Application of Prediction Analysis to Cross Classifications of Ordinal Data
Identifieur interne : 001325 ( Istex/Corpus ); précédent : 001324; suivant : 001326Application of Prediction Analysis to Cross Classifications of Ordinal Data
Auteurs : Jörg E. Von Eye ; Jörg E. Brandtst DterSource :
- Biometrical Journal [ 0323-3847 ] ; 1988.
English descriptors
Abstract
Prediction analysis (PA) of cross classifications is characterized as a method for the analysis of local prediction hypotheses, that is, hypotheses that link particular predictor states to particular states of criteria. To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.
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DOI: 10.1002/bimj.4710300604
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To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Prof. Dr. von Eye</name><affiliations><json:string>College of Human Development, The Pennsylvania State University</json:string><json:string>Alexander von Eye, The Pennsylvania State University College of Health and Human Development S ‐ 110 Henderson Human Development Bldg. 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To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.</abstract><qualityIndicators><score>6.476</score><pdfVersion>1.3</pdfVersion><pdfPageSize>612 x 792 pts (letter)</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>814</abstractCharCount><pdfWordCount>5570</pdfWordCount><pdfCharCount>32698</pdfCharCount><pdfPageCount>15</pdfPageCount><abstractWordCount>123</abstractWordCount></qualityIndicators><title>Application of Prediction Analysis to Cross Classifications of Ordinal Data</title><refBibs><json:item><host><author></author><title>Agresti, A.,1984:Analysis of ordinal categorical data.New York:Wiley.</title></host></json:item><json:item><host><author></author><title>Bachman, J. G.,Johnson, L. D., &O'Malley, P. M.,1980:Monitoring the future: Questionnaire responses from the Nation's high school seniors, 1979.Ann. Arbor. MI:Survey Research Center.</title></host></json:item><json:item><host><author></author><title>Berztiss, A. 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F.,1973:The study of behavioral development.New York:Academic Press.</title></host></json:item></refBibs><genre><json:string>article</json:string></genre><host><volume>30</volume><publisherId><json:string>BIMJ</json:string></publisherId><pages><total>15</total><last>665</last><first>651</first></pages><issn><json:string>0323-3847</json:string></issn><issue>6</issue><subject><json:item><value>Article</value></json:item></subject><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><eissn><json:string>1521-4036</json:string></eissn><title>Biometrical Journal</title><doi><json:string>10.1002/(ISSN)1521-4036</json:string></doi></host><categories><wos><json:string>science</json:string><json:string>statistics & probability</json:string><json:string>mathematical & computational biology</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>mathematics & statistics</json:string><json:string>statistics & probability</json:string></scienceMetrix></categories><publicationDate>1988</publicationDate><copyrightDate>1988</copyrightDate><doi><json:string>10.1002/bimj.4710300604</json:string></doi><id>03D3919DF6D38E626330B223828B1E404DB8A0C1</id><score>0.9074204</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/03D3919DF6D38E626330B223828B1E404DB8A0C1/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/03D3919DF6D38E626330B223828B1E404DB8A0C1/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/03D3919DF6D38E626330B223828B1E404DB8A0C1/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Application of Prediction Analysis to Cross Classifications of Ordinal Data</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Berlin</pubPlace><availability><p>Copyright © 1988 WILEY‐VCH Verlag GmbH & Co. KGaA</p></availability><date>1988</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Application of Prediction Analysis to Cross Classifications of Ordinal Data</title><author xml:id="author-1"><persName><surname>von Eye</surname></persName><roleName type="degree">Prof. Dr.</roleName><affiliation>College of Human Development, The Pennsylvania State University</affiliation><affiliation>Alexander von Eye, The Pennsylvania State University College of Health and Human Development S ‐ 110 Henderson Human Development Bldg. 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J.</title><idno type="pISSN">0323-3847</idno><idno type="eISSN">1521-4036</idno><idno type="DOI">10.1002/(ISSN)1521-4036</idno><imprint><publisher>WILEY‐VCH Verlag</publisher><pubPlace>Berlin</pubPlace><date type="published" when="1988"></date><biblScope unit="volume">30</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="651">651</biblScope><biblScope unit="page" to="665">665</biblScope></imprint></monogr><idno type="istex">03D3919DF6D38E626330B223828B1E404DB8A0C1</idno><idno type="DOI">10.1002/bimj.4710300604</idno><idno type="ArticleID">BIMJ4710300604</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1988</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Prediction analysis (PA) of cross classifications is characterized as a method for the analysis of local prediction hypotheses, that is, hypotheses that link particular predictor states to particular states of criteria. To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>keywords</head><item><term>Prediction analysis</term></item><item><term>Ordinal variables</term></item><item><term>Log‐linear models</term></item><item><term>Prediction success</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1987-05">Received</change><change when="1988">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/03D3919DF6D38E626330B223828B1E404DB8A0C1/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>WILEY‐VCH Verlag</publisherName><publisherLoc>Berlin</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1521-4036</doi><issn type="print">0323-3847</issn><issn type="electronic">1521-4036</issn><idGroup><id type="product" value="BIMJ"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="BIOMETRICAL JOURNAL">Biometrical Journal</title><title type="short">Biom. 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KGaA</copyright><eventGroup><event type="manuscriptReceived" date="1987-05"></event><event type="manuscriptRevised" date="1987-08"></event><event type="firstOnline" date="2007-01-19"></event><event type="publishedOnlineFinalForm" date="2007-01-19"></event><event type="xmlConverted" agent="Converter:JWSART34_TO_WML3G version:2.3.2 mode:FullText source:HeaderRef result:HeaderRef" date="2010-03-09"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-02-22"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-15"></event></eventGroup><numberingGroup><numbering type="pageFirst">651</numbering><numbering type="pageLast">665</numbering></numberingGroup><correspondenceTo><lineatedText><line>Alexander von Eye, The Pennsylvania State University College of Health and Human Development S ‐ 110 Henderson Human Development Bldg. University Park, PA 16802 USA</line><line>Jochen Brandtstädter, Brandtstädter Universität Trier FB 1 – Psychologie – Tarforst D ‐ 5500 Trier</line></lineatedText></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:BIMJ.BIMJ4710300604.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="0"></count><count type="tableTotal" number="2"></count><count type="referenceTotal" number="29"></count></countGroup><titleGroup><title type="main" xml:lang="en">Application of Prediction Analysis to Cross Classifications of Ordinal Data</title><title type="short" xml:lang="en">Prediction Analysis</title></titleGroup><creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1" corresponding="yes"><personName><honorifics>Prof. Dr.</honorifics><givenNames>Alexander</givenNames><familyName>von Eye</familyName></personName></creator><creator xml:id="au2" creatorRole="author" affiliationRef="#af2" corresponding="yes"><personName><honorifics>Prof. Dr.</honorifics><givenNames>Jochen</givenNames><familyName>Brandtstädter</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="af1" countryCode="US" type="organization"><unparsedAffiliation>College of Human Development, The Pennsylvania State University</unparsedAffiliation></affiliation><affiliation xml:id="af2" countryCode="US" type="organization"><unparsedAffiliation>Department of Psychology, University of Trier</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">Prediction analysis</keyword><keyword xml:id="kwd2">Ordinal variables</keyword><keyword xml:id="kwd3">Log‐linear models</keyword><keyword xml:id="kwd4">Prediction success</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Prediction analysis (<i>PA</i>) of cross classifications is characterized as a method for the analysis of local prediction hypotheses, that is, hypotheses that link particular predictor states to particular states of criteria. To evaluate the success of a prediction, <i>PA</i> compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in <i>PA</i>. Numerical examples are given from the areas of cognitive development and drug research.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Application of Prediction Analysis to Cross Classifications of Ordinal Data</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Prediction Analysis</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Application of Prediction Analysis to Cross Classifications of Ordinal Data</title></titleInfo><name type="personal"><namePart type="termsOfAddress">Prof. Dr.</namePart><namePart type="family">von Eye</namePart><affiliation>College of Human Development, The Pennsylvania State University</affiliation><affiliation>Alexander von Eye, The Pennsylvania State University College of Health and Human Development S ‐ 110 Henderson Human Development Bldg. University Park, PA 16802 USAJochen Brandtstädter, Brandtstädter Universität Trier FB 1 – Psychologie – Tarforst D ‐ 5500 Trier</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="termsOfAddress">Prof. Dr.</namePart><namePart type="family">Brandtstädter</namePart><affiliation>Department of Psychology, University of Trier</affiliation><affiliation>Alexander von Eye, The Pennsylvania State University College of Health and Human Development S ‐ 110 Henderson Human Development Bldg. University Park, PA 16802 USAJochen Brandtstädter, Brandtstädter Universität Trier FB 1 – Psychologie – Tarforst D ‐ 5500 Trier</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>WILEY‐VCH Verlag</publisher><place><placeTerm type="text">Berlin</placeTerm></place><dateIssued encoding="w3cdtf">1988</dateIssued><dateCaptured encoding="w3cdtf">1987-05</dateCaptured><copyrightDate encoding="w3cdtf">1988</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="tables">2</extent><extent unit="references">29</extent></physicalDescription><abstract lang="en">Prediction analysis (PA) of cross classifications is characterized as a method for the analysis of local prediction hypotheses, that is, hypotheses that link particular predictor states to particular states of criteria. To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.</abstract><subject lang="en"><genre>keywords</genre><topic>Prediction analysis</topic><topic>Ordinal variables</topic><topic>Log‐linear models</topic><topic>Prediction success</topic></subject><relatedItem type="host"><titleInfo><title>Biometrical Journal</title></titleInfo><titleInfo type="abbreviated"><title>Biom. J.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Article</topic></subject><identifier type="ISSN">0323-3847</identifier><identifier type="eISSN">1521-4036</identifier><identifier type="DOI">10.1002/(ISSN)1521-4036</identifier><identifier type="PublisherID">BIMJ</identifier><part><date>1988</date><detail type="volume"><caption>vol.</caption><number>30</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>651</start><end>665</end><total>15</total></extent></part></relatedItem><identifier type="istex">03D3919DF6D38E626330B223828B1E404DB8A0C1</identifier><identifier type="DOI">10.1002/bimj.4710300604</identifier><identifier type="ArticleID">BIMJ4710300604</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 1988 WILEY‐VCH Verlag GmbH & Co. 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