Mastering Spearman Rank Correlation Analysis in R

// Spearman correlation in R //

I'll show the details that will enhance your data analysis with specifying the correct alternative hypothesis, requesting a different confidence interval - if needed and how to classify the magnitude of your correlation aka. effect.

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

📚 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 Spearman rank correlation coefficient
0:27 One-sided vs. two-sided testing
1:18 Interpretation: I) p value
1:51 Interpretation: II) correlation coefficient r
2:08 Interpretation: III) effect size classification

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[davidalfonsoriveraruiz]

Thanks, i have to correlate one normal distributed variable (antibody concentration) and other non-normal variable (percentage of killed bacteria), thus spearman correlation is an option because my data are not completely normal and some data is repeated (tie data). A question, i need to transform my raw data variable into ranks before the test? or the the raw data is transformed by default, if i understand well, when you say that both of your variables are at the ordinary scale that means that you are not using your raw data, your previously transformed into ranks?.