Search results

Praise for the First Edition of Statistical Analysis with Missing Data ""An important contribution to the applied statistics literature ... I give the book high marks for unifying and making accessible much of the past and current work in this important area.""--William E. Strawderman, Rutgers University ""This book ... provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statistician's bookshelf.""-The Statistician ""The book should be studied in the statistical methods department in every statistical agency.""-

1: Introduction -- 2: Multivariate Normal Distribution -- 3: Reliability Alpha, Principles Component Analysis and Exploratory Factor Analysis -- 4: Confirmatory Factor Analysis -- 5: Multiple Regression with a Single Dependent Variable -- 6: System of Equations -- 7: Canonical Correlation Analysis -- 8: Categorical Dependent Variables -- 9: Rank-Ordered Data -- 10: Error in Variables - Analysis of Covariance Structure - Structural Equation Models -- 11: Testing Mediation and Moderation Effects -- 12: Cluster Analysis -- 13: Analysis of Similarity and Preference Data -- Appendices: A: Rules in Matrix Algebra -- B: Statistical Tables -- C: Description of Data Sets

The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet eliciting truthful answers in surveys is challenging, especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Many researchers, however, have used a simple difference-in-means estimator, which prevents the efficient examination of multivariate relationships between respondents' characteristics and their responses to sensitive items. Moreover, no systematic means exists to investigate the role of underlying assumptions. We fill these gaps by developing a set of new statistical methods for list experiments. We identify the commonly invoked assumptions, propose new multivariate regression estimators, and develop methods to detect and adjust for potential violations of key assumptions. For empirical illustration, we analyze list experiments concerning racial prejudice. Open-source software is made available to implement the proposed methodology.

A survey is given of random graphs and random graph processes which can be used to describe and analyze networks that are changing with time. Marko‐vian change over time, log‐linear models for change, and conditionally uniform models for change are described. It is noted that estimation is usually complex if the random graph involves dependent dyads. Models with deterministic change over time may be a way to avoid the difficulties implied by dependent dyads. Logit regression methods are described that can be used to estimate such models.

The battle at Kursk in 1943 is often referred to as the greatest tank battle in the history of warfare. This volume makes extensive use of German archival documents as well as various Russian books and articles. As well as an account of the battle, it addresses methodological issues.