Modelling method effects as individual causal effects
Identifieur interne : 001129 ( Main/Exploration ); précédent : 001128; suivant : 001130Modelling method effects as individual causal effects
Auteurs : Steffi Pohl [Allemagne] ; Rolf Steyer [Allemagne] ; Katrin Kraus [Suède]Source :
- Journal of the Royal Statistical Society: Series A (Statistics in Society) [ 0964-1998 ] ; 2008-01.
English descriptors
- KwdEn :
Abstract
Summary. Method effects often occur when different methods are used for measuring the same construct. We present a new approach for modelling this kind of phenomenon, consisting of a definition of method effects and a first model, the method effect model, that can be used for data analysis. This model may be applied to multitrait–multimethod data or to longitudinal data where the same construct is measured with at least two methods at all occasions. In this new approach, the definition of the method effects is based on the theory of individual causal effects by Neyman and Rubin. Method effects are accordingly conceptualized as the individual effects of applying measurement method j instead of k. They are modelled as latent difference scores in structural equation models. A reference method needs to be chosen against which all other methods are compared. The model fit is invariant to the choice of the reference method. The model allows the estimation of the average of the individual method effects, their variance, their correlation with the traits (and other latent variables) and the correlation of different method effects among each other. Furthermore, since the definition of the method effects is in line with the theory of causality, the method effects may (under certain conditions) be interpreted as causal effects of the method. The method effect model is compared with traditional multitrait–multimethod models. An example illustrates the application of the model to longitudinal data analysing the effect of negatively (such as ‘feel bad’) as compared with positively formulated items (such as ‘feel good’) measuring mood states.
Url:
DOI: 10.1111/j.1467-985X.2007.00517.x
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 001B19
- to stream Istex, to step Curation: 001A02
- to stream Istex, to step Checkpoint: 000543
- to stream Main, to step Merge: 001231
- to stream Main, to step Curation: 001129
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Modelling method effects as individual causal effects</title><author><name sortKey="Pohl, Steffi" sort="Pohl, Steffi" uniqKey="Pohl S" first="Steffi" last="Pohl">Steffi Pohl</name></author><author><name sortKey="Steyer, Rolf" sort="Steyer, Rolf" uniqKey="Steyer R" first="Rolf" last="Steyer">Rolf Steyer</name></author><author><name sortKey="Kraus, Katrin" sort="Kraus, Katrin" uniqKey="Kraus K" first="Katrin" last="Kraus">Katrin Kraus</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:D0BFF03C6DA96E4BCAD5ED77126F71FDE13C5974</idno><date when="2008" year="2008">2008</date><idno type="doi">10.1111/j.1467-985X.2007.00517.x</idno><idno type="url">https://api.istex.fr/document/D0BFF03C6DA96E4BCAD5ED77126F71FDE13C5974/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001B19</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001B19</idno><idno type="wicri:Area/Istex/Curation">001A02</idno><idno type="wicri:Area/Istex/Checkpoint">000543</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Checkpoint">000543</idno><idno type="wicri:doubleKey">0964-1998:2008:Pohl S:modelling:method:effects</idno><idno type="wicri:Area/Main/Merge">001231</idno><idno type="wicri:Area/Main/Curation">001129</idno><idno type="wicri:Area/Main/Exploration">001129</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Modelling method effects as individual causal effects</title><author><name sortKey="Pohl, Steffi" sort="Pohl, Steffi" uniqKey="Pohl S" first="Steffi" last="Pohl">Steffi Pohl</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Friedrich‐Schiller‐Universität, Jena</wicri:regionArea><wicri:noRegion>Jena</wicri:noRegion><wicri:noRegion>Jena</wicri:noRegion></affiliation></author><author><name sortKey="Steyer, Rolf" sort="Steyer, Rolf" uniqKey="Steyer R" first="Rolf" last="Steyer">Rolf Steyer</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Friedrich‐Schiller‐Universität, Jena</wicri:regionArea><wicri:noRegion>Jena</wicri:noRegion><wicri:noRegion>Jena</wicri:noRegion></affiliation></author><author><name sortKey="Kraus, Katrin" sort="Kraus, Katrin" uniqKey="Kraus K" first="Katrin" last="Kraus">Katrin Kraus</name><affiliation wicri:level="1"><country xml:lang="fr">Suède</country><wicri:regionArea>University of Uppsala</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of the Royal Statistical Society: Series A (Statistics in Society)</title><idno type="ISSN">0964-1998</idno><idno type="eISSN">1467-985X</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><pubPlace>Oxford, UK</pubPlace><date type="published" when="2008-01">2008-01</date><biblScope unit="volume">171</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="41">41</biblScope><biblScope unit="page" to="63">63</biblScope></imprint><idno type="ISSN">0964-1998</idno></series><idno type="istex">D0BFF03C6DA96E4BCAD5ED77126F71FDE13C5974</idno><idno type="DOI">10.1111/j.1467-985X.2007.00517.x</idno><idno type="ArticleID">RSSA517</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0964-1998</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Causality</term><term>Method effect</term><term>Multitrait</term><term>Negative item formulation</term><term>Structural equation modelling</term><term>multimethod</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Summary. Method effects often occur when different methods are used for measuring the same construct. We present a new approach for modelling this kind of phenomenon, consisting of a definition of method effects and a first model, the method effect model, that can be used for data analysis. This model may be applied to multitrait–multimethod data or to longitudinal data where the same construct is measured with at least two methods at all occasions. In this new approach, the definition of the method effects is based on the theory of individual causal effects by Neyman and Rubin. Method effects are accordingly conceptualized as the individual effects of applying measurement method j instead of k. They are modelled as latent difference scores in structural equation models. A reference method needs to be chosen against which all other methods are compared. The model fit is invariant to the choice of the reference method. The model allows the estimation of the average of the individual method effects, their variance, their correlation with the traits (and other latent variables) and the correlation of different method effects among each other. Furthermore, since the definition of the method effects is in line with the theory of causality, the method effects may (under certain conditions) be interpreted as causal effects of the method. The method effect model is compared with traditional multitrait–multimethod models. An example illustrates the application of the model to longitudinal data analysing the effect of negatively (such as ‘feel bad’) as compared with positively formulated items (such as ‘feel good’) measuring mood states.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>Suède</li></country></list><tree><country name="Allemagne"><noRegion><name sortKey="Pohl, Steffi" sort="Pohl, Steffi" uniqKey="Pohl S" first="Steffi" last="Pohl">Steffi Pohl</name></noRegion><name sortKey="Steyer, Rolf" sort="Steyer, Rolf" uniqKey="Steyer R" first="Rolf" last="Steyer">Rolf Steyer</name></country><country name="Suède"><noRegion><name sortKey="Kraus, Katrin" sort="Kraus, Katrin" uniqKey="Kraus K" first="Katrin" last="Kraus">Katrin Kraus</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Rhénanie/explor/UnivTrevesV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001129 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 001129 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Rhénanie
|area= UnivTrevesV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:D0BFF03C6DA96E4BCAD5ED77126F71FDE13C5974
|texte= Modelling method effects as individual causal effects
}}
| This area was generated with Dilib version V0.6.31. |  |