Recovering a Basic Space from Issue Scales in R

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dc.citation.issueNumber7en_US
dc.citation.journalTitleJournal of Statistical Softwareen_US
dc.citation.volumeNumber69en_US
dc.contributor.authorPoole, Keith T.en_US
dc.contributor.authorLewis, Jeffrey B.en_US
dc.contributor.authorRosenthal, Howarden_US
dc.contributor.authorLo, Jamesen_US
dc.contributor.authorCarroll, Royceen_US
dc.date.accessioned2017-05-02T21:09:55Z
dc.date.available2017-05-02T21:09:55Z
dc.date.issued2016en_US
dc.description.abstractbasicspace is an R package that conducts Aldrich-McKelvey and Blackbox scaling to recover estimates of the underlying latent dimensions of issue scale data. We illustrate several applications of the package to survey data commonly used in the social sciences. Monte Carlo tests demonstrate that the procedure can recover latent dimensions and reproduce the matrix of responses at moderate levels of error and missing data.en_US
dc.identifier.citationPoole, Keith T., Lewis, Jeffrey B., Rosenthal, Howard, et al.. "Recovering a Basic Space from Issue Scales in R." <i>Journal of Statistical Software,</i> 69, no. 7 (2016) Foundation for Open Access Statistics: https://doi.org/10.18637/jss.v069.i07.
dc.identifier.doihttps://doi.org/10.18637/jss.v069.i07en_US
dc.identifier.urihttps://hdl.handle.net/1911/94113
dc.language.isoengen_US
dc.publisherFoundation for Open Access Statistics
dc.rightsThis work is licensed under the Creative Commons Attribution 3.0 Unported Licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/en_US
dc.subject.keywordmultivariateen_US
dc.subject.keywordRen_US
dc.subject.keywordscalingen_US
dc.titleRecovering a Basic Space from Issue Scales in Ren_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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