Generalizing the Regression Model: Techniques for Longitudinal and Contextual Analysis

This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of each technique showing how it was used to address substantive and interesting research questions. It takes a step-by-step approach in the presentation of each topic, using mathematical derivations where necessary, but primarily emphasizing how the methods involved can be implemented, are used in addressing representative substantive problems that span a number of disciplines, and can be interpreted in words. The book demonstrates the analyses in STATA and SAS.

Format: Paperback / softback
Length: 688 pages
Publication date: 02 March 2021
Publisher: SAGE Publications Inc

This comprehensive text delves into the realm of statistical analysis, encompassing a wide range of models and techniques. It begins by introducing regression, the fundamental framework for predicting the relationship between a dependent variable and one or more independent variables. The text then explores the general linear model, a versatile tool for analyzing data with continuous outcomes. Structural equation modeling (SEM) is introduced as a powerful method for modeling complex relationships between variables and their latent variables. The hierarchical linear model is presented as a way to handle data with both continuous and categorical variables. Growth curve models are discussed to analyze the growth patterns of individuals or groups over time. Panel data analysis is introduced to study the dynamics of relationships among individuals or groups over an extended period. Event history models are used to analyze the timing of events and their effects on outcomes.

Throughout the text, published implementations of each technique are showcased, demonstrating how they have been applied to address substantive and interesting research questions across various disciplines. The presentation of each topic follows a step-by-step approach, utilizing mathematical derivations when necessary, but primarily emphasizing the practical aspects and interpretation of the methods in terms of real-world problems.

The book aims to bridge the gap between theoretical concepts and practical application, making statistical analysis accessible to students and researchers alike. It demonstrates the analyses using popular statistical software packages such as STATA and SAS, providing hands-on experience for those who wish to apply these techniques in their own research endeavors.

In summary, Generalizing the Regression Model serves as an invaluable resource for students and practitioners seeking to expand their knowledge and expertise in statistical analysis. It offers a comprehensive coverage of regression, general linear models, SEM, hierarchical linear models, growth curve models, panel data analysis, and event history models, accompanied by practical examples and implementations. By providing a step-by-step approach and emphasizing practical applications, this text equips readers with the tools and knowledge necessary to apply statistical techniques in real-world research scenarios.

Weight: 1060g
Dimension: 204 x 255 x 30 (mm)
ISBN-13: 9781506342092