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

Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

28.2K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
28.2K
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

13.6K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
13.6K
Sampling Distribution01:12

Sampling Distribution

17.9K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
17.9K
Chemical Formulas02:52

Chemical Formulas

61.2K
A chemical formula presents information about the proportions of atoms constituting a particular chemical compound or molecule, mainly using symbols of elements and numbers. At times other symbols, such as dashes, parentheses, brackets, commas, plus, and minus signs, are also used. A chemical formula can be one of three types – molecular, empirical, and structural.
61.2K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.0K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.0K
Ionic Compounds: Formulas and Nomenclature03:34

Ionic Compounds: Formulas and Nomenclature

87.2K
An element composed of atoms that readily lose electrons (a metal) can react with an element composed of atoms that readily gain electrons (a nonmetal) to produce ions through complete electron transfer. The compound formed by this transfer is stabilized by the electrostatic attractions (ionic bonds) between the oppositely charged ions.
87.2K

You might also read

Related Articles

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

Sort by
Same author

Directional dominance on stature and cognition in diverse human populations.

Nature·2015
Same author

Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations.

American journal of human genetics·2015
Same author

A comparison of location of acute symptomatic vs. 'silent' small vessel lesions.

International journal of stroke : official journal of the International Stroke Society·2015
Same author

Rare and low-frequency variants and their association with plasma levels of fibrinogen, FVII, FVIII, and vWF.

Blood·2015
Same author

Coupled changes in brain white matter microstructure and fluid intelligence in later life.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2015
Same author

Current versus lifetime depression, APOE variation, and their interaction on cognitive performance in younger and older adults.

Psychosomatic medicine·2015
Same journal

A Simple Approach for Differential Test Functioning Based on Sum Scores.

Educational and psychological measurement·2026
Same journal

Evaluating Factor Retention in Large Factor Analysis Models: A Simulation Study Comparing 15 Methods.

Educational and psychological measurement·2026
Same journal

Agreement and Alignment in Binary Rating Tasks: Strategic Convergence as an Equilibrium Outcome.

Educational and psychological measurement·2026
Same journal

Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing.

Educational and psychological measurement·2026
Same journal

Identification and Diagnosis of Misreporting in Surveys.

Educational and psychological measurement·2026
Same journal

The Aggregated Latent Profile Index: Measuring Person Profile Differentiation Within a Bootstrap-Validated Latent Profile Space.

Educational and psychological measurement·2026
See all related articles

Related Experiment Video

Updated: Jan 31, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K

Have Standard Formulas Correcting Correlations for Range Restriction Been Adequately Tested?: Minor Sampling

Wendy Johnson1, Ian J Deary1, Thomas J Bouchard2

  • 1University of Edinburgh, Edinburgh, UK.

Educational and Psychological Measurement
|December 19, 2018
PubMed
Summary
This summary is machine-generated.

Study samples often have less variability than their source populations due to selection bias. Formulas to correct these distortions were tested using real data, revealing limitations, especially with non-normal distributions.

Keywords:
adjustment formulasdistortionrange restrictionskewstatistical biasstudy participation

More Related Videos

Bronchoalveolar Lavage BAL for Research; Obtaining Adequate Sample Yield
11:47

Bronchoalveolar Lavage BAL for Research; Obtaining Adequate Sample Yield

Published on: March 24, 2014

75.3K
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
07:22

Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives

Published on: April 10, 2017

9.9K

Related Experiment Videos

Last Updated: Jan 31, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K
Bronchoalveolar Lavage BAL for Research; Obtaining Adequate Sample Yield
11:47

Bronchoalveolar Lavage BAL for Research; Obtaining Adequate Sample Yield

Published on: March 24, 2014

75.3K
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
07:22

Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives

Published on: April 10, 2017

9.9K

Area of Science:

  • Psychometrics
  • Statistical Genetics
  • Population Health

Background:

  • Study samples often exhibit reduced variability compared to source populations due to selection bias.
  • Existing formulas aim to correct correlation distortions caused by range restriction.
  • Empirical validation of these correction formulas is scarce, with prior tests relying on simulated data.

Purpose of the Study:

  • To empirically test the accuracy of formulas designed to correct for range restriction distortions in correlation.
  • To evaluate the performance of these correction methods using real-world data with known population and subsample characteristics.
  • To investigate the impact of non-normal data distributions on the effectiveness of range restriction correction formulas.

Main Methods:

  • Utilized the 6-Day Sample, a subsample of the Scottish Mental Survey 1947, for empirical testing.
  • Compared correlations from the full sample with those from the restricted subsample, both before and after applying correction formulas.
  • Simulated sample selection processes and analyzed cognitive ability data from the Minnesota Study of Twins Reared Apart to assess generalizability.

Main Results:

  • Correction formulas showed limitations in accurately reproducing full-sample correlations, particularly for cognitive tests with slight deviations from normal distributions (skewness, kurtosis).
  • Maximum likelihood estimates offered minimal improvement over standard correction methods.
  • Simulations using the Minnesota Study of Twins Reared Apart data yielded comparable results, indicating widespread issues with range restriction correction.

Conclusions:

  • Current formulas for correcting range restriction distortions may be insufficient, especially when data deviate from normality.
  • Further research is needed to develop more robust statistical methods for addressing selection bias in correlational studies.
  • The findings highlight challenges in accurately estimating population-level associations from selected study samples.