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9780803941786 Academic Inspection Copy

Data Theory and Dimensional Analysis

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This book considers how real-world observations can be interpreted to convert them into data to be analysed, so as to facilitate more effective use of sealing techniques. The author then introduces readers to the most appropriate strategies for different research situations. Some of the basic scaling questions, such as the importance of measurement levels, the kinds of variables needed for Likert or Guttman scales, and when to use multidimensional sealing instead of factor analysis are examined. The latest research on data theory work, previously only available in technical journals, is now made accessible to social scientists.
William G. Jacoby is a Professor in the Department of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan, where he serves as Director of the Inter-University Consortium for Political and Social Research (ICPSR) Summer Training Program in Quantitative Methods of Social Research. Professor Jacoby joined the MSU faculty in 2003. Previously, he held positions at the University of South Carolina, Ohio State University, and the University of Missouri. He received his Ph.D. from the University of North Carolina, Chapel Hill in 1983. Professor Jacoby's main professional interests are mass political behavior (public opinion, political attitudes, voting behavior) and quantitative methodology (measurement theory, scaling methods, statistical graphics, modern regression). His current research focuses on citizen ideology and belief system organization, value choices and their implications for subsequent political orientations, measuring policy priorities in the American states, the implications of measurement assumptions for statistical models, and graphical strategies for data analysis. Recently, Professor Jacoby has taught courses on public opinion, regression analysis, scaling methods, and statistical graphics.
Introduction Measurement Data Theory Dimensionality Data Theory and Scaling Methods Alternating Least Squares, Optimal Scaling Conclusions
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