Hideki Toyoda

Using Non-Normal SEM to Resolve the ACDE Model in the Classical Twin Design

One of the biggest problems in classical twin studies is that it cannot estimate additive genetic (A), non-additive genetic (D), shared environmental (C), and non-shared environmental (E) effects, simultaneously, because the model, referred to as the ACDE model, has negative degrees of freedom when using Structural Equation Modeling (SEM). Therefore, instead of the ACDE model, the ACE model or the ADE model is actually used. However, using the ACE or ADE models almost always leads to biased estimates. In the present paper, the univariate ACDE model is developed using non-normal Structural Equation Modeling (nnSEM). In SEM, (1st- and) 2nd-order moments, namely, (means and) covariances are used as information. However, nnSEM uses higher-order moments as well as (1st- and) 2nd-order moments. nnSEM has a number of advantages over SEM. One of which is that nnSEM can specify models that cannot be specified using SEM because of the negative degrees of freedom. Simulation studies have shown that the proposed method can decrease the biases. There are other factors that have possible effects on phenotypes, such as higher-order epistasis. Since the proposed method cannot estimate these effects, further research on developing a more exhaustive model is needed.

[Graphical modeling for constructs: an integrated solution for factor analysis and GM].

This research develops a new method for analyzing relation for factors which combines graphical modeling (GM) and factor analysis. In this method, estimation of the inverse of the variance-covariance matrix is done in the framework of factor analysis, and then the data-model fit is investigated using GM. The partial correlation coefficients of the estimated model are calculated, and the estimation of parameters is repeated until discovery of the worst fit index. In order to confirm the effectiveness of this method, three correlation matrices were analyzed as a real data study. In first and second case, intelligence models of Harman and Turstone were restructured using this method. In third case, EQ model was structured using it. The results show that this method can be apply GM for latent variables and a good assistant to set up path models for factors.