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Authordc.contributor.authorAntino, Mirko 
Authordc.contributor.authorAlvarado, Jesús M. 
Authordc.contributor.authorAsún Inostroza, Rodrigo 
Authordc.contributor.authorBliese, Paul 
Cita de ítemdc.identifier.citationSociological Methods & Research 2020, Vol. 49(4) 839-867es_ES
Abstractdc.description.abstractThe need to determine the correct dimensionality of theoretical constructs and generate valid measurement instruments when underlying items are categorical has generated a significant volume of research in the social sciences. This article presents two studies contrasting different categorical exploratory techniques. The first study compares Mokken scale analysis (MSA) and two-factor-based exploratory techniques for noncontinuous variables: item factor analysis and Normal Ogive Harmonic Analysis Robust Method (NOHARM). Comparisons are conducted across techniques and in reference to the common principal component analysis model using simulated data under conditions of two-dimensionality with different degrees of correlation (r = .0 to .6). The second study shows the theoretical and practical results of using MSA and NOHARM (the factorial technique which functioned best in the first study) on two nonsimulated data sets. The nonsimulated data are particularly interesting because MSA was used to solve a theoretical debate. Based on the results from both studies, we show that the ability of NOHARM to detect dimensionality and scalability is similar to MSA when the data comprise two uncorrelated latent dimensions; however, NOHARM is preferable when data are drawn from instruments containing latent dimensions weakly or moderately correlated. This article discusses the theoretical and practical implications of these findings.es_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.uri*
Sourcedc.sourceSociological Methods & Researches_ES
Keywordsdc.subjectMokken scale analysises_ES
Keywordsdc.subjectPrincipal component analysises_ES
Keywordsdc.subjectFactor analysises_ES
Títulodc.titleRethinking the Exploration of Dichotomous Data: Mokken Scale Analysis Versus Factorial Analysises_ES
Document typedc.typeArtículo de revistaes_ES
dcterms.accessRightsdcterms.accessRightsAcceso Abierto
Indexationuchile.indexArtículo de publicación ISI
Indexationuchile.indexArtículo de publicación SCOPUS

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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile