Beschreibung

This book explores the fundamentals of multidimensional scaling (MDS) and how this analytic method can be used in applied setting for educational and psychological research. The book tries to make MDS more accessible to a wider audience in terms of the language and examples that are more relevant to educational and psychological research and less technical so that the readers are not overwhelmed by equations. The goal is for readers to learn the methods described in this book and immediately start using MDS via available software programs. The book also examines new applications that have previously not been discussed in MDS literature. It should be an ideal book for graduate students and researchers to better understand MDS.

Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research is divided into three parts. Part I covers the basic and fundamental features of MDS models pertaining to applied research applications. Chapters in this section cover the essential features of data that are typically associated with MDS analysis such as preference ration or binary choice data, and also looking at metric and non-metric MDS models to build a foundation for later discussion and applications in later chapters. Part II examines specific MDS models and its applications for education and psychology. This includes spatial analysis methods that can be used in MDS to test clustering effect of items and individual differences MDS model (INDSCAL). Finally, Part III focuses on new applications of MDS analysis in these research fields. These new applications consist of profile analysis, longitudinal analysis, mean-level change, and pattern change. The book concludes with a historical review of MDS development as an analytical method and a look to future directions.

Explains the fundamental elements of MDS models and its application in education and psychological research
Covers topics relevant to current practices in educational or psychological research and practice
Uses non-technical language to describe the methods in order to make it easy for readers without a background in MDS can understand

Autorentext

Cody S. Ding completed his Ph.D. in University of Minnesota, Minneapolis. He was trained as a psychologist with emphasis on developmental psychology, methodology, and measurement. Over years, he has been working as a professor at universities, teaching educational and psychological assessment, research designs, quantitative analysis, and other methodological related courses. In addition, he has been working with many school districts and the state educational agencies on various evaluation projects to improve student learning and school practices.

With expertise in quantitative research design and analysis, in particular experimental design and survey research, Dr. Ding focuses on multidimensional scaling, data visualization, and longitudinal modeling. Psychometric expertise includes item response theory and multidimensional scaling applied to measurement and evaluation research. Dr. Ding has been actively involving in research on multidimensional scaling as latent growth model and cognitive measurement model in education and psychological assessment. His research interests include exploratory latent variable analysis via multidimensional scaling (MDS), applications of Item Response Theory (IRT), Structural Equation Modeling (SEM), and Bayesian network in studying psychosocial adaptation of adolescents and young adults.

Dr. Cody Ding has been conducting cutting-edge methodological research and providing high-level consulting services to quantitative researchers in the social and behavioral sciences, including several evaluation grants that investigate the program effectiveness on student learning.

Inhalt
Chapter 1: Introduction.- Chapter 2: Data issues of MDS.- Chapter 3: Metric vas non-metric MDS model.- Chapter 4: Model selection and interpretation.- Chapter 5: Basic MDS analysis.- Chapter 6: Visualization of data structure by MDS.- Chapter 7: Individual differences MDS analysis.- Chapter 8: MDS Preference Analysis.- Chapter 9: Configuration Similarities.- Chapter 10: Latent Profile analysis.- Chapter 11: Longitudinal analysis using MDS.- Chapter 12: Testing pattern hypotheses with MDS.- Chapter 13: Mean-level change vs. pattern change.- Chapter 14: Historical review.