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Modelling method effects as individual causal effects
Friedrich-Schiller-Universität, Jena, Germany.
Friedrich-Schiller-Universität, Jena, Germany .
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science, Statistics.
2008 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 171, no 1, 41-63 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2008. Vol. 171, no 1, 41-63 p.
Keyword [en]
Causality, Method effect, Multitrait-multimethod, Negative item formulation, Structural equation modelling
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-101045DOI: 10.1111/j.1467-985X.2007.00517.xISI: 000252330700004OAI: oai:DiVA.org:uu-101045DiVA: diva2:211614
Available from: 2009-04-16 Created: 2009-04-16 Last updated: 2011-04-26Bibliographically approved

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