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Focusing on the methods that are commonly used by social scientists, this text introduces the regression methods for analysing spatial data.

A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied.

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects.

As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. This work identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved.

Can taxometric procedures be used to distinguish types (species, latent classes, taxa) from continua (dimensions, latent traits, factors); and, if so, how? Aimed at demystifying this process, Waller and Meehl unpack Meehl''s work on the MAXCOV-HITMAX procedure to reveal the underlying rationale of MAXCOV in simple terms and show how this technique can be profitably used in a variety of disciplines by researchers in their taxonomic work.

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: * An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3* New section on multivariate growth models in Chapter 6 * A discussion of research synthesis or meta-analysis applications in Chapter 7* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

Clearly and precisely shows how SEM can be used to answer or provide insight to substantive questions, specifically by weaving a small set of empirical examples and data throughout the chapters

In this book Ton Heinen explores topics such as: how to estimate the parameters of latent class analysis models and latent trait models; methods for model selection; and ways to examine the correspondence between discrete latent trait models and certain restricted latent class models.

This book presents a general approach to missing data problems in event history analysis which is based on the similarities between log-linear models, hazard models and event history models.

Non-linear analysis of categorical variables, that is, a variable that can sort objects into a limited number of distinct groups called `categories', is a useful technique for social scientists, particularly those who do survey research. This book introduces the reader to the application of a particular approach to categorical analysis, the GIFI system, or multiple correspondence analysis. Using illustrative examples from a variety of disciplines, van de Geer shows how to perform these techniques using standard computer programs, such as SPSS. The book explains when to use particular programs, what conditions need to be met for effective use of each program, and how to interpret the results based on the use of each of these programs. Detai

Written by a sociologist, a graph theorist, and a statistician, this title provides you with a solid statistical foundation from which to analyse network data.

This book offers readers a systematic review of the origins, history, and statistical foundations of PSM and illustrates how to use PSM methods for solving evaluation problems.

This book provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The accompanying website includes R code and the dataset used in the book.