relationship of two variables: non parametric tests Flashcards
It examines the relationship between two variables, being the nonparametric counterpart of Pearson’s correlation. Therefore, in this case, a normal distribution of the data is not required.
Spearman’s Rank Correlation
uses the ranks of the data rather than the original data
Spearman’s Rank Correlation
*hence the name rank correlation
Pearson would look at exact finish times (1:23:45, 1:24:02, etc.)
Spearman just looks at who came 1st, 2nd, 3rd, etc.
it’s more flexible:
Works even when data is messy or uneven
Useful when you have outliers (really high or low numbers)
Spearman Correlation Coefficient known as
rho rs
it is used for
Spearman’s Rank Correlation:
Mann-Whitney U Test:
Wilcoxon Signed Rank Test:
Kruskal-Wallis Test:
Spearman’s Rank Correlation:
ordinal and continuous
Mann-Whitney U Test:
ordinal and dichotomous unpaired
Wilcoxon Signed Rank Test:
ordinal and dichotomous paired
Kruskal-Wallis Test:
ordinal and nominal
spearman correlation value of r and the degree of correlation
no - 0
weak/ low - 0.01 and 0.35
average 0.36 and 0.7
strong/ high 0.71 and 0.99
perfect - 1
Used to determine if there’s a difference between two samples, the rank sum of the two samples are used rather than the means as in the t-test for independent samples.
Mann-Whitney U Test
the non-parametric counterpart to the t-test for independent samples; it is subjected to less stringent assumptions than the t-test.
Mann-Whitney U Test
converts scores to ranks
compare rank positions
more flexible, work with messy data
independent variable vs dependent variable
independent variable:
are like buttons you press (on and off)
example - gender, medication, production
dependent variable:
are like thermometers that show different levels
example - salary, wellbeing, weight
Used to test whether the mean values of two dependent groups differ significantly from each other.
Wilcoxon Signed Rank Test
Used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable.
Kruskal-Wallis Test
it is a non-parametric test used when the assumptions for one way analysis of variance are not met.
The Kruskal-Wallis
it test is a non-parametric test and is therefore subject to considerably fewer assumptions than its parametric counterpart, the t-test for dependent samples.
Kruskal-Wallis Test