relationship of two variables: non parametric tests Flashcards

1
Q

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.

A

Spearman’s Rank Correlation

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2
Q

uses the ranks of the data rather than the original data

A

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)

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3
Q

Spearman Correlation Coefficient known as

A

rho rs

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4
Q

it is used for
Spearman’s Rank Correlation:
Mann-Whitney U Test:
Wilcoxon Signed Rank Test:
Kruskal-Wallis Test:

A

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

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5
Q

spearman correlation value of r and the degree of correlation

A

no - 0
weak/ low - 0.01 and 0.35
average 0.36 and 0.7
strong/ high 0.71 and 0.99
perfect - 1

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6
Q

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.

A

Mann-Whitney U Test

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7
Q

the non-parametric counterpart to the t-test for independent samples; it is subjected to less stringent assumptions than the t-test.

A

Mann-Whitney U Test

converts scores to ranks
compare rank positions
more flexible, work with messy data

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8
Q

independent variable vs dependent variable

A

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

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9
Q

Used to test whether the mean values of two dependent groups differ significantly from each other.

A

Wilcoxon Signed Rank Test

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10
Q

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.

A

Kruskal-Wallis Test

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11
Q

it is a non-parametric test used when the assumptions for one way analysis of variance are not met.

A

The Kruskal-Wallis

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12
Q

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.

A

Kruskal-Wallis Test

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