Examining the cross-national applicability of multi-item, multi-dimensional measures using generalizability theory
Author(s) -
S. Durvasula,
Richard G. Netemeyer,
J. Craig Andrews,
Steven Lysonski
Publication year - 2006
Publication title -
journal of international business studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.819
H-Index - 195
eISSN - 1478-6990
pISSN - 0047-2506
DOI - 10.1057/palgrave.jibs.8400210
Subject(s) - generalizability theory , measure (data warehouse) , econometrics , measurement invariance , confirmatory factor analysis , computer science , structural equation modeling , psychology , data science , mathematics , data mining , statistics , machine learning
Establishing the applicability of multi-item measures is important for making valid inferences when testing theories cross-nationally. Typically, researchers have relied upon the tenets of classical measurement theory (CT) using confirmatory factor model invariance testing to conclude that a unidimensional measure is applicable across countries. However, two important issues remain unresolved via CT techniques: (1) if the measure is found not to be invariant, CT tells us little as to why the measure varies across countries; and (2) if the measure is multi-dimensional, what factors affect its cross-national applicability? Our research seeks to address these issues and the cross-national measurement applicability of multi-dimensional scales via generalizability theory (GT). In this paper, we use a cross-national data set and simulated data sets to demonstrate the usefulness of GT to cross-national multi-dimensional measurement
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