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.
