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.
--
Diana Michl, M.A.
PhD candidate
International Experimental
and Clinical Linguistics
Universit?t Potsdam
www.ling.uni-potsdam.de/staff/dmichl
www.duoinfernale.eu
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