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Article: Computation of individual latent variable scores from data with multiple missingness patterns

TitleComputation of individual latent variable scores from data with multiple missingness patterns
Authors
KeywordsFactor analysis
Factor score
Latent variable score
Missingness
SEM
Software
Issue Date2007
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244
Citation
Behavior Genetics, 2007, v. 37 n. 2, p. 408-422 How to Cite?
AbstractLatent variable models are used in biological and social sciences to investigate characteristics that are not directly measurable. The generation of individual scores of latent variables can simplify subsequent analyses. However, missing measurements in real data complicate the calculation of scores. Missing observations also result in different latent variable scores having different degrees of accuracy which should be taken into account in subsequent analyses. This manuscript presents a publicly available software tool that addresses both these problems, using as an example a dataset consisting of multiple ratings for ADHD symptomatology in children. The program computes latent variable scores with accompanying accuracy indices, under a 'user-specified' structural equation model, in data with missing data patterns. Since structural equation models encompass factor models, it can also be used for calculating factor scores. The program, documentation and a tutorial, containing worked examples and specimen input and output files, is available at http://statgen.iop.kcl.ac.uk/lsc . © 2006 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/81666
ISSN
2015 Impact Factor: 3.268
2015 SCImago Journal Rankings: 1.457
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCampbell, DDen_HK
dc.contributor.authorRijsdijk, FVen_HK
dc.contributor.authorSham, PCen_HK
dc.date.accessioned2010-09-06T08:20:32Z-
dc.date.available2010-09-06T08:20:32Z-
dc.date.issued2007en_HK
dc.identifier.citationBehavior Genetics, 2007, v. 37 n. 2, p. 408-422en_HK
dc.identifier.issn0001-8244en_HK
dc.identifier.urihttp://hdl.handle.net/10722/81666-
dc.description.abstractLatent variable models are used in biological and social sciences to investigate characteristics that are not directly measurable. The generation of individual scores of latent variables can simplify subsequent analyses. However, missing measurements in real data complicate the calculation of scores. Missing observations also result in different latent variable scores having different degrees of accuracy which should be taken into account in subsequent analyses. This manuscript presents a publicly available software tool that addresses both these problems, using as an example a dataset consisting of multiple ratings for ADHD symptomatology in children. The program computes latent variable scores with accompanying accuracy indices, under a 'user-specified' structural equation model, in data with missing data patterns. Since structural equation models encompass factor models, it can also be used for calculating factor scores. The program, documentation and a tutorial, containing worked examples and specimen input and output files, is available at http://statgen.iop.kcl.ac.uk/lsc . © 2006 Springer Science+Business Media, LLC.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0001-8244en_HK
dc.relation.ispartofBehavior Geneticsen_HK
dc.subjectFactor analysisen_HK
dc.subjectFactor scoreen_HK
dc.subjectLatent variable scoreen_HK
dc.subjectMissingnessen_HK
dc.subjectSEMen_HK
dc.subjectSoftwareen_HK
dc.titleComputation of individual latent variable scores from data with multiple missingness patternsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0001-8244&volume=37&issue=2&spage=408&epage=422&date=2007&atitle=Computation+of+individual+latent+variable+scores+from+data+with+multiple+missingness+patternsen_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10519-006-9123-2en_HK
dc.identifier.pmid17120140-
dc.identifier.scopuseid_2-s2.0-33947280410en_HK
dc.identifier.hkuros133085en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33947280410&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume37en_HK
dc.identifier.issue2en_HK
dc.identifier.spage408en_HK
dc.identifier.epage422en_HK
dc.identifier.isiWOS:000245407100014-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCampbell, DD=16041366500en_HK
dc.identifier.scopusauthoridRijsdijk, FV=6701830835en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.citeulike1228929-

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