[R-sig-ME] First post: binomial model for omission of items of questionnaire, and advice on reading

[D. Rizopoulos]

Hi Chris,

You can have a look at my course notes for Repeated Measurements data; participants of my course have mainly a epidemiological, public health or medical background: http://www.drizopoulos.com/courses/EMC/CE08.pdf

I have also created a shiny app that replicates all analysis in the course: https://github.com/drizopoulos/Repeated_Measurements

In the near future the course will also be available online, using a blended-learning format.

Best,
Dimitris

- - - - - -
Dimitris Rizopoulos
Professor of Biostatistics
Erasmus University Medical Center
The Netherlands

From: Chris Evans <chrishold using psyctc.org<mailto:chrishold using psyctc.org>>
Date: Thursday, 09 May 2019, 8:26 PM
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using 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)])
ID itemN positive missed
1 1 1 FALSE 1
2 2 1 FALSE 0
3 3 1 FALSE 0
4 4 1 FALSE 0
5 5 1 FALSE 1
6 6 1 FALSE 0

"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.

I analysed my data with:

> res3 <- glmer(missed ~ positive + (1 | ID), family = binomial, data = longDat)
> res3
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: missed ~ positive + (1 | ID)
Data: longDat
AIC BIC logLik deviance df.resid
12597.534 12619.165 -6295.767 12591.534 9997
Random effects:
Groups Name Std.Dev.
ID (Intercept) 1.056
Number of obs: 10000, groups: ID, 1000
Fixed Effects:
(Intercept) positiveTRUE
0.6102 -0.7833
> se <- sqrt(diag(vcov(res1)))
> (tab <- cbind(Est = fixef(res3), LL = fixef(res3) - 1.96 * se, UL = fixef(res3) + 1.96 * se))
Est LL UL
(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?!

TIA,

Chris


--
Chris Evans <chris using psyctc.org> Skype: chris-psyctc
Visiting Professor, University of Sheffield <chris.evans using sheffield.ac.uk>
I do some consultation work for the University of Roehampton <chris.evans using roehampton.ac.uk> and other places but this <chris using psyctc.org> remains my main Email address.
I have "semigrated" to France, see: https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.psyctc.org%2Fpelerinage2016%2Fsemigrating-to-france%2F&data=02%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C5b955bbee7254e48256d08d6d4abcdfd%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C636930231694193093&sdata=luUc6XwSc6go4c45clYHcawAkXyUpsQwWT3p4QX51yU%3D&reserved=0 if you want to book to talk, I am trying to keep that to Thursdays and my diary is now available at: https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.psyctc.org%2Fpelerinage2016%2Fecwd_calendar%2Fcalendar%2F&data=02%7C01%7Cd.rizopoulos%40erasmusmc.nl%7C5b955bbee7254e48256d08d6d4abcdfd%7C526638ba6af34b0fa532a1a511f4ac80%7C0%7C0%7C636930231694193093&sdata=gVRBIgRL2WJo1MZzrxbDXO11ksKccQiWTsWWoAERYZU%3D&reserved=0
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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