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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.6K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

7.3K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
7.3K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

37.0K
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...
37.0K
Scaling01:26

Scaling

657
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
657
Dimensional Analysis01:23

Dimensional Analysis

2.4K
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
2.4K
Dimensional Analysis01:27

Dimensional Analysis

759
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
759

You might also read

Related Articles

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

Sort by
Same author

Diagnostic work-up and systemic treatment for advanced non-squamous non-small-cell lung cancer in four Southeast Asian countries.

ESMO open·2022
Same author

European physical and rehabilitation medicine organisms--origins and developments.

Europa medicophysica·2006
Same author

Behavioral outcomes following below-knee amputation in the coordination between balance and leg movement.

Gait & posture·2005
Same author

Reorganization of equilibrium and movement control strategies after total knee arthroplasty.

Journal of rehabilitation medicine·2002
Same author

Equilibrium and movement control strategies in trans-tibial amputees.

Prosthetics and orthotics international·2000
Same author

Reorganization of equilibrium and movement control strategies in patients with knee arthritis.

Scandinavian journal of rehabilitation medicine·1999
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Multidimensional Scaling Methods for Many-Object Sets: A Review.

L Tsogo, M H Masson, A Bardot

    Multivariate Behavioral Research
    |January 9, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Multidimensional scaling (MDS) can be inefficient for large datasets due to the large number of pairwise comparisons. This review explores efficient similarity task methods for handling extensive object sets in MDS.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Quantification of Orofacial Phenotypes in Xenopus
    09:26

    Quantification of Orofacial Phenotypes in Xenopus

    Published on: November 6, 2014

    10.4K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
    08:12

    A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

    Published on: March 1, 2022

    3.0K
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K
    Quantification of Orofacial Phenotypes in Xenopus
    09:26

    Quantification of Orofacial Phenotypes in Xenopus

    Published on: November 6, 2014

    10.4K

    Area of Science:

    • Psychology
    • Statistics
    • Data Science

    Background:

    • Multidimensional scaling (MDS) reconstructs object relationships from dissimilarity data.
    • Traditional MDS requires numerous pairwise comparisons, becoming inefficient for large datasets (n > 30).

    Purpose of the Study:

    • To address the inefficiency of similarity tasks in multidimensional scaling for large object sets.
    • To review methods that enhance the efficiency of similarity judgments while preserving scaling solution quality.

    Main Methods:

    • Review of existing literature on similarity judgment methods.
    • Analysis of techniques designed to reduce the number of pairwise comparisons in large-scale MDS.

    Main Results:

    • Identified several methods to improve the efficiency of similarity tasks in MDS.
    • These methods aim to reduce the computational burden associated with large numbers of stimuli.

    Conclusions:

    • Efficient similarity task methods are crucial for applying MDS to large datasets.
    • The reviewed techniques offer practical solutions for scalable multidimensional scaling analyses.