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Why are sets of items from different constructs loading on the same factor? In my experience, many psychological tests have multiple scales where the correlations between some scales can be quite high (e.g., .6 to .8 correlations). In such a case, there may not be a huge difference between a model where all these items load on one factor versus a model ...

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The "1" means that the coefficient for that particular path has been set (fixed) to 1, as mentioned by Peter Flom. The diagram you refer to is reproduced here: In order for a latent variable to have a scale either its variance must be fixed (often to 1), or a path to one of it's indicators must be fixed (usually to 1), as in this case. The same applies ...

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There is a complete explanation p176: http://psychology.concordia.ca/fac/kline/Library/k13b.pdf

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A "1" on diagrams like this usually means that the coefficient has been set to 1 by the person doing the analysis. Such settings are often necessary to allow model identification.

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What comes first here? Understanding the data or being committed to using a particular technique? Why precisely do you think the PCA is going to help? Perhaps it is beyond your control, or too late any way, but the questionnaire sounds badly designed. If a rating of "perceived usefulness" is needed, why not ask something like "on a scale of 1 to 5, please ...

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As a general advice, your sample size is quite small. It's not such a no-no as some people claim but depending on the specifics of the data, it's not too surprising to have unstable or unexpected results in a factor analysis. A big question in all this is how you selected the number of factors to extract. There is no objective easy-to-determine â€śnumber of ...

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I may be misunderstanding the phrase "indeterminancy of scale", but I believe it is set to one for identifiability. (That is, the number of unknowns in this system of equations should not exceed the number of equations.) Without setting one of the links to one, there are too many unknowns. Is that the same thing as indeterminancy of scale? In most SEM ...

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Here's how I see it. Technically, you are right. Simply adding the scores (or averaging them) weights them all equally and this may not be the optimal solution. However, it does have certain advantages: 1) It is simple. Factor analysis is not. OK, readers of this list probably understand factor analysis; but what about journal editors, dissertation ...

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FA is for unconstrained variables. First transform the data to make it this way. Use any inverse CDF (I use the quantile function for the normal distribution, qnorm in R) for variables that start out between 0 and 1; use any log function for positive variables (if zero can occur, add 1 first.) Then do factor analysis. The results will be for the ...

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Using factor analysis for scale construction is a bit of an art. It is common to drop items that load to a substantial degree on more than one factor after factor rotation. That said, a few alternative ideas: Consider whether you have extracted enough factors. Sometimes when you extract more factors cross-loading items or items that don't load much at ...

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This book by J. Scott Long should suit your purposes.

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It might be better to try to classify people before doing the EFA, using e.g. cluster analysis which is designed for that purpose. However, if, for some reason, you want to classify on the factors, I would first make a bunch of plots: Univariate density plots of each factor, then some bivariate plots, what else I would do depends on the number of factors. ...

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