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Experimental Research in IT Analysis – practical part

Experimental Research in IT Analysis – practical part. Hanna Venesvirta. Example analysis etc.

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Experimental Research in IT Analysis – practical part

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  1. Experimental Research in ITAnalysis – practical part Hanna Venesvirta

  2. Example analysis etc. Experimental task: select an object as fast as possible.Depend variable: selection time.One independent variable (diameter of an object) with three levels (diameter: 25, 30, and 40 mm).All the participants made the same task: within subject design. one-way within subject ANOVA.

  3. Difference in the means! Is it significant?

  4. What is needed for a graph?

  5. Error bars to a graph? (Applies to MS Office 2007 & 2010) - Make the graph - Select the data series from the graph -> ”chart tools” is available - Select ”layout” – ”error bars” – ”more error bars options” - Here we use only ”positive” error bars – select ”plus” - ”Error amount” == ”custom” – select ”specify value” – select from the sheet the values you have calculated for the deviation (e.g., S.E.M.s) for ”positive error value” (MS Office 2013? E.g. http://www.officetooltips.com/excel/tips/adding_error_bars.html )

  6. Data to SPSS? Three ways (at least): 1) Input by hand Might be painful and cause errors 2) Copy-and-paste (from, e.g., Excel) Paste variable names to ”variable view” and data to ”data view” Note: SPSS is picky with variable names – avoid at least spaces and umlauts

  7. Data to SPSS? 3) Open data from, e.g., Excel Open – data – browse to data location – select the file type you are using Note: for the SPSS analysis you use only the data matrix (so no means or deviations etc. are used here)

  8. Parametric tests

  9. From the output, find table ”tests of within-subjects effects”

  10. - So there is a difference, but where? -> pairwise comparisons Comparing 25 mm to 30 mm, 25 mm to 40 mm, and 30 mm to 40 mm -> 3 comparisons Two ways: 1) via the GLM menu (showed first) - calculates automatically the Bonferroni correction but does not tell the t-values 2) via pairwise t-tests-menu (2nd) - gives t-values - remember to calculate the B-correction by hand (α/no. of comparisons)

  11. From the output, find ”pairwise comparisons” under ”estimated mariginal means”: Check in which order your columns were and you will find out where the difference is. size 1=25 mm 2=30 mm 3=40 mm

  12. From the output, find ”paired samples test”

  13. Reporting parametric ANOVA

  14. Reporting pairwise comparisons (1) Check in which order your columns were and you will find out where the difference is. 1=25 mm 2=30 mm 3=40 mm ”MD” stands for ”Mean Difference”

  15. Reporting pairwise comparisons (2)

  16. Non-parametric one way within subjects analysis of variance:Friedman test

  17. From the output, find ”test statistics” NPar Tests Friedman Test

  18. Post hocs for Friedman: Wilcoxon (Repeated Measures) Signed-Rank test

  19. From the output, find ”test statistics” Wilcoxon Signed Ranks Test

  20. Reporting Friedman’s test

  21. Reporting Wilcoxon test Note: doubleclick the table (twice) and you will see more accurate p-value (on this case p = 0.000517 so it is significant in 0.01 level as 0.01/3=0.0033..).

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