The Third Edition of John W. Creswell’s best-selling Research Design enables readers to compare three approaches to research – qualitative, quantitative, and mixed methods – in a single research methods text. The book examines these methodologies side by side within the process of research, from the beginning steps of philosophical assumptions to the writing and presenting of research.
Structural Equation Modeling: Foundations and Extensions
By: David Kaplan
Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face.
Thoroughly revised to address recent developments, this new edition includes:
• The foundations of SEM, including path analysis and factor analysis.
• Traditional SEM for continuous latent variables, including latent growth curve modeling for continuous growth factors, and issues in testing assumptions of SEM.
• SEM for categorical latent variables, including latent class analysis, Markov models (latent and mixed latent), and growth mixture modeling.
• Philosophical issues in the practice of SEM, including the problem of causal inference.
A Mathematical Primer for Social Statistics
By: John Fox
A Mathematical Primer for Social Statistics: Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. It is, however, surprising how far one can go with a relatively modest mathematical background. The proposed monograph aims to provide that background, introducing the areas of mathematics that are most centrally important to applied social statistics: matrices, linear algebra, and vector geometry; basic differential and integral calculus, including multivariable and matrix calculus, and the application of calculus to optimization problems; and probability and estimation, including the basics of probability theory, discrete and continuous random variables, commonly encountered statistical distributions, principles of estimation, the method of maximum likelihood and the basics of Bayesian inference.
Event History Analysis with Stata
By: Hans-Peter Blossfeld, Gtz Rohwer, and Katrin Golsch
Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models.
The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data, to data organization, to applications using the software, to the interpretation of results. The book also demonstrates, through example, how to implement hypotheses tests and how to choose the right model. The strengths and limitations of various techniques are emphasized in each example, along with an introduction to the model, details on how to input data, and the related Stata commands. Each application is accompanied by a brief explanation of the underlying statistical concept.
Readers are offered the unique opportunity to easily run and modify all of the book’s application examples on a computer, by visiting the author’s Web site at http://www.uni-bamberg.de/sowi/soziologie-i/eha/. Examples include survival rates of patients in medical studies; unemployment periods in economic studies; and the time it takes a criminal to break the law after his release in a criminological study.