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

Diana Michl dmichl at uni-potsdam.de
Sun Apr 9 22:12:42 CEST 2017

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.)


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

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