Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.
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Transcript of Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.
![Page 1: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/1.jpg)
Stats 2022n
Non-Parametric Approaches to DataChp 15.5 & Appendix E
![Page 2: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/2.jpg)
OutlineChp 15.5 alternative to
Spearman Correlation Example Pearson correlation
Appendix E
Mann - Whitney U-Test Example independent measures t test
Wilcoxon signed-rank test Example repeated-measures t test
Kruskal – Wallace Test independent measures ANOVA)
Friedman Test (repeated measures ANOVA)
![Page 3: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/3.jpg)
A note on ordinal scales
• An ordinal scale :
Example – Grades
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A note on ordinal scales
Ordinal scales allow ranking
Example – Grades
![Page 5: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/5.jpg)
Why use ordinal scales?
• Some data is easier collected as ordinal
–
–
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The case for ranking data
1. Ordinal data needs to be ranked before it can be tested (via non-parametric tests)
2. Transforming data through ranking can be a useful tool
![Page 7: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/7.jpg)
Ranking data (rank transform) can be a useful tool
– If assumptions of a test are not (or cannot be) met…
– Common if data has:• Non linear relationship …• Unequal variance…• High variance …
– Data sometimes requires rank transformation for analysis
The case for ranking data
![Page 8: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/8.jpg)
Rank Transformation
Group A Group B8 54
98 8258 9278 23
A Ranks B Ranks1 38 64 75 2
![Page 9: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/9.jpg)
Rank Transformation
Group A Group B8 68 68 27 1
What if ties?....
![Page 10: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/10.jpg)
Ordinal TransformationRanking Data, If Ties
Groupscores
(ordered) rankrank
(tie adjusted)B 1 1 1B 2 2 2B 6 3 3.5B 6 4 3.5A 7 5 5A 8 6 7A 8 7 7A 8 8 7
Group A Group B8 68 68 27 1
A Ranks B Ranks1 52 73.5 73.5 7
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Chp 15.5Spearman Correlation
![Page 12: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/12.jpg)
Spearman Correlation
Only requirement – ability to rank order data• Data already ranked• Rank transformed data
Rank transform useful if relationship non-linear…
![Page 13: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/13.jpg)
Spearman Correlation
Participant x yA 4 9B 2 6C 2 2D 10 10E 3 8F 7 10
0 2 4 6 8 10 120
2
4
6
8
10
12
Participant x y x rank y rankA 4 9 4 4B 2 6 1.5 2C 2 2 1.5 1D 10 10 6 5.5E 3 8 3 3F 7 10 5 5.5
1 2 3 4 5 6 70
1
2
3
4
5
6
Example
![Page 14: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/14.jpg)
Spearman Correlation
x rank y rank xy x2 y2
4 4 16 16 161.5 2 3 2.25 41.5 1 1.5 2.25 16 5.5 33 36 30.253 3 9 9 95 5.5 27.5 25 30.25
21 21 90 90.5 90.5
x 21 y 21 x2 90.5 y2 90.5 xy 90
Calculation
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Spearman Correlation
x 21 y 21 x2 90.5 y2 90.5 xy 90
Calculation
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Spearman Correlation
x rank y rank D D2
4 4 0 01.5 2 0.5 0.251.5 1 -0.5 0.256 5.5 -0.5 0.253 3 0 05 5.5 0.5 0.25
0 1
= = =
Spearman Correlation Special Formula
![Page 17: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/17.jpg)
Spearman Correlation
x rank y rank D D2
4 4 0 01.5 2 0.5 0.251.5 1 -0.5 0.256 5.5 -0.5 0.253 3 0 05 5.5 0.5 0.25
0 1
𝑟 𝑠=1−6∑D2
n (n2−1 )=0.9714
Spearman Correlation Special Formula
v.s.
![Page 18: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/18.jpg)
Hypothesis testing with spearman
• Same process as Pearson – (still using table B.7)
![Page 19: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/19.jpg)
Appendix E
Mann - Whitney U-TestWilcoxon signed-rank test
Kruskal – Wallace Test Friedman Test
![Page 20: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/20.jpg)
Mann - Whitney U-Test
– Requirements• •
– Hypotheses: • •
![Page 21: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/21.jpg)
Mann - Whitney U-Test
Illustration
Sample A Ranks
Sample B Ranks
1 6
2 7
3 8
4 9
5 10
Sample A Ranks
Sample B Ranks
1 2
3 4
5 6
7 8
9 10
Extreme difference due to conditionsDistributions of ranks unequal
No difference due to conditionsDistributions of ranks unequal
![Page 22: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/22.jpg)
Mann - Whitney U-TestExample
Group ScoreA 8A 98A 58A 78A 42A 14A 63A 84B 54B 82B 92B 23B 53B 41B 28B 25
Group A Group B8 54
98 8258 9278 2342 5314 4163 2884 25
ranked (sorted) according to valuesGroup Score Rank
A 8 1A 14 2B 23 3B 25 4B 28 5B 41 6A 42 7B 53 8B 54 9A 58 10A 63 11A 78 12B 82 13A 84 14B 92 15A 98 16
![Page 23: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/23.jpg)
Mann - Whitney U-Test
Group RankA 1A 2B 3B 4B 5B 6A 7B 8B 9A 10A 11A 12B 13A 14B 15A 16
A Ranks B Ranks1 32 47 5
10 611 812 914 1316 15
A Ranks B Ranks1 0 3 22 0 4 27 4 5 2
10 6 6 211 6 8 312 6 9 314 7 13 616 8 15 7UA 37 UB 27
verify: 8*8= 64 37+27=64
U=27
Computing U by hand
![Page 24: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/24.jpg)
Mann - Whitney U-TestComputing U via formula
A Ranks B Ranks1 32 47 5
10 611 812 914 1316 15
R 73 63
RA = 73 RB = 63
= 8 U=27
![Page 25: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/25.jpg)
Mann - Whitney U-TestEvaluating Significance with U
U=27
alpha = 0.05, 2 tails, df(8,8)
Critical value = 13
U > critical value, we fail to reject the null
The ranks are equally distributed between samples
H0:
H1:
![Page 26: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/26.jpg)
Mann - Whitney U-TestWrite-Up
The original scores were ranked ordered and a Mann-Whitney U-test was used to compare the ranks for the n = 8 participants in treatment A and the n = 8 participants in treatment B. The results indicate no significant difference between treatments, U = 27, p >.05, with the sum of the ranks equal to 27 for treatment A and 37 for treatment B.
![Page 27: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/27.jpg)
Mann - Whitney U-TestEvaluating Significance Using Normal Approximation
¿(8 ) (8 )2
=32
¿√ (8 ) (8 ) (8+8+1 )12
=√90.66666667=9.52190
With n>20, the MW-U distribution tends to approximate a normal shape, and so, can be evaluated using a z-score statistic as an alternative to the MW-U table.
U=27 = 8 Note: n not > 20!
![Page 28: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/28.jpg)
Mann - Whitney U-TestEvaluating Significance Using Normal Approximation
¿32
¿9.52190
¿(27 )− (32 )9.52190
=−0.5251
alpha = 0.052 tailsCritical value: z = ± 1.96
-0.5251 is not in the critical regionFail to reject the null.
![Page 29: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/29.jpg)
Wilcoxon signed-rank test
Hypotheses:• H 0:
• H 1:
participant Condition 1 Ciondition 2 differenceA 1 3 -2B 6 2 4C 9 10 -1D 7 10 -3E 9 4 5F 3 9 -6G 2 2 0H 9 1 8I 9 1 8J 3 5 -2K 1 4 -3
Requirements• Two related samples (repeated measure)• Rank ordered data
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Wilcoxon signed-rank test
Participant DifferenceA -2B 4C -1D -3E 5F -6H 8I 8J -2K -3
Sorted and ranked by magnitudeParticipant Difference Rank
C -1 1A -2 2.5J -2 2.5D -3 4B 4 5E 5 6F -6 7H 8 8.5I 8 8.5
![Page 31: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/31.jpg)
Wilcoxon signed-rank test
Sorted and ranked by magnitudeParticipant Difference Rank
C -1 1A -2 2.5J -2 2.5D -3 4B 4 5E 5 6F -6 7H 8 8.5I 8 8.5
Positiverank scores
Negativerank scores
5 16 2.5
8.5 2.58.5 4
7 R 28 17
T= 17
![Page 32: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/32.jpg)
Wilcoxon signed-rank test
T= 17n=10alpha = .05two talescritical value = 8
T obtained > critical value, fail to reject the null
The difference scores are not systematically positive or systematically negative.
![Page 33: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/33.jpg)
Wilcoxon signed-rank test
The 11 participants were rank ordered by the magnitude of their difference scores and a Wilcoxon T was used to evaluate the significance of the difference between treatments. One sample was removed due to having a zero difference score. The results indicate no significant difference, n = 10, T = 17, p <.05, with the positive ranks totaling 28 and the negative ranks totaling 17.
Write up
![Page 34: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/34.jpg)
Wilcoxon signed-rank test
Participant Difference RankC 0 1.5A 0 1.5J -2 3D -3 4B 4 5
Positiverank scores
Negativerank scores
1.5 1.54 3
R 5.5 4.5
A note on difference scores of zero
Participant Difference RankC 0 1A 0 1.5J 0 1.5D -3 3B 4 4
Participant Difference RankC 0A 2 1.5J -2 1.5D -3 3B 4 4
N = 4
N = 5
N = 4
Positiverank scores
Negativerank scores
1.5 1.55 3
4 R 6.5 8.5
Positiverank scores
Negativerank scores
1.5 1.54 3
R 5.5 4.5
![Page 35: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/35.jpg)
Wilcoxon signed-rank testEvaluating Significance Using Normal ApproximationT= 17 n= 10 Note: n not > 20!
![Page 36: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/36.jpg)
Wilcoxon signed-rank testEvaluating Significance Using Normal Approximation
T = 17
alpha = 0.052 tailsCritical value: z = ± 1.96
-0.21847 is not in the critical regionFail to reject the null.
![Page 37: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/37.jpg)
Interim Summary
Calculation of Mann-Whitney or Wilcoxon is fair game on test.
When to use Mann-Whitney or Wilcoxon
• If data is already ordinal or ranked
• If assumptions of parametric test are not met
![Page 38: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/38.jpg)
Kruskal – Wallace Test
• Alternative to independent measures ANOVA
• Expands Mann – Whitney
• Requirements
• Null –
![Page 39: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/39.jpg)
Kruskal – Wallace Test
• Rank ordered data (all conditions)
![Page 40: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/40.jpg)
Kruskal – Wallace Test
For each treatment condition• n: n for each group• T: sum of ranks for each groupOverall• N: Total participants
Statistic identified with H
Distribution approximates same distribution as chi-squared (i.e. use the chi squared table)
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Friedman Test
• Alternative to repeated measures ANOVA
• Expands Wilcoxon test
• Requirements
• Null
![Page 42: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/42.jpg)
Friedman Test
• Rank ordered data (within each participant)
![Page 43: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/43.jpg)
Friedman Test
• For each treatment condition– n: n for each group– r: sum of ranks for each condition
• Overall– k: Total groups
• Uses distribution for hypothesis testing. Chi square statistic for ranks.
![Page 44: Stats 2022n Non-Parametric Approaches to Data Chp 15.5 & Appendix E.](https://reader037.fdocuments.in/reader037/viewer/2022103006/56649ef95503460f94c0ae73/html5/thumbnails/44.jpg)
Summary
Groups 2 3+
Independent measure
Repeated measure
Groups 2 3+
Independent measure
Repeated measure
Ratio Data Ranked Data