dc.creatorRdz Navarro, Karina
dc.creatorYang Wallentin, Fan
dc.date.accessioned2020-05-06T15:24:13Z
dc.date.available2020-05-06T15:24:13Z
dc.date.created2020-05-06T15:24:13Z
dc.date.issued2020
dc.identifierPsicothema 2020, Vol. 32, No. 1, 115-121
dc.identifier10.7334/psicothema2019.235
dc.identifierhttps://repositorio.uchile.cl/handle/2250/174450
dc.description.abstractBackground: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike. making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifics interactions only, misleading results may be obtained. Method: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. Results: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. Conclusions: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers.
dc.languageen
dc.publisherColegio Oficial de Psicólogos de Asturias
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourcePsicothema
dc.subjectEcuaciones estructurales no lineales
dc.subjectModeración
dc.subjectEfectos de interacción
dc.subjectEfectos cuadráticos
dc.subjectEspecificación del modelo
dc.titleSpecification issues in nonlinear SEM The moderation that wasn't
dc.typeArtículo de revista


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