8. factorial designs PDF

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Factorial designs...

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8. Factorial Designs

Sway Significant main effect of activity “There was a significant main effect of activity [F (1, 208) = 51.67, p < .001], which indicates that when ignoring incidental hardship that the activity does affect people’s judgement of perceived moral character. Volunteering resulted in a higher perceived moral character score.”

Non-Significant main effect of incidental hardship “There was a non-significant main effect of incidental hardship [F (1, 208) = 2.69, p = .102], which indicates that when ignoring activity there was no perceived difference in Geoff’s moral character depending on whether the concert was good or bad.”

Significant interaction of activity*incidental hardship “There was a significant interaction between activity and incidental hardship [F (1, 208) = 5.18, p = . 024], which indicates that together the interaction of activity and incidental hardship does affect people’s judgement of perceived moral character. Can be seen that the interaction of volunteering activity and high incidental hardship resulted in highest score of perceived moral character. When Geoff was playing basketball, incidental hardship didn’t make any difference.”

Can we trust our model? Histogram of standardised residuals – want it to be a normal curve.

Using an analysis plan to compare results Do the results match ours? What differences are there? Yes. Apply the analysis plan to their results ! Is there anything missing? Do the authors report assumptions test? -

What can you conclude from this? How does it compare to your own assumption tests?

Do the authors reach same conclusions that we did? -

Slight differences in f and p values  Hint: check degrees of freedom – what’s different? How does their graph compare to ours?

How do the authors describe/interpret their results?

Stats lab report Data: cognitive psychology experiments Analyse, report and write up results of one of these experiments

Attention and food experiment – Do food logos increase craving under both low and high perceptual load? -

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Predictors: perceptual load (IV1) and distractor (IV2) o How is perceptual load measured? o How is distractor measured? Outcome: overall craving o How is overall craving measured? Hypothesis: craving will not increase as much under high perceptual load Stats test = two-way repeated measures ANOVA

How will you check the data for the analysis you’ve chosen? -

What do you need to check in the data? What are the assumptions of the analysis? How will you check these assumptions? What will you do if they are violated?

How will you find the answer to your research question? -

Which results will test your hypothesis? How will you know which effects are significant?

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How will you know which effects are meaningful?...