Determining the Number of Factors to Retain in an Exploratory Factor Analysis Using Comparison Data of Known Factorial Structure.
Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenva...
| Publicado en: | Psychological Assessment Vol. 24; no. 2; pp. 282 - 293 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
American Psychological Association
Jun2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=77896058&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 77896058 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10403590 POL jtl: Psychological Assessment issn: 10403590 maglogo: N pubinfo: dt: Jun2012 vid: 24 iid: 2 pid: 34 pub: American Psychological Association artinfo: ui: 77896058 10.1037/a0025697 ppf: 282 ppct: 11 formats: tig: atl: Determining the Number of Factors to Retain in an Exploratory Factor Analysis Using Comparison Data of Known Factorial Structure. aug: au: Ruscio, John Roche, Brendan affil: Department of Psychology, The College of New Jersey su: Personality assessment Factor analysis Factor structure Sampling errors Eigenvalues Educational evaluation sug: subj: Personality assessment Factor analysis Factor structure Sampling errors Eigenvalues Educational evaluation keyword: comparison data exploratory factor analysis Kaiser criterion number of factors parallel analysis comparison data exploratory factor analysis Kaiser criterion number of factors parallel analysis ab: Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenvalues of randomly generated data with those for the actual data. PA takes into account sampling error, and at present it is widely considered the best available method. We introduce a variant of PA that goes even further by reproducing the observed correlation matrix rather than generating random data. Comparison data (CD) with known factorial structure are first generated using 1 factor, and then the number of factors is increased until the reproduction of the observed eigenvalues fails to improve significantly. We evaluated the performance of PA, CD with known factorial structure, and 7 other techniques in a simulation study spanning a wide range of challenging data conditions. In terms of accuracy and robustness across data conditions, the CD technique outperformed all other methods, including a nontrivial superiority to PA. We provide program code to implement the CD technique, which requires no more specialized knowledge or skills than performing PA. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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