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Reproducible Research Using R An Open Educational Resource

Christian Martinez

Section 6.9 CramerV

Cramer’s V tells you the strength of the relationship between two categorical variables, similar to correlation coefficient It is important to note that this measures effect size. This is not a statistic. We can utilize the cramerV() command from the rcompanion package ([D.1.23]).
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library(rcompanion)

cramerV(contingency_table)

Cramer V 
0.2300265
The value derived from this is 0.23. Again, similar to the correlation coefficient, there are guidelines that can be utilized to understand the strength.
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library(kableExtra)

cramerV_table <- data.frame(
  "Cramer V Value" = c("0.00-0.10", "0.10-0.30", "0.30-0.50", ">0.50"),
  "Strength of Relationship" = c("Very Weak", "Weak", "Moderate", "Strong"),
  check.names = FALSE
)

kable(cramerV_table,
      booktabs = TRUE,
      caption = "Guidelines for interpreting the strength of association using Cramer's V. These ranges provide general heuristics for describing effect size in categorical analyses and should be interpreted in context rather than as strict cutoffs.")

Table: Guidelines for interpreting the strength of association using Cramer's V. These ranges provide general heuristics for describing effect size in categorical analyses and should be interpreted in context rather than as strict cutoffs.

|Cramer V Value |Strength of Relationship |
|:--------------|:------------------------|
|0.00-0.10      |Very Weak                |
|0.10-0.30      |Weak                     |
|0.30-0.50      |Moderate                 |
|>0.50          |Strong                   |
Treatment, overall, had a weak-moderate effect on outcome overall. This suggests that while treatment type and infection outcome are related, the relationship isn’t strong — meaning that other factors likely play a larger role.
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