Sorry for the mistake, and thanks for the reply.
Here are the results of > str(sub_data)
tibble [263 x 10] (S3: tbl_df/tbl/data.frame)
$ part : chr [1:263] "Part1" "Part2" "Part3" "Part4" ...
$ WF : chr [1:263] "SA" "SA" "SA" "SA" ...
$ gender : chr [1:263] "male" "female" "female" "female" ...
$ age : num [1:263] 37 28 44 16 33 20 26 50 40 49 ...
$ education: chr [1:263] "postgrad" "postgrad" "postgrad" "seconadry or
below" ...
$ SA_hum : num [1:263] 10 8 9 4 2 8 9 5 10 7 ...
$ EA_hum : num [1:263] 6 7 10 8 8 9 10 9 9 10 ...
$ SA_cul : num [1:263] 7 8 9 5 1 8 9 4 10 9 ...
$ EA_cul : num [1:263] 10 7 9 9 9 8 10 8 10 9 ...
$ better : chr [1:263] "SA" "SA" "EA" "EA" ...
Hope this is okay.
Best
On Wed, 17 Jun 2020 at 19:12, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:
Dear Saudi,
Your attachment got stripped from the mail. This is explained under the
general instructions at https://www.r-project.org/mail.html#instructions.
The problem seems to be in the data. So post the data in a suitable
format. Or send us the output of `str(sub_data)`
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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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
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Op wo 17 jun. 2020 om 16:14 schreef Saudi Sadiq <saudisadiq at gmail.com>:
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 10 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).
To rule out the effect of which subtitle was watched first, 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. The info
regarding
this is called WF (watched first).
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. Actually, I wanted better to be
the only dependent factor and asking participants 'which subtitle is
better?' could be enough, but I wanted to have detailed information of
why
a subtitle is better by asking participants specific questions (regarding
which subtitle is more humorous and closer to Egyptian culture). Most of
the time, the total of the hum + cul = better, but sometimes it is not
(e.g. the sum for subtitle EA could be bigger than for SA, but the
participant prefers SA in the better column).
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 do the models 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?
I hope I am not violating the rules here as I am attaching the dataset (
sub_data) just in case someone would like to have a look at it.
All the best
--
Saudi Sadiq,
--
Saudi Sadiq,
Lecturer, Minia University, Egypt
Academia <http://york.academia.edu/SaudiSadiq>, Reserachgate
<https://www.researchgate.net/profile/Saudi_Sadiq>, Google Scholar
<https://scholar.google.co.uk/citations?user=h0latzcAAAAJ&hl=en>,
Publons
<https://publons.com/researcher/2950905/saudi-sadiq/>
Certified Translator by (Egyta) <https://www.egyta.com/>
Associate Fellow of the Higher Education Academy, UK
<https://www.heacademy.ac.uk/>