Using The Multivariate Multilevel Logistic Regression Model To Detect Dif

Using the Multivariate Multilevel Logistic Regression Model to Detect DIF

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.

Handbook of Quantitative Methods for Educational Research

As part of their research activities, researchers in all areas of education develop measuring instruments, design and conduct experiments and surveys, and analyze data resulting from these activities. Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Commonly referred to as quantitative methods, these techniques cover a range of statistical tests and tools. Quantitative research is essentially about collecting numerical data to explain a particular phenomenon of interest. Over the years, many methods and models have been developed to address the increasingly complex issues that educational researchers seek to address. This handbook serves to act as a reference for educational researchers and practitioners who desire to acquire knowledge and skills in quantitative methods for data analysis or to obtain deeper insights from published works. Written by experienced researchers and educators, each chapter in this handbook covers a methodological topic with attention paid to the theory, procedures, and the challenges on the use of that particular methodology. It is hoped that readers will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area.