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2 nd Year Practical: Factorial Designs

2 nd Year Practical: Factorial Designs. Dr. Jonathan Stirk. Statistical Analysis Using E-Merge, E-Data Aid and SPSS. Repeated Measure Design- (Fully-Within Subjects). Research Hypothesis : Does coping strategy influence pain?

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2 nd Year Practical: Factorial Designs

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  1. 2nd Year Practical: Factorial Designs Dr. Jonathan Stirk Statistical Analysis Using E-Merge, E-Data Aid and SPSS

  2. 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”)

  3. Individual data Note: some individuals always report pain, others are very resistant. Repeated measure design reduces subject variability. Time levels

  4. Group data / Interaction Graph

  5. 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.

  6. 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.

  7. 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)

  8. Distance Near Far Compatibility Compatible Incompatible Design structure for example exp’t For an individual subject, cell means are an average across a number of trials!

  9. 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

  10. E-Merge 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

  11. 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

  12. Remove any practise trials from analysisFilter by ‘Procedure[Block]’

  13. Filtering out trials Check the box for the trials that you wish to INCLUDE in the analysis!

  14. Hide unnecessary columns and filter correct response trials 1 = correct 0 = incorrect

  15. 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

  16. 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

  17. Raw means for subjects SPSS

  18. 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

  19. Paste into SPSS and rename variables

  20. Bring conditions over (be consistent!)

  21. 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

  22. Results 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!

  23. FCE Possible Interaction Graph RT (msecs) Incompatible FCE Compatible near far

  24. 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

  25. 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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