This is a three-part assignment in which you will demonstrate your ability to: Analyze components of a
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 named course number_assignment number_Last Name_First Initial , for example, PSY7625_u01a1_Smith_K . The deadline for submitting your work is 11:59 PM CST on Sunday of Week 2.
The data file for this assignment is bpstudy.sav , located 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 bpstudy.sav, 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 ( n 1) and sample size for females ( n 2).
- The means for males ( M 1) and females ( M 2) on HR1.
- The calculated mean difference ( M 1 – M 2).
- The standard deviations for males ( s 1) and females ( s 2) 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 (
M
1 –
M
2) by either
s
1 or
s
2. Violation of the homogeneity of variance assumption requires calculation of
S
pooled. Homogeneity assumed:
- Cohen's d = ( M 1 – M 2) ÷ s 1 or Cohen's d = ( M 1 – M 2) ÷ s 2
- To be comprehensive, report Cohen's d based on a calculation with s 1 and a calculation with s 2. 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 s 1 or s 2).
- α err prob = standard alpha level.
- Sample size group 1 = n 1 from Section 1 above.
- Sample size group 2 = n 2 from Section 1 above.
- Click Calculate .
Provide a screen shot 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 α err prob.
- Specify Power (1 - β) = .80.
- Set the Allocation ratio to 1 (i.e., equal sample sizes).
- Press Calculate.
Provide a screen shot of your G*Power output. Interpret the meaning of a .80 power value. Specifically, report the estimated n 1, n 2, 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 N s.
In conclusion, reflect on the importance of conducting an a priori power analysis in psychological research plans.
Deliverable: Word Document
