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Foundations Of Factor Analysis Second Edition

Author: Stanley A Mulaik
Publisher: CRC Press
ISBN: 1420099817
Size: 17.99 MB
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Providing a practical, thorough understanding of how factor analysis works, Foundations of Factor Analysis, Second Edition discusses the assumptions underlying the equations and procedures of this method. It also explains the options in commercial computer programs for performing factor analysis and structural equation modeling. This long-awaited edition takes into account the various developments that have occurred since the publication of the original edition. New to the Second Edition A new chapter on the multivariate normal distribution, its general properties, and the concept of maximum-likelihood estimation More complete coverage of descriptive factor analysis and doublet factor analysis A rewritten chapter on analytic oblique rotation that focuses on the gradient projection algorithm and its applications Discussions on the developments of factor score indeterminacy A revised chapter on confirmatory factor analysis that addresses philosophy of science issues, model specification and identification, parameter estimation, and algorithm derivation Presenting the mathematics only as needed to understand the derivation of an equation or procedure, this textbook prepares students for later courses on structural equation modeling. It enables them to choose the proper factor analytic procedure, make modifications to the procedure, and produce new results.

Multivariable Modeling And Multivariate Analysis For The Behavioral Sciences

Author: Brian S. Everitt
Publisher: CRC Press
ISBN: 1439807701
Size: 18.35 MB
Format: PDF, ePub, Mobi
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Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues. The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations. Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com

Latent Markov Models For Longitudinal Data

Author: Francesco Bartolucci
Publisher: CRC Press
ISBN: 1466583711
Size: 52.23 MB
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Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Linear Causal Modeling With Structural Equations

Author: Stanley A. Mulaik
Publisher: CRC Press
ISBN: 9781439800393
Size: 10.73 MB
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Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal relations directly by perceiving quantities in magnitudes and motions of causes that are conserved in the effects of causal exchanges. The author surveys the basic concepts of graph theory useful in the formulation of structural models. Focusing on SEM, he shows how to write a set of structural equations corresponding to the path diagram, describes two ways of computing variances and covariances of variables in a structural equation model, and introduces matrix equations for the general structural equation model. The text then discusses the problem of identifying a model, parameter estimation, issues involved in designing structural equation models, the application of confirmatory factor analysis, equivalent models, the use of instrumental variables to resolve issues of causal direction and mediated causation, longitudinal modeling, and nonrecursive models with loops. It also evaluates models on several dimensions and examines the polychoric and polyserial correlation coefficients and their derivation. Covering the fundamentals of algebra and the history of causality, this book provides a solid understanding of causation, linear causal modeling, and SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models.

Applied Survey Data Analysis

Author: Steven G. Heeringa
Publisher: CRC Press
ISBN: 9781420080674
Size: 57.38 MB
Format: PDF
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Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches. Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133886190
Size: 17.60 MB
Format: PDF, Kindle
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To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Strukturgleichungsmodellierung

Author: Rolf Weiber
Publisher: Springer-Verlag
ISBN: 3642350127
Size: 77.16 MB
Format: PDF, Kindle
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Alle Analysen werden an einem durchgehenden Fallbeispiel konkret und transparent durchgeführt Strukturgleichungsmodelle sind in allen Wissenschaftsdisziplinen von größter Bedeutung, da sie das Standardinstrument zur empirischen Prüfung von Hypothesensystemen darstellen. Dabei stehen oftmals die Beziehungen zwischen hypothetischen Konstrukten (latenten Variablen) im Fokus des Interesses. Das Buch zeichnet den gesamten Prozess der Strukturgleichungsmodellierung von der Konzeptualisierung theoretischer Konstrukte über die Spezifikation von Messmodellen, die Reliabilitäts- und Validitätsprüfung mittels konfirmatorischer Faktorenanalyse bis hin zur Prüfung von kausalen Wirkhypothesen mittels Kovarianzstrukturanalyse sowie PLS nach. Alle Arbeitsschritte werden an einem durchgehenden Fallbeispiel unter Verwendung von SPSS, AMOS und SmartPLS veranschaulicht und jeweils konkrete Anwendungsempfehlungen gegeben. Ebenso werden weiterführende Aspekte wie die Mehrgruppenkausalanalyse, die Spezifikation reflektiver und formativer Messansätze sowie MIMIC-Modelle einsteigergerecht behandelt.