Hi Saudi,
I can only make a guess, but that is that a variable having a unique
value for each participant has been read in as a factor. I assume that
"better" is some combination of "hum" and "cul" and exactly what is
WF?
Jim
On Thu, Jun 11, 2020 at 5:27 AM Saudi Sadiq <saudisa...@gmail.com> wrote:
>
> Dear Sir/Madam,
>
> Hope everyone is safe and sound. I appreciate your help a lot.
>
> I am evaluating two Arabic subtitles of a humorous English scene and asked
> 263 participants (part) to evaluate the two subtitles (named Standard
> Arabic, SA, and Egyptian Arabic, EA) via a questionnaire that asked them to
> rank the two subtitles in terms of how much each subtitle is
>
> 2) more humorous (hum),
>
> 5) closer to Egyptian culture (cul)
>
>
>
> The questionnaire contained two 1-10 linear scale questions regarding the 2
> points clarified, with 1 meaning the most humorous and closest to Egyptian
> culture, and 1 meaning the least humorous and furthest from Egyptian
> culture. Also, the questionnaire had a general multiple-choice question
> regarding which subtitle is better in general (better). General information
> about the participants were also collected concerning gender (categorical
> factor), age (numeric factor) and education (categorical factor).
>
> Two versions of the questionnaire were relied on: one showing the ‘SA
> subtitle first’ and another showing the ‘EA subtitle first’. Nearly half
> the participants answered the first and nearly half answered the latter.
>
> I am focusing on which social factor/s lead/s the participants to evaluate
> one of the two subtitles as generally better and which subtitle is more
> humorous and closer to Egyptian culture. Each of these points alone can be
> the dependent factor, but the results altogether can be linked.
>
> I thought that mixed effects analyses would clarify the picture and answer
> the research questions (which factor/s lead/s participants to favour a
> subtitle over another?) and, so, tried the lme4 package in R and ran many
> models but all the codes I have used are not working.
>
> I ran the following codes, which yielded Error messages, like:
>
> model1<- lmer (better ~ gender + age + education + WF + (1 | part),
> data=sub_data)
>
> Error: number of levels of each grouping factor must be < number of
> observations (problems: part)
>
>
>
> Model2 <- glmer (better ~ gender + age + education + WF + (1 | part), data
> = sub_data, family='binomial')
>
> Error in mkRespMod(fr, family = family) :
>
> response must be numeric or factor
>
>
>
> Model3 <- glmer (better ~ age + gender + education + WF + (1 | part), data
> = sub_data, family='binomial', control=glmerControl(optimizer=c("bobyqa")))
>
> Error in mkRespMod(fr, family = family) :
>
> response must be numeric or factor
>
>
>
> Why does the model crash? Does the problem lie in the random factor part
> (which
> is a code for participants)? Or is it something related to the mixed
> effects analysis?
>
> Best
> Saudi Sadiq
>
> [[alternative HTML version deleted]]
>
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