I would like to perform factor analysis to identify latent variables and create indices from my data set that has 144 variables (mostly ordinal) all together. Before doing so I would like impute my missing data however I cannot do this for the entire data set without reducing the number of variables in it first (error message). My questions are:
1) How should I handle this? Can I run a factor analysis to see which variables need imputing, then go back perform multiple imputation and re-do the factor analysis this time without missing values...is this silly?
2)Can I group my binary variables together without using factor analysis? This would partially reduce the nr of variables in my data set.
error message: The imputation model for inCL_care contains more than 100 parameters. No missing values will be imputed. Reducing the number of effects in the imputation model, by merging sparse categories of categorical variables, chane gin the measurement level of ordinal variables to scale, removing two-way interactions, or specyfying constraints on the roles of some variables, may resolve the problem. Alternatively increase the max.number of parameters allowed on the MAXMODELPARAM keyword of the IMPUTE sub-command.