Suppose that two raters (rater A and rater B) each assign physical attractiveness scores (0 = not at all

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 7 facial photographs. Pearson r is a common index of inter-rater 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. Generally r’ s of .8 to .9 are considered desirable when reliability is assessed.

Compute the Pearson correlation between the Rater A/Rater B attractiveness ratings. What is the obtained r value?
Is your obtained r statistically significant? (Unless otherwise specified, use a = .05 two tailed for all significance tests).
Are the rater A and rater B scores "reliable"? Is there good or poor agreement between raters?

When the Titanic capsized, was there a difference in the probability of being saved for women passengers in first class versus women passengers in third class? There were a total of 309 women in first and third class.
- Compute a phi coefficient using the observed cell frequencies in the table above.
- Also compute a chi-square statistic for the observed frequencies in the table above.
- Write up your results in paragraph form. Was there a statistically significant association between being in first class and being saved, when we look at the passenger survival data from the Titanic? How strong was the association between being in first class and being saved (versus being in third class and being lost)?
To complete this question, use the data from Table 9.2 on page 375 of your Warner text which shows GNP per capita (X), and mean life satisfaction, for a set of 19 nations (Y). Enter the data into IBM SPSS.

Run the <Analyze><Descriptives> procedure; obtain the mean and standard deviation for each variable.
Do appropriate preliminary data screening, then run the bivariate regression to predict life satisfaction from GNP. Write up the results in paragraph form including: statistical significance, effect size, and nature of relationship. Use the Save command to save the unstandardized residuals as a new variable in your PASW worksheet. Include effect size and nature of relationship even if the regression is not statistically significant.
Show that you can use the sums of squares in the ANOVA table in the IBM SPSS printout to reproduce the value of R ² on the IBM SPSS printout.

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