about to exploratory factor analysis
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about to exploratory factor analysis
 
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Hi all,
I have a question. Namely, running exploratory factor analysis (EFA) in spss, it gives explained variance proportion for each eiganvalue. Besides, the variables that have loading less than .30 can be dropped out from data set according to this analysis. Herein, reporting the total explained variance in articles, is it necessary or appropriate to repeat the EFA with reduced data set and report the total explained variance obtained from second analysis? thanks in advance ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
Re: about to exploratory factor analysis
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What is the purpose of the factor analysis?
What did you use for a stopping rule for the number of factors to retain? What discipline's journals are you thinking about? What do they report? Art Kendall Social Research Consultants On 1/27/2012 7:33 AM, [hidden email] wrote: ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARDHi all, I have a question. Namely, running exploratory factor analysis (EFA) in spss, it gives explained variance proportion for each eiganvalue. Besides, the variables that have loading less than .30 can be dropped out from data set according to this analysis. Herein, reporting the total explained variance in articles, is it necessary or appropriate to repeat the EFA with reduced data set and report the total explained variance obtained from second analysis? thanks in advance ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
Art Kendall
Social Research Consultants |
Re: about to exploratory factor analysis
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In reply to this post by arifozer@msn.com
The explained variance proportion is an attribute of the underlying
components or factors. Loadings on each factor is an attribute of the observed variables, and can be changed by rotation of the factors. Discarding variables with lower loadings is not an analytical requirement but a heuristic decision, dictated by the desire to have a more frugal model with the minimum possible number of variables. If some variable appears to have low loadings, especially in the first few factors, and this is not greatly changed via rotation, eliminating those variables may be a smart move. A general warning, however, is that even variables that have low loadings in the first factor extracted may have a heavier weight in some other factor. Sometimes the problem is not defined by several observed variables mostly explained by the first factors (as in the classical case of several inter-correlated cognitive tests explained by the first factor or "general intelligence"). Sometimes, indeed, observed variables are better explained by several of the underlying factors, and it may also be the case that the applicable theory justifies this multi-factor analysis. Thus a variable with low loading on Factor 1 may have a larger loading on Factor 2 or 3. Besides, in some cases several variables, having load loadings individually, may make, all together, a substantial contribution. If these variables (or some of them) somehow can be interpreted as belonging in the same underlying dimension (e.g. by being all correlated to the same factor, even if that factor is not the first) or if they reflect variables with important theoretical functions, then retaining them may be wise. For instance, suppose in an instance of the same classical analysis of cognitive ability you have some variable (or variables) reflecting the nervousness of the subjects in the test situation: even if that variable (or variables) have a relatively low loading on the first factor, they may be important to retain. Finally, it may be the case that through rotation of factors you may get a better (and more easily interpretable) picture of the importance of those variables. Hector -----Mensaje original----- De: SPSSX(r) Discussion [mailto:[hidden email]] En nombre de [hidden email] Enviado el: Friday, January 27, 2012 09:33 Para: [hidden email] Asunto: about to exploratory factor analysis Hi all, I have a question. Namely, running exploratory factor analysis (EFA) in spss, it gives explained variance proportion for each eiganvalue. Besides, the variables that have loading less than .30 can be dropped out from data set according to this analysis. Herein, reporting the total explained variance in articles, is it necessary or appropriate to repeat the EFA with reduced data set and report the total explained variance obtained from second analysis? thanks in advance ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ----- No virus found in this message. Checked by AVG - www.avg.com Version: 2012.0.1901 / Virus Database: 2109/4768 - Release Date: 01/26/12 ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD |
Re: about to exploratory factor analysis
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In reply to this post by arifozer@msn.com
I can speak in particular to the question of factor analyses
of rating scales with a few dozen clinical items. - If your data is other, please describe. I've done a huge number of these over the years, and I very seldom saw an item that failed to load at least 0.30 on at least one accepted factor. When that happened, it was always clear, on inspection, that the item was "bad" - mis-written, ambiguous, or irrelevant to the general theme of the other items (even if it would be relevant elsewhere). In that case, it was appropriate to drop the item explicitly, with explanation, and to re-run the analysis. If I had *many* dozens of items, or a different sort of items, I think I would have to consider, mainly, what my written explanation would be for dropping the items. How exploratory is the whole study? How badly, or for what reason, do the dropped items fail an "eyeball" test? -- Rich Ulrich > Date: Fri, 27 Jan 2012 07:33:10 -0500 > From: [hidden email] > Subject: about to exploratory factor analysis > To: [hidden email] > > Hi all, > > I have a question. Namely, running exploratory factor analysis (EFA) in spss, > it gives explained variance proportion for each eiganvalue. Besides, the > variables that have loading less than .30 can be dropped out from data set > according to this analysis. Herein, reporting the total explained variance in > articles, is it necessary or appropriate to repeat the EFA with reduced data > set and report the total explained variance obtained from second analysis? > thanks in advance > |
Re: about to exploratory factor analysis
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In reply to this post by Hector Maletta
The factors you get depend on the variables you use. Consequently, the distribution of total variance of all variables among the various factors depends on what variables you use. Each variables is regarded as standardized with variance=1, and this implies that k variables have total variance=k. If you drop one variable, total variance drops to k-1. This total variance, whatever it is, is distributed among the various factors, of which you can obtain up to the same number of observable variables (k or k-1 in the above examples). Once you drop one or more variables, factor analysis starts all over again, from scratch, taking only into account the remaining variables. Remember that factors are not objectively existing entities, but mathematical constructs that are linear functions of observed variables. Hector De: Arif OZER [mailto:[hidden email]] Hi all, Thanks for replies, but I think that I didnt express myself in the previous e-mails. Actually my question is that whether it is necessary to repeat exploratory factor analysis to determine the total variance, after the first analysis that is dropped variables or we must use to report the proportion of total variance obtained from the first analysis? thanks a lot again. web: http://goo.gl/TEzDi > From: [hidden email] No virus found in this message. Version: 2012.0.1901 / Virus Database: 2109/4770 - Release Date: 01/27/12 |
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