Using Parcels to Convert Path Analysis Models Into Latent Variable Models

Abstract

The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as indicators of the latent variables, path analysis models of the aggregated variables, and models in which reliability estimates were used to correct for measurement error in path analysis models. Results showed that point estimates of path coefficients were smallest for the path analysis models and largest for the latent variable models. It is concluded that, whenever possible, it is better to use a latent variable model in which parcels are used as indicators than a path analysis model using total scale scores.Structural equation modeling is a data analytic technique commonly used to examine patterns of relationships among constructs. Often, some or all of these constructs are measured by multi-item scales. The researcher then has several options for specifying the constructs in the structural equation model. These options have different implications for the parameter estimates and fit of the model and each of these options has advantages and disadvantages. In this article we suggest using parcels as indicators of latent variables, where parcels are aggregations (sums or averages) of several individual items. Advantages of using parcels as indicators of latent variables in structural equation models will be discussed.

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