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

32.3K
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...
32.3K
Cluster Sampling Method01:20

Cluster Sampling Method

14.2K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.2K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

3.1K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
3.1K
Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions03:03

Mixtures of Gases: Dalton's Law of Partial Pressures and Mole Fractions

43.7K
Unless individual gases chemically react with each other, the individual gases in a mixture of gases do not affect each other’s pressure. Each gas in a mixture exerts the same pressure that it would exert if it were present alone in the container. The pressure exerted by each individual gas in a mixture is called its partial pressure.
43.7K
Dalton's Law of Partial Pressure01:11

Dalton's Law of Partial Pressure

2.5K
The partial pressure of a gas is a measure of the thermodynamic activity of the gas's molecules. The pressure that a gas would create if it occupied the total volume available is called the gas's partial pressure. If two or more gases are mixed together in a container, the molecules move randomly and collide with each other, causing them to reach thermal equilibrium. When the gases have the same temperature, their molecules have the same average kinetic energy. Thus, each gas obeys the...
2.5K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.2K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.2K

You might also read

Related Articles

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

Sort by
Same author

Multi-method validation of the new computerized test of fluid intelligence MatriKS.

Behavior research methods·2026
Same author

Error patterns on a computerized version of Raven's progressive matrices in specific learning disorders.

Frontiers in psychology·2026
Same author

The Uncertainty in Illness Questionnaire (UIQ): development and validation of a clinically oriented measure for patients and caregivers.

Health and quality of life outcomes·2026
Same author

Null hypothesis significance testing vs. Bayesian inference using generalized linear mixed models with binary outcomes: a case study under practical design constraints.

Frontiers in psychology·2026
Same author

Psychometric properties of the Usability and Acceptability Scale (UAS) for evaluating digital tools in children and adolescent users.

Frontiers in psychology·2026
Same author

The placebo effect in reading performance: A cross-over experimental study.

Acta psychologica·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
Same journal

Psychometric functions from multiple responses : Dedicated to the memory of Colin L. Mallows.

Behavior research methods·2026
Same journal

Low-cost, open-source, full-stack software and Arduino-based hardware for control of commercially available animal behavior systems.

Behavior research methods·2026
Same journal

PyNeon: A Python package for the analysis of Neon multimodal mobile eye-tracking data.

Behavior research methods·2026
Same journal

Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Jan 25, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K

Extracting partially ordered clusters from ordinal polytomous data.

Debora de Chiusole1, Andrea Spoto2, Luca Stefanutti1

  • 1FISPPA Department, University of Padua, Padova, Italy.

Behavior Research Methods
|May 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces the k-median clustering algorithm for building polytomous knowledge structures from ordinal data. The k-median algorithm, using Manhattan distance, outperforms k-modes for ordered responses in knowledge space theory applications.

Keywords:
Clustering algorithmsKnowledge space theoryPolytomous KSTk-mediank-modes

More Related Videos

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.5K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.7K

Related Experiment Videos

Last Updated: Jan 25, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.5K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

11.7K

Area of Science:

  • Educational Measurement and Psychometrics
  • Data Mining and Machine Learning
  • Knowledge Representation and Reasoning

Background:

  • Knowledge space theory models knowledge states as ordered clusters of individuals.
  • Existing extensions for polytomous data lack methods for constructing polytomous structures.
  • Need for algorithms that can handle ordinal response scales in knowledge structure analysis.

Purpose of the Study:

  • To propose an adaptation of the k-median clustering algorithm for building polytomous knowledge structures.
  • To extend existing clustering methods to handle ordinal data in knowledge space theory.
  • To evaluate the efficacy of the proposed k-median algorithm compared to k-modes.

Main Methods:

  • Adaptation of the k-median clustering algorithm for ordinal data.
  • Replacement of Hamming distance with Manhattan distance to account for response order.
  • Utilizing the median as the central tendency measure instead of the mode.

Main Results:

  • Simulation studies and empirical data application demonstrate the effectiveness of k-median.
  • K-median algorithm shows theoretical and practical advantages over k-modes for ordinal scales.
  • Manhattan distance's sensitivity to level order is crucial for polytomous structure analysis.

Conclusions:

  • The k-median algorithm is a promising data-driven procedure for building polytomous knowledge structures.
  • Preference for k-median over k-modes when dealing with ordinal response data in knowledge space theory.
  • The proposed method enhances the analysis of complex knowledge states represented by polytomous items.