[R-sig-ME] a question about partitioning effects

[John Haman]

I'm curious about the basis for the logistic regression model here. A 
correct response seems to imply that there is a true categorization (or 
coarsening) of sounds on the gradient from [s] to [sh]. Is there? Or is 
the correct classification in the eye of the researcher?

To try to answer your actual question, have you thought about modeling 
the data as

glmer(category ~ AQ*gender*stimulus_char + (1 | listener))

or some variation thereof? This would treat AQ, gender, and stimulus as 
fixed effects and supply a random intercept for each listener.

-John

On 11/10/19 1:15 PM, John Kingston wrote:
> I have a data set in which a group of listeners were asked to 
> categorize a series of sounds that stepped incrementally from an [s] 
> to a [sh], as in "see" and "she". Members of this series were followed 
> by one of three vowels, each one spoken by one of four speakers. Call 
> these the "stimulus" characteristics. Past work predicts how 
> differences between the vowels and speakers' voices would influence 
> listeners' choice of [s] or [sh] as the category to which a particular 
> step in the series belongs, and those predictions are clearly 
> confirmed by the results.
>
> In addition to these stimulus characteristics and their predicted 
> effects, I also have "listener" characteristics, namely, their gender 
> -- there are 23 women and 24 men -- and their scores on Simon 
> Baron-Cohen's Autism Spectrum Questionnaire -- it provides a total AQ 
> score and scores on five subsets of questions for each listener.
>
> The interest in this study is how a listener's gender and their total 
> AQ or subset scores influence the effects of stimulus characteristics 
> on their categorization performance. In a mixed effects logistic 
> regression model like this, I would ordinarily represent differences 
> between listeners as a random effect, but here I also want to include 
> the gender and total and subset AQ scores as fixed effects, in 
> interactions with the fixed effects that represent the stimulus 
> characteristics.
>
> I should add that I have an analogous data set collected from a 
> different group of listeners, evenly divided between women and men, in 
> which the stimulus characteristics are the same but the other listener 
> characteristics are scores from the five subsets of the Big Five 
> personality questionnaire. As the subsets measure what are supposed to 
> be independent personality traits, there is no total Big Five score. 
> So my question is more general; namely, how do I model the effects of 
> these measures of listeners' personality traits while also capturing 
> uncontrolled differences between listeners?
>