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When data consist of grouped observations or clusters, and there is
a risk that measurements within the same group are not independent,
group-specific random effects can be added to a regression model in
order to account for such within-group associations. Regression
models that contain such group-specific random effects are called
mixed-effects regression models, or simply mixed models. Mixed
models are a versatile tool that can handle both balanced and
unbalanced datasets and that can also be applied when several
layers of grouping are present in the data; these layers can either
be nested or crossed. In linguistics, as in many other fields, the
use of mixed models has gained ground rapidly over the last decade.
This methodological evolution enables us to build more
sophisticated and arguably more realistic models, but, due to its
technical complexity, also introduces new challenges. This volume
brings together a number of promising new evolutions in the use of
mixed models in linguistics, but also addresses a number of common
complications, misunderstandings, and pitfalls. Topics that are
covered include the use of huge datasets, dealing with non-linear
relations, issues of cross-validation, and issues of model
selection and complex random structures. The volume features
examples from various subfields in linguistics. The book also
provides R code for a wide range of analyses.
When data consist of grouped observations or clusters, and there is
a risk that measurements within the same group are not independent,
group-specific random effects can be added to a regression model in
order to account for such within-group associations. Regression
models that contain such group-specific random effects are called
mixed-effects regression models, or simply mixed models. Mixed
models are a versatile tool that can handle both balanced and
unbalanced datasets and that can also be applied when several
layers of grouping are present in the data; these layers can either
be nested or crossed. In linguistics, as in many other fields, the
use of mixed models has gained ground rapidly over the last decade.
This methodological evolution enables us to build more
sophisticated and arguably more realistic models, but, due to its
technical complexity, also introduces new challenges. This volume
brings together a number of promising new evolutions in the use of
mixed models in linguistics, but also addresses a number of common
complications, misunderstandings, and pitfalls. Topics that are
covered include the use of huge datasets, dealing with non-linear
relations, issues of cross-validation, and issues of model
selection and complex random structures. The volume features
examples from various subfields in linguistics. The book also
provides R code for a wide range of analyses.
In Paradigm and Paradox, Dirk Geeraerts formulated many of the
basic tenets that were to form what Cognitive Linguistics is today.
Change of Paradigms -New Paradoxes links back to this seminal work,
exploring which of the original theories and ideas still stand
strong, which new questions have arisen and which ensuing new
paradoxes need to be addressed. It thus reveals how Cognitive
Linguistics has developed and diversified over the past decades.
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