this week.
- Based on the dataset you chose, construct a research question that can be answered with a Pearson correlation and bivariate regression.
- Once you perform your correlation and bivariate regression analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document.
For this Assignment:
Write a 2- to 3-paragraph analysis of your correlation and bivariate regression results for each research question. Do not forget to evaluate if the correlation and bivariate regression assumptions are met and report the effect size. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be.
Use proper APA format, citations, and referencing for your analysis, research question, and display of output.
By Day 7
Expert Solution Preview
Introduction:
As a medical professor responsible for designing and conducting lectures, evaluating student performance, and providing feedback through assignments and examinations, this week’s focus on correlation and bivariate regression provides students with a crucial tool in their medical research arsenal. In this week, we will learn how to construct research questions, analyze research design, evaluate correlation and bivariate regression measures, and eventually interpret the results of correlation and bivariate regression tests. We’ll also analyze the implications for social change and evaluate research related to correlation and bivariate regression.
Answer to Week 8 Content:
Correlation tests are commonly utilized but are frequently misinterpreted due to the use of the term “correlation” in a colloquial context. This week’s learning objectives include constructing research questions, evaluating research design, and analyzing results related to correlation and bivariate regression. Ordinary least squares regression is also introduced as a vital tool in social science research. The required resources include the “Social statistics for a diverse society” textbook and the “Using IBM® SPSS® statistics for research methods and social science statistics” book, among others. The datasets for this week include the Afrobarometer dataset, the High School Longitudinal Study dataset, and the 2014 General Social Survey dataset. The Laureate Education’s video on correlation and bivariate regression and the Walden University’s Research Design Alignment Table are also included in the resources. The assignment involves using Pearson correlation and bivariate regression to answer a research question and analyzing the results while evaluating if the correlation and bivariate regression assumptions are met and reporting the effect size.
