Recently I am conducting a research on the relationship between motivation/attitude variables (Gardner's model) and English language proficiency in the Philippines. I encountered a problem: missing values. I used a 160-item scale in my study, consisting of around 10 subscales, where each item has a 7-point Likert-type response set, with values from 1 to 7. Some respondents failed to answer some items.
I'd like to try "Multiple Imputation" using SPSS 18. But I have some questions, hope you can help out:
For example, the variable "Interest in foreign languages" is measured by a 10-item (Q1-Q10) scale, but some respondents left a few items unanswered. And again, "Attitudes toward English-speaking people" is measured by 8-item (e.g., Q11-Q18) scale. I wonder if I can impute missing values on a dataset with variable names such as, "ID, sex, age, Q1, Q2, Q3, Q4,...Q18, Final grade"? Or do I really have to add up the items first to get a subscale score before "Multiple Imputation"?
Do I have to recode those negatively worded items before "Multiple Imputation"? For example, if Q1, Q3, Q5, Q7, Q9 are negatively worded, do I have to recode them first?
It seems AMOS 18 cannot do "Calculate Estimates" on those imputed data. Do you think I should just average the five imputed values for each missing data to get a new value, from which I can build a new dataset so that AMOS 18 will have to handle only one complete dataset, rather than the five imputed datasets plus the original? Is averaging the five imputed values the right way of "POOLING"?