Order Description


Discussion: Relationships and Causation
When considering the world of relationships between variables, it is a common mistake to assume causation when a correlation is present. A high correlation between

variables does not necessarily indicate causation. A study may show that there is a positive correlation between salary and the quality of work of individual employees

in that the more employees are paid, the better their performance. However, there may be no causation because other factors may impact the quality of an individual’s

work, such as training and experience. Also, consider if there is a positive correlation between employee training and quality of work of individual employees. Should

a researcher safely assume that a causal relationship exists here in that the better training that employees receive, the better their performance? While strong

correlations prompt researchers to take notice of possible causality, researchers must also be aware of attentional bias and prior beliefs when interpreting

correlations. It is, therefore, important to examine how causation is established. In this Discussion, you will distinguish between the two concepts of causation and

correlation and apply them to your potential Doctoral Study.

To prepare for this Discussion, review Lesson 31 in the Green and Salkind (2017) text and consider the correlation to your potential Doctoral Study topic. Your

potential topic may or may not be appropriate for correlational methods, but for the purpose of this Discussion, assume it is.

By Day 3
Post an analysis of the difference between causation and correlation within the context of your DBA doctoral research study. In your analysis, do the following:

Assess the implications for professional practice when a researcher implies causation after using correlation (e.g., bivariate correlation) analyses.
Explain why the results of bivariate correlation analyses are considered weak in terms of internal validity.
Explain how would you extend or modify a research design to examine a true cause-and-effect relationship.
Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and at least one additional scholarly source.

Refer to the Week 5 Discussion Rubric for specific grading elements and criteria. Your Instructor will use this rubric to assess your work.

Required Readings
Important Note: Some of the readings found in this course are more than 5 years old. Although we strive to use current references whenever possible, several of the

articles/resources found in this course are seminal, or foundational, works.

Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson.

Unit 8, “Correlation, Regression, and Discriminant Analysis Procedures”
Lesson 31, “Pearson Product-Moment Correlation Coefficient” ( 187–192)
Bleske-Rechek, A., Morrison, K. M., & Heidtke, L. D. (2015). Causal inference from descriptions of experimental and non-experimental research: Public understanding of

correlation-versus-causation. Journal of General Psychology, 142(1), 48–70. doi:10.1080/00221309.2014.977216

Note: You will access this article from the Walden Library databases.

Coogan, L. L. (2015). Teaching across courses: Using the concept of related markets from economics to explain statistics’ causation and correlations. B>Quest, 1–10.

Retrieved from https://www.westga.edu/~bquest/

Note: You will access this article from the Walden Library databases.