The Pearson correlation coefficient can be easily calculated in R using the cor.test()-function.
In this video, I'll show you the details that will enhance your data analysis with specifying the correct alternative hypothesis, requesting a different confidence interval - if needed and demonstrating how to classify the magnitude of your correlation also known as effect size.

For the latter I will also refer to Cohen (1992): A Power Primer and the thresholds provided, mainly applicable for the behavioral sciences.

The Pearson correlation coefficient is also reffered to as the Bravais-Pearson correlation coefficient or sometimes only Pearson's r.


📚 Literature
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📚 Cohen, J. (1992): Quantitative methods in psychology: A power primer. Psychological bulletin, pp. 155-159.


⏰ Timestamps:
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0:00 Introduction
0:09 Requirements for the Pearson correlation coefficient
0:14 Using cor.test() in R
0:27 One-sided vs. two-sided testing
1:11 Interpretation: I) p value
1:42 Interpretation: II) correlation coefficient r
2:00 Interpretation: III) effect size classification

If you have any questions or suggestions regarding the Pearson correlation in R, please use the comment function. Thumbs up or down to decide if you found the video helpful.

#useR #statorials


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