Assuming that initial conditions are exogenous, the random effects variance is restricted to zero.
This approach allows one to model both endogenous (or intrinsic) variance and exogenous effects induced by experimental design.
At the outset, our conceptual SEM model involved 121 parameters (the initial measurement model included three types of parameters to be estimated: variances for exogenous variables, covariances between latent factors, and factor loadings), with approximately 33 indicators (there were at least 3 indicators per latent construct, and we hypothesized 11 latent constructs).
The lower confidence interval of the RMSEA was.000 and the upper limit.059, thus indicating a well-fitting model (see Figure 3 and Tables 3 and 4). Figure 3 Standardized path coefficients of Self-Direction and Apprenticeship Conation with exogenous variable correlations and explained variances of the dependent variables in the Adapted Path Model.
The co-variances for the exogenous variables are the same as reported in Table 3, whereas those for the well-being measures feature in Figure 1.
For testing the above model using structural equation modeling (SEM), as recommended by Hatcher, the variances of the exogenous variables (latent constructs) need to be specified as free parameters to be estimated [ 24].
To solve the resulting scale indeterminancy issue caused by estimating the variances of the exogenous variables in the above model, one factor loading for each latent construct was fixed to 1 (Item # Relieve for Effectiveness; Item # To Plan for Convenience; Item # Overall for Global Satisfaction) [ 24].
In other words, if two variables are believed to cause each other, a third variable (the instrument) is needed to partition the variance into endogenous and exogenous components.
In the IVs estimation, the estimate is based only on the part of variance that we argue is exogenous, that is, as identified by our instrument.
The first stage V-IGARCH (1, 1) endogenous mean and conditional variance returns are measured with exogenous factors from the second stage V-IGARCH (1, 1) models.
For such enrollment levels variance of class size is mainly exogenous and we argue that this allows estimation of quasi-experimental class size effects.
