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I have a dataset that includes a number of variables that need to be reverse coded. I have already completed my missing data analysis, however, I did not reverse code the items prior to replacing the missing data (using the EM algorithm). I am also about to complete an exploratory factor analysis (EFA) on the data, followed by a confirmatory factor analysis (CFA) using SPSS.

Two questions:

  1. Should I have reverse coded all items prior to replacing the missing data? Will this have influenced which numbers were used to replace the missing data, and if I reverse code items now, will it be wrong?

  2. Should I have reverse coded the items prior to the EFA and CFA? I have heard that it can be okay to leave the reverse-coded items in EFA, as they will still load on the correct factors, but just be negatively loaded. However, I am wondering if this is correct/best practice?

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  1. I don't think it should matter because you're just changing the scale of the variables, not the relationship between a given variable and other variables.

  2. Yes, you're right, it should just affect whether loadings are negative or positive. If you're ultimately planning on average items together, then I would just reverse code them anyways because it's easier to interpret.

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