Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
Nominal Level of Measurement00:56

Nominal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...
Interval Level of Measurement00:55

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Box plots: use and interpretation.

Transfusion·2008
Same author

Effects of insulin therapy on inflammatory mediators in infants undergoing cardiac surgery with cardiopulmonary bypass.

Cytokine·2008
Same author

Tumor growth decreases NK and B cells as well as common lymphoid progenitor.

PloS one·2008
Same author

[Expression of vascular endothelial growth factor in different breast tissues and clinical significance thereof].

Zhonghua yi xue za zhi·2008
Same author

Immobilization of hemoglobin on the gold colloid modified pretreated glassy carbon electrode for preparing a novel hydrogen peroxide biosensor.

Applied biochemistry and biotechnology·2008
Same author

Induction of time-dependent oxidative stress and related transcriptional effects of perfluorododecanoic acid in zebrafish liver.

Aquatic toxicology (Amsterdam, Netherlands)·2008

Related Experiment Video

Updated: Jun 16, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

An approach to solve group-decision-making problems with ordinal interval numbers.

Zhi-Ping Fan1, Yang Liu

  • 1Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang 110004, China. zpfan@mail.neu.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 23, 2010
PubMed
Summary

This study introduces a new method for ranking alternatives using ordinal interval numbers in group decision making. The approach enhances efficiency and accuracy for uncertain preference information.

More Related Videos

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

Related Experiment Videos

Last Updated: Jun 16, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
06:16

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting

Published on: June 6, 2020

Area of Science:

  • Decision Sciences
  • Operations Research
  • Information Science

Background:

  • Ordinal interval numbers represent uncertain preferences in group decision making (GDM).
  • Existing research inadequately addresses the application of ordinal interval numbers in GDM ranking.
  • Efficient and accurate ranking is crucial for large datasets of ordinal interval numbers.

Purpose of the Study:

  • To develop a novel approach for ranking alternatives based on ordinal interval numbers in GDM.
  • To address the challenge of efficiently and accurately ranking alternatives with uncertain preference information.
  • To provide a theoretical and algorithmic framework for utilizing ordinal interval numbers in decision-making.

Main Methods:

  • Defined the concept of possibility degree for comparing two ordinal interval numbers.
  • Constructed a collective expectation possibility degree matrix from pairwise comparisons.
  • Developed an optimization model and a corresponding algorithm to rank alternatives.

Main Results:

  • The proposed method effectively ranks alternatives using ordinal interval numbers under uncertainty.
  • The collective expectation possibility degree matrix captures group preferences.
  • The optimization model provides a systematic way to determine the ranking order.

Conclusions:

  • The new approach offers an efficient and accurate solution for ranking problems involving ordinal interval numbers in GDM.
  • The method provides a robust framework for handling uncertain preference information.
  • Demonstrated the practical applicability through illustrative examples.