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]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.