Adopting the most conservative criterion for assessing invariance (i.e., unadjusted p-value), we thus sought to establish a partially metric invariant measurement model that constrained the factor loadings for all seven non-referent SFQ subscales and four of the six non-referent KDCS subscales (all except the Sleep and Social Support subscales) to be invariant across the VETERAN and DOPPS samples.
68– 69 DCMs typically require setting up an underlying anatomical model that constrains the possible routes of activity propagation between brain regions.
We compared the results for the 11 clusters whose models included equations with more than three parameters with a run of model selection that constrained each equation to no more than three parameters and found that the models constrained for the number of parameters had BICs comparable to the ones with no constraint in five cases and resulted in lower BIC in two cases.
The unconstrained base line model was compared with 3 other CFA models that were constrained at increasingly stringent levels (see Table 3).
The null model (a) that constrains both datasets to have equivalent dN/dS ratios for the class of sites under positive selection is strongly rejected (p < 0.0001; df = 1) while the null model (b) constraining the proportions of selected sites across datasets was not rejected (p < 0.165; df = 1).
Additional analyses were based on a four-factor model that constrained the factor loadings to be equal across gender groups.
Therefore, we ran a model that constrained these paths to equivalence and then examined the chi-difference test between these nested models.
We estimated time-dated phylogenetic trees by enforcing a variable-rates clock model that constrained all root-to-tip path lengths to have the same total time but allowed the average rates of sound evolution to vary throughout the tree.
Model M1, a neutrality model that constrained ω to be either 0 or 1, was compared to both M2, a selection model that added an additional ω ratio class estimated from the data, and M3, a selection model that used an unconstrained discrete distribution to model classes of ω ratios.
In addition to a null model that constrained the effective population size in donor (N D ) and recipient (N R ) to be identical (so that there is no bottleneck at transmission), models with constant, exponential and logistic demographic functions for the recipient population were fitted.
