[R-sig-ME] Nutrition questionnaire data
Doug Adams
fog0 at gmx.com
Wed Jun 16 17:42:00 CEST 2010
Thank you Tim! -- those are definitely helpful too.
On Tue, Jun 15, 2010 at 8:01 AM, Tim Carnus <Tim.Carnus at ucd.ie> wrote:
> Hi Dennis,
>
> Not sure if you are aware of the following paper:
>
> Doran H, Bates D, Bliese P, Dowling M (2007). “Estimating the Multilevel
> Rasch Model: With
> the lme4 Package.” Journal of Statistical Software, 20(2). URL
> http://www.jstatsoft.
> org/v20/i02/.
>
> It seems that the Rasch model is a similar model to what you are hoping
> to fit to your data. They reduce the 1:5 responses to a binary response
> in a first step and mention then how to fit the model to the ordinal
> response.
>
> Another option for you would be MCMCglmm which I think is capable of
> fitting mixed models to ordinal data but also allows for different
> distribution in the response variable.
>
> Best regards,
>
> Tim Carnus
>
>
> On Mon, 2010-06-14 at 18:12 -0600, Doug Adams wrote:
>> Thanks very much Dennis. You're right: it does use a Likert scale,
>> although one subscale is binary (which is another mini-problem on its
>> own, though one aspect less complex than the rest of the data of
>> course).
>>
>> Maybe I will try a MANOVA approach to see how it works out. I'll also
>> check out the lavaan package. I'm still struggling to wrap my head
>> around everything you've said, but that's not your fault! I've just
>> got lots of learning to do still... : )
>>
>> Doug
>>
>>
>> On Sat, Jun 12, 2010 at 11:53 PM, Dennis Murphy <djmuser at gmail.com> wrote:
>> > Hi:
>> >
>> > On Sat, Jun 12, 2010 at 8:55 PM, Doug Adams <fog0 at gmx.com> wrote:
>> >>
>> >> Hello,
>> >>
>> >> It's been a while since I've posted, although I've been using R
>> >> sig-mixed-models as a reference a lot lately.
>> >>
>> >> I've got some data from a questionnaire that I'd like to analyze, and
>> >> I want to make sure my syntax is right. There were multiple groups of
>> >> subjects (residents, medical students...) being surveyed, and the
>> >> questions were also grouped into subscales. So basically, I have the
>> >> "response" for each question & for each subject, and questions &
>> >> subjects are crossed. Further, questions are nested within subscales,
>> >> while subjects are nested within groups -- and groups & subscales are
>> >> crossed.
>> >>
>> > But:
>> > - the subscales are properties of the questionnaire (response)
>> > - the groups are properties of the subjects
>> >
>> > I would tend to view this problem more as one with a multivariate response
>> > where you could use 'MANOVA-like' concepts - for example, one contrast
>> > matrix for the subjects, another for the responses (subscales) and a test
>> > statistic that uses one or both using matrix multiplication. The problem, I
>> > suspect, is that your questionnaire is on a Likert scale, so multivariate
>> > normal
>> > assumptions would be specious.
>> >
>> > In a sense, you're caught between modeling paradigms: on one hand, it's
>> > reminiscent of the multivariate response approach to repeated measures
>> > applied to an entire questionnaire scale, where the subjects would be
>> > considered fixed blocks in a MANOVA (associated with different types of
>> > health providers, an intrinsic factor); on the other hand, the subjects are
>> > random and you'd prefer to use a mixed model approach. The problem with the
>> > latter is that AFAIK no one has extended linear or generalized mixed models
>> > to the case of multivariate responses, and I'm rather sure that neither nlme
>> > or
>> > lme4 is designed for that type of problem at present. I wonder if some type
>> > of latent variable or structural equation model might be better suited for
>> > this
>> > task - the question is whether such models can handle ordinal responses,
>> > as would normally be the case with Likert scales. (Of course, if the
>> > questions yield plausibly normal distributions, that's a different
>> > matter...) The
>> > recently released lavaan package might be useful if the latent variable
>> > route
>> > looks promising.
>> >
>> > Something for you to cogitate over :)
>> >
>> > HTH,
>> > Dennis
>> >
>> >
>> >
>> >>
>> >> So here's kind of what it looks like:
>> >>
>> >> subscale 1
>> >> subscale 2 subscale 3
>> >> q1 q2 q3 q4 q5
>> >> q6 q7 q8 q9 q10 q11 q12
>> >> Attending s1 # # # # # #
>> >> # # # # # #
>> >> s2 # # # # #
>> >> # # # # # # #
>> >> s3 # # # # #
>> >> # # # # # # #
>> >> Student s4 # # # # # #
>> >> # # # # # #
>> >> s5 # # # # #
>> >> # # # # # # #
>> >> s6 # # # # #
>> >> # # # # # # #
>> >> Resident s7 # # # # # #
>> >> # # # # # #
>> >> s8 # # # # #
>> >> # # # # # # #
>> >> s9 # # # # #
>> >> # # # # # # #
>> >>
>> >>
>> >> I hope that comes out right ASCII-wise in this post! : ) Anyway,
>> >> there are more subjects and questions and such than in this little
>> >> visual of course. Is this correct?
>> >>
>> >> lmer(response ~ group + (1|subject) + (1|question), data=NL, REML=TRUE)
>> >>
>> >> Thanks so much,
>> >> Doug Adams
>> >>
>> >>
>> >> P.S. - If there's a reference for the modeling syntax used in R -- or
>> >> in lme4 specifically if need be -- and how each operator works, please
>> >> let me know. For example, if there were a help document that says
>> >> things like " A\B means A is nested within B ," or " the | symbol
>> >> denotes the the following factor is at a higher level in the model, "
>> >> etc., that would be so helpful. I've learned a lot from context, from
>> >> Pinheiro & Bates (though that's for nlme), and from this forum, but I
>> >> haven't been able to find something like a formula syntax reference
>> >> like that.
>> >>
>> >> _______________________________________________
>> >> R-sig-mixed-models at r-project.org mailing list
>> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> >
>> >
>>
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