From: "Pierce, Steven" <pierces1 at msu.edu>
To: "Chris Evans" <chrishold at psyctc.org>
Sent: Friday, 10 May, 2019 14:22:52
Subject: RE: [R-sig-ME] First post: binomial model for omission of items of
questionnaire, and advice on reading
Here are some reading suggestions that may help you learn more about using mixed
effects models. I could provide more (mixed models is a huge topic area), but
these are a good start. Best of luck in your continuing journey to learn how to
use mixed models.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis:
Modeling change and event occurrence . New York, NY: Oxford University Press.
? First half of the book is a very reader-friendly intro to mixed effect models,
second half focuses on discrete-time survival analysis.
Gelman, A., & Hill, J. (2007). Data analysis using regression and
multilevel/hierarchical models . New York, NY: Cambridge University Press.
? Shows the connection between basic regression and mixed effects models. Good
foundational resource.
Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction
to basic and advanced multilevel modeling (2nd ed.). London, UK: Sage.
? Good foundational resource.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in
cross-sectional multilevel models: A new look at an old issue. Psychological
Methods, 12(2), 121-138. doi:10.1037/1082-989X.12.2.121
? Centering affects how one interprets the coefficients in models. This is an
excellent paper for getting a handle on that issue.
Acting Director; Associate Director
Center for Statistical Training & Consulting (CSTAT)
Michigan State University
Office Phone & Fax: (517) 353-1051
E-mail: pierces1 at msu or Steve.Pierce at cstat.msu.edu
-----Original Message-----
From: Chris Evans <chrishold at psyctc.org>
Sent: Thursday, May 9, 2019 2:26 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] First post: binomial model for omission of items of
questionnaire, and advice on reading
I've followed this list for some years now and learned much about analyses of
mixed models from it but I'm pretty sure this is my first post and I suspect
it's embarrassingly obvious and that leads to its second part: advice on
reading.
The immediate question is about testing whether participants omitting an item of
a questionnaire relates to the cueing, negative or positive of the item. The
data look like this:
head(longDat[,c(7,3,4,6)])
"itemN" is a factor as at the moment I'm not testing any order effect through
completion of the questionnaire. The variable "positive" is the cueing and
"missed" is whether the item was omitted by the participant or not.
I think a reasonable model is that people vary in a general willingness to omit
items and that there might in addition to that random variance, be an effect of
cueing, probably that negatively cued items are less likely to be omitted but I
wouldn't want a directional test. As it happens in this questionnaire there are
10 items, three positively cued and seven negatively cued. I've simulated data
so the ten items have rather different omission rates and the cueing has an
effect on top of those.
res3 <- glmer(missed ~ positive + (1 | ID), family = binomial, data = longDat)
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation)
['glmerMod']
Family: binomial ( logit )
Formula: missed ~ positive + (1 | ID)
AIC BIC logLik deviance df.resid
12597.534 12619.165 -6295.767 12591.534 9997
Number of obs: 10000, groups: ID, 1000
se <- sqrt(diag(vcov(res1)))
(tab <- cbind(Est = fixef(res3), LL = fixef(res3) - 1.96 * se, UL = fixef(res3)
+ 1.96 * se))
(Intercept) 0.6101525 0.5330314 0.6872737
positiveTRUE -0.7832788 -0.8603999 -0.7061576
That all seems fine and to fit with the parameters that I'd put into simulating
the data but I'm sufficiently new to this to want to check with people more
experienced than I am if that does seem the right approach. I may have some
follow up work where there are more ways to classify the ten items (including
order).
My tangential question is about recommended reading for someone who comes out of
medicine through psychotherapy so doesn't really have algebra, let alone matrix
algebra and Bayesian theory say, running in my veins. I have many
peer-reviewed, empirical, quantitative papers from the last three decades,
almost all based on my having to do my own statistical analyses as I've rarely
worked anywhere where I've had either money to pay for statistical help or a
resident statistician. However, I'm fairly new to multilevel models (as you can
see!) but I'm increasingly seeing them as vital to the sorts of data I analyse.
Where should I start?!
Chris Evans < [ mailto:chris at psyctc.org | chris at psyctc.org ] > Skype:
chris-psyctc
Visiting Professor, University of Sheffield < [
mailto:chris.evans at sheffield.ac.uk | chris.evans at sheffield.ac.uk ] >
I do some consultation work for the University of Roehampton < [
mailto:chris.evans at roehampton.ac.uk | chris.evans at roehampton.ac.uk ] > and
other places but this < [ mailto:chris at psyctc.org | chris at psyctc.org ] >
remains my main Email address.
Beware: French time, generally an hour ahead of UK. That page will also take you
to my blog which started with earlier joys in France and Spain!
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