[R] Questionnaire Analysis virtually without continuous Variables

[Joshua Wiley]

 Hi Sacha,

You're right that this is not an R related question really (would be better somewhere like crossvalidated.com).

If basically everyone catches 0/1 birds, then I would consider dichotomizing:

Y <- as.integer(caught >= 1)

then check cross tabs to make sure there are no zero cells between predictors and outcome:

xtabs(~Y + dogs + guns, data=yourdata)

then use the glmer() function to model the nested random effects.

m <- glmer(Y ~ dog + gun + (1 | household) + (1 | village) + (1 | district), data = yourdata, family=binomial)

summary(m)

Cheers,

Josh

On Aug 4, 2012, at 7:12, Sacha Viquerat <dawa.ya.moto at googlemail.com> wrote:

> Hello!
> I am doing an analysis on a questionnaire of hunters taken in 4 different districts of some mysterious foreign country. The aim of the study was to gather info on the factors that determine the hunting success of a peculiarly beautiful bird in that area. All variables are factors, i.e. they are variables such as "Use of Guns - yes / no", "Use of Dogs - yes / no" and the likes. The response is upposed to be "number of Birds caught", which was designed to be the only continuous variable. However, in reality the number of caught birds is between 0 and 1, sometimes hunters answered with 2. Unfortunately, it is not the questioner who is burdened with the analysis, but me. I am struggling to find an appropriate approach to the analysis. I don't really consider this as count data, since it would be very vulnerable to overinflation (and a steep decline for counts above 0). I can't really suggest binomial models either, since the lack of explanatory, continuous data renders such an approach quite vague. I also struggle with the random design of the survey (households nested within villages nested within districts). Adding to that, hunters don't even target the bird as their prime objective. The bird is essentially a by-catch, most often used for instant consumption on the hunting trip. I therefore doubt that any analysis makes more than a little sense, but I will not yet succumb to failure. Any ideas?
> 
> Thanks in advance!
> 
> PS: I just realized that this is not a question related to R but to statistics in general. Apologies for that!
> 
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