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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:

  1. Run Exploratory Factor Analysis on data to identify factor patterns (would CFA be more appropriate here?)
  2. 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.

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  • $\begingroup$ Thanks for sticking with this. I think this is a good question now (+1). Hopefully, you will be able to get all the help you need. $\endgroup$ – gung Apr 23 at 1:18
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    $\begingroup$ Agree with @gung that this is a good question. One clarification: are you adamant about using factor analysis or are you open to an alternative approach (with some similarities)? Item Response Theory (IRT) models would be very useful for this purpose and I could create an answer around that if it would be useful for you. $\endgroup$ – jsakaluk Apr 24 at 18:46
  • $\begingroup$ @jsakaluk thanks for your answer - I am not glued to factor analysis, it is just what I have encountered the most when doing some research on the topic (especially used in psychology). Anything that might address my goal would be highly appreciated! (IRT or any other model - note that I'm not very familiar with IRT) $\endgroup$ – Grint Apr 25 at 20:36
  • $\begingroup$ @jsakaluk hi! An answer would be really appreciated if possible $\endgroup$ – Grint May 1 at 14:28
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Rather than remove questions that do not load on any factor, you might remove some of questions loading on the same factor. The idea is that such questions are highly correlated, and hence provide partly redundant information.

On the other hand, questions that do not load on any factors are highly specific questions, unlike any other, and should be retained if you deem the answer informative for the issue of interest.

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  • $\begingroup$ Thanks. Do you think EFA or CFA is appropriate in this case? This is because I already have the theoretical constructs each question belongs to. How can I decide which questions to remove that load on the same factor? $\endgroup$ – Grint May 9 at 17:16
  • $\begingroup$ I am not sure about EFA or CFA. The choice of which questions to remove I believe is highly problem-dependent. I think a bit of trial and error will show you which questions can be removed with minimal degradation of the amount of variance accounted for by the common factors. $\endgroup$ – F. Tusell May 9 at 18:07
  • $\begingroup$ Thanks again, could you expand on some ways to try this? (when you say "trial and error") $\endgroup$ – Grint May 9 at 19:06

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