Difference Between Causation and Correlation Essay

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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.

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This mean that when the gathering of data is done through experimental means and any misleading data is alleviated then the presence of a correlation implies there is causation.

In order to make causal inference it is required that there is confidence from the outcomes of the ANOVA and t tests although not necessarily with outcomes of the regression or correlation techniques. An experimental research often entails smaller experimental treatment numbers and that the data gathered from this research is evaluated conveniently with two groups ANOVA and t tests (Coogan, 2015).ANOVA and the t test are learned during experimental research studies. At times researchers mistake experimental methods with statistical techniques (Bleske-Rechek, Morrison & Heidtke, 2015). Using correlational design adds to the existing problem. Whenever students are trained on using correlational design for the description of non-experimental data collection methods and warned against the challenges relating to inferring causality from the data, the mistake students make is confusing the technique of correlational statistics with the method of correlational data (Coogan, 2015). Using correlational technique for design description will make the entire research to become non-experimental when it is supposed to be observational.

Correlation can be understood as association. More accurately it can be understood as a measure to the depth with which 2 variables have a relationship. If for instance increase in value of a variable is associated with the increase in value of another variable then the two variables are said to have positive correlation (Bleske-Recheks, Morrison & Heidtke, 2015). For instance there is relationship between….....

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References

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

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 http://www.westga.edu/~bquest/

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