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c&ls model in fig. 2 can be modified to remove the latent-consistency with covariance-data inconsistencies of item 3, y ₃. the modified model uses the y ₂ y ₃ pair but removes y ₃ from work context; placing it in its own latent-factor, x ₃. the modified model then uses the y ₂ y ₃ pair but removes y ₃ from work context; placing it in its own latent-factor, x ₂. both of these modifications make it possible to use the y ₂ y ₃ pair within the same model as the y ₅ y ₆ y ₈ pair. in so doing, the modified model of fig. 3 clearly improves the covariance data fit ( ²=116.6 with 54 degrees of freedom and p.0000) in a manner similar to that achieved by removing item 13.

from a theoretical perspective, models of this type have been proposed to accommodate additional effects that might arise from c&ls measurement-invariance attempts to resolve covariance-inconsistencies [ 20 ]. such additional effects are included in the model of fig. 3, but the third modification in this model improves the covariance data fit just as well as the second modification. why might these results occur?

therefore, we were interested to see if there were any correlations between any of the above items and the work-latent without the availability of any exogenous controls. so, for the five new exogenous regressors, we computed spearman correlations against the work latent (as in model 3), controlling for the previous five exogenous regressors. and, of interest to us here, were the negative correlations of literature index items against the work latent. using bonferroni corrections, we set our p-value to be less than 0.005.

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