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

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.0K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.0K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.5K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.5K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

641
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
641
Compacting Factor test01:22

Compacting Factor test

689
The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
689
Fast Fourier Transform01:10

Fast Fourier Transform

1.2K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
1.2K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K

You might also read

Related Articles

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

Sort by
Same author

Review: Smart agri-systems for the pig industry.

Animal : an international journal of animal bioscience·2022
Same author

Recruiting and retaining first-year college students in online health research: Implementation considerations.

Journal of American college health : J of ACH·2022
Same author

Modelling the links between farm characteristics, respiratory health and pig production traits.

Scientific reports·2021
Same author

Influence of temperature on prevalence of health and welfare conditions in pigs: time-series analysis of pig abattoir inspection data in England and Wales.

Epidemiology and infection·2020
Same author

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

Journal of neural engineering·2017
Same author

Applying dynamic data collection to improve dry electrode system performance for a P300-based brain-computer interface.

Journal of neural engineering·2016
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 26, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Factor Recovery in Binary Data Sets: A Simulation.

L M Collins, N Cliff, D J McCormick

    Multivariate Behavioral Research
    |January 24, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study found phi coefficients slightly better than tetrachorics for identifying factors in binary data. For factor structure recovery, phi coefficients generally outperformed tetrachorics, making them preferable for most factor analysis applications.

    More Related Videos

    A Multimodal Wide-Field Fourier-Transform Raman Microscope
    06:48

    A Multimodal Wide-Field Fourier-Transform Raman Microscope

    Published on: December 30, 2025

    736
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.8K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    8.1K
    A Multimodal Wide-Field Fourier-Transform Raman Microscope
    06:48

    A Multimodal Wide-Field Fourier-Transform Raman Microscope

    Published on: December 30, 2025

    736
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.8K

    Area of Science:

    • Psychometrics
    • Statistical analysis
    • Data science

    Background:

    • Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors.
    • Binary data, consisting of only two possible values, presents unique challenges in factor analysis due to its discrete nature.
    • Assessing the performance of different correlation coefficients is crucial for accurate factor recovery in binary datasets.

    Purpose of the Study:

    • To compare the performance of phi coefficients and tetrachoric correlations in factor analysis of binary data.
    • To evaluate two key dimensions: accuracy of nontrivial factor identification and factor structure recovery.
    • To provide recommendations for the preferred method in practical factor analysis applications.

    Main Methods:

    • The study employed factor analysis techniques to analyze binary data.
    • Performance was assessed using phi coefficients and tetrachoric correlations.
    • Two primary evaluation criteria were used: identification of meaningful factors and reconstruction of known factor structures.

    Main Results:

    • Both phi coefficients and tetrachoric correlations showed poor performance in identifying nontrivial factors, with phi coefficients performing marginally better.
    • Factor structure recovery was generally good when the correct number of factors was specified.
    • Phi coefficients demonstrated superior factor structure recovery and better prevention of item misclassification compared to tetrachoric correlations.
    • Tetrachoric correlations were more effective at including relevant items but resulted in more Heywood cases (unrealistic negative variances).

    Conclusions:

    • Phi coefficients are generally recommended over tetrachoric correlations for factor analysis of binary data due to better overall performance in structure recovery and fewer estimation issues.
    • While tetrachoric correlations may be better at item inclusion, the prevalence of Heywood cases suggests caution in their application.
    • The findings support the use of phi coefficients for more robust factor analysis with binary datasets in most practical scenarios.