Teach Yourself Cluster Analysis, Conjoint Analysis, and Econometrics Techniques

 
9781493530403: Teach Yourself Cluster Analysis, Conjoint Analysis, and Econometrics Techniques

The purpose of this e-book is to make the case for the application of the classifications and econometrics techniques on issues addressed by social and behavioral scientists. This e-book will address such classification and econometrics techniques as cluster analysis, conjoint analysis, seemingly unrelated regression, and simultaneous equations modeling. Classification techniques will be discussed in length on subjects such as hierarchical agglomerative clustering, k-means clustering, and two-step clustering. Descriptive and prescriptive in nature, the e-book will start with a detailed pedagogical introduction to each of these techniques followed by a detailed description of the standards used in the application of these techniques. The author will go over the purpose and rationale for using each statistical test and provide a clear exposition of why and when each technique should be used. Each technique will be explained in lay man’s terms, difficult concepts using illustrative examples that are easily understood. Mathematical prerequisite is generally low; the author assumes her reader has some familiarity with descriptive statistics and multivariate regression. After reading the e-book, the reader will be able to understand each technique and apply it to social science related research without having to know the meaning of Greek symbols and equations. In this e-book, syntax and output for each technique will be discussed and the author will provide a clear explanation of how to interpret the output. Readers will know how to modify the syntax provided in the e-book and apply them to their own programs to use. Programming syntax in SPSS and R are also provided. These syntax will help readers make sense of the results when they use SPSS software featuring cluster analysis and R software featuring conjoint analysis, seemingly unrelated regression, and simultaneously equations modeling. The purpose of the examples used in this book is to illustrate the use of various classification and econometrics techniques and should not be considered definitive.

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About the Author:

Hui Liew, Ph.D., is a faculty member at the Department Sociology, Averett University in Virginia, USA. She obtained her master’s and doctorate degrees in sociology and applied statistics from Arizona State University, Pennsylvania State University, and Mississippi State University. The focus of her research is on aging, medical sociology/demography, substance abuse, health inequality, and quantitative methodology. She has published over 20 research and scientific papers; majority of which focus on the use of semi-parametric mixture modeling (SPMM), cluster analysis, conjoint analysis, and simultaneous equations modeling.

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Hui P Liew Ph.D.
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