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This volume presents contributions on handling data in which the
postulate of independence in the data matrix is violated. When this
postulate is violated and when the methods assuming independence
are still applied, the estimated parameters are likely to be
biased, and statistical decisions are very likely to be incorrect.
Problems associated with dependence in data have been known for a
long time, and led to the development of tailored methods for the
analysis of dependent data in various areas of statistical
analysis. These methods include, for example, methods for the
analysis of longitudinal data, corrections for dependency, and
corrections for degrees of freedom. This volume contains the
following five sections: growth curve modeling, directional
dependence, dyadic data modeling, item response modeling (IRT), and
other methods for the analysis of dependent data (e.g., approaches
for modeling cross-section dependence, multidimensional scaling
techniques, and mixed models). Researchers and graduate students in
the social and behavioral sciences, education, econometrics, and
medicine will find this up-to-date overview of modern statistical
approaches for dealing with problems related to dependent data
particularly useful.
This volume presents contributions on handling data in which the
postulate of independence in the data matrix is violated. When this
postulate is violated and when the methods assuming independence
are still applied, the estimated parameters are likely to be
biased, and statistical decisions are very likely to be incorrect.
Problems associated with dependence in data have been known for a
long time, and led to the development of tailored methods for the
analysis of dependent data in various areas of statistical
analysis. These methods include, for example, methods for the
analysis of longitudinal data, corrections for dependency, and
corrections for degrees of freedom. This volume contains the
following five sections: growth curve modeling, directional
dependence, dyadic data modeling, item response modeling (IRT), and
other methods for the analysis of dependent data (e.g., approaches
for modeling cross-section dependence, multidimensional scaling
techniques, and mixed models). Researchers and graduate students in
the social and behavioral sciences, education, econometrics, and
medicine will find this up-to-date overview of modern statistical
approaches for dealing with problems related to dependent data
particularly useful.
This book offers a comprehensible overview of the statistical
approach called the person-centered method. Instead of analyzing
means, variances and covariances of scale scores as in the common
variable-centered approach, the person-centered approach analyzes
persons or objects grouped according to their characteristic
patterns or configurations in contingency tables. This second
edition explores the relationship between two statistical methods:
log-linear modeling (LLM) and configural frequency analysis (CFA).
Both methods compare expected frequencies with observed
frequencies. However, while LLM searches for the underlying
dependencies of the involved variables in the data (model-fitting),
CFA examines significant residuals in non-fitting models. New
developments in the second edition include: Configural Mediation
Models, CFA with covariates, moderator CFA, and CFA modeling
branches in tree-based methods. The new developments enable the use
of categorical together with continuous variables, which makes CFA
a very powerful statistical tool. This new edition continues to
utilize R-package confreq (derived from Configural Frequency
Analysis), much updated since the first edition and newly adjusted
to the new R base program 4.0. An electronic supplement is now
available with 18 R-scripts and many datasets.
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