No abstract

Although the literature on alternatives to effect indicators is growing, there has been little attention given to evaluating causal and composite (formative) indicators. This paper provides an overview of this topic by contrasting ways of assessing the validity of effect and causal indicators in structural equation models (SEMs). It also draws a distinction between composite (formative) indicators and causal indicators and argues that validity is most relevant to the latter. Sound validity assessment of indicators is dependent on having an adequate overall model fit and on the relative stability of the parameter estimates for the latent variable and indicators as they appear in different models.If the overall fit and stability of estimates are adequate, then a researcher can assess validity using the unstandardized and standardized validity coefficients and the unique validity variance estimate. With multiple causal indicators or with effect indicators influenced by multiple latent variables, collinearity diagnostics are useful. These results are illustrated with a number of correctly and incorrectly specified hypothetical models.

No abstract

The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data.