V11

Question 1

linear models

Answer 1

• modelling a relationship

- response ~ predictor

Question 2

how to include several factors into a linear model

Answer 2

example:
- pitch ~ politeness + e

with added sex
- pitch ~ politeness + sex + e

Question 3

random effects

Answer 3

• every person has a slightly different voice pitch, and this is going to be a factor that affects all responses from the same subject, thus rendering these different responses inter-dependent rather than independent
• model individual differences by assuming different random intercepts for each individual(subject)

pits ~politenesss + sex + (1|subject) + e

Question 4

how to find best model for different effects

Answer 4

• anova(model1.null,model2.model)

- if model is significantly different from null then this is preferred, also if it has lower AIC

Question 5

random intercept model

Answer 5

• fixed effects (attitude, gender) are all the sae for all subject and item -> our model is a random intercept model
• in model we account for baseline-differences in pitch, but we assume that whatever the effect of politeness is, its going to be the same for all subjects

thats why we said (1|subject), (1|scenario)

Question 6

random slope model

Answer 6

• assume some are more polite than others
• > random slope model, where subjects and items are not only allowed to have different intercepts, but where they are also allowed to have different slopes for the effect of politeness

(1+attitude|subject), (1+attitude|scenario)
-> means that you tell the model to expect differing baseline levels of frequency ( the intercept represented by 1) as well as differing responses to the main factor in question(attitude)