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Books > Social sciences > Psychology > Psychological methodology
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The SAGE Handbook of Quantitative Methods in Psychology (Hardcover)
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The SAGE Handbook of Quantitative Methods in Psychology (Hardcover)
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`I often... wonder to myself whether the field needs another book,
handbook, or encyclopedia on this topic. In this case I think that
the answer is truly yes. The handbook is well focused on important
issues in the field, and the chapters are written by recognized
authorities in their fields. The book should appeal to anyone who
wants an understanding of important topics that frequently go
uncovered in graduate education in psychology' - David C Howell,
Professor Emeritus, University of Vermont Quantitative psychology
is arguably one of the oldest disciplines within the field of
psychology and nearly all psychologists are exposed to quantitative
psychology in some form. While textbooks in statistics, research
methods and psychological measurement exist, none offer a unified
treatment of quantitative psychology. The SAGE Handbook of
Quantitative Methods in Psychology does just that. Each chapter
covers a methodological topic with equal attention paid to
established theory and the challenges facing methodologists as they
address new research questions using that particular methodology.
The reader will come away from each chapter with a greater
understanding of the methodology being addressed as well as an
understanding of the directions for future developments within that
methodological area. Drawing on a global scholarship, the Handbook
is divided into seven parts: Part One: Design and Inference:
addresses issues in the inference of causal relations from
experimental and non-experimental research, along with the design
of true experiments and quasi-experiments, and the problem of
missing data due to various influences such as attrition or
non-compliance. Part Two: Measurement Theory: begins with a chapter
on classical test theory, followed by the common factor analysis
model as a model for psychological measurement. The models for
continuous latent variables in item-response theory are covered
next, followed by a chapter on discrete latent variable models as
represented in latent class analysis. Part Three: Scaling Methods:
covers metric and non-metric scaling methods as developed in
multidimensional scaling, followed by consideration of the scaling
of discrete measures as found in dual scaling and correspondence
analysis. Models for preference data such as those found in random
utility theory are covered next. Part Four: Data Analysis: includes
chapters on regression models, categorical data analysis,
multilevel or hierarchical models, resampling methods, robust data
analysis, meta-analysis, Bayesian data analysis, and cluster
analysis. Part Five: Structural Equation Models: addresses topics
in general structural equation modeling, nonlinear structural
equation models, mixture models, and multilevel structural equation
models. Part Six: Longitudinal Models: covers the analysis of
longitudinal data via mixed modeling, time series analysis and
event history analysis. Part Seven: Specialized Models: covers
specific topics including the analysis of neuro-imaging data and
functional data-analysis.
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