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Prism Lab Stats

Prism Lab Stats. Dr. Roger Newport & Laura Condon. Room B47. Drop-In Sessions: Tuesdays 12-2pm. roger.newport@nottingham.ac.uk http://www.psychology.nottingham.ac.uk/staff/rwn/Teaching/C82MPR.html. How a standard experiment might look Condition 1 Pre Exp (e-prime?) Post Condition 2

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Prism Lab Stats

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  1. Prism Lab Stats Dr. Roger Newport & Laura Condon Room B47. Drop-In Sessions: Tuesdays 12-2pm. roger.newport@nottingham.ac.uk http://www.psychology.nottingham.ac.uk/staff/rwn/Teaching/C82MPR.html

  2. How a standard experiment might look Condition 1 Pre Exp (e-prime?) Post Condition 2 Pre Exp (e-prime?) Post We only need to record the last few pre-exposure and the first few post-exposure in each manipulation

  3. Manipulation 1 Table of means for ANOVA 1 3 2 } 2.0 S1 -6 -5 -3 } S2 -4.7 Sn

  4. We could stick it all into a…. Two-factor ANOVA 2-way repeated measures Analysis of Variance. Each subject participates in ALL conditions

  5. What does the 2-factor ANOVA test for? • Differences between levels of Factor A (“main effect of A”) • Differences between levels of Factor B (“main effect of B”) • Other differences (“interaction”) • So the 2-factor ANOVA involves computing three separate F ratios - three independent hypothesis tests!

  6. What would we do if the interaction was significant? Unplanned comparisons All possible comparisons M1pr v M1po M1pr v M2pr M1pr v M2po M1po v M2pr M1po v M2po M2pre v M2po

  7. What would we do if the interaction was significant? Unplanned comparisons All possible comparisons M1pr v M1po M1pr v M2pr M1pr v M2po M1po v M2pr M1po v M2po M2pre v M2po

  8. What would we do if the interaction was significant? Unplanned comparisons All possible comparisons M1pr v M1po M1pr v M2pr M1pr v M2po M1po v M2pr M1po v M2po M2pre v M2po alpha = .0083

  9. What do we really want to know? What were our hypotheses? Why did we do the experiment? Planned comparisons All possible comparisons M1pr v M1po M1pr v M2pr M1pr v M2po M1po v M2pr M1po v M2po M2pre v M2po alpha = .05

  10. Advantages of planned comparisons: Simplifies analysis Reduces risk of type I errors (& don’t have to adjust alpha level) Interpretation is easy as each comparison is derived from a specific hypothesis No tricky interactions to interpret Only have results that you are interested in Disadvantages: SPSS does not (easily) do the comparisons we need

  11. The outcome we predicted How do we perform a planned comparison? Not just a t-test because we must take into account the variability of the whole model How do we do it in SPSS when SPSS doesn’t do it? Show there is no difference here But that there is a difference here

  12. MS Acomp F = Acomp MS S/A Testing the planned comparisons F ratios need to be calculated for each comparison and for that we need the mean square the mean square is calculated by:

  13. Do a 2x2 ANOVA…

  14. …to get the residual mean square from the whole model

  15. Then do a one way ANOVA on the comparison in question…

  16. …to get the mean square for the comparison

  17. MS Acomp F = Acomp MS S/A Divide one by the other to get the F ratio

  18. MS Acomp F = Acomp MS S/A Do the same for the other comparison

  19. Evaluating the planned comparison’s null hypothesis Critical F's for comparisons use the degrees of freedom for the numerator and the denominator of the F-ratio. Bung these values into an online F ratio calculator e.g. http://faculty.vassar.edu/lowry/tabs.html#f Or use a book of tables You can’t trust anything you find online, so test the calculator with these values: F = 5.99; Numerator = 1; Denominator = 6. You should get a p value of 0.05 Note that this is different to both the 2 way interaction and the 1 way significance values In my example there are 1 and 7 degrees of freedom for this comparison. F(1, 7) = 10.51, p= 0.0142

  20. Now then, although you have run three ANOVAs you do not have to report this. If you specify planned comparisons there is no need to describe or report the omnibus. You do, however, have to state that you are performing planned comparisons and give the rationale/hypothesis for them early doors (i.e. at the end of the introduction)

  21. General Why Specific What/How Outcome Specific Why General Prism adaptation Refer to literature Other variable Hypotheses and predictions Method - replicability F[1,5] = 2.4, p<0.05 SE bars on graphs Results Describe outcomes Why you got those outcomes Refer to literature What it all means Future research Follow the guidelines on the prism web page

  22. What is known about PA (that is relevant to the study) What is known about our manipulation (that is relevant) How one should affect the other Hypothesis / brief method of testing

  23. Target Response time Measurement Make good use of pictures to describe your experimental setup If people were right handed (for example) say how you know Make sure it is prelicable

  24. Collation of means etc. Type of analysis (not package used) No need to report the omnibus F[1,5] = 2.4, p<0.05 SE bars on graphs Give direction of effects Give means in table or graph, not both Figure 1: blah de blah de blah

  25. What were the main results? What do they mean? Do they fit previous research - why? What are the implications of your results? How do they add to the literature? If suggesting further research give concrete examples of how to go about it and how it would have a bearing on your results? Come to a conclusion

  26. There are further instructions about how to present your reports on the course web page. Ignore them at your peril (by which I mean: ignore them and lose marks)

  27. Do not put too much information on one page as this will make it really really difficult for your audience to take in both what is on the screen and what you are saying. They won’t have time to read it and they will start to lose interest. At the same time do not simply read out exactly what is on the page because your audience will be able to do that for themselves. You should try to give the impression that you know what you are talking about and above all do not mumble Do Not Use Text That Is Too Small  Bullet points are good  They emphasise important points  And they can remind you what to say next, but they can also look rubbish if not done very well

  28. If you choose a fancy background Make sure you can still read the text If you choose a fancy background Make sure you can still read the text If you choose a fancy background Make sure you can still read the text If you choose a fancy background Make sure you can still read the text If you choose a fancy background Make sure you can still read the text

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