[R-sig-ME] Random effects in multinomial regression in R?

[Souheyla GHEBGHOUB]

I read that in multinomial regression, all independent variables should be
variables that we manipulate. Can I still have pretest as IV without
skewing my results?

Best,
Souheyla

On Fri, 22 Mar 2019, 23:31 Souheyla GHEBGHOUB, <souheyla.ghebghoub using gmail.com>
wrote:

> Thank you both. I will look into this and see :)
>
> Best,
> Souheyla
>
> On Fri, 22 Mar 2019, 22:02 Uanhoro, James, <uanhoro.1 using buckeyemail.osu.edu>
> wrote:
>
>> In standard regression models, the assumption is predictor variables are
>> measured without error. Test scores will have measurement error, hence
>> Doran's comment when test scores are used as covariates. See: Hausman, J.
>> (2001). Mismeasured Variables in Econometric Analysis: Problems from the
>> Right and Problems from the Left. *Journal of Economic Perspectives*,
>> *15*(4), 57–67. https://doi.org/10.1257/jep.15.4.57
>> I will note that many practitioners ignore this issue, and it is quite
>> common to use predictors measured with error. Consider the number of times
>> people use polychotomized income measures, or SES measures as predictors,
>> or some other "construct".
>> On Mar 22 2019, at 5:39 pm, Souheyla GHEBGHOUB <
>> souheyla.ghebghoub using gmail.com> wrote:
>>
>> Dear Doran,
>>
>> Could you explain more this point to me, please?
>>
>> Thank you,
>> Souheyla
>>
>> On Fri, 22 Mar 2019, 21:19 Doran, Harold, <HDoran using air.org> wrote:
>>
>> Yes, but conditioning on the pre-test means you are using a variable
>> measured with error and the estimates you obtain and now inconsistent, and
>> that¹s a pretty big sin.
>>
>> On 3/22/19, 3:49 PM, "Souheyla GHEBGHOUB" <souheyla.ghebghoub using gmail.com>
>> wrote:
>>
>> Dear René,
>>
>> Thank you for your feedback to me. You are right, dropping the pretest
>> from
>> covariate if I predict change definitely makes sense to me! But the fact
>> that i need to control for the starting levels of participants makes it
>> obligatory for me to chose the second way, which is predicting posttest
>> instead of change to have pretest scores controlled for.
>>
>> You also chose (1+group | word) , which is new to me. Does it intend to
>> assume the effect of group to vary across words, which is something
>> applicable to my data, right?
>> I will discuss all this with my supervisor, and may reply here again in
>> few
>> days if you do not mind.
>> Thank you very much
>> Souheyla
>> University of York
>>
>>
>> On Fri, 22 Mar 2019 at 13:42, René <bimonosom using gmail.com> wrote:
>>
>> Hi Souheyla,
>>
>> it seems to me that you will run into problems with your coding of
>> change
>> (gain, no gain and decline) because the 'change' is by
>> definition/calculation depending on the predictor pretest.
>> See, according to your coding scheme:
>> Change = decline can only occur if pretest=1 (not by pretest=0).
>> Change = gain can only occur if pretest = 0 (not by pretest=1)
>> Change = No Gain can occur if pretest= 1 or 0
>> In other words:
>> If pretest = 1 then the possible outcomes can be decline or no gain
>> If pretest = 0 then the possible outcomes can be gain or no gain
>>
>> And if the model result shows you then that the pre-test is
>> significantly
>> related to p(change-outcome), I guess there is no surprise in it, is it?
>>
>> So the first solution to this would be simply kicking the pre-test
>> predictor out of the model completely, and predict:
>> mod1 <- brm(Change ~ Group + (1|Subject) + (1+Group|Word),...)
>> (Btw.: actually the first Hierarchical Bayes Model question I see on the
>> mixed-effects mailing list :))
>>
>> Attempt for a further clarification on which random slopes would reflect
>> the model's design:
>> If you have a within-subjects design, by-subject random slopes are
>> possible for the within-subject variable (e.g. if there are two sets of
>> words/lists [e.g. abstract vs. concrete words] for each participant, and
>> you test whether there is a performance-difference between these
>> word-lists, then you can implement by-subject random slopes for words,
>> because each participant has seen both sets.) If each participant has
>> seen
>> only one list (i.e. between subjects design) by subject random slopes
>> for
>> words are not appropriate, because there is no 'slope' by participant
>> (i.e.
>> by definition, having a slope requires at least two observations...).
>> This
>> is always a good rule of thumb without thinking about it too heavily :)
>> Ans as you see: you can define a random slope for words:
>> (1+Group|Word),
>> because each word has been presented in each group (i.e. there can be a
>> slope for each word). And intuitively speaking the Treatment-effect can
>> vary depending on the stimuli you use, and the slope makes sense. (You
>> also
>> see in this example that the treatment effect can also vary by subjects,
>> but in fact, this subject effect variation IS EQUAL to the effect you
>> want
>> to test, and having by subject group random slopes would eliminate the
>> fixed effect...)
>>
>> Anyway, there is a second possibility to define your model, depending on
>> how you want to interpret it. In the previous model you can say
>> something
>> about the type-of-change likelihoods depending on the treatment group.
>> But
>> you could implement the model as binomial as well (i.e. logistic
>> regression)
>>
>> mod2 <- brm(posttest ~ pretest*Group + (1|Subject) + (1+Group|Word),...)
>>
>> And what you would expect here would be an interaction between pre-test
>> and Group. For instance; if pretest=0 & treatment 1 then posttest larger
>> than with pretest=0 & treatment 2; but not when pretest=1 (because this
>> is
>> a plausible no gain situation). And so on...
>> (And in this model there are no also no further random slopes hidden in
>> your design :))
>> Hope this helps.
>>
>> Best, René
>>
>>
>> Am Do., 21. März 2019 um 14:01 Uhr schrieb Souheyla GHEBGHOUB <
>> souheyla.ghebghoub using gmail.com>:
>>
>> Dear Philip,
>>
>> I understand , here is the structure of my data in case it could help.
>>
>> I have 3 groups of participants (control, treatment1, treatment2). Each
>> group was tested twice, once before treatment (pretest) and once after
>> treatment (posttest).
>> In each test, they were tested on knowledge of 28 words, scores are
>> dichotomous (0 = unknown , 1 = known). Tests are the same.
>>
>> I calculated change from pretest to posttest :
>> if pretest 0 and posttest 0 = no gain
>> if pretest 1 and posttest 1 = no gain
>> if pretest 0 and posttest 1 = gain
>> if pretest 1 and posttest 0 = decline
>> So I ended up with a dependent variable called Change with 3 levels
>> (no_gain, gain, decline) and I tried to predict it using Group and
>> Pretest
>> as covariates using multinomial logit model. mod0 <- brm(Change ~
>> Pretest
>> +
>> Group) I would like to add random effects for subjects but don't know
>> what's the best form when Time factor is absent.
>>
>> I hope other statisticians who read this could help
>> Thank you
>> Souheyla
>>
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