Regression

As a consumer of research, you know that relationships are of critical importance. You must first know if a relationship exists between two variables before you can determine if one variable may account for another. In this week's readings, you focused on correlations that are used to tell you if two variables are related to one another, but you also now know that you cannot infer causation from a significant correlation alone. That is, you might find that years of education and salary are related, but that does not tell you if more education causes your salary to increase. Correlations also do not allow you to predict a participant's score on one variable, based on his or her score on another variable. One way to predict one score from another is by using regression. For example, if you wanted to know what salary you could expect in your field if you went back to school for another 2 years, regression could help you make that prediction.

In this Discussion you will apply regression to a research scenario of your choosing.

To prepare: Imagine a situation in which you would like to predict an outcome. Think about why you would choose to use regression rather than correlation. Why is prediction more important than simply describing a relationship?

Post by Day 3 a description of a scenario where you would like to predict an outcome based on a predictor variable. Describe how regression would help you make your prediction. Apply the following terms to your scenario (making sure to fully explain each concept in relation to your example): criterion, predictor, linear regression line, correlation (positive or negative), and proportion of variance accounted for (R2).

Respond by Day 6 to at least one of your peers in one of the following ways:

Challenge key points in the scenario in which they use regression.

Support key points in the scenario in which they use regression.

Resources:

Readings

Heiman, G. (2015). Behavioral sciences STAT 2 (2nd ed). Stamford, CT: Cengage.

Chapter 10, "Describing Relationships Using Correlation and Regression" (pp.162-181)

Chapter 13, "Chi Square and Nonparametric Procedures" (pp.218-229 only)

Chapter 10 Review Card (p. 10.4)

Chapter 13 Review Card (p. 13.4)

Media

Bjorkman, S. (2014). SPSS tutorial - Chi-square. Retrieved from http://screencast.com/t/mJsqW8p7

Note: This video demonstrates calculating and interpreting chi-square analyses in SPSS.

Ludwig, T. E. (n.d.a). Correlation [Interactive media]. Retrieved June 11, 2013, from http://bcs.worthpublishers.com/WebPub/Psychology/psychsim5/PsychSim5%20Tutorials/Correlation/Correlation.htm

Note: This site offers additional information about correlations, including interactive media examples.

Son, J. (2011). Statistics - Regression [Video file]. Retrieved from http://www.youtube.com/user/EducatorVids2?& 

This video explains linear regression, including predictor and response variables.

StatsLectures. (2011d). SPSS - Pearson's r (+hypothesis test) [Video file]. Retrieved from http://www.youtube.com/watch? 


This video shows how to calculate Pearson's r in SPSS. In addition, a hypothesis test is conducted to determine if the Pearson's r is significant.

Optional Resources

BBC (Producer). (2010). The joy of stats [Video series]. Retrieved from http://www.bbc.co.uk/programmes/p00cgkfk

"Hans Rosling's 200 Countries, 200 Years, 4 Minutes"

University of South Carolina. (n.d.a). Regression applet. Retrieved June 11, 2013, from http://www.stat.sc.edu/~west/javahtml/Regression.html

University of South Carolina. (n.d.b). Understanding correlation. Retrieved June 11, 2013, from http://www.stat.tamu.edu/~west/applets/rplot.html