Series:

Quantitative Applications in the Social Sciences

3 total works

Cover of Understanding Regression Assumptions

Through the use of careful explanations and examples, Berry shows the reader how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in textbooks, Berry moves on to explore in detail the "substantive" meaning of each assumption (such as lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of autocorrelation). Aimed at improving social science applications of regression, this volume is a must for every student′s and researcher′s library.


Cover of Nonrecursive Causal Models
Where an assumption of unidirectionality in causal effects is unrealistic, ′recursive′ models cannot be used, and more complex ′nonrecursive′ models are necessary. Unfortunately, many nonrecursive models (unlike recursive models) are ′unidentified′, which makes meaningful parameter estimation impossible. Even when they are identified, it would be inappropriate to use OLS regression techniques (appropriate for recursive models) for the purpose of estimation. The concept of identification, and the factors that lead to it are explained; and various tests for determination are provided. Illustrations from a variety of social science disciplines are used throughout the book.

Cover of Multiple Regression in Practice

The authors provide a systematic treatment of the major problems involved in using regression analysis. They clearly and concisely discuss the consequences of violating the assumptions of the regression model, procedures for detecting violations, and strategies for dealing with these problems.