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

5-Number Summary01:04

5-Number Summary

5.4K
In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
5.4K
Measures of Central Tendency02:16

Measures of Central Tendency

19.1K
The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
19.1K
Outliers and Influential Points01:08

Outliers and Influential Points

5.6K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.6K
Skewness01:06

Skewness

16.1K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
16.1K
Quartile01:15

Quartile

8.2K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
8.2K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

358
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
358

You might also read

Related Articles

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

Sort by
Same author

State media control influences large language models.

Nature·2026
Same author

Adaptive Randomization in Conjoint Survey Experiments.

Political analysis : an annual publication of the Methodology Section of the American Political Science Association·2026
Same author

The causal impact of segregation on a disparity: A gap-closing approach.

Sociological science·2026
Same author

Causal Effect of Character Gender on Readers' Preferences.

Proceedings of the Computational Humanities Research Conference ...·2026
Same author

The causal effect of parent occupation on child occupation: A multivalued treatment with positivity constraints.

Sociological methods & research·2026
Same author

The decade-long growth of government-authored news media in China under Xi Jinping.

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

Related Experiment Video

Updated: Dec 6, 2025

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K

Comment: Summarizing income mobility with multiple smooth quantiles instead of parameterized means.

Ian Lundberg1, Brandon M Stewart2

  • 1Department of Sociology and Office of Population Research, Princeton University, ianlundberg.org.

Sociological Methodology
|October 12, 2020
PubMed
Summary

This study introduces a new method for analyzing economic mobility by visualizing income distributions across parent income levels. It offers a more informative and robust alternative to traditional elasticity measures.

Keywords:
economic mobilityintergenerational elasticityintergenerational mobilitystratification

More Related Videos

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.6K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.0K

Related Experiment Videos

Last Updated: Dec 6, 2025

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.6K
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
07:54

Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence

Published on: October 25, 2011

19.0K

Area of Science:

  • Sociology
  • Economics
  • Statistics

Background:

  • Economic mobility studies traditionally use intergenerational elasticity (IGE) to summarize offspring income distributions relative to parent income.
  • Conventional IGE often relies on geometric means, with recent proposals focusing on arithmetic means, each having limitations.
  • Mitnik and Grusky (2020) proposed a parametric strategy for estimating the arithmetic mean of offspring income.

Purpose of the Study:

  • To propose a novel strategy for assessing economic mobility that overcomes limitations of existing summary statistics.
  • To offer a more comprehensive and robust method for understanding the relationship between parent and offspring income.
  • To address the sensitivity of mean-based measures to income distribution tails.

Main Methods:

  • Decomposition of the intergenerational elasticity (IGE) into choices of summary statistic and functional form.
  • Development of a visualization strategy using smooth functions of parent income to display multiple quantiles of offspring income.
  • Comparison of the proposed quantile visualization method with traditional mean-based approaches (geometric and arithmetic).

Main Results:

  • The proposed method visualizes multiple quantiles of the offspring income distribution as smooth functions of parent income.
  • This approach effectively addresses issues associated with geometric means and the sensitivity of arithmetic means to extreme incomes.
  • The visualization strategy provides richer insights into economic mobility compared to single-number summary statistics.

Conclusions:

  • Visualizing income quantiles offers a superior method for studying economic mobility, providing more information than single summary statistics.
  • The proposed method avoids the statistical pitfalls of both geometric and arithmetic means in analyzing income distributions.
  • Findings have broad implications for regression analysis, highlighting the potential undesirability of using the mean as a default summary statistic due to its sensitivity to distributional tails.