[R-sig-ME] Using individual differences from model A as predictor in model B

[Koen Neijenhuijs]

Hi Thierry,

we've been doing this for the past couple of days, and realised that
certain lag-models might represent our construct of motivation best. Thanks
for the advice!

Best,

Koen

2016-12-02 14:20 GMT+01:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:

> Dear Koen,
>
> Have you tried writing down the model you envision as a (set of)
> mathematical expressions? This can help to better understand what you want
> and how you can fit it.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> 2016-12-02 13:31 GMT+01:00 Koen Neijenhuijs <kn.journal.news at gmail.com>:
>
>> Dear Thierry,
>>
>> thanks for the quick response! That would be the easiest model, but I'm
>> not entirely sure whether this model represents motivation the way we
>> envision it. In essence what you propose is a model where we test whether
>> adherence has changed from week1&week2 to week3, and whether this change is
>> different for the two different groups. However, this model does not have a
>> concrete moderation of the individual differences inside of the fixed
>> effect, which is what we're after. In essence, the research question isn't
>> so much about the two different groups (the groups merely exist to balance
>> the experiment itself, and is thus a control variable), but what the effect
>> of the manipulation is for participants of different intrinsic motivations.
>> Our problem is that intrinsic motivation is inherently intertwined with the
>> dependent variable, and while putting time in the model as you propose is
>> one way to approach the question, the interaction of BeforeAfter*Treatment
>> doesn't allow us to disentagle the question regarding motivation.
>>
>> Kind regards,
>>
>> Koen
>>
>> 2016-12-02 13:23 GMT+01:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:
>>
>>> Dear Koen,
>>>
>>> I think you can fit this in a single model. Here a a few options:
>>>
>>> with lme4:
>>> Adherence ~ BeforeAfter * Treatment + (1 + BeforeAfter|Participant)
>>> Adherence ~ BeforeAfter * Treatment + (1| Participant:BeforeAfter)
>>>
>>> with INLA:
>>> Adherence ~ BeforeAfter * Treatment + f(Time, model = "rw1", replicate =
>>> Participant)
>>>
>>> The BeforeAfter:Treatment interaction is the effect you are interested
>>> in. The lme4 random effect allow for an additional treatment effect for
>>> individual participants. The INLA random effect allows for correlated
>>> random intercept along Time for the individual participant. rw1 stands for
>>> random walk of order 1, which models the differences between consecutive
>>> time points.
>>>
>>> Best regards,
>>>
>>>
>>> ir. Thierry Onkelinx
>>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>>> and Forest
>>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>>> Kliniekstraat 25
>>> 1070 Anderlecht
>>> Belgium
>>>
>>> To call in the statistician after the experiment is done may be no more
>>> than asking him to perform a post-mortem examination: he may be able to say
>>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>>> The plural of anecdote is not data. ~ Roger Brinner
>>> The combination of some data and an aching desire for an answer does not
>>> ensure that a reasonable answer can be extracted from a given body of data.
>>> ~ John Tukey
>>>
>>> 2016-12-02 12:03 GMT+01:00 Koen Neijenhuijs <kn.journal.news at gmail.com>:
>>>
>>>> Dear all,
>>>>
>>>>
>>>> we've run an experiment with two groups, which we followed for 3 weeks.
>>>> Each participant got three trials per week, and our dependent variable
>>>> is
>>>> the adherence, defined as whether they replied to the trial or not. In
>>>> the
>>>> third week, we introduced a manipulation, which was balanced across the
>>>> two
>>>> groups. We want to test the effect of the manipulation, moderated for
>>>> intrinsic motivation to adhere to the trials. We are struggling with the
>>>> operationalization of intrinsic motivation.
>>>>
>>>> We ran a binomial mixed-effect model on the data of the first two
>>>> weeks, to
>>>> estimate intrinsic motivation. So far, we've come up with three methods
>>>> to
>>>> do so, but each comes with their own concerns. I was hoping to hear your
>>>> thoughts on this.
>>>>
>>>> 1. The first method is simply to use the aggregated (sum) adherence of
>>>> each
>>>> participant. This method would be seemingly valid, as the model on the
>>>> first two weeks shows no main effect of time, group, nor the interaction
>>>> time*group. However, I am reluctant to go this route as this method is
>>>> less
>>>> detailed than the other options.
>>>>
>>>> 2. The second method is to extract the random-adjusted intercept and
>>>> random-adjusted slope of time (random effects + fixed effects), per
>>>> participant. The interaction of these two represent intrinsic
>>>> motivation as
>>>> it inherits both the intercept of adherence as well as its' development
>>>> over time; this combination is capable of representing every possible
>>>> motivation timeline (start high and go lower over time; start high and
>>>> stay
>>>> high over time; start low and go up over time; etc). However, using this
>>>> method, to test the effect we're interested in will result in a
>>>> three-way
>>>> interaction (intercept*slope*manipulation), and a four-way interaction
>>>> to
>>>> check moderation of prior group characteristics. It is unlikely we have
>>>> enough power to test this, as our sample size is limited.
>>>>
>>>> 3. The third method is to extract the prediction equation from the
>>>> model of
>>>> the first two weeks and apply this to the data of the third week. This
>>>> method will give us one representation of motivation instead of two,
>>>> which
>>>> does include both fixed and random effects. However, as the method is
>>>> applied to data of the third week, I am uncertain whether it is valid
>>>> as a
>>>> representation of intrinsic motivation over the first two weeks.
>>>>
>>>> Sorry for the long wall of text. What are your thoughts on this? Are
>>>> there
>>>> other ways of operationalizing individual differences on adherence in
>>>> the
>>>> first two weeks to use as an independent variable on adherence in the
>>>> third
>>>> week?
>>>>
>>>> Cheers,
>>>>
>>>> Koen
>>>>
>>>>         [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
>>>
>>
>

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