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

Coefficient of Correlation01:12

Coefficient of Correlation

8.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.1K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.4K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.4K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

7.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
7.6K
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

886
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
886
Correlation and Regression00:53

Correlation and Regression

2.9K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.9K
Correlations02:20

Correlations

35.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
35.7K

You might also read

Related Articles

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

Sort by
Same author

Large cities lose their growth advantage as countries urbanize.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Who is using AI to code? Global diffusion and impact of generative AI.

Science (New York, N.Y.)·2026
Same author

Bridging the short-term and long-term dynamics of economic structural change.

Nature communications·2025
Same author

The coherence of US cities.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Skill dependencies uncover nested human capital.

Nature human behaviour·2025
Same author

Knowledge diffusion in the network of international business travel.

Nature human behaviour·2020

Related Experiment Video

Updated: Dec 31, 2025

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

The value of complementary co-workers.

Frank M H Neffke1,2

  • 1Growth Lab, Harvard Kennedy School, Harvard University, Cambridge, MA 02138, USA.

Science Advances
|January 4, 2020
PubMed
Summary

Teamwork boosts worker value. Similar skills among coworkers can be costly, but complementary expertise is beneficial. This coworker complementarity grows throughout a career, explaining wage differences and high earnings in large cities and establishments.

More Related Videos

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

1.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Dec 31, 2025

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
Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

1.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Area of Science:

  • Socioeconomics
  • Labor Economics
  • Organizational Behavior

Background:

  • Societal knowledge is distributed among specialized workers.
  • Effective teams require coordination to cover diverse expertise.
  • Understanding coworker interdependencies is crucial for economic productivity.

Purpose of the Study:

  • To analyze coworker interdependencies arising from distributed knowledge in a population-wide dataset.
  • To investigate how coworker qualifications impact individual economic value.
  • To provide a unifying framework for understanding wage differentials and returns to education.

Main Methods:

  • Utilized a population-wide dataset of Swedish workers' educational specializations over a 10-year period.
  • Analyzed interdependencies among co-workers based on their qualifications.
  • Examined the relationship between coworker complementarity and individual economic outcomes.

Main Results:

  • Having co-workers with similar qualifications is economically detrimental.
  • Co-workers with complementary qualifications significantly increase a worker's value.
  • The benefit of coworker complementarity intensifies over a worker's career.

Conclusions:

  • The value of an individual's knowledge is contingent on their team's diverse skill set.
  • Coworker complementarity explains variations in educational returns, wages in large establishments, and high urban wages.
  • Strategic team composition is vital for maximizing individual and organizational economic success.