The correlation study measures various variables and their relationships to each other. Statistical tests can be used to examine the relationships with regard to their strength (significance), the regression indicates the functional relationship between variables.


The goal of this method is to make predictions based on one variable about the other variable. It should be noted that a correlation does not allow for causality assumptions, since the variables could be influenced by a third variable.


There are different correlation coefficients that are used depending on the measurement level and the variables involved. The best known are:

  1. Bravais/Pearson: This correlation coefficient is used for metrically scaled data.
  2. Spearman's: This correlation coefficient is used for ordinally scaled data.

For mixed scaled data the lowest measurement level determines the choice of the correlation coefficient.


Core Literature

  • [1] Tichy, W., & Padberg, F. (2007). Empirische Methodik in der Softwaretechnik im Allgemeinen und bei der Software-Visualisierung im Besonderen. In Software Engineering 2007–Beiträge zu den Workshops–Fachtagung des GI-Fachbereichs Softwaretechnik. Gesellschaft für Informatik e. V.
  • [2] Artusi, R., Verderio, P., & Marubini, E. (2002). Bravais-Pearson and Spearman correlation coefficients: meaning, test of hypothesis and confidence interval. The International journal of biological markers17(2), 148-151.
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