# Provide a screen shot 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.

October 8, 2021
###### How did your analysis compare and contrast with your peers’? Did they see something you did not? What insights can you bring to their thinking?
October 8, 2021

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 ( 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 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).
• α err prob = standard alpha level.
• Sample size group 1 = n1 from Section 1 above.
• Sample size group 2 = n2 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 n1, n2, and total 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.