Internal Multi-Dimensional Scaling of Categorical Variables

Abstract

The purpose of the study in the dissertation is to translate raw categorized data into numerical values on which standard statistical analyses can be performed. When raw observations are recorded on a nominal scale, they are to be transformed so that the resulting numbers can be regarded as lying on an interval scale. A scalling technique is developed on the basis of a generalization of Lancaster's approach (canonical correlation for two sets). The report also presents computer programs starting from data in contingency tables which are converted into a correlation matrix. Initial values are used in order to start the minimum-determinant process. Various initial weights and the final minimum-determinant solution are compared.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1974
Accession Number
AD0782706

Entities

People

  • Jeffrey Chit-fu Chang
  • Rolf E. Bargmann

Organizations

  • University of Georgia

Tags

DTIC Thesaurus Topics

  • Computer Programs
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Estimators
  • Information Science
  • Literature Surveys
  • Maximum Likelihood Estimation
  • Network Science
  • Normal Distribution
  • Numerical Analysis
  • Procedures (Computers)
  • Square Roots
  • Standards
  • Statistical Algorithms
  • Statistical Analysis

Readers

  • Regression Analysis.
  • Theoretical Analysis.