[R-sig-ME] (no subject)

Meir Barneron meir.barneron at gmail.com
Sun Aug 14 09:22:06 CEST 2016

Great, thank you for your recommendations.


2016-08-11 19:48 GMT+03:00 Dan McCloy <drmccloy at uw.edu>:

> [please keep mailing list in cc]
> I don't know much about your study design details, and there are
> situations where it would matter to include such nested groupings.  For
> example, imagine that some of the "items" were problems with very similar
> solutions, and after solving one of them, it was easier to solve subsequent
> ones of the same type.  The random grouping of items might have led to one
> fairly homogeneous item group (that would be easier) and other item groups
> that were more heterogeneous (and harder).  In such a circumstance, the
> nested grouping would capture the extent to which the random groupings
> yielded non-balanced groups (with respect to some dimension that is
> desirable to balance, such as "difficulty" in this example).
> If you are worried about this possibility, then use the nested group
> syntax as previously suggested.  On the other hand, if you are confident in
> your assertion that "the groups do not have any theoretical relations and
> all was divided totally randomly" and thus believe that the groups are
> equivalent/balanced on all the dimensions that matter, then it is unlikely
> that modeling the nesting is going to yield different answers / better
> model fits / etc.  But as I said before, your dataset is small enough that
> you could just try it both ways and compare the two models...
On Thu, Aug 11, 2016 at 12:32 AM, Meir Barneron <meir.barneron at gmail.com>

> Hey Dan,
> thank you very much for your answer.
> If I understood you, your advice is to run a model only with a random error
> on subjects and items because the group were created in a random manner?
> thanks again
> Meir
> 2016-08-08 21:12 GMT+03:00 Dan McCloy <drmccloy at uw.edu>:
>> The GLMM FAQ has some information about specifying nested random
>> effects.  See especially the "model specification" table [1] and the
>> "nested or crossed" section [2].  Something like this may be what you're
>> looking for:
>> grade ~ Time + (Time | SubjectGroup / Subject) + (Time | ItemGroup / Item)
>> Given that you've assigned groups randomly, I'm unsure if there will be
>> much benefit to modeling the groups this way (unless maybe "grade" for one
>> subject/item is somehow influenced by the other subjects or items in the
>> group?).  Anyway, the dataset is small enough that it will be easy to try
>> and see what happens.  Also note that if "Time" has only two values, it can
>> be treated as a factor (like "pre" and "post" treatment).
>> [1]: https://rawgit.com/bbolker/mixedmodels-misc/master/glmmFAQ.h
>> tml#model-specification
>> [2]: https://rawgit.com/bbolker/mixedmodels-misc/master/glmmFAQ.h
>> tml#nested-or-crossed
>> -- dan
>> Daniel McCloy
>> http://dan.mccloy.info/
>> Postdoctoral Research Associate
>> Institute for Learning and Brain Sciences
>> University of Washington
> On Thu, Aug 4, 2016 at 2:22 AM, Meir Barneron <meir.barneron at gmail.com>
> wrote:
>> Hi everyone,
>> I am relatively new in MEMs, and trying to tigure out what is the best
>> model fo my data. The data itself is relatively simple but the design more
>> complicated..
>> To make it simpler, I am interested in investigating if there is a
>> difference between two measures made at two point in time (1 and 2), that
>> is all. My dependent variable is a grade. I do not enter into details in
>> order to keep it as simple as possible. My theory predicts that the grades
>> will be smaller at time 2 compared to time 1.
>> Basically I have 30 subjects, and 100 Items and I want to make sure that
>> there is an effect after controlling for subjects and items. Here is the
>> design.
>> Before the experiment I randomly selected 30 subjects from a pool. I also
>> randomly selected 100 items from a pool. Next,  I randomly divided the 30
>> subjects into 5 groups of 6 subjects. I also randomly divided the 100
>> items into 5 groups of 20 items. The groups do not have any theoretical
>> relations and all was divided totally randomly.
>> Then I assigned one group of 20 items to one group of 6 subjects. Within
>> each group, each 6 subjects saw each 20 items. For each Items, each
>> subject
>> gave me one grade at Time 1, and one at Time 2.
>> My question is how to model this design. One possibility I have tried
>> if to ignore the group and took into account only the subjects and the
>> items.
>> This is my syntax:
>> model1 <- lmer(Grade ~ 1 + Time +
>>                  (1 + Time | Subject) +
>>                  (1 + Time | Item),
>>                REML=F, data = NITE1)
>> Does anyone have an idea how to take the "group" into account?
>> Alternaltvely, do you think the model I built is sufficient?
>> Thank you in advance
>> Meir
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