Hide metadata

dc.date.accessioned2020-08-17T19:57:10Z
dc.date.available2020-08-17T19:57:10Z
dc.date.created2020-07-28T09:53:00Z
dc.date.issued2020
dc.identifier.citationScherer, Ronny . Analysing PIAAC Data with Structural Equation Modelling in Mplus. Large-Scale Cognitive Assessment: Analyzing PIAAC Data. 2020, 165-208 Springer Nature
dc.identifier.urihttp://hdl.handle.net/10852/78483
dc.description.abstractStructural equation modelling (SEM) has become one of the most prominent approaches to testing substantive theories about the relations among observed and/or unobserved variables. Applying this multivariate procedure, researchers are faced with several methodological decisions, including the treatment of indicator variables (e.g. categorical vs. continuous treatment), the handling of missing data, and the selection of an appropriate level of analysis. The PIAAC data pose additional issues, such as the clustering of individual-level data, the large number of participating countries, the representation of performance scores by a set of plausible values, and the differences in the selection probabilities. Therefore, a flexible software package is required to handle them. This chapter introduces readers to analysing PIAAC data with SEM in the software Mplus by (a) presenting the key concepts behind SEM, (b) discussing the complexities of the PIAAC data and their possible handling, (c) illustrating the specification and evaluation of measurement and structural models, and (d) pointing to current developments in the areas of measurement invariance testing and multilevel SEM. Sample input and output files are provided.
dc.languageEN
dc.publisherSpringer Nature
dc.relation.ispartofMethodology of Educational Measurement and Assessment
dc.relation.ispartofseriesMethodology of Educational Measurement and Assessment
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAnalysing PIAAC Data with Structural Equation Modelling in Mplus
dc.typeChapter
dc.creator.authorScherer, Ronny
cristin.unitcode185,18,7,0
cristin.unitnameCentre for Educational Measurement
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.cristin1820681
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=Large-Scale Cognitive Assessment: Analyzing PIAAC Data&rft.spage=165&rft.date=2020
dc.identifier.startpage165
dc.identifier.endpage208
dc.identifier.pagecount293
dc.identifier.doihttps://doi.org/10.1007/978-3-030-47515-4_8
dc.identifier.urnURN:NBN:no-81545
dc.type.documentBokkapittel
dc.type.peerreviewedPeer reviewed
dc.source.isbn978-3-030-47515-4
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/78483/1/Scherer2020_Chapter_AnalysingPIAACDataWithStructur.pdf
dc.type.versionPublishedVersion
cristin.btitleLarge-Scale Cognitive Assessment: Analyzing PIAAC Data


Files in this item

Appears in the following Collection

Hide metadata

Attribution 4.0 International
This item's license is: Attribution 4.0 International