## CHI-RESOURCES@LISTSERV.ACM.ORG

 Options: Use Classic View Use Monospaced Font Show Text Part by Default Show All Mail Headers Topic: [<< First] [< Prev] [Next >] [Last >>]

 Linear Mixed-Effects Models with R online course \$10 USD Geoffrey Hubona <[log in to unmask]> Fri, 2 Oct 2015 11:29:06 -0400 text/plain (47 lines) ```​Includes lifetime 24/7 access on any device and a Certificate of Completion for those who complete the online course. *Linear Mixed Effects Models with R * https://www.udemy.com/linear-mixed-effects-models-with-r/?couponCode=OCTFOR10 *Linear Mixed-Effects Models with R* is a 7-session course that teaches the requisite knowledge and skills necessary to fit, interpret and evaluate the estimated parameters of linear mixed-effects models using R software. Alternatively referred to as nested, hierarchical, longitudinal, repeated measures, or temporal and spatial pseudo-replications, linear mixed-effects models are a form of least-squares model-fitting procedures. They are typically characterized by two (or more) sources of variance, and thus have multiple correlational structures among the predictor independent variables, which affect their estimated effects, or relationships, with the predicted dependent variables. These multiple sources of variance and correlational structures must be taken into account in estimating the "fit" and parameters for linear mixed-effects models. The structure of mixed-effects models may be additive, or non-linear, or exponential or binomial, or assume various other ‘families’ of modeling relationships with the predicted variables. However, in this "hands-on" course, coverage is restricted to linear mixed-effects models, and especially, how to: (1) choose an appropriate linear model; (2) represent that model in R; (3) estimate the model; (4) compare (if needed), interpret and report the results; and (5) validate the model and the model assumptions. Additionally, the course explains the fitting of different correlational structures to both temporal, and spatial, pseudo-replicated models to appropriately adjust for the lack of independence among the error terms. The course does address the relevant statistical concepts, but mainly focuses on implementing mixed-effects models in R with ample R scripts, ‘real’ data sets, and live demonstrations. No prior experience with R is necessary to successfully complete the course as the first entire course section consists of a "hands-on" primer for executing statistical commands and scripts using R. Other Research Methods online courses for \$10 here: http://tinyurl.com/udemyrm     ---------------------------------------------------------------                 To unsubscribe, send an empty email to        mailto:[log in to unmask]     For further details of CHI lists see http://listserv.acm.org     --------------------------------------------------------------- ```