Hardback : HK$1,256.00
This book presents recent developments in the theory and application of latent variable models (LVMs) by some of the most prominent researchers in the field. Topics covered involve a range of LVM frameworks including item response theory, structural equation modeling, factor analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications.
The book is divided into two sections, although several chapters cross these content boundaries. Part one focuses on complexities which involve the adaptation of latent variables models in research problems where real-world conditions do not match conventional assumptions. Chapters in this section cover issues such as analysis of dyadic data and complex survey data, as well as analysis of categorical variables. Part two of the book focuses on drawing real-world meaning from results obtained in LVMs. In this section there are chapters examining issues involving assessment of model fit, the nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences.
This book appeals to researchers and graduate students interested in the theory and application of latent variable models. As such, it serves as a supplementary reading in graduate level courses on latent variable models. Prerequisites include basic knowledge of latent variable models.
This book presents recent developments in the theory and application of latent variable models (LVMs) by some of the most prominent researchers in the field. Topics covered involve a range of LVM frameworks including item response theory, structural equation modeling, factor analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications.
The book is divided into two sections, although several chapters cross these content boundaries. Part one focuses on complexities which involve the adaptation of latent variables models in research problems where real-world conditions do not match conventional assumptions. Chapters in this section cover issues such as analysis of dyadic data and complex survey data, as well as analysis of categorical variables. Part two of the book focuses on drawing real-world meaning from results obtained in LVMs. In this section there are chapters examining issues involving assessment of model fit, the nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences.
This book appeals to researchers and graduate students interested in the theory and application of latent variable models. As such, it serves as a supplementary reading in graduate level courses on latent variable models. Prerequisites include basic knowledge of latent variable models.
M. C. Edwards, R. C. MacCallum, Introduction: Complexity and Meaning in Latent Variable Modeling. Part I. Complexities in Latent Variable Modeling.R. Cudeck, J. R. Harring, Estimating the Correlation between Two Variables when Individuals are Measured Repeatedly. R. Gonzalez, D. Griffin, Deriving Estimators and Their Standard Errors in Dyadic Data Analysis: Examples Using a Symbolic Computation Program. P. F. Craigmile, M. Peruggia, T. Van Zandt, A Bayesian Hierarchical Model for Response Time Data Providing Evidence for Criteria Changes Over Time. I. Moustaki, A Review of Estimation Methods for Latent Variable Models. G. Zhang, C.T. Lee, Standard Errors for Ordinary Least Squares Estimates of Parameters in Structural Equation Modeling. L. Cai, Three Cheers for the Asymptotically Distribution Free Theory of Estimation and Inference: Some Recent Applications in Linear and Nonlinear Latent Variable Modeling. K. A. Duncan, S. N. MacEachern, Nonparametric Bayesian Modeling of Item Response Curves with a Three Parameter Logistic Prior Mean. W. A. Nicewander, Exact Solutions for IRT Latent Regression Slopes and Latent Variable Intercorrelations. S. du Toit, Analysis of Structural Equation Models Based on a Mixture of Continuous and Ordinal Random Variables in the Case of Complex Survey Data. Part II. Drawing Meaning from Latent Variable Models.R. E. Millsap, A Simulation Paradigm for Evaluating Approximate Fit. R. C. MacCallum, T. Lee, M. W. Browne, Fungible Parameter Values in Latent Curve Models. A. Shapiro, Statistical Inference of Moment/Covariance Structures. J. L. Rodgers, W. H. Beasley, Fisher, Gosset, and Alternative Hypothesis Significance Testing (AHST): Using the Bootstrap to Test Scientific Hypotheses about the Multiple Correlation. S. M. Boker, M. Martin, On The Equilibrium Dynamics of Meaning. K. Tateneni, M. Schiller, Applying Components Analysis to Attitudinal Segmentation.
Michael C. Edwards is an Associate Professor in
the Quantitative Area of the Department of Psychology at The Ohio
State University. He received his PhD in 2005 from the L.L.
Thurstone Psychometric Laboratory at the University of North
Carolina at Chapel Hill. His research focuses on measurement issues
in the social sciences with specific topics including
multidimensional item response theory, computerized adaptive
testing, local dependence, and measurement invariance.
Robert C. MacCallum is Professor Emeritus of
Psychology at both the University of North Carolina at Chapel Hill
and Ohio State University. He received his graduate training at the
University of Illinois and then spent 28 years on the faculty in
the Quantitative Psychology program at Ohio State University,
moving to UNC in 2003. His research interests focus on methods for
analysis and modeling of correlational and longitudinal data,
including factor analysis, structural equation modeling, and latent
curve models. Within these areas he has worked on various issues
including model estimation and evaluation, power analysis for
testing models, and the nature and management of sources of error
in modeling. His current interests involve the study of uncertainty
inherent in results of statistical models. He is former Director of
the L. L. Thurstone Psychometric Laboratory at UNC, and former
president of the Society for Multivariate Experimental Psychology.
In 2011 he received the Samuel J. Messick Award for distinguished
scientific contributions from Division 5 of the American
Psychological Association.
“This fine collection of papers is a fitting tribute to Michael
Browne, one of the giants of contemporary psychometrics. Readers
interested in latent variable models are sure to enjoy this
valuable edited volume.” - Kenneth A. Bollen, University of North
Carolina- Chapel Hill, USA
“This exciting volume adds impressively to the structural equation
modeling literature by giving top contributors in our field a new
forum for presenting their current thinking about pressing issues."
- John R. Nesselroade, University of Virginia, USA “An inspiring
book on latent variable modeling in honor of a great man and
scientist. It offers an excellent sample of new applications and
methodologies, an enjoyable snapshot of an important and expanding
field.” – Paul de Boeck, University of Amsterdam, the Netherlands
“This book will be a significant contribution to the field and I am
enthusiastic about its publication. … The editors … recruited a
strong list of contributors. ...This book would be appropriate for
advanced graduate students, [and] applied researchers … and …
statisticians. It could be used in conjunction with a more general
text for a … course on structural equation modeling.” – Jason T.
Newsom, Portland State University, USA
“The range of topics ... [will] be of interest to researchers and
graduate students. ... [It] represents a considerable contribution
to the literature on latent variable modeling. .... It draws on the
expertise of a number of well-known researchers in the field."-
Ronald H. Heck, University of Hawaii at Manoa, USA“A significant
contribution to the field.” – Melanie M. Wall, Columbia University,
USA
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