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

Language01:16

Language

917
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
917
Common Ion Effect03:24

Common Ion Effect

46.8K
Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Châtelier’s principle. Consider the dissolution of silver iodide:
46.8K
Components of Language01:24

Components of Language

821
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
821
Language Development01:22

Language Development

916
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
916
Language and Cognition01:27

Language and Cognition

801
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
801
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

9.1K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
9.1K

You might also read

Related Articles

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

Sort by
Same author

Bias of Odds Ratio Estimate in Fisher's Exact Test.

International journal of methods in psychiatric research·2026
Same author

The permutation test: a simple way to test hypotheses.

Nurse researcher·2024
Same author

Bias correction for Cohen's <i>d</i>.

The Journal of general psychology·2023
Same author

Bootstrap Estimate of Bias for Intraclass Correlation.

Journal of applied measurement·2020
Same author

A Note on the Relation between Item Difficulty and Discrimination Index.

Journal of applied measurement·2019
Same author

Common language effect size for correlations.

The Journal of general psychology·2019
Same journal

Understanding the Added Value of Action Limits for QTL Monitoring.

Therapeutic innovation & regulatory science·2026
Same journal

Implementing Project Optimus in Oncology Dosage Optimization: Where are We Now?

Therapeutic innovation & regulatory science·2026
Same journal

Validation of the Ontario Protocol Assessment Level (OPAL) Tool for Assessing Clinical Trial Complexity and Supporting Workforce Planning in the Italian Clinical Research Context.

Therapeutic innovation & regulatory science·2026
Same journal

The Effect of Covariate Adjustment for Cox Regression in Cardiovascular Outcome Trials.

Therapeutic innovation & regulatory science·2026
Same journal

Current Status and Construction Requirements of Drug Clinical Trial Institutions in Sichuan Province in China.

Therapeutic innovation & regulatory science·2026
Same journal

The CARE Program: An Initiative of Patient-Focused Drug Development for Rare Diseases by NMPA.

Therapeutic innovation & regulatory science·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K

Multivariate Common Language Effect Size.

Xiaofeng Steven Liu1

  • 11 University of South Carolina, Columbia, SC, USA.

Therapeutic Innovation & Regulatory Science
|September 18, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a common language effect size for multiple outcome variables. This probability measure, derived from the Mahalanobis distance, is easy to understand and applicable to comparing two or more groups.

Keywords:
Mahalanobis distancediscriminant functionmultivariate effect size

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Related Experiment Videos

Last Updated: Feb 5, 2026

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.5K

Area of Science:

  • Statistics
  • Multivariate Analysis
  • Psychometrics

Background:

  • Effect sizes are crucial for interpreting the magnitude of differences between groups.
  • Existing effect size measures for multivariate outcomes can be complex and difficult to interpret.
  • A common language effect size facilitates broader understanding and application of statistical findings.

Purpose of the Study:

  • To develop a universally understood effect size measure for multivariate data.
  • To convert the standardized multivariate effect size (Mahalanobis distance) into an interpretable probability.
  • To provide a method for comparing effect sizes across two or more groups.

Main Methods:

  • Standardized multivariate effect size (Mahalanobis distance) was calculated.
  • This measure was converted into a probability representing the likelihood of one individual having a larger discriminant function value than another.
  • The proposed probability serves as a common language effect size.

Main Results:

  • A novel probability-based common language effect size for multivariate outcomes was derived.
  • This effect size is simple to calculate and readily comprehensible to non-experts.
  • The method is applicable for comparing effects in both two-group and multiple-group scenarios.

Conclusions:

  • The proposed probability offers an intuitive and accessible measure of effect size for multivariate analyses.
  • This common language effect size enhances the interpretability and practical utility of multivariate statistical findings.
  • It provides a valuable tool for researchers across various scientific disciplines to communicate effect magnitudes effectively.