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Latent Growth Curve Modeling

Latent Growth Curve Modeling Author Kristopher J. Preacher
ISBN-10 9781412939553
Release 2008-06-27
Pages 96
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Provides easy-to-follow, didactic examples of several common growth modeling approaches



Latent Class Analysis

Latent Class Analysis Author Allan L. McCutcheon
ISBN-10 0803927525
Release 1987-05-01
Pages 96
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Latent class analysis is a powerful tool for analyzing the structure of relationships among categorically scored variables. It enables researchers to explore the suitability of combining two or more categorical variables into typologies or scales. It also provides a method for testing hypotheses regarding the latent structure among categorical variables.



An Introduction to Latent Variable Growth Curve Modeling

An Introduction to Latent Variable Growth Curve Modeling Author Terry E. Duncan
ISBN-10 9781135601249
Release 2013-05-13
Pages 272
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This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples. Updated throughout, the second edition features three new chapters—growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research. This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.



Latent Curve Models

Latent Curve Models Author Kenneth A. Bollen
ISBN-10 9780471455929
Release 2006
Pages 285
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An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs. This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include: Chapter introductions and summaries that provide a quick overview of highlights Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics Extensive footnoting that points the way to the primary literature for more information on particular topics With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.



Causal Analysis with Panel Data

Causal Analysis with Panel Data Author Steven E. Finkel
ISBN-10 0803938969
Release 1995-01-17
Pages 98
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Panel data — information gathered from the same individuals or units at several different points in time — are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions.



The Reviewer s Guide to Quantitative Methods in the Social Sciences

The Reviewer   s Guide to Quantitative Methods in the Social Sciences Author Gregory R. Hancock
ISBN-10 9781135172992
Release 2010-04-26
Pages 448
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The Reviewer’s Guide is designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its uniquely structured chapters address traditional and emerging quantitative methods of data analysis.



Human Trafficking

Human Trafficking Author Joan Reid
ISBN-10 9781317227342
Release 2017-10-02
Pages 155
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Human trafficking involves the violation of societal norms and often activates criminal justice responses including police, courts, juvenile justice, and child protective services. Due to the complex nature of human trafficking, some behaviours that facilitate human trafficking cannot be easily identified and assigned to conventional crime categories. As a result of this complexity, criminologists have yet to fully explore the problem of human trafficking. In recent years, however, there has been a growing interest among criminologists in human trafficking and its intersections with the criminal justice system and overlap with conventional types of crime. This edited collection of research aims to underscore these intersections in order to further improve the description, explanation, and prevention of human trafficking. Research contained in this book provides a step forward by describing police perceptions and responses to human trafficking while also providing insight into victims with reports on victim perceptions of their treatment by the police. Most notably, this volume has moved research on human trafficking beyond descriptive frequencies to sophisticated multivariate analyses. This book was originally published as a special issue of the Journal of Crime and Justice.



A Unified Model for the Analysis of Individual Latent Trajectories

A Unified Model for the Analysis of Individual Latent Trajectories Author Chueh-An Hsieh
ISBN-10 MSU:31293030635555
Release 2010
Pages 308
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A Unified Model for the Analysis of Individual Latent Trajectories has been writing in one form or another for most of life. You can find so many inspiration from A Unified Model for the Analysis of Individual Latent Trajectories also informative, and entertaining. Click DOWNLOAD or Read Online button to get full A Unified Model for the Analysis of Individual Latent Trajectories book for free.



Longitudinal Structural Equation Modeling

Longitudinal Structural Equation Modeling Author Todd D. Little
ISBN-10 9781462510276
Release 2013-02-26
Pages 386
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Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website (www.guilford.com/little-materials) provides datasets for all of the examples--which include studies of bullying, adolescent students' emotions, and healthy aging--with syntax and output from LISREL, Mplus, and R (lavaan).



Missing Data

Missing Data Author Paul D. Allison
ISBN-10 9781452207902
Release 2001-08-13
Pages 104
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Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.



Fixed Effects Regression Models

Fixed Effects Regression Models Author Paul D. Allison
ISBN-10 9781483389271
Release 2009-04-20
Pages 136
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This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here



Longitudinal and Panel Data

Longitudinal and Panel Data Author Edward W. Frees
ISBN-10 0521535387
Release 2004-08-16
Pages 467
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An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.



The SAGE Handbook of Quantitative Methodology for the Social Sciences

The SAGE Handbook of Quantitative Methodology for the Social Sciences Author David Kaplan
ISBN-10 9781483365879
Release 2004-06-21
Pages 528
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The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.



Multilevel Modeling

Multilevel Modeling Author Douglas A. Luke
ISBN-10 0761928790
Release 2004-07-08
Pages 79
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A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.



Modern Methods for Robust Regression

Modern Methods for Robust Regression Author Robert Andersen
ISBN-10 9781412940726
Release 2008
Pages 107
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Geared towards both future and practising social scientists, this book takes an applied approach and offers readers empirical examples to illustrate key concepts. It includes: applied coverage of a topic that has traditionally been discussed from a theoretical standpoint; empirical examples to illustrate key concepts; a web appendix that provides readers with the data and the R-code for the examples used in the book.



Multilevel Analysis

Multilevel Analysis Author Joop J. Hox
ISBN-10 9781317308676
Release 2017-09-14
Pages 348
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Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.



The Association Graph and the Multigraph for Loglinear Models

The Association Graph and the Multigraph for Loglinear Models Author Harry J. Khamis
ISBN-10 9781452238951
Release 2011-01-12
Pages 136
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The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.