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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

703
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
703
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

47.8K
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.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
47.8K
Two-Way ANOVA01:17

Two-Way ANOVA

3.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.6K
Test for Homogeneity01:23

Test for Homogeneity

2.5K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.5K
Group Design02:01

Group Design

11.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
11.0K
One-Way ANOVA01:18

One-Way ANOVA

14.5K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
14.5K

You might also read

Related Articles

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

Sort by
Same author

Site-directed mutagenesis of tyrosine hydroxylase. Role of serine 40 in catalysis.

The Journal of biological chemistry·1992
Same author

Microsurgical inguinal varicocelectomy with delivery of the testis: an artery and lymphatic sparing technique.

The Journal of urology·1992
Same author

Clusterin production in the obstructed rabbit kidney: correlations with loss of renal function.

Journal of the American Society of Nephrology : JASN·1992
Same author

Anatomical approach to varicocelectomy.

Seminars in urology·1992
Same author

No-scalpel vasectomy.

Seminars in urology·1992
Same author

Intraoperative varicocele anatomy: a macroscopic and microscopic study.

The Journal of urology·1992
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

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

A Two-Group Classification Procedure For Multivariate Dichotomous Responses.

M Goldstein

    Multivariate Behavioral Research
    |January 26, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a new classification method for multivariate binary data using orthogonal series expansion. The approach effectively handles data sparseness and achieves optimal, parsimonious modeling for independent samples.

    More Related Videos

    A Two-interval Forced-choice Task for Multisensory Comparisons
    07:13

    A Two-interval Forced-choice Task for Multisensory Comparisons

    Published on: November 9, 2018

    11.6K
    Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
    08:13

    Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

    Published on: May 18, 2020

    7.1K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K
    A Two-interval Forced-choice Task for Multisensory Comparisons
    07:13

    A Two-interval Forced-choice Task for Multisensory Comparisons

    Published on: November 9, 2018

    11.6K
    Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
    08:13

    Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

    Published on: May 18, 2020

    7.1K

    Area of Science:

    • Statistics
    • Machine Learning
    • Data Analysis

    Background:

    • Multivariate binary data analysis presents challenges, particularly with sparse datasets.
    • Existing discrete methods often struggle with frequency data sparseness.

    Purpose of the Study:

    • To introduce a novel two-group classification procedure for multivariate binary data.
    • To address the issue of data sparseness in classification tasks.
    • To develop a parsimonious modeling approach.

    Main Methods:

    • Utilizes an orthogonal series expansion for estimating state probabilities.
    • Applies a classification rule designed for independent samples.
    • Incorporates a method to achieve model parsimony under optimality conditions.

    Main Results:

    • The proposed procedure effectively classifies multivariate binary data.
    • The method successfully mitigates problems associated with data sparseness.
    • Achieves parsimonious models while maintaining optimality.

    Conclusions:

    • The developed classification procedure offers a robust solution for analyzing sparse multivariate binary data.
    • Orthogonal series expansion provides an effective tool for probability estimation in this context.
    • The method enhances the applicability of discrete analysis to frequency data.