Suppose that two raters (Rater A and Rater B) each assign physical attractiveness scores (0 = not at all attractive to 10 = extremely attractive) to a set of seven facial photographs. Pearson’s r can be used as an index of interrater reliability or agreement on quantitative ratings. A correlation of +1 would indicate perfect rank-order agreement between raters, while an r of 0 would indicate no agreement about judgments of relative attractiveness. An r of .8 to .9 is considered desirable when reliability is assessed. For this example, Rater A’s score is the X variable and Rater B’s score is the Y variable. The ratings are as follows: Photo Rater A Rater B 1 3 5 2 5 5 3 8 9 4 7 8 5 6 4 6 10 9 7 5 4 Compute the Pearson correlation between the Rater A and Rater B attractiveness ratings. What is the obtained r value? Is your obtained r statistically significant (using α =.05, two tailed)? Are the Rater A and Rater B scores “reliable”? Is there good or poor agreement between raters?
Suppose that two raters (Rater A and Rater B) each assign physical attractiveness scores (0 = not at all attractive to 10 = extremely attractive) to a set of seven facial photographs. Pearson’s r can be used as an index of interrater reliability or agreement on quantitative ratings. A correlation of +1 would indicate perfect rank-order agreement between raters, while an r of 0 would indicate no agreement about judgments of relative attractiveness. An r of .8 to .9 is considered desirable when reliability is assessed. For this example, Rater A’s score is the X variable and Rater B’s score is the Y variable. The ratings are as follows: Photo Rater A Rater B 1 3 5 2 5 5 3 8 9 4 7 8 5 6 4 6 10 9 7 5 4 Compute the Pearson correlation between the Rater A and Rater B attractiveness ratings. What is the obtained r value? Is your obtained r statistically significant (using α =.05, two tailed)? Are the Rater A and Rater B scores “reliable”? Is there good or poor agreement between raters?
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.6: Summarizing Categorical Data
Problem 23PPS
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Suppose that two raters (Rater A and Rater B) each assign physical attractiveness scores (0 = not at all attractive to 10 = extremely attractive) to a set of seven facial photographs. Pearson’s r can be used as an index of interrater reliability or agreement on quantitative ratings. A
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Photo |
Rater A |
Rater B |
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1 |
3 |
5 |
|
2 |
5 |
5 |
|
3 |
8 |
9 |
|
4 |
7 |
8 |
|
5 |
6 |
4 |
|
6 |
10 |
9 |
|
7 |
5 |
4 |
- Compute the Pearson correlation between the Rater A and Rater B attractiveness ratings. What is the obtained r value?
- Is your obtained r statistically significant (using α =.05, two tailed)?
- Are the Rater A and Rater B scores “reliable”? Is there good or poor agreement between raters?
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