I'm a resident physician working on my doctor's thesis and I'm trying to analyse data from a survey with R. I have basic mathematic and rookie statistics skills.
Participants of the survey have been shown four pictures of people (average man, average woman, attractive man, attractive woman) with four disfigurements (strabism, acne, piercing, tattoo), crossed in a latin square OR the control group (every face without disfigurement), randomized equally (20% each group). Participants were told they are gonna have a surgical treatment or a medical checkup (randomized) by the physician shown and they had to answer on a Likert-Scale from 0-10 (0 very improbable, 10 very probable) how much they would like to get the medical treatment from this physician. They also had to rate the physicians regarding to attractivity, competence, honesty, intelligence, kindness and reliability again on a Likert-Scale from 0-10 and also how important these attributes for the participants are.
Summary
participant_id character unique number
answer ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
attractivity ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
competence ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
honesty ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
intelligence ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
kindness ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
reliability ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_attractivity ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_competence ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_honesty ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_intelligence ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_kindness ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
weight_reliability ordered factor 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
situation factor Internist, Surgeon
face factor Man_Average, Woman_Average, Man_Attractive, Woman_Attractive
disfigurement factor None, Strabism, Acne, Piercing, Tattoo
I have 4009 unique participants with four answer blocks each, so 16036 rows in wide format in total.
I'm beginning with answer as dependent variable and situation, face and disfigurement as independent variables as well as participant_id as a random factor in a cumulative link mixed-effect model with clmm from the ordinal package for R. There seem to be significant interactions between situation, face and disfigurement, so I implemented the model as follows:
model.clmm <- clmm(answer ~ situation * disfigurement * face + (1 | participant_id), data = answers_full, Hess = TRUE, threshold = "symmetric")
I would like to do post-hoc tests with the emmeans package.
Question One: Am I (in general) on the right way with this strategy or totally wrong?
Question Two: Is it acceptable to do pairwise comparisons given the fact that there are interactions or what are the alternatives?
Question Three: To analyse all of the given answers, is it acceptable to do multiple univariate ordinal regressions or should I switch to multivariate ordinal regression (e.g. with the mvord package)?
Thank you very much in advance! Kindest regards, Pascal