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

[Phillip Alday]

There's also brms, which supports ordinal regression with the lme4 syntax in a Bayesian framework.

Phillip


> On 10 Apr 2017, at 05:42, Diana Michl <dmichl at uni-potsdam.de> wrote:
> 
> Hi Josh,
> 
> I see you have a discrete, ordinal outcome variable - a scale of 1-4, so 
> 4 possible answers that are ordered.
> The lmer-function in lme4 actually needs a continuous outcome variable 
> (so answers like 1.4, 3.67 should be possible). I recommend the R 
> package "ordinal": It's especially designed for your kind of response 
> variable. It allows you to fit the model the same way, except it would 
> be a logistic regression, but it's not all that different.
> 
> (Ok, lme4 might give you the same results still, but it's cleaner this way.)
> 
> Diana
> 
> 
> 
> Am 08.04.2017 um 22:26 schrieb Joshua Rosenberg:
>> 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> 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
>>> 
>> 
>> 
>> 
> 
> -- 
> Diana Michl, M.A.
> PhD candidate
> International Experimental
> and Clinical Linguistics
> Universität Potsdam
> www.ling.uni-potsdam.de/staff/dmichl
> www.duoinfernale.eu
> 
> 
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