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dc.contributor.authorMatta, Tyler H
dc.contributor.authorRutkowski, Leslie
dc.contributor.authorRutkowski, David
dc.contributor.authorLiaw, Yuan-Ling
dc.date.accessioned2018-11-20T06:13:46Z
dc.date.available2018-11-20T06:13:46Z
dc.date.issued2018
dc.identifier.citationLarge-scale Assessments in Education. 2018 Nov 19;6(1):15
dc.identifier.urihttp://hdl.handle.net/10852/65601
dc.description.abstractThis article provides an overview of the R package lsasim, designed to facilitate the generation of data that mimics a large scale assessment context. The package features functions for simulating achievement data according to a number of common IRT models with known parameters. A clear advantage of lsasim over other simulation software is that the achievement data, in the form of item responses, can arise from multiple-matrix sampled test designs. Furthermore, lsasim offers the possibility of simulating data that adhere to general properties found in the background questionnaire (mostly ordinal, correlated variables that are also related to varying degrees with some latent trait). Although the background questionnaire data can be linked to the test responses, all aspects of lsasim can function independently, affording researchers a high degree of flexibility in terms of possible research questions and the part of an assessment that is of most interest.
dc.language.isoeng
dc.rightsThe Author(s); licensee Springer International Publishing Ltd.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlelsasim: an R package for simulating large-scale assessment data
dc.typeJournal article
dc.date.updated2018-11-20T06:13:47Z
dc.creator.authorMatta, Tyler H
dc.creator.authorRutkowski, Leslie
dc.creator.authorRutkowski, David
dc.creator.authorLiaw, Yuan-Ling
dc.identifier.cristin1654935
dc.identifier.doihttps://doi.org/10.1186/s40536-018-0068-8
dc.identifier.urnURN:NBN:no-67910
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/65601/1/40536_2018_Article_68.pdf
dc.type.versionPublishedVersion
cristin.articleid15


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