Multilevel Analysis with Few Clusters : Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference

Lade...
Vorschaubild
Dateien
Elff_2-1444sl6y7ch828.pdf
Elff_2-1444sl6y7ch828.pdfGröße: 444.92 KBDownloads: 204
Datum
2021
Autor:innen
Heisig, Jan Paul
Schaeffer, Merlin
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
EU-Projektnummer
DFG-Projektnummer
Projekt
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
British Journal of Political Science. Cambridge University Press. 2021, 51(1), pp. 412-426. ISSN 0007-1234. eISSN 1469-2112. Available under: doi: 10.1017/S0007123419000097
Zusammenfassung

Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
320 Politik
Schlagwörter
multilevel analysis; cross-national comparison; comparative politics; methodology; statistical inference; maximum likelihood
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690ELFF, Martin, Jan Paul HEISIG, Merlin SCHAEFFER, Susumu SHIKANO, 2021. Multilevel Analysis with Few Clusters : Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference. In: British Journal of Political Science. Cambridge University Press. 2021, 51(1), pp. 412-426. ISSN 0007-1234. eISSN 1469-2112. Available under: doi: 10.1017/S0007123419000097
BibTex
@article{Elff2021-01Multi-51168,
  year={2021},
  doi={10.1017/S0007123419000097},
  title={Multilevel Analysis with Few Clusters : Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference},
  number={1},
  volume={51},
  issn={0007-1234},
  journal={British Journal of Political Science},
  pages={412--426},
  author={Elff, Martin and Heisig, Jan Paul and Schaeffer, Merlin and Shikano, Susumu}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/51168">
    <dc:rights>Attribution-NonCommercial-ShareAlike 4.0 International</dc:rights>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Schaeffer, Merlin</dc:creator>
    <dcterms:abstract xml:lang="eng">Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.</dcterms:abstract>
    <dc:creator>Heisig, Jan Paul</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-10-02T09:19:21Z</dcterms:available>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/42"/>
    <dc:creator>Elff, Martin</dc:creator>
    <dcterms:title>Multilevel Analysis with Few Clusters : Improving Likelihood-Based Methods to Provide Unbiased Estimates and Accurate Inference</dcterms:title>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-sa/4.0/"/>
    <dc:contributor>Heisig, Jan Paul</dc:contributor>
    <dc:contributor>Shikano, Susumu</dc:contributor>
    <dc:contributor>Schaeffer, Merlin</dc:contributor>
    <dcterms:issued>2021-01</dcterms:issued>
    <dc:contributor>Elff, Martin</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/51168"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/51168/1/Elff_2-1444sl6y7ch828.pdf"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/51168/1/Elff_2-1444sl6y7ch828.pdf"/>
    <dc:creator>Shikano, Susumu</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-10-02T09:19:21Z</dc:date>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja