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# Kruskal Wallis test, Friedman test, Spearman Correlation

Kruskal Wallis test, Friedman test, Spearman Correlation

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### Kruskal Wallis test, Friedman test, Spearman Correlation

1. 1. Kruskal-Wallis test Friedman test Spearman’s Correlation Dr. S. A. Rizwan, M.D., Public Health Specialist, Saudi Board of Preventive Medicine, Riyadh, Kingdom of Saudi Arabia. With thanks to Dr. Tajali Nazir Shora
2. 2. Checklist of four questions • Q1. What scales of measurement has been used? • Q2. Which hypothesis has been tested? • Q3. If the hypothesis of difference has been tested, are the samples independent or dependent? • Q4. How many sets of measures are involved? 2
3. 3. Kruskal-Wallis test 3
4. 4. KW test • The Kruskal-Wallis test is a nonparametric test that can be used to determine whether three or more independent samples were selected from populations having the same distribution. H0: There is no difference in the group medians Ha: There is a difference in the group medians 4
5. 5. KW test • This test is appropriate for use under the following circumstances: – we have three or more conditions that you want to compare; – each condition is performed by a different group of participants; – the data do not meet the requirements for a parametric test. 5
6. 6. Requirements • The data: – not normally distributed; – if the variances for the different conditions are markedly different; – if the data are measurements on an ordinal scale. • If the data meet the requirements for a parametric test, it is better to use a one-way independent-measures Analysis of Variance (ANOVA) 6
7. 7. Given three or more independent samples, the test statistic H for the Kruskal-Wallis test is Where: N represents the number of participants, Tc is the rank total for each group nc is the number of participants in each group Reject the null hypothesis when H is greater than the critical number. (always use a right tail test.) KW test 7
8. 8. Procedure 1 • Combine the observations of the various groups 2 • Arrange them in order of magnitude from lowest to highest 3 • Assign ranks to each of the observations and replace them in each of the groups 4 • Original ratio data has therefore been converted into ordinal or ranked data 5 • Ranks are summed in each group and the test statistic, H is computed 6 • Ranks assigned to observations in each of the groups are added separately to give rank sums 8
9. 9. Example • Does physical exercise alleviate depression? – We find some depressed people and check that they are all equivalently depressed to begin with. Then we allocate each person randomly to one of three groups: • no exercise; • 20 minutes of jogging per day; or • 60 minutes of jogging per day. – At the end of a month, we ask each participant to rate how depressed they now feel, on a Likert scale that runs from 1 ("totally miserable") through to 100 (ecstatically happy"). 9
10. 10. Appropriate test? • We have three separate groups of participants. • In each group each participant gives us a single score on a rating scale. (Ratings are examples of an ordinal scale of measurement, and so the data are not suitable for a parametric test) 10
11. 11. KW test The Kruskal-Wallis test will tell us if the differences between the groups are so large that they are unlikely to have occurred by chance. 11
12. 12. KW test 12
13. 13. Step 1: Rank all of the scores 13
14. 14. 14
15. 15. Step 2: Find "Tc", the total of the ranks for each group. Just add together all of the ranks for each group in turn. • Tc1 (the rank total for the "no exercise" group) is 76.5. • Tc2 (the rank total for the "20 minutes" group) is 79.5. • Tc3 (the rank total for the "60 minutes" group) is 144. 15
16. 16. Step 3: Find "H". 16
17. 17. KW test 17
18. 18. 18
19. 19. Step 4: Calculate the degrees of freedom • The degrees of freedom is the number of groups minus one. Degrees of Freedom = Number of Groups - 1 • Here we have three groups, and so we have 2 d.f. 19
20. 20. Step 5: Assess the significance of H • Assessing the significance of H depends on – the number of participants and – the number of groups. 20
21. 21. KW test • Three groups, with ≤ 5 participants in each group, then use the special table for small sample sizes. • For more than five participants per group, treat H as Chi-Square. 21
22. 22. 22
23. 23. 23
24. 24. KW test • H is statistically significant if it is equal to or larger than the critical value of Chi-Square for particular df • Here, we have 8 participants per group, and so we treat H as Chi-Square. • H is 7.27, with 2 df 24
25. 25. KW test • Look along the row that corresponds to number of degrees of freedom. • We look along the row for 2 df • We compare our obtained value of H to each of the critical values in that row of the table, starting on the left hand side and stopping once our value of H is no longer equal to or larger than the critical value. 25
26. 26. KW test • So here, we start by comparing our H of 7.27 to 5.99. With 2 degrees of freedom, a value of Chi- Square as large as 5.99 is likely to occur by chance only 5 times in a hundred: i.e. it has a p of .05. • Our obtained value of 7.27 is even larger than this, and so this tells us that our value of H is even less likely to occur by chance. • Our H will occur by chance with a probability of less than 0.05. 26
27. 27. KW test • Move on, and compare our H to the next value in the row, 9.21. 9.21 will occur by chance one time in a hundred, i.e. with a p of .01. However, our H of 7.27 is less than 9.21, not bigger than it. • This tells us that our value of H is not so large that it is likely to occur with a probability of 0.01. 27
28. 28. KW test • The likelihood of obtaining a value of H as large as the one we've found, purely by chance, is somewhere between 0.05 and 0.01 - i.e. pretty unlikely, and so we would conclude that there is a difference of some kind between our three groups. 28
29. 29. KW test • Note that the Kruskal-Wallis test merely tells you that the groups differ in some way: you need to inspect the group means or medians • However in this particular case, the interpretation seems fairly straightforward: exercise does seem to reduce self-reported depression, but only in the case of participants who are doing 60 minutes. • There seems to be no difference between those participants who took 20 minutes of exercise per day, and those who did not exercise at all. 29
30. 30. KW test • We could write this up as follows: • "A Kruskal-Wallis test revealed that there was a significant effect of exercise on depression levels (H (2) = 7.27, p < .05). • Inspection of the group means suggests that compared to the "no exercise" control condition, depression was significantly reduced by 60 minutes of daily exercise, but not by 20 minutes of exercise". 30
31. 31. KW test • The post hoc test for a significant KW is called Dunn’s test with Bonferroni correction for multiple testing 31
32. 32. Friedman Test 32
33. 33. Friedman Test • This is similar to the Wilcoxon singed rank test, except that we can use it with three or more conditions. • Each subject participates in all of the different conditions of the experiment. 33
34. 34. FT • Does background music affect the performance of Laboratory workers? • We take a group of five workers, and measure each worker's productivity (in terms of the number of Stool Microscopies per hour) thrice – once while the worker is listening to “Easy- listening music’, – once while the worker is listening to ‘Marching Band music,’ and – once while the same worker is working in ‘Silence.’34
35. 35. Step 1: Rank each subject's scores individually Worker No Music Easy Listening Marching Band Raw Score Ranked Score Raw Score Ranked Score Raw Score Ranked Score 1 4 1 5 2 6 3 2 2 1 7 2.5 7 2.5 3 6 1.5 6 1.5 8 3 4 3 1 7 3 5 2 5 3 1 8 2 9 3 35
36. 36. Step 2: Find the rank total for each condition Worker No Music Easy Listening Marching Band Raw Score Ranked Score Raw Score Ranked Score Raw Score Ranked Score 1 4 1 5 2 6 3 2 2 1 7 2.5 7 2.5 3 6 1.5 6 1.5 8 3 4 3 1 7 3 5 2 5 3 1 8 2 9 3 Total 5.5 11 13.5 36
37. 37. Step 3: Work out χr2 • Where : – C is the number of conditions, – N is the number of subjects – ΣTc2 is the sum of the squared rank totals for each condition. 37
38. 38. FT • To get ΣTc2: • Take each rank total and square it. Worker No Music Easy Listening Marching Band Raw Score Ranked Score Raw Score Ranked Score Raw Score Ranked Score 1 4 1 5 2 6 3 2 2 1 7 2.5 7 2.5 3 6 1.5 6 1.5 8 3 4 3 1 7 3 5 2 5 3 1 8 2 9 3 Total 5.5 11 13.5 Square of Rank Total 30.25 121 182.25 38
39. 39. FT 39
40. 40. Step 4: Calculate degrees of freedom • The degrees of freedom are given by the number of conditions minus one. C - 1 = 3 - 1 = 2. 40
41. 41. Step 5: Assess the statistical significance of Xr2 • It depends on the number of subjects and the number of groups. – more than 9 subjects, we can use a Chi-Square table. – less than nine subjects, special tables of critical values are available. • Compare obtained Xr2 value to the critical value of Chi-Square for df 41
42. 42. Number of Subjects Number of Conditions 42
43. 43. FT • If your obtained value of Xr2 is bigger than the critical Chi-Square value, the difference between conditions is statistically significant. • As with the Kruskal- Wallis test, Friedman's test only tells you that some kind of difference exists; • Inspection of the median score for each condition will usually be enough • Post hoc test for FT is called Dunn’s test 43
44. 44. Spearman’s Correlation 44
45. 45. Types of correlation • Pearson’s • Spearman’s • Hoeffding’s D • Distance correlation • Mutual Information and the Maximal Information Coefficient 45
46. 46. Correlation coefficient • A correlation coefficient is a succinct (single- number) measure of the strength of association between two variables. • There are various types of correlation coefficient for different purposes. • Commonly used correlation coeff. are – Pearson's r – Spearman's rho 46
47. 47. Difference between Pearson’s r and Spearman’s rho • Spearman’s rho makes fewer assumptions about the nature of the data on which the correlation is to be performed • The data need only be measured on an ordinal scale • Pearson’s “r” is a measure of the strength of the linear relationship between two variables • Spearman’s rho is a measure of the strength of the monotonic relationship between them. 47
48. 48. Difference between Pearson’s r and Spearman’s rho • If a monotonic relationship exists, it simply means that one of the variables increases (or decreases) by some amount when the value of the other variable changes. • A linear relationship is thus a special case of a monotonic relationship. • Thus, if there is a monotonic but non-linear relationship between two variables, it’s better to use Spearman’s rho because Pearson’s “r” will tend to underestimate the strength of the relationship. 48
49. 49. Difference between Pearson’s r and Spearman’s rho • Spearman’s rho won’t be “fooled” by the fact that the relationship isn’t a linear one. 49
50. 50. Monotonic and non-monotonic 50 Linear relationships
51. 51. Monotonic and non-monotonic • Monotonic variables increase (or decrease) in the same direction, but not always at the same rate. • Linear variables increase (or decrease) in the same direction at the same rate. 51
52. 52. How to decide a correlation test? • Before running a test, you should make a scatter plot first to view the overall pattern of your data. • The strength and direction of a monotonic relationship between two variables can be measured by the Spearman Rank-Order Correlation. • If your variables are monotonic and linear, a more appropriate test might be Pearson’s Correlation as long as the assumptions for Pearson’s are met. for example, if your data is highly skewed, has high kurtosis, or is heteroscedastic, you cannot use Pearson’s. You can, however, still use Spearman’s, which is a non- parametric test. 52
53. 53. Example for Spearman’s rho • Vitamin treatment enhances the memory of people who take it • If there is a correlation between the number of vitamin treatments that a subject has had, and their performance on some memory test. We would have two scores for each subject: number of vitamin treatments, and that person’s memory-test score. 53
54. 54. Spearman’s rho 54
55. 55. Spearman’s rho 55
56. 56. Problems In Interpreting Correlations • Correlation does not imply causality • Factors affecting the size of the correlation – The smaller the sample, the more likely it is to show sampling variation, and the more variable the correlation can be. – Thus, when a correlation coefficient is calculated, it does not represent the one-and-only correlation between those two variables for that population. – Different samples might produce higher or lower correlations. 56
57. 57. Sample and population correlations Here are the limits within which 80% of sample r’s will fall, when the true correlation (i.e., in the population) is zero 57
58. 58. Requirements for Pearson’s correlation Linearity of the relationship Homoscedasticity Effect of discontinuous distributions 58
59. 59. Linearity of the relationship • Pearson’s r is a measure of the linear relationship between two variables and Spearman’s rho is a measure of the monotonic relationship between them. • Two variables may have a very strong relationship to each other, but of a non-linear or non-monotonic kind (for example they might have a curvilinear relationship). • If so, the correlation coefficient will underestimate the strength of the relationship between the two variables. • This is why you should always make a scatterplot of the data, so that you can look at the trend before calculating a correlation. 59
60. 60. Homoscedasticity • Homoscedasticity (equal variability): For Pearsons’ r to be valid, scores should have a reasonably constant amount of variability at all points in their distribution 60
61. 61. Effect of discontinuous distributions 61
62. 62. Take home messages • KW test NP equivalent of One Way ANOVA, >2 independent groups • FT test NP equivalent of Repeated Measures ANOVA, >2 paired groups • Spearman’s rank order correlation NP equivalent of Pearson’s correlation for monotonic relationships 62
63. 63. Thank you Kindly email your queries to sarizwan1986@outlook.com 63