Dear Linda,
A few years ago we solved a similar problem by means of a two-stage
bootstrap analysis. Our data consisted of mispronunciations in
various categories (no error, error interrupted, error
noninterrupted, other error, etc).
First, we did a two-stage resampling of the data set (i.e. first
resampling subjects, and then resampling responses within resampled
subjects). Then we ran a multinomial logistic regression on the
selected responses, yielding a regression coefficient for each term
in the model.
The above steps were repeated 250 times.
The resulting regression coefficients, 250 for each term in the
model), were used to determine the 95%CI (i.e. the P025 to P975
interval) for each term.
For more details and results, see:
Nooteboom, S.G., & Quené, H. (2008). Self-monitoring and feedback: a
new attempt to find the main cause of lexical bias in phonological
speech errors. Journal of Memory and Language, 58 (3), 837-861.
[doi:10.1016/j.jml.2007.05.003].
Hope this helps! With kind regards, Hugo Quené
Date: Mon, 4 May 2009 11:36:09 +0200
From: "Linda Mortensen" <[email protected]>
Subject: [R-lang] How to use mixed-effects models on multinomial data
To: <[email protected]>
Message-ID:
<[email protected]>
Content-Type: text/plain; charset="iso-8859-1"
Dear R-language experts,
I'm trying to run a logistic regression on ordered multinomial data. - The dependent variable is number of correct items with accuracy ranging from 0 to 5. I want to use a mixed-effects model, but am unsure about how to use this model to fit multinomial data. Can you help?
Thanks
Linda
Linda Mortensen
Post-doctoral research fellow
Department of Psychology
University of Copenhagen
?ster Farimagsgade 2A
1353 Copenhagen K
Denmark
Tel.: +45 3532 4889
E-mail: [email protected]
--
Dr Hugo Quené | assoc prof in Phonetics | Utrecht inst of
Linguistics OTS | Utrecht University | Trans 10 | 3512 JK Utrecht |
The Netherlands | T +31 30 253 6070 | F +31 30 253 6000 |
[email protected] | www.hugoquene.nl | www.hum.uu.nl
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