Hypothesis Testing And Model Selection In The Social Sciences
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Hypothesis Testing and Model Selection in the Social Sciences
Author: David L. Weakliem
language: en
Publisher: Guilford Publications
Release Date: 2016-04-25
Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital]www.guilford.com/weakliem-materials[/ital]) supplies data and syntax files for the book's examples.
The Essential Guide to Doing Your Research Project
This practical book sets out how to approach each stage of your research project, from choosing a research design and methodology to collecting and analysing data and communicating your results – and showcases best practice along the way. Packed with pragmatic guidance for tackling research in the real world, this fourth edition: Offers support for diving into a project using digital data, with how-to guidance on conducting online and social media research Empowers you to confidently disseminate your work and present with impact Helps you map out your research journey and put a plan in place with decision trees in every chapter Challenges you to be reflective and critical about the research you consume and undertake Zina O′Leary′s detailed and down-to-earth approach gives you the research skills and momentum you need to successfully complete your research project.
Multilevel Modeling Methods with Introductory and Advanced Applications
Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation. In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs. Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.