1. Ethics of Research - Review and Application + Some “Catch-up” Items Lawrence R. Gordon Psychology Research Methods I

2. Bystander Response to Arterial Bleeding 4 Shotland & Heinold (1985) 4 Research question? 4 Methodological issues (relating to participants’ rights, informed consent, deception, etc.) 4 Overall YEA/NAY? 4 Revisions?

3. Southern Culture of Honor 4 Cohen, Nisbett, Bowdle, & Schwarz (1996) 4 Research question? 4 Methodological issues (relating to participants’ rights, informed consent, deception, etc.) 4 Overall YEA/NAY? 4 Revisions?

4. WHY REVISIT THIS NOW? 4 You are about to conduct research at UVM 4 We want you to know more about the local process 4 You have learned more since the earlier introduction that may provide a context 4 Resources: UVM Human Subjects site: –http:/www.uvm.edu/~reshmpg/test/irb-home.htm –http://www.uvm.edu, then Research, Human Subjects

5. What Does the IRB Do? 4 Its chief function: Considers costs and benefits of the research 4 Asks, is the research question worth the use of human participants? 4 Because human participants do not need to participate in studies, their rights are the highest priority

6. Submitting Protocols 4 In general --- wide range 4 As part of a class –Exempt from review –Expedited review –Full review: IRB meetings 4 Strong focus on Informed Consent –Lay summary –Consent Form –Often combined

7. Lay Summary 4 No jargon! (hence “lay”) 4 Elements: Title Invitation to participate Aims - hypothesis Background - WHY conducted Procedures - include time commitment Risks/Discomforts/Inconveniences Benefits - personal & societal Costs Many optional elements

8. Statement of Consent 4 Elements Have read lay summary Understand procedure, risks, and benefits Participation voluntary; may withdraw any time Confidentiality to extent of the law Whom to contact if questions Signature Sometimes sign certifying a debriefing was given 4 Example of combined form -- Goodwin p. 50 For simple exempt study not terribly complicated

9. Issues or questions? 4 Yes? No? 4 Then we’ll move on to some further ideas in statistics that may be of help in understanding your analyses and output

10. Some ideas behind the statistics 4 Nature of “test statistics” (vs. descriptive ) –e.g., t and F, so far…. 4 Suppose the null hypothesis is true, what is the value of “Treatment”? 4 Suppose “Treatment”=0, what is the value of TestStat? 4 What happens as “Treatment” gets larger: to TestStat? to prob(TestStat|Null true) -- “p=”?

11. Some ideas behind the stats (cont.) 4 What is this “df” thing? –E.g.,, for n scores –df = n-1 here, why? what’s it mean? – Kinda “techie,” but if the mean, X-bar, is known, then only n-1 scores are “free to vary,” hence only n-1 “degrees of freedom” or “df” –Example -- suppose you know the mean of 3 scores is 10, then if 2 are:the third must be: 12, 8? 8,7? 13, 12?

12. So, df in articles, etc.? Can be useful... 4 For independent groups means --- –t(28) means there were 30 scores, because for this, df=(n 1 -1)+(n 2 -1)= n 1 +n 2 -2 4 For paired means (repeated measures) –t(28) means there were 29 pairs of matched scores, df = n-1 pairs of related scores 4 Examples (blasts from the past)...

13. ANSWERS REVISITED “Having Fun” Example Inferential Statistics

14. Repeated-measures Definitional Example 4 “Family therapy for anorexia” (1994) 4 SPSS -- standard analysis for paired-samples:

15. So, df in articles, etc.? Can be useful…(continued) 4 For k independent groups means --- –“F(2,24)” means that there were 3 levels of the IV  “Effect” df = k-1 24 “df for error”  ”Error” df = 24 here 27 scores in all  Effect df + Error df = N-1 Sourcedf Effect 2 Error24 Total26 If equal size groups, how many Ps per group? 4 Example... “F(2,215)=5.314” 

16. MEM 2002: Between-Groups

17. So, df in articles, etc.? Can be useful... (continued) 4 For k levels in repeated-meas ANOVA --- –F(2,24) means that there were 3 levels of the IV  “Effect” df = k-1 24 “df for error”  ”Error” df = 24 here Error = 2(n-1)=24, so n-1=12  13 Participants! ?? scores in all  Ss df + Effect df + Error df = N-1 Source df Subjects n-1 Effectk-1 Error(n-1)(k-1) Totalnk-1 = N-1 scores 4 Example... “F(3,69)=5.60” 

18. Mandel et al. (1995), from Handout last class: “Listening times to sound stimuli” 4 “Across all 24 subjects {itemized values of 4 means}…an [ANOVA] revealed …{means}were significantly different with a main effect of name category, F(3,69)=5.60, p=.0017.” 4 ANOVA Summary Table SourcedfSSMS F p Subjects23 Listen Time 35.60.0017 Error69 Total95 For “F(3,69), how many scores were there? 3+69=72 + 24 Ss = 96 (24 Ss x 4 scores each!) 4 EXAMPLE (SPSS -- Memory)…”F(2,434)=27.562” 

19. MEM 2002: Within-Ss

20. MEM 2001: Within-Ss N=200 Ps

21. Some ideas behind the Statistics…POWER 4 Recall the abstract definition of a test statistic: 4 We want to find effects if they’re there, and Power is the probability of doing that. 4 The larger the test statistic, the greater our chance of doing that. 4 Therefore, we want to maximize the numerator and minimize the denominator, but how?

22. Influences on Power of the NHST … Pr(Reject null|Null false) 4 Level of significance used (  ); e.g. more power if.05 than if.01 (set a priori) 4 Size of the treatment effect: more power if larger effects (increase numerator) 4 Size of the sample: more power if N larger (decrease denominator by increasing df for error) 4 Experimental control and procedure (increase power by decreasing error variability in denominator) 4 Choice of design -- often within-Ss more powerful by reducing individual differences -- “error variance” 4 Review Goodwin pp. 136-141.

23. WRAPUP 4 Will go on to final major experimental design next two classes --- factorial designs and their interaction effects. Extremely important --- most frequently used designs!