Analyzing average and conditional effects with multigroup multilevel structural equation models
Identifieur interne : 000239 ( Ncbi/Merge ); 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
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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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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Psychol</journal-id><journal-id journal-id-type="iso-abbrev">Front Psychol</journal-id><journal-id journal-id-type="publisher-id">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type="epub">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">24795668</article-id><article-id pub-id-type="pmc">4006036</article-id><article-id pub-id-type="doi">10.3389/fpsyg.2014.00304</article-id><article-categories><subj-group subj-group-type="heading"><subject>Psychology</subject><subj-group><subject>Methods Article</subject></subj-group></subj-group></article-categories><title-group><article-title>Analyzing average and conditional effects with multigroup multilevel structural equation models</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Mayer</surname><given-names>Axel</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/124619"></uri></contrib><contrib contrib-type="author"><name><surname>Nagengast</surname><given-names>Benjamin</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref><uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/84927"></uri></contrib><contrib contrib-type="author"><name><surname>Fletcher</surname><given-names>John</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Steyer</surname><given-names>Rolf</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><uri xlink:type="simple" xlink:href="http://community.frontiersin.org/people/u/115823"></uri></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>Department of Methodology and Evaluation Research, Institute of Psychology, University of Jena</institution><country>Jena, Germany</country></aff><aff id="aff2"><sup>2</sup><institution>Department of Data Analysis, Ghent University</institution><country>Ghent, Belgium</country></aff><aff id="aff3"><sup>3</sup><institution>Center for Educational Science and Psychology, University of Tübingen</institution><country>Tübingen, Germany</country></aff><aff id="aff4"><sup>4</sup><institution>Department of Education, University of Oxford</institution><country>Oxford, UK</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Tobias Koch, Freie Universität Berlin, Germany</p></fn><fn fn-type="edited-by"><p>Reviewed by: Felix Thoemmes, Cornell University, USA; Johannes Textor, Universiteit Utrecht, Netherlands</p></fn><corresp id="fn001">*Correspondence: Benjamin Nagengast, Center for Educational Science and Psychology, University of Tübingen, Room 302, Europastr. 6, 72072 Tübingen, Germany e-mail: <email xlink:type="simple">benjamin.nagengast@uni-tuebingen.de</email></corresp><fn fn-type="other" id="fn002"><p>This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology.</p></fn></author-notes><pub-date pub-type="epreprint"><day>24</day><month>1</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>23</day><month>4</month><year>2014</year></pub-date><pub-date pub-type="collection"><year>2014</year></pub-date><volume>5</volume><elocation-id>304</elocation-id><history><date date-type="received"><day>15</day><month>11</month><year>2013</year></date><date date-type="accepted"><day>24</day><month>3</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014 Mayer, Nagengast, Fletcher and Steyer.</copyright-statement><copyright-year>2014</copyright-year><license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><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></abstract><kwd-group><kwd>multilevel analysis of covariance</kwd><kwd>average effects</kwd><kwd>multilevel structural equation modeling</kwd><kwd>conditional effects</kwd><kwd>quasi-experimental designs</kwd></kwd-group><counts><fig-count count="1"></fig-count><table-count count="4"></table-count><equation-count count="31"></equation-count><ref-count count="79"></ref-count><page-count count="16"></page-count><word-count count="12202"></word-count></counts></article-meta></front></pmc><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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