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Agreement among raters is of great importance in many domains. For
example, in medicine, diagnoses are often provided by more than one
doctor to make sure the proposed treatment is optimal. In criminal
trials, sentencing depends, among other things, on the complete
agreement among the jurors. In observational studies, researchers
increase reliability by examining discrepant ratings. This book is
intended to help researchers statistically examine rater agreement
by reviewing four different approaches to the technique. The first
approach introduces readers to calculating coefficients that allow
one to summarize agreements in a single score. The second approach
involves estimating log-linear models that allow one to test
specific hypotheses about the structure of a cross-classification
of two or more raters' judgments. The third approach explores
cross-classifications or raters' agreement for indicators of
agreement or disagreement, and for indicators of such
characteristics as trends. The fourth approach compares the
correlation or covariation structures of variables that raters use
to describe objects, behaviors, or individuals. These structures
can be compared for two or more raters. All of these methods
operate at the level of observed variables. This book is intended
as a reference for researchers and practitioners who describe and
evaluate objects and behavior in a number of fields, including the
social and behavioral sciences, statistics, medicine, business, and
education. It also serves as a useful text for graduate-level
methods or assessment classes found in departments of psychology,
education, epidemiology, biostatistics, public health,
communication, advertising and marketing, and sociology. Exposure
to regression analysis and log-linear modeling is helpful.
"Configural Frequency Analysis" (CFA) provides an up-to-the-minute
comprehensive introduction to its techniques, models, and
applications. Written in a formal yet accessible style, actual
empirical data examples are used to illustrate key concepts.
Step-by-step program sequences are used to show readers how to
employ CFA methods using commercial software packages, such as SAS,
SPSS, SYSTAT, S-Plus, or those written specifically to perform CFA.
CFA is an important method for analyzing results involved with
categorical and longitudinal data. It allows one to answer the
question of whether individual cells or groups of cells of
cross-classifications differ significantly from expectations. The
expectations are calculated using methods employed in log-linear
modeling or a priori information. It is the only statistical method
that allows one to make statements about empty areas in the data
space.
Applied and or person-oriented researchers, statisticians, and
advanced students interested in CFA and categorical and
longitudinal data will find this book to be a valuable resource.
Developed since 1969, this method is now used by a large number of
researchers around the world in a variety of disciplines, including
psychology, education, medicine, and sociology. "Configural
Frequency Analysis" will serve as an excellent text for courses on
configural frequency analysis, categorical variable analysis, or
analysis of contingency tables. Prerequisites include an
understanding of descriptive statistics, hypothesis testing,
statistical model fitting, and some understanding of categorical
data analysis and matrix algebra.
"Configural Frequency Analysis" (CFA) provides an up-to-the-minute
comprehensive introduction to its techniques, models, and
applications. Written in a formal yet accessible style, actual
empirical data examples are used to illustrate key concepts.
Step-by-step program sequences are used to show readers how to
employ CFA methods using commercial software packages, such as SAS,
SPSS, SYSTAT, S-Plus, or those written specifically to perform CFA.
CFA is an important method for analyzing results involved with
categorical and longitudinal data. It allows one to answer the
question of whether individual cells or groups of cells of
cross-classifications differ significantly from expectations. The
expectations are calculated using methods employed in log-linear
modeling or a priori information. It is the only statistical method
that allows one to make statements about empty areas in the data
space.
Applied and or person-oriented researchers, statisticians, and
advanced students interested in CFA and categorical and
longitudinal data will find this book to be a valuable resource.
Developed since 1969, this method is now used by a large number of
researchers around the world in a variety of disciplines, including
psychology, education, medicine, and sociology. "Configural
Frequency Analysis" will serve as an excellent text for courses on
configural frequency analysis, categorical variable analysis, or
analysis of contingency tables. Prerequisites include an
understanding of descriptive statistics, hypothesis testing,
statistical model fitting, and some understanding of categorical
data analysis and matrix algebra.
A comprehensive resource for analyzing a variety of categorical
data, this book emphasizes the application of many recent advances
of longitudinal categorical statistical methods. Each chapter
provides basic methodology, helpful applications, examples using
data from all fields of the social sciences, computer tutorials,
and exercises. Written for social scientists and students, no
advanced mathematical training is required. Step-by-step command
files are given for both the CDAS and the SPSS software
programs.
Agreement among raters is of great importance in many domains. For
example, in medicine, diagnoses are often provided by more than one
doctor to make sure the proposed treatment is optimal. In criminal
trials, sentencing depends, among other things, on the complete
agreement among the jurors. In observational studies, researchers
increase reliability by examining discrepant ratings. This book is
intended to help researchers statistically examine rater agreement
by reviewing four different approaches to the technique.
The first approach introduces readers to calculating coefficients
that allow one to summarize agreements in a single score. The
second approach involves estimating log-linear models that allow
one to test specific hypotheses about the structure of a
cross-classification of two or more raters' judgments. The third
approach explores cross-classifications or raters' agreement for
indicators of agreement or disagreement, and for indicators of such
characteristics as trends. The fourth approach compares the
correlation or covariation structures of variables that raters use
to describe objects, behaviors, or individuals. These structures
can be compared for two or more raters. All of these methods
operate at the level of observed variables.
This book is intended as a reference for researchers and
practitioners who describe and evaluate objects and behavior in a
number of fields, including the social and behavioral sciences,
statistics, medicine, business, and education. It also serves as a
useful text for graduate-level methods or assessment classes found
in departments of psychology, education, epidemiology,
biostatistics, public health, communication, advertising and
marketing, and sociology. Exposure to regression analysis and
log-linear modeling is helpful.
Preface.- 1 Questions that Can Be Answered with CFA.- 2 Elements of
CFA.- 3 Models of CFA.- 4 Models of Longitudinal CFA.- 5 Designs
for CFA.- 6 Special Variables in CFA.- 7 The CFA Treasure Chest.- 8
CFA Software.- Index.
A comprehensive resource for analyzing a variety of categorical
data, this book emphasizes the application of many recent advances
of longitudinal categorical statistical methods. Each chapter
provides basic methodology, helpful applications, examples using
data from all fields of the social sciences, computer tutorials,
and exercises. Written for social scientists and students, no
advanced mathematical training is required. Step-by-step command
files are given for both the CDAS and the SPSS software
programs.
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.
Social change, such as the consequences of German unification, is
likely to impact normative as well as maladaptive development
during adolescence. Beyond documenting effects by comparing
adolesecents' psychosocial development at various time periods of
the unification process, this book offers insights into the
macro-and-micro-level mechanisms that bring about the changes, such
as the demands by new social insitutions or challenges facing
families.
This unique book provides a comprehensive and detailed coverage of
configural frequency analysis (CFA), the most useful method of
analysis of categorical data in person-oriented research. It
presents the foundations, methods, and models of CFA and features
numerous empirical data examples from a range of disciplines that
can be reproduced by the readers. It also addresses computer
applications, including relevant R packages and modules. Configural
frequency analysis is a statistical method that allows the
processing of important and interesting questions in categorical
data. The perspective of CFA differs from the usual perspective of
relations among variables; its focus is on patterns of variable
categories that stand out with respect to specific hypotheses, and
as such, CFA allows for testing numerous substantive hypotheses.
The book describes the origins of CFA and their relation to
chi-square analysis as well as the developments that are based on
log-linear modeling. The models covered range from simple models of
variable independence to complex models that are needed when causal
hypotheses are tested. Empirical data examples are provided for
each model. New models are introduced for person-oriented mediation
analysis and locally optimized time series analysis, and new
results concerning the characteristics of CFA methods are bolstered
using Monte Carlo simulations. Primarily intended for researchers
and students in the social and behavioral sciences, the book will
also appeal to anyone who deals with categorical data from a
person-centered perspective.
General Linear Model methods are the most widely used in data
analysis in applied empirical research. Still, there exists no
compact text that can be used in statistics courses and as a guide
in data analysis. This volume fills this void by introducing the
General Linear Model (GLM), whose basic concept is that an observed
variable can be explained from weighted independent variables plus
an additive error term that reflects imperfections of the model and
measurement error. It also covers multivariate regression, analysis
of variance, analysis under consideration of covariates, variable
selection methods, symmetric regression, and the recently developed
methods of recursive partitioning and direction dependence
analysis. Each method is formally derived and embedded in the GLM,
and characteristics of these methods are highlighted. Real-world
data examples illustrate the application of each of these methods,
and it is shown how results can be interpreted.
Dieser Band stellt umfassend die Methoden der
Konfigurationsfrequenzanalyse (KFA) vor, eines von G.A. Lienert
erstmals eingebrachten Verfahrens zur Testung von Hypothesen in
Bezug auf Haufigkeiten in individuellen Zellen oder Gruppen einer
Kreuzklassifikation. Die Autoren, die die Methode weiterentwickelt
haben, bieten eine umfassende Darstellung der Grundlagen, Modelle
und konkreten Anwendungsfalle in der psychologischen und
sozialwissenschaftlichen, personen-orientierten Forschung. Dabei
werden die Anfange der KFA und ihr Bezug zur Chi-Quadrat Analyse
ebenso beschrieben wie die Entwicklungen, die auf log-linearen
Modellen basieren. Fur jedes Modell und fur jede Fragestellung, die
mit der KFA untersucht werden koennen, werden empirische
Datenbeispiele prasentiert. Neue Ergebnisse werden durch
Monte-Carlo Simulationen untermauert sowie neue Modelle entwickelt
und vorgestellt.Das Buch richtet sich zum einen an Leser*innen, die
uber grundlegendes Hintergrundwissen in der angewandten Statistik
aus einfuhrenden Kursen und Kursen uber log-lineare Modelle
verfugen. Aber auch Leserinnen und Leser ohne diese Kenntnisse
koennen von diesem Buch profitieren, weil alle noetigen technischen
Elemente eigens eingefuhrt und erklart werden. Computerprogramme
werden vorgestellt und in Beispielen angewendet. Insgesamt stellt
sich die KFA als statistische Methode dar, mit der fur kategoriale
Daten wichtige und interessante Fragen bearbeitet werden koennen,
die im Kontext der Anwendung von Routinemethoden der Statistik
nicht zuganglich sind.
General Linear Model methods are the most widely used in data
analysis in applied empirical research. Still, there exists no
compact text that can be used in statistics courses and as a guide
in data analysis. This volume fills this void by introducing the
General Linear Model (GLM), whose basic concept is that an observed
variable can be explained from weighted independent variables plus
an additive error term that reflects imperfections of the model and
measurement error. It also covers multivariate regression, analysis
of variance, analysis under consideration of covariates, variable
selection methods, symmetric regression, and the recently developed
methods of recursive partitioning and direction dependence
analysis. Each method is formally derived and embedded in the GLM,
and characteristics of these methods are highlighted. Real-world
data examples illustrate the application of each of these methods,
and it is shown how results can be interpreted.
This book presents an introduction to the methodology of structural
equation modeling, illustrates its use, and goes on to argue that
it has revolutionary implications for the study of natural systems.
A major theme of this book is that we have, up to this point,
attempted to study systems primarily using methods (such as the
univariate model) that were designed only for considering
individual processes. Understanding systems requires the capacity
to examine simultaneous influences and responses. Structural
equation modeling (SEM) has such capabilities. It also possesses
many other traits that add strength to its utility as a means of
making scientific progress. In light of the capabilities of SEM, it
can be argued that much of ecological theory is currently locked in
an immature state that impairs its relevance. It is further argued
that the principles of SEM are capable of leading to the
development and evaluation of multivariate theories of the sort
vitally needed for the conservation of natural systems.
Supplementary information can be found at the authors website,
http: //www.jamesbgrace.com/. Details why multivariate analyses
should be used to study ecological systems Exposes unappreciated
weakness in many current popular analyses Emphasises the future
methodological developments needed to advance our understanding of
ecological systems
Regression Analysis for Social Sciences presents methods of
regression analysis in an accessible way, with each method having
illustrations and examples. A broad spectrum of methods are
included: multiple categorical predictors, methods for curvilinear
regression, and methods for symmetric regression. This book can be
used for courses in regression analysis at the advanced
undergraduate and beginning graduate level in the social and
behavioral sciences. Most of the techniques are explained
step-by-step enabling students and researchers to analyze their own
data. Examples include data from the social and behavioral sciences
as well as biology, making the book useful for readers with
biological and biometrical backgrounds. Sample command and result
files for SYSTAT are included in the text.
Key Features
* Presents accessible methods of regression analysis
* Includes a broad spectrum of methods
* Techniques are explained step-by-step
* Provides sample command and result files for SYSTAT
This book provides developmental researchers with the basic tools
for understanding how to utilize categorical variables in their
data analysis. Covering the measurement of individual differences
in growth rates, the measurement of stage transitions, latent class
and log-linear models, chi-square, and more, the book provides a
means for developmental researchers to make use of categorical
data.
The book covers:
* Measurement and repeated observations of categorical data
* Catastrophe theory
* Latent class and log-linear models
* Applications
These edited volumes present new statistical methods in a way that
bridges the gap between theoretical and applied statistics. The
volumes cover general problems and issues and more specific topics
concerning the structuring of change, the analysis of time series,
and the analysis of categorical longitudinal data. The book targets
students of development and change in a variety of fields -
psychology, sociology, anthropology, education, medicine,
psychiatry, economics, behavioural sciences, developmental
psychology, ecology, plant physiology, and biometry - with basic
training in statistics and computing.
These edited volumes present new statistical methods in a way that
bridges the gap between theoretical and applied statistics. The
volumes cover general problems and issues and more specific topics
concerning the structuring of change, the analysis of time series,
and the analysis of categorical longitudinal data. The book targets
students of development and change in a variety of fields -
psychology, sociology, anthropology, education, medicine,
psychiatry, economics, behavioural sciences, developmental
psychology, ecology, plant physiology, and biometry - with basic
training in statistics and computing.
Structural Equation Modeling (SEM) is a technique that is used to estimate, analyze and test models that specify relationships among variables. This book explains the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study, and the history of the development of SEM. It provides examples of analyses on biological data including multi-group models, means models, p-technique and time-series. In addition, the book discusses computer applications and contrasts three popular SEM software packages. Data sets and programs in the book can be downloaded from http://nrmsc.usgs.gov/products/Pugesek_SEM.htm.
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