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Related Concept Videos

How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Dealing with categorical data in a multidimensional context: The multidimensional balanced worth.

Carmen Herrero1, Antonio Villar2

  • 1Department of Economics, FAE. University of Alicante, 03080, Alicante, Spain.

Social Science Research
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new protocol for evaluating population performance across multiple dimensions using categorical data. It extends the balanced worth concept to compare likelihoods of achieving higher performance levels, aiding fields like medicine and social sciences.

Keywords:
Categorical variablesMultidimensional evaluationRelative performance

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Area of Science:

  • Multidimensional analysis
  • Social sciences
  • Statistical evaluation

Background:

  • Evaluating population performance across multiple dimensions with categorical data is complex.
  • Existing methods may not adequately capture multidimensional performance.
  • The balanced worth concept offers a potential framework for such evaluations.

Purpose of the Study:

  • To present a novel evaluation protocol for multidimensional performance assessment.
  • To enable relative performance evaluation of populations using categorical outcomes.
  • To extend the balanced worth concept to a multidimensional context.

Main Methods:

  • Development of an evaluation protocol based on comparing likelihoods of higher performance.
  • Extension of the balanced worth concept to a multidimensional setting.
  • Application of the protocol to empirical case studies.

Main Results:

  • The proposed protocol effectively evaluates relative population performance in multidimensional contexts.
  • The method is applicable to various fields including Medicine, Social Sciences, and Engineering.
  • Empirical applications demonstrate its utility in analyzing life satisfaction and poverty.

Conclusions:

  • The developed protocol provides a robust framework for multidimensional categorical data evaluation.
  • This approach enhances comparative analysis of population performance across diverse domains.
  • The methodology offers valuable insights for policy and research in social and economic areas.