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  1. Generalized multilevel structural equation modeling.S. Rabe-Hesketh, A. Skrondal & A. Pickles - unknown
    A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models, combine features of generalized linear mixed models and structural equation models and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced (...)
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  • Evaluation of model fit in nonlinear multilevel structural equation modeling.Karin Schermelleh-Engel, Martin Kerwer & Andreas G. Klein - 2014 - Frontiers in Psychology 5.
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  • Experimental and quasi-experimental designs for generalized causal inference.William R. Shadish - 2001 - Boston: Houghton Mifflin. Edited by Thomas D. Cook & Donald Thomas Campbell.
    Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; (...)
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  • Association, causation, and marginal structural models.James M. Robins - 1999 - Synthese 121 (1-2):151-179.
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