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From my understanding, the best way to deal with ordered ordinal data type is by applying the logistic regression.

In my analysis, I would like to generate an odd ratio and confidence interval. These are the code: (the sample data is attached in a picture) enter image description here

library(mice)
library(nnet)
library(MASS)
dat <- read.csv(file = 'data.csv')
female <- dat$Em3 == 1
NaFm05 <- sum(female) * 0.05
dat$Em3[sample(which(female),NaFm05)]<-NA
datImpute<-dat[,-1] ## to exclude the gender in the imputation process
imp <-mice(datImpute, m=5, printFlag=FALSE, maxit = 30, seed=2525,meth='polr')
fit <- with(imp, multinom(Perspective ~ Em1 + Em2 + Em3 +
SN1+SN2+SN3+Ec1+Ec2+Ec3,
model =T))
tab051 <- summary(pool(fit), conf.int = TRUE) ## do i need to include exponentiate = TRUE to calculate Odd ratio?
tab051[-1] <- round(tab051[-1],3)

After I run this code, I guess the results are not sensible.. Do you have any suggestions to improve this code?

My another questions:

  1. is it possible if I apply the logistic regression for the imputation of missing data whilst for the analysis, I apply the linear regression?
  2. from my reading, to impute the ordered ordinal data, we need to perform factor() all relevant data. Is it true and compulsory?
  3. is it possible to apply PMM for imputation in a survey and apply linear regression for missing data analysis
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