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Structural Equations with Latent Variables
Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is...
Latent Curve Models
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 str...
Testing Structural Equation Models
What is the role of fit measures when respecifying a model? Should the means of the sampling distributions of a fit index be unrelated to the size of the sample? Is it better to estimate the statistical power of the chi-square test than to turn to fit indices? Exploring these and related questions, well-known scholars examine the methods of testing structural equation models (SEMS) with and without measurement error, as estimated by such programs as EQS, LISREL and CALIS.
Handbook of Structural Equation Modeling
"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--
Handbook of Causal Analysis for Social Research
What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.
Nonrecursive Models
Nonrecursive Models provides explicit guidance to researchers on the estimation and assessment of nonrecursive simultaneous equation models in a clear, condensed and precise form. It guides readers through the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.
Introducing LISREL
Introducing Lisrel provides a comprehensive introduction to Lisrel for structural equation modeling using a non-technical, user-friendly approach. It shows the major steps associated with the formulation and testing of a model.
Time Matters
What do variables really tell us? When exactly do inventions occur? Why do we always miss turning points as they transpire? When does what doesn't happen mean as much, if not more, than what does? Andrew Abbott considers these fascinating questions in Time Matters, a diverse series of essays that constitutes the most extensive analysis of temporality in social science today. Ranging from abstract theoretical reflection to pointed methodological critique, Abbott demonstrates the inevitably theoretical character of any methodology. Time Matters focuses particularly on questions of time, events, and causality. Abbott grounds each essay in straightforward examinations of actual social scientific...
