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2nd Year Practical: Factorial Designs
Dr. Jonathan Stirk
Statistical Analysis Using E-Merge, E-Data Aid and SPSS
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Repeated Measure Design-(Fully-Within Subjects)
Research Hypothesis: Does coping strategy influence pain?
Dependent Variable: Report of pain level 0..50 (0=no pain, 50=excruciating).
Independent Variables:– Coping strategy: Concentrate on Pain vs. Avoidance. – Time hand has been in ice water (3 levels: 30, 60, 90 sec).
8 subjects participate in all conditions (“repeated measures 2 x 3 design”)
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Individual data
0
5
10
15
20
25
30
35
40
45
1 2 3 4 5 6
Mary
Jane
Jill
Jean
Corey
Pam
Jennifer
Jersey
Concentrate Avoid
Individual Effects
Note: some individuals always report pain, others are very resistant. Repeated measure design reduces subject variability.
Time levels
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Group data / Interaction Graph
Group Effects
0
5
10
15
20
25
30
1 2 3
Time
Pa
in R
ati
ng
Concentrate
Avoidance
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ANOVA
Source SS df MS F P
Coping 46 1 46.02 1.87 0.213
related Error 172 7 24.54
Time 2140 2 1070.02 36.69 0.001
related Error 408 14 29.16
Coping*Time 288 2 144.02 21.09 0.001
related Error 96 14 6.83
Subjects 1055 7 150.74
• No main effect of coping strategy.
• Main effect of time: more time = more pain.
• Interaction: Avoidance better for short periods, but worse with longer intervals.
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Your data
For each individual, enter their mean score for each condition/cell into your analysis.
Use E-Merge, E-Data Aid & SPSS. If each factor has only 2-levels, no need for
pairwise comparisons. Interaction is probably important.
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Example data analysis
IV’s: – flanker-target separation (distance)
2 levels: near & far
– Flanker-target response compatibility 2 levels: compatible & incompatible
DV:– Time taken to correctly identify target (RT in
milliseconds)
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Design structure for example exp’t
Incompatible
Compatible
Compatibility
FarNear
Distance
2X
3X 4X
1X
For an individual subject, cell means are an average across a number of trials!
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Merging separate data files
Currently you will have a directory which contains your e-prime file and a number of separate .edat files
Each subject run will create a single *.edat data file – E.g. ‘*-1-1.edat’, ‘*-2-1.edat’ etc.
Merge these into 1 large file using E-Merge This produces a merge file (*.emrg) You should open this merge file using E-Data Aid
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Select Unmerged files (check they are all from the same experiment)
Click MERGE and name the merged file with something sensible
Ctrl-Left click will also choose each file
E-Merge
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E-Data Aid
Open E-data aid and open the merged file This will contain all the trials for EVERY
subject Filter data ready for analysis and output the
raw mean data for each participant
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Remove any practise trials from analysisFilter by ‘Procedure[Block]’
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Filtering out trials
Check the box for the trials that you wish to INCLUDE in the analysis!
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Hide unnecessary columns and filter correct response trials
1 = correct
0 = incorrect
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Choose to analyze correct trials only
Choose the name of the slide that participants made responses on to filter for accuracy– E.g. StimDisplay.ACC in
the example experiment
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Use ‘Analyze’ to get raw data
To get the means for your data use the ANALYZE option in E-Data Aid (Looks like a calculator)
This will open the window seen on the right
– Row- Subject– Column- F_Compatability &– F_distance (or whatever your 2
factor columns have been named)
– Data- StimDisplay.RT
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Raw means for subjects
StimDisplay.RT:Mean by Subject and F_Compatability, F_distance
Mean StimDisplay.RT
Mean StimDispl
ay.RT
Mean StimDispl
ay.RT
Mean StimDispl
ay.RT
compatible compatible incompatible incompatible
Subject far near far near
1 428.93 515.50 474.69 487.21
2 596.08 444.19 450.00 455.38
3 457.20 441.20 467.00 535.67
SPSS
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E-Data ready for export / copy
This analysis provides the MEANS for the 4 conditions (2x2 combinations) you selected
This can now be exported or copied into SPSS Just select the data only (not the headings) and
press Ctrl-C to copy Open SPSS, create 4 columns and paste data
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Paste into SPSS and rename variables
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Bring conditions over (be consistent!)
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Run analysis!
Name the TWO IV’s and define the number of levels of each (2)
Start with the factor which is highest up in your raw data table e.g. compatibility then distance
ADD each in turn
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ResultsTests of Within-Subjects Effects
Measure: RT
14.410 1 14.410 .004 .956
14.410 1.000 14.410 .004 .956
14.410 1.000 14.410 .004 .956
14.410 1.000 14.410 .004 .956
7328.776 2 3664.388
7328.776 2.000 3664.388
7328.776 2.000 3664.388
7328.776 2.000 3664.388
2.297 1 2.297 .001 .984
2.297 1.000 2.297 .001 .984
2.297 1.000 2.297 .001 .984
2.297 1.000 2.297 .001 .984
8512.237 2 4256.119
8512.237 2.000 4256.119
8512.237 2.000 4256.119
8512.237 2.000 4256.119
2348.921 1 2348.921 .671 .499
2348.921 1.000 2348.921 .671 .499
2348.921 1.000 2348.921 .671 .499
2348.921 1.000 2348.921 .671 .499
6997.645 2 3498.823
6997.645 2.000 3498.823
6997.645 2.000 3498.823
6997.645 2.000 3498.823
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
Sphericity Assumed
Greenhouse-Geisser
Huynh-Feldt
Lower-bound
SourceCOMPAT
Error(COMPAT)
DIST
Error(DIST)
COMPAT * DIST
Error(COMPAT*DIST)
Type III Sumof Squares df Mean Square F Sig.
We can see on the basis of only 3 participants that there are NO SIGNIFICANT MAIN EFFECTS for Compatibility or Distance. Also there are no interactions.
This is not what we might expect!
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Possible Interaction Graph
RT (msecs)
near far
Incompatible
Compatible
FCEFCE
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And Finally
Next week you will be presenting your experiments to the class
Each presentation can be given be either 1 or all members of the group
Total time of each presentation should be XX minutes
You must use POWERPOINT so save file in your user-space or on a floppy disk or USB-drive
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ANOVA help
For additional help on related (within-subjects) ANOVA see
– Keppel, G., Saufley, W.H.,Tokunaga, H. (1992) Introduction to Design and Analysis. (in library)
– Sprinthall, R.C.(2003). Basic Statistical Analysis, 7th Edition.– Howell, D. (1992). Statistical methods for psychology. – Dancey, C.P & Reidy, J. (2002). Statistics without maths for
psychology. – Or any other major stats text
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