Analyzing average and conditional effects with multigroup multilevel structural equation models
Identifieur interne : 000239 ( Ncbi/Checkpoint ); précédent : 000238; suivant : 000240Analyzing average and conditional effects with multigroup multilevel structural equation models
Auteurs : Axel Mayer [Allemagne, Belgique] ; Benjamin Nagengast [Allemagne] ; John Fletcher [Royaume-Uni] ; Rolf Steyer [Allemagne]Source :
- Frontiers in Psychology [ 1664-1078 ] ; 2014.
Abstract
Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been the recommended approach for analyzing treatment effects in quasi-experimental multilevel designs with treatment application at the cluster-level. In this paper, we introduce the generalized ML-ANCOVA with linear effect functions that identifies average and conditional treatment effects in the presence of treatment-covariate interactions. We show how the generalized ML-ANCOVA model can be estimated with multigroup multilevel structural equation models that offer considerable advantages compared to traditional ML-ANCOVA. The proposed model takes into account measurement error in the covariates, sampling error in contextual covariates, treatment-covariate interactions, and stochastic predictors. We illustrate the implementation of ML-ANCOVA with an example from educational effectiveness research where we estimate average and conditional effects of early transition to secondary schooling on reading comprehension.
Url:
DOI: 10.3389/fpsyg.2014.00304
PubMed: 24795668
PubMed Central: 4006036
Affiliations:
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sortKey="Mayer, Axel" sort="Mayer, Axel" uniqKey="Mayer A" first="Axel" last="Mayer">Axel Mayer</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Methodology and Evaluation Research, Institute of Psychology, University of Jena</institution><country>Jena, Germany</country></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea></wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Department of Data Analysis, Ghent University</institution><country>Ghent, Belgium</country></nlm:aff><country xml:lang="fr">Belgique</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Nagengast, Benjamin" sort="Nagengast, Benjamin" uniqKey="Nagengast B" first="Benjamin" last="Nagengast">Benjamin Nagengast</name><affiliation wicri:level="1"><nlm:aff id="aff3"><institution>Center for Educational Science and Psychology, University of Tübingen</institution><country>Tübingen, Germany</country></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Fletcher, John" sort="Fletcher, John" uniqKey="Fletcher J" first="John" last="Fletcher">John Fletcher</name><affiliation wicri:level="1"><nlm:aff id="aff4"><institution>Department of Education, University of Oxford</institution><country>Oxford, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Steyer, Rolf" sort="Steyer, Rolf" uniqKey="Steyer R" first="Rolf" last="Steyer">Rolf Steyer</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Methodology and Evaluation Research, Institute of Psychology, University of Jena</institution><country>Jena, Germany</country></nlm:aff><country xml:lang="fr">Allemagne</country><wicri:regionArea></wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Psychology</title><idno type="eISSN">1664-1078</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been the recommended approach for analyzing treatment effects in quasi-experimental multilevel designs with treatment application at the cluster-level. In this paper, we introduce the generalized ML-ANCOVA with linear effect functions that identifies average and conditional treatment effects in the presence of treatment-covariate interactions. We show how the generalized ML-ANCOVA model can be estimated with multigroup multilevel structural equation models that offer considerable advantages compared to traditional ML-ANCOVA. The proposed model takes into account measurement error in the covariates, sampling error in contextual covariates, treatment-covariate interactions, and stochastic predictors. We illustrate the implementation of ML-ANCOVA with an example from educational effectiveness research where we estimate average and conditional effects of early transition to secondary schooling on reading comprehension.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Aiken, L S" uniqKey="Aiken L">L. S. Aiken</name></author><author><name sortKey="West, S G" uniqKey="West S">S. G. 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Bosker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sobel, M E" uniqKey="Sobel M">M. E. Sobel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Steiger, J H" uniqKey="Steiger J">J. H. Steiger</name></author><author><name sortKey="Fouladi, R T" uniqKey="Fouladi R">R. T. Fouladi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Steyer, R" uniqKey="Steyer R">R. Steyer</name></author><author><name sortKey="Gabler, S" uniqKey="Gabler S">S. Gabler</name></author><author><name sortKey="Von Davier, A A" uniqKey="Von Davier A">A. A. von Davier</name></author><author><name sortKey="Nachtigall, C" uniqKey="Nachtigall C">C. Nachtigall</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Steyer, R" uniqKey="Steyer R">R. Steyer</name></author><author><name sortKey="Mayer, A" uniqKey="Mayer A">A. Mayer</name></author><author><name sortKey="Fiege, C" uniqKey="Fiege C">C. Fiege</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Steyer, R" uniqKey="Steyer R">R. Steyer</name></author><author><name sortKey="Partchev, I" uniqKey="Partchev I">I. Partchev</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ukoumunne, C C" uniqKey="Ukoumunne C">C. C. Ukoumunne</name></author><author><name sortKey="Gulliford, M C" uniqKey="Gulliford M">M. C. Gulliford</name></author><author><name sortKey="Chinn, S" uniqKey="Chinn S">S. Chinn</name></author><author><name sortKey="Sterne, J A C" uniqKey="Sterne J">J. A. C. Sterne</name></author><author><name sortKey="Burney, P G J" uniqKey="Burney P">P. G. J. Burney</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Vanderweele, T J" uniqKey="Vanderweele T">T. J. VanderWeele</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Allemagne</li><li>Belgique</li><li>Royaume-Uni</li></country></list><tree><country name="Allemagne"><noRegion><name sortKey="Mayer, Axel" sort="Mayer, Axel" uniqKey="Mayer A" first="Axel" last="Mayer">Axel Mayer</name></noRegion><name sortKey="Nagengast, Benjamin" sort="Nagengast, Benjamin" uniqKey="Nagengast B" first="Benjamin" last="Nagengast">Benjamin Nagengast</name><name sortKey="Steyer, Rolf" sort="Steyer, Rolf" uniqKey="Steyer R" first="Rolf" last="Steyer">Rolf Steyer</name></country><country name="Belgique"><noRegion><name sortKey="Mayer, Axel" sort="Mayer, Axel" uniqKey="Mayer A" first="Axel" last="Mayer">Axel Mayer</name></noRegion></country><country name="Royaume-Uni"><noRegion><name sortKey="Fletcher, John" sort="Fletcher, John" uniqKey="Fletcher J" first="John" last="Fletcher">John Fletcher</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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