Ethics of Research - Review and Application + Some “Catch-up” Items
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Transcript of Ethics of Research - Review and Application + Some “Catch-up” Items
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Ethics of Research - Review and Application
+ Some “Catch-up” Items
Lawrence R. Gordon
Psychology Research Methods I
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Bystander Response to Arterial Bleeding Shotland & Heinold (1985) Research question? Methodological issues (relating to
participants’ rights, informed consent, deception, etc.)
Overall YEA/NAY? Revisions?
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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?
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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
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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
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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
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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
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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
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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
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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=”?
T estS ta tV ariab ility T rea tm en t E rror
V ariab ility E rror
( )
( )
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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 ?
V ar
SS
d f
X X
n
2
1
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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)...
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ANSWERS REVISITED“Having Fun” Example
Inferential Statistics
Independent Samples Test
-6.353 98 .000 -3.880Equal variancesassumed
Estimate of 10minute interval
t df Sig. (2-tailed)Mean
Difference
t-test for Equality of Means
Group Statistics
50 8.604 2.722
50 12.484 3.353
Experimental Conditions'More fun' (Captions)
'Less fun' (No Captions)
Estimate of 10minute interval
N Mean Std. Deviation
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Repeated-measuresDefinitional Example “Family therapy for anorexia” (1994) SPSS -- standard analysis for paired-samples:
Paired Samples Statistics
83.229 17 5.017
90.494 17 8.475
Before therapy
After therapy
Pair1
Mean N Std. Deviation
Paired Samples Correlations
17 .538 .026Before therapy& After therapy
Pair1
N Correlation Sig.
Paired Samples Test
-7.265
7.157
-4.185
16
.001
Mean
Std. Deviation
Paired Differences
t
df
Sig. (2-tailed)
Before therapy- After therapy
Pair 1
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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-1Source df
Effect 2Error 24
Total 26
• If equal size groups, how many Ps per group?
Example... “F(2,215)=5.314”
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MEM 2002: Between-GroupsDescriptives: Total # Correct, both Trials
TOTAL
73 28.7945 5.65184 16.00 41.00
73 31.9041 5.13746 16.00 42.00
72 29.7361 6.82372 8.00 41.00
218 30.1468 6.02503 8.00 42.00
Non-specific
Imagery Instructions
Imagery Instructionsplus Picture
Total
N Mean Std. Deviation Minimum Maximum
ANOVA
TOTAL
371.070 2 185.535 5.314 .006
7506.233 215 34.913
7877.303 217
Between Groups
Within Groups
Total
Sum ofSquares df Mean Square F Sig.
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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”
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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 TableSource df SS MS F p
Subjects 23Listen 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”
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MEM 2002: Within-Ss
Serial Position Curve
THIRDS
321
Me
an
# C
orre
ctly R
eca
lle
d
10.8
10.6
10.4
10.2
10.0
9.8
9.6
9.4
9.2
Tests of Within-Subjects Effects
Measure: MEASURE_1
176.113 2 88.057 27.562 .000
1386.554 434 3.195
SourceTHIRDS
Error(THIRDS)
Sum ofSquares df Mean Square F Sig.
Descriptive Statistics
10.7248 218
9.4633 218
9.9587 218
Total of 1st Third
Total of 2nd Third
Total of 3rd Third
Mean N
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MEM 2001: Within-Ss2. THIRDS
Measure: MEASURE_1
10.465
8.985
9.355
THIRDS1
2
3
Mean
MEM2001: Mean correct by Thirds
Serial Position Curve
THIRDS
321
Me
an
# C
orre
ctly R
eca
lle
d
11.0
10.5
10.0
9.5
9.0
8.5
Tests of Within-Subjects Effects
Measure: MEASURE_1
237.293 2 118.647 29.390 .000
1606.707 398 4.037
SourceTHIRDS
Error(THIRDS)
Sum ofSquares df Mean Square F Sig.
N=200 Ps
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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?
T estS ta tV ariab ility T rea tm en t E rror
V ariab ility E rror
( )
( )
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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.
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WRAPUP
Will go on to final major experimental design next two classes --- factorial designs and their interaction effects. Extremely important --- most frequently used designs!