Data generation for composite-based structural equation modeling methods
نویسندگان
چکیده
منابع مشابه
Missing data techniques for structural equation modeling.
As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling m...
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Many times in both educational and social science research it is impossible to collect data that is complete. When administering a survey, for example, people may answer some questions and not others. This missing data causes a problem for researchers using structural equation modeling (SEM) techniques for data analyses. Because SEM and multivariate methods require complete data, several method...
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Model validation involves quantitatively comparing model predictions with experimental observations, both of which contain uncertainty. A building block approach to model validation may proceed through various levels, such as material to component to subsystem to system. This paper presents a structural equation modeling-based Bayesian approach to make use of the low-level data for system-level...
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ژورنال
عنوان ژورنال: Advances in Data Analysis and Classification
سال: 2020
ISSN: 1862-5347,1862-5355
DOI: 10.1007/s11634-020-00396-6