To develop a new questionnaire for measuring lifeguards' vigilance, after gathering data from different literature, I found 20 scales contributing to lifeguards' vigilance. Then I started to design questions for each scale in the way that in each scale, there are some questions (4 to 10 questions) with different face but with the same concept. Finally I have come up to 142 items. Now I want to perform factor analysis for construct validity. I don't know if I could perform FA at the scale level instead of item level. (Scales and items in each scale have face validity too.)
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$\begingroup$ You need to explain what you want to learn from the factor analysis or why you are considering it. It's not an end in itself, you don't have to do it, at any level. $\endgroup$– GalaCommented Sep 8, 2013 at 7:02
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$\begingroup$ this is for construct validity of questionnaire $\endgroup$– Alireza aminaeeCommented Sep 8, 2013 at 7:36
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2 Answers
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You should perform factor analysis at the item level. In other words, you should input all of the items into a single factor analysis. This is assuming, of course, that you have enough cases (500+?). This would allow you to know what items measure what different constructs. Factor analysis at the scale level would be possible, but quite unorthodox. This might work if you have too few cases. This would assume that items within you scales all measure the same thing, which they likely do not, and this is what factor analysis is designed to test.
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$\begingroup$ Why does FA of a separate scale
assume that items within your scale all measure the same thing? $\endgroup$– ttnphnsCommented Sep 8, 2013 at 16:29 -
$\begingroup$ Well if you are doing FA on scales (rather than items), you are summing the items to calculate the scales. If you are summing items, then this assumes that each item generally measures the same construct. Consider a measure of depression (or anything, really), if you sum items, you are adding depression severity and it is assumed that every item measures depression severity. $\endgroup$– BehacadCommented Sep 8, 2013 at 17:25
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$\begingroup$ i want to add an example to clarify the case. as i said i found 20 scales. one of these scales, for example, is concentration. for this scale i design 6 questions to cover different aspects of life guarding. these questions although designed for different aspect of life-guarding but all of them measure concentration and they all received face validity by experts . and i did the same with other scales. now when performing factor analysis 33 variables are extracted and those questions designed for concentration is distributed in different factors. what should i do now $\endgroup$ Commented Sep 8, 2013 at 17:30
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$\begingroup$ dear ttnphns. yes. items within my scales all measure the same thing $\endgroup$ Commented Sep 8, 2013 at 17:36
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1$\begingroup$ That is the point of factor analysis, it empirically tells you whether they measure the same construct. Those might be the same construct (e.g., anxiety), or they might not. For example, children anxiety and underwater body anxiety may well best be conceptualized as distinct construct (despite both being anxiety-related). $\endgroup$– BehacadCommented Sep 8, 2013 at 18:22
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My understanding is that confirmatory factor analysis (CFA) is typically run separately for each scale in order to verify the expected factor structure of each. If your intent is to use these scales as IVs and DVs in an analysis (e.g., multiple regression), I'd use this route. However, if your intention is to perform an exploratory factor analysis on all of your items across scales and search for "emergent" factors not intended by the original authors, I think an argument could be made for the latter approach.
