A journey around alpha and omega to estimate internal consistency reliability
Based on recent psychometric developments, this paper presents a conceptual and practical guide for estimating internal consistency reliability of measures obtained as item sum or mean. The internal consistency reliability coefficient is presented as a by-product of the measurement model underlying...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | español |
| OAI Identifier: | oai:ddd.uab.cat:173917 |
| Acceso en línea: | https://ddd.uab.cat/record/173917 https://dx.doi.org/urn:doi:10.6018/analesps.33.3.268401 |
| Access Level: | acceso abierto |
| Palabra clave: | Reliability-SEM Reliability Internal consistency Cronbach's alpha Coefficient alpha Coefficient omega Congeneric measures Tau-equivalent measures Confirmatory factor analysis Fiabilidad Consistencia interna Alfa de Cronbach Coeficiente alfa Coeficiente omega Medidas congenéricas Medidas tau-equivalentes Análisis factorial confirmatorio |
| Sumario: | Based on recent psychometric developments, this paper presents a conceptual and practical guide for estimating internal consistency reliability of measures obtained as item sum or mean. The internal consistency reliability coefficient is presented as a by-product of the measurement model underlying the item responses. A three-step procedure is proposed for its estimation, including descriptive data analysis, test of relevant measurement models, and computation of internal consistency coefficient and its confidence interval. Provided formulas include: (a) Cronbach's alpha and omega coefficients for unidimensional measures with quantitative item response scales, (b) coefficients ordinal omega, ordinal alpha and nonlinear reliability for unidimensional measures with dichotomic and ordinal items, (c) coefficients omega and omega hierarchical for essentially unidimensional scales presenting method effects. The procedure is generalized to weighted sum measures, multidimensional scales, complex designs with multilevel and/or missing data and to scale development. Four illustrative numerical examples are fully explained and the data and the R syntax are provided. |
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