Abstract
In this paper we will evaluate some tests for path models with small sample sizes. There are well-known tests for structural equation models in general. However, these tests are based on statistical assumptions, like the normality distribution of the variables and/or large samples, which are not very realistic. We propose to use a test based on the so-called parametric bootstrap method as a means for selection of a model. This means that we will use some resampling method and assess the empirical distribution of some test statistics. Using this distribution it is possible to decide whether a model fits the data or not. This approach is nowadays an important topic within Computation Statistics. See, for instance, Wegman (1988), Wilcox (2001), Martinez and Martinez (2002). We restrict ourselves to a sub-class of structural equation models, the path models. The reason for this restriction is that in path models we do not have latent variables. Applying our method to structural equation models with latent variables is much more complicated and will be discussed in a future paper.
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Mooijaart, A., van Montfort, K. (2004). Statistical Power in PATH Models for Small Sample Sizes. In: van Montfort, K., Oud, J., Satorra, A. (eds) Recent Developments on Structural Equation Models. Mathematical Modelling: Theory and Applications, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-1958-6_1
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DOI: https://doi.org/10.1007/978-1-4020-1958-6_1
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