Understanding Research Ethics and Applications: A Guide for Researchers

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

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

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

4. WHY REVISIT THIS NOW? • You are about to conduct research at UVM • We want you to know more about the local process • You have learned more since the earlier introduction that may provide a context • 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? • Its chief function: Considers costs and benefits of the research • Asks, is the research question worth the use of human participants? • Because human participants do not need to participate in studies, their rights are the highest priority

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

7. Lay Summary • No jargon! (hence “lay”) • 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 • 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 • Example of combined form -- Goodwin p. 50 • For simple exempt study not terribly complicated

9. Issues or questions? • Yes? No? • 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 • Nature of “test statistics” (vs. descriptive) • e.g., t and F, so far…. • Suppose the null hypothesis is true, what is the value of “Treatment”? • Suppose “Treatment”=0, what is the value of TestStat? • What happens as “Treatment” gets larger: to TestStat? to prob(TestStat|Null true) -- “p=”?

11. Some ideas behind the stats (cont.) • 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... • For independent groups means --- • t(28) means there were 30 scores, because for this, df=(n1-1)+(n2-1)= n1+n2-2 • For paired means (repeated measures) • t(28) means there were 29 pairs of matched scores, df = n-1 pairs of related scores • Examples (blasts from the past)...

13. ANSWERS REVISITED“Having Fun” Example Inferential Statistics

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

15. So, df in articles, etc.? Can be useful…(continued) • 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 Error 24 Total 26 • If equal size groups, how many Ps per group? • Example... “F(2,215)=5.314” 

16. MEM 2002: Between-Groups

17. So, df in articles, etc.? Can be useful... (continued) • 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 Effect k-1 Error (n-1)(k-1) Total nk-1 = N-1 scores • Example... “F(3,69)=5.60” 

18. Mandel et al. (1995), from Handout last class: “Listening times to sound stimuli” • “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.” • ANOVA Summary Table Source df SS MS F p Subjects 23 Listen Time 3 5.60 .0017 Error 69 Total 95 For “F(3,69), how many scores were there? 3+69=72 + 24 Ss = 96 (24 Ss x 4 scores each!) • 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 • Recall the abstract definition of a test statistic: • We want to find effects if they’re there, and Power is the probability of doing that. • The larger the test statistic, the greater our chance of doing that. • 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) • Level of significance used (); e.g. more power if .05 than if .01 (set a priori) • Size of the treatment effect: more power if larger effects (increase numerator) • Size of the sample: more power if N larger (decrease denominator by increasing df for error) • Experimental control and procedure (increase power by decreasing error variability in denominator) • Choice of design -- often within-Ss more powerful by reducing individual differences -- “error variance” • Review Goodwin pp. 136-141.

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