|Assignment ID Number||AFFGEHU83939HD|
|Type of Document||Essay|
This is a three-part assignment in which you will demonstrate your ability to:
Analyze components of a t test required for power analysis.
Compute and interpret a post hoc power analysis.
Compute and interpret an a priori power analysis.
In addition to IBM SPSS, you will also use the G*Power software to complete this assignment. Answer each question, providing IBM SPSS or G*Power analysis output when necessary to support your answer. Save your work in a Word file. The deadline for submitting your work is 11:59 p.m. Central time on Sunday of week 2.
The data file for this assignment, BP Study Dataset, is given in the resources. You will be conducting a post hoc power analysis and an a priori power analysis on an independent samples t test of gender as the grouping variable (male = 1; female = 2) and HR1 (heart rate) as the outcome variable. There are three sections of this assignment. After reporting the t test results, you will then conduct a post hoc power analysis followed by an a priori power analysis.
Section 1: Reporting the t Test Results
Using BP Study Dataset, conduct an independent samples t test in SPSS with gender as the grouping variable (male = 1; female = 2) and HR1 (heart rate) as the outcome variable.
Paste the SPSS output and then report:
The sample size for males ( n1) and sample size for females ( n2).
The means for males ( M1) and females ( M2) on HR1.
The calculated mean difference ( M1 M2).
The standard deviations for males ( s1) and females ( s2) on HR1.
The Levene test (homogeneity of variance assumption) and interpretation.
t, degrees of freedom, t value, and probability value. State whether or not to reject the null hypothesis. Interpret the results.
Calculate Cohen’s d effect size from the SPSS output and interpret it. Specifically, if the homogeneity of variance assumption is met, divide the mean difference ( M1 M2) by either s1 or s2. Violation of the homogeneity of variance assumption requires calculation of Spooled. Homogeneity assumed:
Cohen’s d = ( M1 M2) ÷ s1 or Cohen’s d = ( M1 M2) ÷ s2
To be comprehensive, report Cohen’s d based on a calculation with s1 and a calculation with s2. Round the effect size to two decimal places. Interpret Cohen’s d with Table 5.2 of your Warner text.
Section 2: Post Hoc Power Analysis
Open G*Power. Select the following options:
Test family = t tests.
Statistical test = Means: Difference between two independent groups (two groups).
Type of power analysis = Post hoc: Compute achieved power.
Tails(s) = Two.
Effect size d = Cohen’s d obtained from Section 1 above (using either s1 or s2).
? err prob = standard alpha level.
Sample size group 1 = n1 from Section 1 above.
Sample size group 2 = n2 from Section 1 above.
Provide a screenshot of your G*Power output. Report the observed power of this post hoc power analysis. Interpret the level of power in terms of rejecting a null hypothesis. Do you have sufficient power to reject a false null hypothesis? Interpret power in terms of committing a Type II error.
Section 3: A Priori Power Analysis
In G*Power, now select:
Type of power analysis = A priori: Compute required sample size.
Input effect size d from Section 1.
Specify ? err prob.
Specify Power (1 – ?) = .80.
Set the Allocation ratio to 1 (that is, equal sample sizes).
Provide a screenshot of your G*Power output. Interpret the meaning of a .80 power value. Specifically, report the estimated n1, n2, and total N to achieve obtain a power of .80. How many total subjects ( N) would be needed to obtain a power of .80? Would you have expected a required N of this size? Why or why not?
Next, in G*Power, change the Cohen’s d effect size value obtained in Section 1 and set it to .50 (conventional “medium” effect size). Click Calculate. How many total subjects ( N) are needed to obtain a power of .80? Compare and contrast these two estimated Ns.
In conclusion, reflect on the importance of conducting an a priori power analysis in psychological research plans.
Use: Warner, R. M. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.
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