Please use this identifier to cite or link to this item: http://arl.liuc.it/dspace/handle/2468/4068
Title: Scale reliability evaluation for a-priori clustered data
Authors: Boari, Giuseppe
Cantaluppi, Gabriele
Nai Ruscone, Marta
Issue Date: 2014
Publisher: Springer
Bibliographic citation: Boari Giuseppe, Cantaluppi Gabriele, Nai Ruscone Marta (2014), Scale reliability evaluation for a-priori clustered data. In: Vicari D., et al. (eds.), Analysis and modeling of complex data in behavioral and social sciences. (Studies in classification, data analysis, and knowledge organization). Springer, p. 37-45. ISBN 978-3-319-06691-2. DOI 10.1007/978-3-319-06692-9_5.
Abstract: According to the classical measurement theory, the reliability of a set of indicators related to a latent variable describing a true measure can be assessed through the Cronbach’s α index. The Cronbach’s α index can be used for τ-equivalent measures and for parallel measures and represents a lower bound for the reliability value in presence of congeneric measures, for which the assessment can properly be made only ex post, once the loading coefficients have been estimated, e.g. by means of a structural equation model with latent variables.Once assumed the existence of an a-priori segmentation based upon a categorical variable Z, we test whether the construct is reliable all over the groups. In this case the measurement model is the same across groups, which means that loadings are equal within each group as well as they do not vary across groups. A formulation of the Cronbach’s α coefficient is considered according to the decomposition of pairwise covariances in a clustered framework, and a test procedure assessing the possible presence of congeneric measures in a multigroup framework is proposed.
URI: http://arl.liuc.it/dspace/handle/2468/4068
Journal/Book: Analysis and modeling of complex data in behavioral and social sciences
ISBN: 978-3-319-06691-2
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