Elements of Dual Scaling: An Introduction to Practical Data Analysis

Elements of Dual Scaling: An Introduction to Practical Data Analysis

Elements of Dual Scaling: An Introduction to Practical Data Analysis

Elements of Dual Scaling: An Introduction to Practical Data Analysis

Synopsis

Quantification methodology of categorical data is a popular topic in many branches of science. Most books, however, are either too advanced for those who need it, or too elementary to gain insight into its potential. This book fills the gap between these extremes, and provides specialists with an easy and comprehensive reference, and others with a complete treatment of dual scaling methodology -- starting with motivating examples, followed by an introductory discussion of necessary quantitative skills, and ending with different perpsectives on dual scaling with examples, advanced topics, and future possibilities.

This book attempts to successively upgrade readers' readiness for handling analysis of qualitative, categorical, and non-metric data, without overloading them. The writing style is very friendly, and difficult topics are always accompanied by simple illlustrative examples.

There are a number of topics on dual scaling which were previously addressed only in journal articles or in publications that are not readily available. Integration of these topics into the standard framework makes the current book unique, and its extensive coverage of relevant topics is unprecedented. This book will serve as both reference and textbook for all those who want to analyze categorical data effectively.

Excerpt

There are a few things you might want to look at before you get involved in dual scaling. They are concerned with the nature of data analysis. As you will see, most points to be mentioned here are what your common sense would dictate. Nevertheless, putting those points in one place may be of some use as an introduction to data analysis.

Data collection is your first topic. If you are a teacher, you may give several tests to a group of students, and their scores on the tests will be your data. If your work is in marketing research, your data may consist of consumers' responses to a set of so-called multiple-choice questions plus their biographical information (e.g., gender, age, profession). If you are a clinical psychologist, patients' responses to an inkblot test may be your data. If you are a public relations officer of a company, a list of complaints from the public would constitute a data set. Whatever your task, you must collect "valid" information, valid in the sense that it is worth analyzing and can be analyzed. This is a very important point with many relevant problems, yet frequently tends to be overlooked or ignored completely.

First of all, you must consider the task of the respondents or subjects: for instance, to answer a set of questions, to compare the taste of Coca Cola and Pepsi, or to rank five candidates for a committee of three. Are these tasks simple and clear enough for your subjects? It is easy to assume, because you are familiar with the area under investigation, that your subjects would be able to answer all the questions, say about pollution problems, social welfare problems, or mandatory retirement issues. If you want to solicit reactions of people in your community to the government's recent tax reform proposal, make the questionnaire short and easy to answer.

Once you know what you want to find out from data analysis, you must collect data suitable for your purposes. If two classes of students are to be compared on their performance in mathematics, for example, make sure that the same mathematics test is given to both classes, and avoid . . .

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