Data analysis

1. Data analysis

2. As usual… • Copy your xxxxx_testing.psydatdata file into the appropriate class_sharefolder for batch analysis • Open your Excel data file and get the mean RT for consistent and conflicting conditions: • Select the testing sheet (not training) • Work out your average scores for the conditions where the non-word was; • Pseudohomophone • Non-word but not a pseudohomophone • Were they different?

3. Group analysis results • This week we aren’t only interested in whether there was an effect of pseudohomophones • We also want to know if the effect was different for the groups A and B • For Group A the training involved (genuine) words that were actually homophones so according to Underwood’s theory these subjects were primed to words sounding similar • For Group B the training was simply words (not homophones) and non-words (also not pseudo-homophones)

4. So we want to know… • Did you all have a pseudohomophone effect? Two ways to think about this: • Compare your pseudohom RTs with control RTs Test if RTpseudohom > RTcontrol • Or subtract your control from pseudohom RTs to create a ‘pseudohomophone effect’ size and compare that against zero Effect = RTpseudohom - RTcontrol Test if effect > 0 • NB the two options above are actually identical mathematically

5. So we want to know… • Did the homophone training have an effect? • Use option 2 from previous slide, and compare the strength of the pseudo-homophone effect (the difference score) between the two conditions EffectgpA= RTpseudohom- RTcontrol EffectgpB= RTpseudohom- RTcontrol Test if EffectgpA > EffectgpB

6. For your report • You’re testing two hypotheses here: • Do people show a pseudohomophone effect in the new procedure? • Does it depend on training words? • For the first hypothesis the DV is reaction time • For the second hypothesis the DV is the strength of pseudohomophone effect • Before leaving make sure you have all the experiment information you’ll need to write your report