Suppose I have a psychological questionnaire asking 30 questions about a person's mental health (on a Likert-scale 1-7). These 30 questions fall into 7 separate, but correlated categories.
The questionnaire has been used for several years, but I would like to develop a shorter version of it for better respondent experience (and to simply reduce survey length)
Suppose I have data collected from around 1000 individuals, and would like to use that information to reduce survey length.
My goal is to remove questions from the survey that might be adding to the survey length but not providing any additional information.
I read that Factor Analysis can be used in cases like this, but am not sure how to apply it in my scenario.
What would the steps look like using Factor Analysis for removing redundant questions from the questionnaire to shorten the survey?
In simple terms, how can Factor Analysis be used to remove questions from my survey without losing relevant information? Would you be able to provide a reference to a book/website that explains Factor Analysis for this purpose?
Here's my understanding so far, but I am not sure these are the right steps or if I am missing anything:
- Run Exploratory Factor Analysis on data to identify factor patterns (would CFA be more appropriate here?)
- Remove items that do not load on any factors
How can identify if a question is not needed? For example, if it is almost the same as another question on the survey.