# Rank-Based Non-Parametric Tests

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1 Rank-Based Non-Parametric Tests

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3 Parametric and Nonparametric Tests Non-Parametric Tests Most of the statistical tests that we have used throughout the semester have relied on certain specific assumptions about the distribution of the involved variables and/or their means, and have been set up to test hypotheses about specific population parameters Such tests (z-tests, t-tests, ANOVAs, Pearson s correlation) are called parametric tests

4 Parametric and Nonparametric Tests Non-Parametric Tests Though these parametric tests are robust to minor violations of their assumptions, they can lead to gross systematic errors when the data are strongly violate the underlying assumptions and can even be undefined for certain types of data (e.g., nominal or non-numerical data). Certain tests do not rely on specific distributional assumptions or test hypotheses about particular population parameters. These tests are generally called nonparametric tests The chi-square tests introduced in the last lecture and Spearman s rank correlation coefficient test were examples of nonparametric tests.

5 Parametric versus Nonparametric Tests The advantages of parametric tests are that Non-Parametric Tests They are more powerful (i.e., you can detect smaller effect sizes with smaller samples) than comparable non-parametric tests when the parametric assumptions are correct (or approximately correct). The hypothesis tests are more specific and easier to interpret. The advantage of nonparametric tests are that They can be used when the distribution of the population is completely unknown They tend to be more robust to ill-behaved (e.g., non-normal, heteroscedastic, & multi-modal) data They are less sensitive to outliers They can be used with nominal and ordinal data

6 Nonparametric tests In the last 70 years or so, statisticians have developed many different nonparametric tests. Those most widely used in the behavioral sciences tend to be rank randomization tests Rank randomization (or rank-permutation) tests are hypothesis tests based on the theoretical distribution of randomly assigned ranks. As a first step, they all require the conversion of raw scores to ordinal ranks This makes them obvious candidates for ordinal data, though these data usually still need to be modified

7 Rank Randomization Tests Advantages of rank-based tests: 1. Ranks are simpler, and rank-based tests are easier to compute 2. They are largely insensitive to the particular form of the population distributions and differences between the distributions underlying the scores in different samples 3. They tend to minimize the effects of large sample variances 4. They are insensitive to outlier scores and make it easier to deal with undetermined scores (e.g., time to task completion) 5. The distribution of randomly assigned ranks can be computed exactly

8 720 permutations Sample 1 Ranks Sample 2 Ranks

9 720 permutations Sample 1 Ranks R 1 = Σr Sample 2 Ranks R 2 = Σr W s = min(r 1,R 2 )

10 Non-Parametric Tests

11 Non-Parametric Tests

12 Rank Randomization Tests Popular rank-based tests: 1. The Mann-Whitney U (or Wilcoxon rank-sum) test Nonparametric analogue to the independent-samples t-test 2. The Wilcoxon signed-rank test Nonparametric analogue to the matched-samples t-test 3. The Kruskal-Wallis test Nonparametric analogue to the one-way ANOVA (independent meas.) 4. The Friedman test Nonparametric analogue to the repeated-measures ANOVA

13 A Note about Computing Ranks All of the rank-based tests will require that you compute ranks based on the total number of scores and from lowest to highest I.e., if you have 3 samples with 5 scores each, the lowest overall score should be assigned the rank 1 and the highest overall score should be assigned the rank 15 In case of ties, each tied score should be assigned the mean of the tied ranks I.e., if the 3 rd and 4 th lowest scores have the same value then you should assign them each a rank of 3.5, with the next highest value receiving a rank of 5. If the 7 th, 8 th, and 9 th scores are all tied, then you should assign them each a rank of 8, with the next highest value receiving a rank of 10.

14 Rank Randomization Tests Aside from converting the raw scores to ranks, the logical steps are similar to those for the parametric hypothesis tests: 1. State the null and alternative hypotheses about the population. 2. Use the null hypotheses to predict the characteristics that the sample ranks should have. 3. Use the samples to compute the test statistic 4. Compare the test statistic with the hypothesis prediction

15 The Mann-Whitney Test This is a test for independent-measures (between subjects) research designs with two groups and is thus an alternative to the independent-measures t-test The basic intuition behind the test is that: A real difference between the two treatments should cause the scores in one sample to be generally larger than the scores in the other sample If all the scores are ranked, the larger ranks should be concentrated in one sample and the smaller ranks should be concentrated in the other sample.

16 The Mann-Whitney Test The null and alternative hypotheses are a bit more vague than for the t-test, but still test for some sort of difference in central tendency H 0 : There is no difference between treatments. Therefore, there is no tendency for ranks in one sample to be systematically higher or lower than in the other sample H 1 : There is a difference between treatments. Therefore, the ranks in one sample should be systematically higher or lower than in the other sample

17 The Mann-Whitney Test When comparing two samples, the Mann-Whitney U statistic for sample 1 represents the sum of the number of scores in sample 2 outranked by scores in sample 1 Treatment A Raw Scores Ranks Treatment B Raw Scores Ranks The smaller of the U values is looked up in the table

18 Example Treatment A Raw Scores Ranks Points Treatment B Raw Scores Ranks Points

19 The Mann-Whitney Test: Steps In practice, the steps for computing the test statistic (U) are: 1. Rank all the observations from smallest to largest 2. Compute the sum of the ranks in each sample, using the following formulas to compute U statistics from the ranks R: U n ( n 1 ), R1 U n ( n R ) 3. U is the smaller of the sums of these counts

20 Example Treatment A Raw Scores Ranks ΣR 1 27 Treatment B Raw Scores Ranks ΣR 2 51 U n1( n1 1) R 2 6(7)

21 U 6 n 6 n In this case, U crit = 5, so we retain the null hypothesis Note: Most tables for rank permutation statistics are set up so that you reject the null hypothesis if the value is smaller than the tabled value.

22 The Mann-Whitney Test: Normal Approximation When n 1 and n 2 are sufficiently large (e.g., n 1,n 2 10) the distribution of rank sums becomes roughly normal and you can use a normal approximation, evaluated against a critical z value, to test for significance. U U nn n n N U U zu U

23 Wilcoxon s Signed Ranks Test This is a test for repeated-measures (within-subjects) research designs with two treatment conditions and is thus an alternative to the repeated-measures t-test The basic intuition behind the test is that: A real difference between the two treatments should cause the difference scores to be generally positive or negative If all the difference scores are ranked and signed (according to whether they represent increases + or decreases -), the ranks should be concentrated in either the positive or negative set.

24 Wilcoxon s Signed Ranks Test Again, the null and alternative hypotheses are a bit more vague than for the repeated measures t-test, but test for some sort of difference in central tendency H 0 : There is no difference between treatments. Therefore, there is no tendency for the ranks of difference scores to be generally positive or negative H 1 : There is a difference between treatments. Therefore, the ranks of the difference scores should be systematically positive or negative

25 Wilcoxon s Signed Rank Test: Steps The steps for computing the test statistic (T) are: 1. Compute the difference scores Non-Parametric Tests 2. Rank all the difference scores from smallest to largest absolute value and assign them positive or negative signs based on whether they represent an increment or decrement 3. Compute separate sums for the positively and negatively signed sets 4. T is the smaller of the resulting signed-rank-sums

26 Example (Suddath et al., 1990)

27 Example T = 9 N = 15 T crit is 25, which is greater than our test statistic, so we would reject the null hypothesis

28 Wilcoxon s Signed Ranks Test: Normal Approximation Again, when n is sufficiently large (e.g., n 20) the distribution of T becomes roughly normal and you can use a normal approximation, evaluated against a critical z value, to test for significance. T T n n1 4 n( n 1)(2n 2) 24 zt T T T n n1 T 4 n( n 1)(2n 1) 24

29 The Kruskal-Wallis One Way ANOVA Non-Parametric Tests This is a test for independent-measures research designs with more than two groups. As its name suggests, it is a nonparametric alternative to the parametric one-way ANOVA The basic intuition behind the test is analogous to that for the parametric one-way ANOVA: A real difference among treatments should cause the variability of scores between groups to be greater than the variability of scores within groups If all the scores are ranked the variability of rank-sums between groups should be greater than the variability of rank-sums within groups

30 The Kruskal-Wallis One Way ANOVA Non-Parametric Tests The null and alternative hypotheses are very similar to those in the parametric one-way ANOVA. H 0 : There is no difference between treatments. There is no tendency for ranks in any sample to be systematically higher or lower than in any other condition. H 1 : There are differences between treatments. The ranks in at least one condition are systematically higher or lower than in another treatment condition

31 Non-Parametric Tests k i k n i j i i T ij i n M M F C x M k T i k n T i i i j j i r r r n r H C Parametric ANOVA: Kruskal-Wallis: 1 1 within between df N k C df k 2 1 C N Comparison of Conceptual Formulas for the Parametric one-way ANOVA and Kruskal-Wallis Test:

32 The Kruskal-Wallis Test: Steps The steps for computing the test statistic (H) are: 1. Rank all scores from lowest to highest across all samples 2. Compute R, the sum of ranks in each sample 3. Plug into the following formula to solve for H: H 2 12 R i 3 N 1 N N n 1 i i

33 The Kruskal-Wallis Test: Normal Approximation As in the other rank-randomization tests, when N is sufficiently large, the distribution of rank-sums becomes approximately normal H is a linear combination (a weighted sum) of squared-rank sums, which means that it can be approximated by the distribution of a sum of squared normal variables For this reason, the significance of H is usually evaluated using a chi-squared distribution with k-1 degrees of freedom.

34 Friedman s Rank Test This is a test for repeated-measures research designs with more than two groups. It is the non-parametric analogue to a one-way repeated-measures ANOVA Just as the repeated measures ANOVA tests for consistent changes between individuals across treatment groups, Friedman s test looks for consistent rankings between individuals across treatment groups It can be used with any repeated-measures data, but is especially useful for measuring inter-rater agreement for rankings

35 The Friedman Test The null and alternative hypotheses are identical to those for the Kruskal-Wallis Test. H 0 : There is no difference between treatments. There is no tendency for ranks in any sample to be systematically higher or lower than in any other condition. H 1 : There are differences between treatments. The ranks in at least one condition are systematically higher or lower than in another treatment condition

36 The Friedman Test: Steps 2 The steps for computing the test statistic ( R ) are: 1. Rank scores across each treatment group for each individual 2. Compute R, the sum of ranks for each group 3. Plug into the following formula to compute the test statistic: 12 ( k 1) R 3 n( k 1) 2 k 2 R i nk( k 1) i

37 Friedman Test Example Sommelier Wine 1 Wine 2 Wine 3 A B C D E F G R (2) 3 n( k 1) k 2 2 R Ri nk( k 1) i 12 7(3)(4) (7)(4)

38 χ 2 Distribution Upper Tail Probability df

39 Friedman Test Example Sommelier Wine 1 Wine 2 Wine 3 A B C D E F G R (2) 3 n( k 1) k 2 2 R Ri nk( k 1) i 12 7(3)(4) (7)(4) crit 5.99, retain H 0 In this case, we would conclude that there is no significant difference in quality between the 3 wines or that the sommeliers are unable to discern any differences.

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