[R-sig-ME] Specifying models nested crossed random effects

[Joshua Rosenberg]

Ah hah - that is helpful! I think it is clear.

Thank you, I will direct people there if they have a similar question.

On Tue, Apr 25, 2017 at 10:40 AM, Ben Bolker <bbolker at gmail.com> wrote:

> http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#nested-or-crossed
> tries to discuss this (last bullet point), but suggested edits for
> clarity are welcome (or pull requests!)
>
> On Tue, Apr 25, 2017 at 10:33 AM, Joshua Rosenberg
> <jmichaelrosenberg at gmail.com> wrote:
> > Diana - thank you very much, I think you're right, and I've found
> > (interestingly, though maybe not surprisingly) there are two identical
> ways
> > to specify this nesting, one is by explicitly identifying the
> participants
> > and signals (i.e., not "Participant 1" nested in "Program A" when there
> is
> > *another* "Participant 1" in "Program B," but rather using a unique
> > participant ID and sample ID for every participant and sample, i.e.
> > (1|program_ID) + (1|sample_ID) or by nesting not unique participant IDs
> and
> > sample IDs within program, i.e. (1|program_ID/participant_ID). Chapter 2
> > <http://lme4.r-forge.r-project.org/book/Ch2.pdf> of the (not published)
> > lme4 book by Bates helped me understand why (basically, lme4 figures out
> > the nesting (or crossing) on its own as long as the random effects are
> not
> > nested implicitly).
> >
> > thanks again. also thinking hard about the ordinal issue - while some
> are,
> > some of our outcomes are composites of multiple items and so aren't
> ordinal.
> >
> > Josh
> >
> > On Sun, Apr 9, 2017 at 5:00 PM, Diana Michl <dmichl at uni-potsdam.de>
> wrote:
> >
> >> Not planning to confuse anyone and I agree with Evan mostly. But it
> seems
> >> to me that even with the fixed effects, it still makes sense to include
> >> participants and programs as nested random effects because they really
> are
> >> nested (one factor (grouping variable) appears only within a particular
> >> level of another factor (grouping variable)).
> >> sample_ID seems fine, but I think it should still be (1|
> >> program_ID/participant_ID).
> >>
> >> Diana
> >>
> >> Am 09.04.2017 um 20:56 schrieb Evan Palmer-Young:
> >>
> >> Thanks for those details, Josh. Interesting design!
> >>
> >> I'm not experienced in interpreting random effects on their own, so
> others
> >> will have better advice on that.
> >>
> >> For your model structure, it sounds like there are three random effects:
> >>
> >> "program_ID"
> >> "participant_ID"
> >> "sample_ID"
> >>
> >> From my reading of lme4 documentation, I think that you have coded
> >> sample_ID correctly and do not need to explicitly nest it within
> program_ID.
> >>
> >> In general, think it may be better form to include both fixed and random
> >> predictors in your model, rather than having separate models to assess
> only
> >> the random effects.
> >>
> >> So your model might be something like,
> >>
> >> interest_model <- lmer(interest ~ ?Instruction_type? + ?time_of_day?  +
> >> ?Working_alone? + (1}program_ID) +  (1|participant_ID) + (1|sample_ID),
> >> data = df)
> >>
> >> Where Instruction_type, time_of_day , Working_alone, are fabricated
> >> variables that might resemble variables you recorded.
> >>
> >> As a disclaimer, this is my second time answering to the list-- welcome!
> >>
> >> Best wishes, Evan
> >>
> >>
> >>
> >>
> >>
> >> On Sat, Apr 8, 2017 at 4:26 PM, Joshua Rosenberg <
> jmichaelrosenberg at gmail.com> wrote:
> >>
> >>
> >> Thank you Evan for your response and thank you for clarifying.
> >>
> >> Responses are in-line below.
> >>
> >>
> >> Thank you for considering this!
> >>
> >> Josh
> >>
> >>
> >> On Sat, Apr 8, 2017 at 3:28 PM, Evan Palmer-Young <ecp52 at cornell.edu> <
> ecp52 at cornell.edu>
> >> wrote:
> >>
> >>
> >> Josh,
> >> Thanks for the questions.
> >> Can you provide a little bit more description about the variables?
> >>
> >>
> >> First, sorry, I had changed some of the variable names in the data and
> >> realize I used different names (and a different outcome) in the
> examples at
> >> the bottom.
> >>
> >> "interest" (one outcome we're measuring) is a variable of participants'
> >> self-reported interest using a 1-4 scale.
> >>
> >> "overall_engagement" is one other (different) outcome: One that was a
> >> composite of variables of students' interest, how hard they were
> >> concentrating,
> >> and how challenging they reported what they were learning was.
> >>
> >> We asked participants (youth) about how interested they were in what
> they
> >> were learning at random intervals using what is called  an experience
> >> sampling method. In our method, youth had phones on which they were
> asked
> >> about what they were thinking / feeling - every youth in the same
> program
> >> (more on the programs in just a moment) was notified to answer our
> >> questions at the same time, although both the instance in time and the
> >> interval between these questions was different between programs.
> >>
> >> "site" = "program" (ID) and program is an indicator for membership in
> one
> >> of the 10 programs.
> >>
> >> Because youth were repeatedly sampled, "participant_ID" is an indicator
> >> for one of about 200 participants.
> >>
> >> "sample_ID" is an indicator unique for each program (it was made from
> the
> >> program_ID, the date, and which of one of four samples it was for that
> >> date). There are about 20 unique values for it for each program, from
> >> around 200 values total.
> >>
> >>
> >>
> >> Does "site" = "program"?
> >> Are participants queried at multiple timepoints? If pre- and
> >> post-program, could this be included as a factor with levels "before"
> and
> >> "afte
> >>
> >>
> >> Yes, the sampling consisted of repeated measures within participant
> >> (around 15-20 responses per participant). It's a bit tricky for me to
> >> describe, but as I mentioned above every youth in the same program was
> >> notified to answer questions at the same time, though both the instance
> in
> >> time and the interval between these questions differed between the 10
> >> programs.
> >>
> >>
> >>
> >> Do you have any particular hypotheses or questions you want to answer
> >> with your model?
> >>
> >>
> >> We're interested in, for a lack of a better word, time point or
> >> situation-specific ("sample_ID") variables' relationships with
> engagement.
> >> We coded video of the programs, including before and when youth were
> >> notified to respond, for example, the type of activity youth were
> >> participating in (i.e., working in groups or individually; doing
> hands-on
> >> activities or listening to the activity leaders). We imagine considering
> >> these as categorical variables.
> >>
> >> Similarly, we're interested in relationships between youth's
> >> characteristics (such as pre-program interest and demographic
> >> characteristics, such as gender) and our outcomes and to a bit of a
> lesser
> >> extent relationships between some program factors and outcomes (though
> with
> >> only 10 programs, we do not imagine we will have statistical power to
> >> detect any / many effects at that level).
> >>
> >> We're interested in sources of variance as a substantive question (how
> >> much of students' engagement is explained by time-point ("sample_ID"),
> >> youth ("participant_ID"), and program ("program_ID") effects?). Though
> this
> >> is a bit secondary to our questions about the specific variables at
> >> time-point, youth, and program levels.
> >>
> >>
> >>
> >> Best wishes, Evan
> >>
> >>
> >>
> >>
> >>
> >> --
> >> Joshua Rosenbergjmichaelrosenberg at gmail.comhttp://joshuamrosenberg.com
> >>
> >>
> >>
> >> --
> >> Diana Michl, M.A.
> >> PhD candidate
> >> International Experimental
> >> and Clinical Linguistics
> >> Universität Potsdamwww.ling.uni-potsdam.de/staff/dmichlwww.
> duoinfernale.eu
> >>
> >>
> >
> >
> > --
> > Joshua Rosenberg
> > jmichaelrosenberg at gmail.com
> > http://joshuamrosenberg.com
> >
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> >
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> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>



-- 
Joshua Rosenberg
jmichaelrosenberg at gmail.com
http://joshuamrosenberg.com

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