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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