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Fri, 2 Oct 2015 11:29:06 -0400
Geoffrey Hubona <[log in to unmask]>
"ACM SIGCHI Resources (Mailing List)" <[log in to unmask]>
Geoffrey Hubona <[log in to unmask]>
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​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 *

*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:

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