Tobacco companies have actively attempted to remake their public image by launching a youth smoking prevention advertisement campaign. Melanie Wakefield (a professor of applied psychology and researcher at the Center for Behavioral Research in Cancer in Victoria, Australia) and her colleagues conducted a study to assess the relation between exposure to tobacco companies' youth smoking prevention advertising and youth smoking-related beliefs, intentions, and behaviors. You use Professor Wakefield's research to design an experiment with two groups of high school students from Ghana. You show the tobacco company's youth smoking prevention ads to one of the groups of students every day for a week at the beginning of their math class. After the week, you assess the groups on the degree to which the students prefer to date/not date smokers, using a 5-point Likert scale. You do not have a prediction about the effect of the ads, as they are supposed to prevent smoking, but Professor Wakefield's findings suggested the ads might have the opposite effect if they have any effect at all. There are 49 students in each group. The first group, who saw the ads, scored an average of 3.4 with a sample standard deviation of 0.8 on the 5-point Likert scale. The second group, who did not see the ads, scored an average of 2.4 with a sample standard deviation of 0.9 on the 5-point Likert scale. You find that the estimated standard error of the difference in means is 0.1720, the estimated Cohen's d is the t statistic is 5.81, and the r2 is Using Cohen's d and Cohen's criteria for evaluating Cohen's d, this is a effect size. Using r2 and Cohen's criteria for evaluating r2, this is a effect size. Hint: There is some subjectivity when applying verbal labels to numerical values. If the Cohen's d or r2 value falls between two categories, acknowledge both categories-for example, small to medium if it is between small and medium.
Tobacco companies have actively attempted to remake their public image by launching a youth smoking prevention advertisement campaign. Melanie Wakefield (a professor of applied psychology and researcher at the Center for Behavioral Research in Cancer in Victoria, Australia) and her colleagues conducted a study to assess the relation between exposure to tobacco companies' youth smoking prevention advertising and youth smoking-related beliefs, intentions, and behaviors. You use Professor Wakefield's research to design an experiment with two groups of high school students from Ghana. You show the tobacco company's youth smoking prevention ads to one of the groups of students every day for a week at the beginning of their math class. After the week, you assess the groups on the degree to which the students prefer to date/not date smokers, using a 5-point Likert scale. You do not have a prediction about the effect of the ads, as they are supposed to prevent smoking, but Professor Wakefield's findings suggested the ads might have the opposite effect if they have any effect at all. There are 49 students in each group. The first group, who saw the ads, scored an average of 3.4 with a sample standard deviation of 0.8 on the 5-point Likert scale. The second group, who did not see the ads, scored an average of 2.4 with a sample standard deviation of 0.9 on the 5-point Likert scale. You find that the estimated standard error of the difference in means is 0.1720, the estimated Cohen's d is the t statistic is 5.81, and the r2 is Using Cohen's d and Cohen's criteria for evaluating Cohen's d, this is a effect size. Using r2 and Cohen's criteria for evaluating r2, this is a effect size. Hint: There is some subjectivity when applying verbal labels to numerical values. If the Cohen's d or r2 value falls between two categories, acknowledge both categories-for example, small to medium if it is between small and medium.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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![# 7. Effect Size for Independent-Measures t Test - Cohen's d and r Squared
Tobacco companies have actively attempted to remake their public image by launching a youth smoking prevention advertisement campaign. Melanie Wakefield, a professor of applied psychology and researcher at the Center for Behavioral Research in Cancer in Victoria, Australia, and her colleagues conducted a study to assess the relation between exposure to tobacco companies’ youth smoking prevention advertising and youth smoking-related beliefs, intentions, and behaviors.
You use Professor Wakefield’s research to design an experiment with two groups of high school students from Ghana. You show the tobacco company’s youth smoking prevention ads to one of the groups of students every day for a week at the beginning of their math class. After the week, you assess the groups on the degree to which the students prefer to date/not date smokers, using a 5-point Likert scale. You do not have a prediction about the effect of the ads, as they are supposed to prevent smoking, but Professor Wakefield’s findings suggested the ads might have the opposite effect if they have any effect at all.
There are 49 students in each group. The first group, who saw the ads, scored an average of 3.4 with a sample standard deviation of 0.8 on the 5-point Likert scale. The second group, who did not see the ads, scored an average of 2.4 with a sample standard deviation of 0.9 on the 5-point Likert scale.
You find that the estimated standard error of the difference in means is 0.1720, the estimated Cohen’s d is [blank], the t statistic is 5.81, and the r² is [blank].
Using Cohen’s d and Cohen’s criteria for evaluating Cohen’s d, this is a [blank] effect size. Using r² and Cohen’s criteria for evaluating r², this is a [blank] effect size.
**Hint**: There is some subjectivity when applying verbal labels to numerical values. If the Cohen’s d or r² value falls between two categories, acknowledge both categories—for example, small to medium if it is between small and medium.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F370a53ca-e37b-40d3-b806-b1d79776021c%2F7528ad52-e4e6-4506-b502-46c3a6036f96%2Fxj3vr1_processed.png&w=3840&q=75)
Transcribed Image Text:# 7. Effect Size for Independent-Measures t Test - Cohen's d and r Squared
Tobacco companies have actively attempted to remake their public image by launching a youth smoking prevention advertisement campaign. Melanie Wakefield, a professor of applied psychology and researcher at the Center for Behavioral Research in Cancer in Victoria, Australia, and her colleagues conducted a study to assess the relation between exposure to tobacco companies’ youth smoking prevention advertising and youth smoking-related beliefs, intentions, and behaviors.
You use Professor Wakefield’s research to design an experiment with two groups of high school students from Ghana. You show the tobacco company’s youth smoking prevention ads to one of the groups of students every day for a week at the beginning of their math class. After the week, you assess the groups on the degree to which the students prefer to date/not date smokers, using a 5-point Likert scale. You do not have a prediction about the effect of the ads, as they are supposed to prevent smoking, but Professor Wakefield’s findings suggested the ads might have the opposite effect if they have any effect at all.
There are 49 students in each group. The first group, who saw the ads, scored an average of 3.4 with a sample standard deviation of 0.8 on the 5-point Likert scale. The second group, who did not see the ads, scored an average of 2.4 with a sample standard deviation of 0.9 on the 5-point Likert scale.
You find that the estimated standard error of the difference in means is 0.1720, the estimated Cohen’s d is [blank], the t statistic is 5.81, and the r² is [blank].
Using Cohen’s d and Cohen’s criteria for evaluating Cohen’s d, this is a [blank] effect size. Using r² and Cohen’s criteria for evaluating r², this is a [blank] effect size.
**Hint**: There is some subjectivity when applying verbal labels to numerical values. If the Cohen’s d or r² value falls between two categories, acknowledge both categories—for example, small to medium if it is between small and medium.
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