[R-sig-ME] R-sig-mixed-models Digest, Vol 155, Issue 8

[John Kingston]

Responding to John Haman:

Thank you for your comments.

I've modeled these results using a logistic regression because the 
sounds at the two ends of the series are an unambiguous [s] and an 
unambiguous [sh], respectively. In English, the native language of all 
the listeners, these two sounds can be analyzed as contrasting in their 
value for just one distinctive feature, [s] is [+anterior] and [sh] is 
[-anterior]. This feature refers to the position of the tongue tip and 
blade. The intermediate steps between the endpoints were made by mixing 
the original [s] and [sh] waveforms in complementary proportions. So 
with respect to both linguistic and physical characteristics of these 
sounds, it's possible to treat responses as binomially distributed.

I have tried a model like the one suggested. My question is whether it's 
possible to interpret the interactions between listener gender and AQ 
scores with stimulus characteristics when other uncontrolled listener 
characteristics are accounted for in the random effect. A model like the 
one proposed, in which there's a random effect of listener on the 
intercept, doesn't strike me as hard to interpret, but what if I wanted 
to examine random effects of listener on the slopes of the fixed effects 
and their interactions? For example, is it still possible to treat a 
random effect of listeners on the interactions of listener gender by a 
stimulus characteristic as accounting for uncontrolled idiosyncrasies of 
those listeners on that interaction?

Best,
John

On 2019-11-11 06:00, r-sig-mixed-models-request using r-project.org wrote:
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> Today's Topics:
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>    1. a question about partitioning effects (John Kingston)
>    2. Re: a question about partitioning effects (John Haman)
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> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Sun, 10 Nov 2019 13:15:48 -0500
> From: John Kingston <jkingston using linguist.umass.edu>
> To: r-sig-mixed-models <r-sig-mixed-models using r-project.org>
> Subject: [R-sig-ME] a question about partitioning effects
> Message-ID: <fca30459879acee26dc6b904983d1c70 using umail.it.umass.edu>
> Content-Type: text/plain; charset="us-ascii"; Format="flowed"
> 
> 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?
> 
> --
> John Kingston
> Professor
> Linguistics Department
> University of Massachusetts
> Integrative Learning Center N434
> 650 N. Pleasant St.
> Amherst, MA 01003
> 1-413-545-6833, fax -2792
> jkingston using linguist.umass.edu
> http://blogs.umass.edu/jkingstn/
> 
> 
> 
> 
> ------------------------------
> 
> Message: 2
> Date: Sun, 10 Nov 2019 18:00:59 -0500
> From: John Haman <mail using johnhaman.org>
> To: r-sig-mixed-models using r-project.org
> Subject: Re: [R-sig-ME] a question about partitioning effects
> Message-ID: <0c253e50-af59-369a-d4fd-0442bd3528b0 using johnhaman.org>
> Content-Type: text/plain; charset="utf-8"; Format="flowed"
> 
> 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?
>> 
> 
> 
> 
> 
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> End of R-sig-mixed-models Digest, Vol 155, Issue 8
> **************************************************

-- 
John Kingston
Professor
Linguistics Department
University of Massachusetts
Integrative Learning Center N434
650 N. Pleasant St.
Amherst, MA 01003
1-413-545-6833, fax -2792
jkingston using linguist.umass.edu
http://blogs.umass.edu/jkingstn/