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.

The politics and economics of the United States are wedded in the political economy of the nation state and the nationalist economic policies. This book proposes that political jurisdictions are not economies but polities, and explores the complex and important economic implications of this thesis.

New edition of a text in which Raudenbush (U. of Michigan) and Bryk (sociology, U. of Chicago) provide examples, explanations, and illustrations of the theory and use of hierarchical linear models (HLM). New material in Part I (Logic) includes information on multivariate growth models, an expanded number of illustrative examples, further data analy

Evaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.

Event history analysis has been a useful method in the social sciences for studying the processes of social change. However, a main difficulty in using this technique is to observe all relevant explanatory variables without missing any variables.

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. It begins with a discussion of log-rate models, modified path models and methods for obtaining maximum likelihood estimates of the parameters of log-linear...

Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and illustrates how it can be used for solving evaluation and causal-inference problems.