week 5 Discussion

• Stangor, C. (2013). Research methods for the behavioral sciences (4th ed., Laureate Education custom ed.). Belmont, CA: Wadsworth Cengage Learning.
  • Chapter 9, “Correlational Research Designs”
  • Chapter 10, “Experimental Research: One-Way Designs”

• Stangor, C. (2011). Research methods for the  behavioral sciences (4th ed.). Belmont, CA: Wadsworth Cengage Learning.     (Note: This reference is only for the PowerPoint version of the  course.) 
  •              
  • PowerPoint  Presentation: Chapter 9             
  • PowerPoint Presentation: Chapter 10    
  This electronic presentation to be used with  Stangor. Research Methods  for the Behavioral Sciences, 4E. From  Stangor. Research Methods for the  Behavioral Sciences, 4E. © 2011  South-Western, a part of Cengage Learning, Inc.  All rights reserved.  Reproduced by permission. Text/images may not be modified  or reproduced  in any way without prior written permission of the publisher. www.cengage.com/permissions

Correlational research describes relationships among variables but cannot indicate that one variable causes something to occur to another variable. Rather, a statistically significant correlation coefficient simply indicates there is a relationship between a predictor variable and an outcome variable.

In correlational research, if there appears to be a statistically significant relationship between two variables, you want to be sure that the relationship truly exists. This goal is challenging to achieve because other variables that you are not studying may complicate the study and the interpretation of the results.

You may find a spurious relationship in which one common causal variable, sometimes referred to as a “third variable,” is responsible for the observed relationship between the predictor variable and the outcome variable. Imagine seeing a news story reporting the findings of a study claiming that children under the age of 17 who viewed R-rated movies showed a greater likelihood of taking up smoking. A third variable that could explain both is permissive parenting. Permissive parenting might lead to children seeing R-rated movies when they are under the age of 17. It might also lead to children taking up smoking as parents may not attend to their children’s whereabouts or discipline them for smoking.

You also may find there are extraneous variables that might influence the outcome variable but do not influence the predictor variable. For example, consider a reported correlation that the number of books in a person’s home (predictor variable) is related to their college GPA (outcome). An extraneous variable could be, for example, a person’s IQ (intelligence quotient) score. The higher IQ might be related to higher college GPA but not necessarily related to the number of books found in a person’s home. There are examples of spurious relations and extraneous variables on pages 162–164 of your course text.

In this Discussion you focus primarily on spurious relations and extraneous variables. After reviewing examples in the course text, you will find your own examples in the media and explain how they affect the relationships between the variables under consideration.

To prepare:

• Review some popular online newspapers and/or online magazines to look for articles on research studies. Look for examples of statements and claims that might reflect a spurious relationship or extraneous variables.
• Find two popular media examples of the following two types of correlations:
  • Find one media example of a correlation that could be argued to be a spurious correlation.
  • Find a second media example that illustrates a correlation that may have an extraneous variable.
• Number the example with the spurious relationship as "1," and the example with extraneous variable as "2."

With these thoughts in mind:

Post by Day 3 the following in the body of your post (do not provide an attached document):

1. For the media example of a spurious correlation: 1) Explain the example and the claim that has been made. 2) Identify the predictor variable and the outcome variable. 3) Identify the correlation. Is it a positive or negative correlation? How did you determine this to be the case? 4) What is your proposed spurious (third) variable? 5) How does your proposed spurious variable simultaneously explain both your outcome variable and predictor variable?
2. For the media example of an extraneous variable: 1) Explain the example and the claim that has been made. 2) Identify the predictor variable and the outcome variable. 3) Identify the correlation. Is it a positive or negative correlation? How did you determine this to be the case? 4) What is your proposed extraneous variable? 5) How does your proposed extraneous variable explain your outcome variable and not the predictor variable?