The Difference between Causation and Correlation within the Context of DBA Doctoral Research Study
Introduction
For growth to occur it is important to understand the concepts of correlation and causation. Correlation can be differentiated from causation in general terms in that correlation assists in the prediction of future events since it indicates what is likely to occur. Causation on the other hand makes it possible to alter the future. Understanding the difference helps ensure that business decisions are made based on measurable variables and tangible facts. When decisions are based on guesswork and assumption there is a high risk that success will be jeopardized (Bleske-Rechek, Morrison & Heidtke, 2015). Prior to making any decision it is fundamental to check that the decision has been made not on assumptions but on proven facts. This discourse analyses difference between correlation and causation with respect to the doctoral research context.
The implications for professional practice when a researcher implies causation after using correlation analyses
While correlation is necessary it is never sufficient for making a causal inference with confidence. It is important to have an appropriate data collection method. In order to make a causal inference it is important to gather data through the control of peripheral variables and experimental means which are likely to mislead the outcome (Bleske-Rechek, Morrison & Heidtke, 2015). Following the gathering of data using this method, if it can be established that the variable that has been manipulated experimentally has some correlation with dependent variable and that the correlation is not necessary linear, then the condition are right for making causal inference. This...
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