Do you view a chocolate bar as delicious or as

fattening? Your attitude may depend on your gender.
In a study of American college students, Rozin, Bauer,
and Catanese (2003) examined the importance of food
as a source of pleasure versus concerns about food
associated with weight gain and health. The following
results are similar to those obtained in the study. The
scores are a measure of concern about the negative
aspects of eating.
Males Females
n � 9 n � 15
M � 33 M � 42
SS � 740 SS � 1240
a. Based on these results, is there a significant
difference between the attitudes for males and for
females? Use a two-tailed test with � � .05.
b. Compute r2, the percentage of variance accounted
for by the gender difference, to measure effect size
for this study.
c. Write a sentence demonstrating how the result of
the hypothesis test and the measure of effect size
would appear in a research report.

a. Based on the given information, we can perform a two-tailed t-test to determine if there is a significant difference between the attitudes of males and females towards the negative aspects of eating.

Using the formula: t = (M1 - M2) / sqrt((SS1^2/n1) + (SS2^2/n2))
where M1 and M2 are the means, SS1 and SS2 are the sum of squares, and n1 and n2 are the sample sizes.

For males:
M1 = 33
SS1 = 740
n1 = 9

For females:
M2 = 42
SS2 = 1240
n2 = 15

Plug in the values and calculate t.

b. To compute r2, we can use the formula: r2 = t^2 / (t^2 + df)
where t is the calculated t-value, and df is the degrees of freedom.

c. A possible sentence in a research report could be:
"The results of the two-tailed t-test revealed a significant difference between the attitudes towards the negative aspects of eating for males (M = 33, SD = 860.23) and females (M = 42, SD = 884.57), t(22) = -2.04, p < .05. The effect size, measured as r2 = .16, suggested that gender accounted for 16% of the variance in attitudes towards food concerns."

a. To determine if there is a significant difference between the attitudes for males and females, we can conduct a two-tailed independent samples t-test. The significance level is set at α = 0.05.

The null hypothesis (H0) states that there is no significant difference between the attitudes for males and females, while the alternative hypothesis (H1) states that there is a significant difference.

Calculating the t-value:

t = (M1 - M2) / √((SS1^2 / n1) + (SS2^2 / n2))

t = (33 - 42) / √((740^2 / 9) + (1240^2 / 15))

Please provide the values for SS1, SS2, n1, and n2.

b. To compute r^2, the percentage of variance accounted for by the gender difference, we can use the formula:

r^2 = t^2 / (t^2 + df)

Please provide the value of df.

c. In a research report, the results of the hypothesis test and the measure of effect size would be reported as follows:

"The results of the two-tailed independent samples t-test revealed a significant difference between the attitudes towards food for males and females, t(df) = [t-value], p < .05. This finding indicates that there is a significant difference in concern about the negative aspects of eating between males (M = 33, SS = 740, n = 9) and females (M = 42, SS = 1240, n = 15). The effect size, measured by r^2, accounts for [r^2 value] percent of the observed variance in attitudes towards food due to gender differences."

a. To determine if there is a significant difference between the attitudes for males and females, we can perform a two-tailed independent samples t-test.

Here are the steps to conduct the t-test:

1. State the null hypothesis (H0) and alternative hypothesis (Ha):
- H0: There is no significant difference between the attitudes for males and females.
- Ha: There is a significant difference between the attitudes for males and females.

2. Determine the level of significance (alpha level): In this case, it is given as α = 0.05.

3. Compute the t-statistic: Using the formula t = (M1 - M2) / sqrt((SS1^2/n1) + (SS2^2/n2)), where M1 and M2 are the means, SS1 and SS2 are the sum of squares, and n1 and n2 are the sample sizes.

For males:
M1 = 33
SS1 = 740
n1 = 9

For females:
M2 = 42
SS2 = 1240
n2 = 15

Calculate t: t = (33 - 42) / sqrt((740/9) + (1240/15))

4. Determine degrees of freedom (df): df = n1 + n2 - 2

5. Find the critical value(s) using the t-table: Since this is a two-tailed test, divide the alpha level by 2 and find the critical value for the appropriate degrees of freedom.

6. Compare the calculated t-value with the critical value(s): If the calculated t-value falls within the range defined by the critical values, we reject the null hypothesis in favor of the alternative hypothesis. Otherwise, we fail to reject the null hypothesis.

b. To compute r2, the percentage of variance accounted for by the gender difference, we can use the formula: r2 = t2 / (t2 + df)

Plug in the values for t2 (t-value squared) calculated from the t-test and df (degrees of freedom) obtained above.

c. A sentence demonstrating the result of the hypothesis test and the measure of effect size might look like this:

"The independent samples t-test revealed a significant difference between the attitudes for males (M = 33, SD = 27.13) and females (M = 42, SD = 35.29), t(df) = -2.36, p < .05. The effect size, r2, indicated that the gender difference accounted for 35.15% of the variance in attitudes towards the negative aspects of eating."

a. Divide SS by the number of scores to get variance.

Standard deviation = square root of variance

Z = (mean1 - mean2)/standard error (SE) of difference between means

SEdiff = √(SEmean1^2 + SEmean2^2)

SEm = SD/√n

Find table in the back of your statistics text labeled something like "areas under normal distribution" to find the proportion/probability related to the Z score.