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  1. 3

    Guidelines on Unequal Sample Sizes in Multilevel Models

    One of the advantages of using multilevel models is their tolerance to heterogeneity of variances between groups (or points in time for multilevel models of change). And one of the main problems with having unequal sizes between groups is that this inequality can create heterogeneous variance...
  2. 3

    Testing Model Fit between Subsets of Data When Using Multilevel Models

    When using multilevel models based on maximum likelihoods, one can use either deviance statistics (e.g., -2LLs) for nested models or information criteria (e.g., Bayesian Information Criterion, BIC) to test which of two models better fit the data--as long as the models are both used to fit the...
  3. 3

    Conducting Factor Analysis on Data with Nominal, Ordinal, and Interval Data

    I would advice on conducting a confirmatory factor analysis on data from an instrument that contains nominal, ordinal, and interval data. I'd think to use, e.g., point biserial, polychoric, and product-moment for each respectively, but what about when they're all in the same CFA? Other posts...
  4. 3

    REGW Post Hoc in R

    Hi Everyone, I've looked and am surprised to be unable to find a way already created to compute the REGW post hoc anlaysis in R. Does anyone know of a package that has it? Thanks!