https://www.selleckchem.com/products/fg-4592.html
A good deal of experimental research is characterized by the presence of random effects on subjects and items. A standard modeling approach that includes such sources of variability is the mixed-effects models (MEMs) with crossed random effects. However, under-parameterizing or over-parameterizing the random structure of MEMs bias the estimations of the Standard Errors (SEs) of fixed effects. In this simulation study, we examined two different but complementary perspectives model selection with likelihood-ratio tests, AIC, and BIC; and
Like
Comment
Share
