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The Effects of Overextraction on Factor and Component Analysis
The effects of overextracting factors and components within and between the methods of maximum likelihood factor analysis (MLFA) and principal component analysis (PCA) were examined. Computer-simulated data sets were generated to represent a range of factor and component patterns. Saturation (a ij =...
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Published in: | Multivariate behavioral research 1992-07, Vol.27 (3), p.387-415 |
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container_title | Multivariate behavioral research |
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creator | Fava, Joseph L. Velicer, Wayne F. |
description | The effects of overextracting factors and components within and between the methods of maximum likelihood factor analysis (MLFA) and principal component analysis (PCA) were examined. Computer-simulated data sets were generated to represent a range of factor and component patterns. Saturation (a
ij
= .8, .6 & .4), sample size (N = 75, 150,225,450), and variable-to-component (factor) ratio (p:m = 12:1,6:1, & 4:1) were conditions manipulated. In Study 1, scores based on the incorrect patterns were correlated with correct scores within each method after each overextraction. In Study 2, scores were correlated between the methods of PCAand MLFA after each overextraction. Overextraction had a negative effect, but scores based on strong component and factor patterns displayed robustness to the effects of overextraction. Low item saturation and low sample size resulted in degraded score reproduction. Degradation was strongest for patterns that combined low saturation and low sample size. Component and factor scores were highly correlated even at maximal levels of overextraction. Dissimilarity between score methods was the greatest in conditions that combined low saturation and low sample size. Some guidelines for researchers concerning the effects of overextraction are noted, as well as some cautions in the interpretation of results. |
doi_str_mv | 10.1207/s15327906mbr2703_5 |
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ij
= .8, .6 & .4), sample size (N = 75, 150,225,450), and variable-to-component (factor) ratio (p:m = 12:1,6:1, & 4:1) were conditions manipulated. In Study 1, scores based on the incorrect patterns were correlated with correct scores within each method after each overextraction. In Study 2, scores were correlated between the methods of PCAand MLFA after each overextraction. Overextraction had a negative effect, but scores based on strong component and factor patterns displayed robustness to the effects of overextraction. Low item saturation and low sample size resulted in degraded score reproduction. Degradation was strongest for patterns that combined low saturation and low sample size. Component and factor scores were highly correlated even at maximal levels of overextraction. Dissimilarity between score methods was the greatest in conditions that combined low saturation and low sample size. Some guidelines for researchers concerning the effects of overextraction are noted, as well as some cautions in the interpretation of results.</description><subject>Biological and medical sciences</subject><subject>Computer Simulation</subject><subject>Correlation</subject><subject>Factor Analysis</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Mathematical Models</subject><subject>Maximum Likelihood Statistics</subject><subject>Overextraction</subject><subject>Principal Components Analysis</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Psychometrics. Statistics. Methodology</subject><subject>Sample Size</subject><subject>Scores</subject><subject>Specification Error</subject><subject>Statistics. Mathematics</subject><issn>0027-3171</issn><issn>1532-7906</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1992</creationdate><recordtype>article</recordtype><sourceid>7SW</sourceid><recordid>eNp9kE1LxDAQhoMo7rr6B0SkBw9eqpO0SZqDwrLs-sHCXtZzSdMUK22yJl11_70p-3ERhIEZ5n3mZXgRusRwhwnwe49pQrgA1haOcEhyeoSG_S7ul8doCEB4nGCOB-jM-w8AYDQVp2hAGM9EqCF6XL7raFpVWnU-slW0-NJO_3ROqq62Jgo1C6N1kTRlNLHtyhptumhsZLPxtT9HJ5VsvL7Y9RF6m02Xk-d4vnh6mYznsUqBdrHiNBO0EkVWUJVhVoKQlBYFSzFJBMOCFpyUGDgIxYkUqqASJCVK4TAEZoRut74rZz_X2nd5W3ulm0Yabdc-x5xBJgBwFlCyRZWz3jtd5StXt9Jtcgx5n1v-N7dwdL3zXxetLg8n-6ACcLMDpFeyqZw0qvYHLqUkTTMI2NUW065WB3X6mjKgtJcftnJtKuta-W1dU-ad3DTW7S2Tf978BRL9kfQ</recordid><startdate>19920701</startdate><enddate>19920701</enddate><creator>Fava, Joseph L.</creator><creator>Velicer, Wayne F.</creator><general>Lawrence Erlbaum Associates, Inc</general><general>Society of Multivariate Experimental Psychology</general><scope>7SW</scope><scope>BJH</scope><scope>BNH</scope><scope>BNI</scope><scope>BNJ</scope><scope>BNO</scope><scope>ERI</scope><scope>PET</scope><scope>REK</scope><scope>WWN</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>19920701</creationdate><title>The Effects of Overextraction on Factor and Component Analysis</title><author>Fava, Joseph L. ; Velicer, Wayne F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-c75895f9b8b5c816d09a55bb6412396195b72d10709c72a9cb5a0a52cc15a0123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Biological and medical sciences</topic><topic>Computer Simulation</topic><topic>Correlation</topic><topic>Factor Analysis</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Mathematical Models</topic><topic>Maximum Likelihood Statistics</topic><topic>Overextraction</topic><topic>Principal Components Analysis</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Psychometrics. Statistics. Methodology</topic><topic>Sample Size</topic><topic>Scores</topic><topic>Specification Error</topic><topic>Statistics. Mathematics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fava, Joseph L.</creatorcontrib><creatorcontrib>Velicer, Wayne F.</creatorcontrib><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Multivariate behavioral research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fava, Joseph L.</au><au>Velicer, Wayne F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ460550</ericid><atitle>The Effects of Overextraction on Factor and Component Analysis</atitle><jtitle>Multivariate behavioral research</jtitle><addtitle>Multivariate Behav Res</addtitle><date>1992-07-01</date><risdate>1992</risdate><volume>27</volume><issue>3</issue><spage>387</spage><epage>415</epage><pages>387-415</pages><issn>0027-3171</issn><eissn>1532-7906</eissn><coden>MVBRAV</coden><abstract>The effects of overextracting factors and components within and between the methods of maximum likelihood factor analysis (MLFA) and principal component analysis (PCA) were examined. Computer-simulated data sets were generated to represent a range of factor and component patterns. Saturation (a
ij
= .8, .6 & .4), sample size (N = 75, 150,225,450), and variable-to-component (factor) ratio (p:m = 12:1,6:1, & 4:1) were conditions manipulated. In Study 1, scores based on the incorrect patterns were correlated with correct scores within each method after each overextraction. In Study 2, scores were correlated between the methods of PCAand MLFA after each overextraction. Overextraction had a negative effect, but scores based on strong component and factor patterns displayed robustness to the effects of overextraction. Low item saturation and low sample size resulted in degraded score reproduction. Degradation was strongest for patterns that combined low saturation and low sample size. Component and factor scores were highly correlated even at maximal levels of overextraction. Dissimilarity between score methods was the greatest in conditions that combined low saturation and low sample size. Some guidelines for researchers concerning the effects of overextraction are noted, as well as some cautions in the interpretation of results.</abstract><cop>Fort Worth, TX</cop><pub>Lawrence Erlbaum Associates, Inc</pub><pmid>26789789</pmid><doi>10.1207/s15327906mbr2703_5</doi><tpages>29</tpages></addata></record> |
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subjects | Biological and medical sciences Computer Simulation Correlation Factor Analysis Fundamental and applied biological sciences. Psychology Mathematical Models Maximum Likelihood Statistics Overextraction Principal Components Analysis Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychometrics. Statistics. Methodology Sample Size Scores Specification Error Statistics. Mathematics |
title | The Effects of Overextraction on Factor and Component Analysis |
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