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

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Coefficient of Correlation01:12

Coefficient of Correlation

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 strength of the linear...
Correlations02:20

Correlations

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...
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

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 of...
Correlation and Regression00:53

Correlation and Regression

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 negative...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

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 other increases, and...

You might also read

Related Articles

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

Sort by
Same author

John P. Campbell (1937-2025).

The American psychologist·2026
Same author

Graduate grade inflation at a U.S. research-intensive university: A 22-year longitudinal analysis.

PloS one·2026
Same author

Planned missingness to reduce survey length: A sheep in wolf's clothing.

Psychological methods·2026
Same author

What do assessment center ratings reflect? Consistency and heterogeneity in variance composition across multiple samples.

The Journal of applied psychology·2025
Same author

Examining Gender-Based Differences in Quantitative Ratings and Narrative Comments in Faculty Assessments by Residents and Fellows.

Journal of graduate medical education·2025
Same author

Personality and personnel selection: Going beyond self-reports and linear relationships.

Current opinion in psychology·2025

Related Experiment Video

Updated: Jul 16, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

A cautionary note on the effects of range restriction on predictor intercorrelations.

Paul R Sackett1, Filip Lievens, Christopher M Berry

  • 1Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA. psackett@umn.edu

The Journal of Applied Psychology
|March 21, 2007
PubMed
Summary

Range restriction significantly impacts predictor intercorrelations, especially in selection composites. This study reveals how composite use can distort correlations between predictors, affecting accuracy.

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

Related Experiment Videos

Last Updated: Jul 16, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

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

Area of Science:

  • Psychometrics
  • Organizational Psychology

Background:

  • Estimating predictor intercorrelations is crucial in personnel selection.
  • Range restriction can attenuate observed correlations, but its effect on intercorrelations within composites is complex.

Purpose of the Study:

  • To investigate the unique challenges in estimating predictor intercorrelations under range restriction.
  • To demonstrate how range restriction affects predictor relationships when used in selection composites.

Main Methods:

  • The study employed simulation methods.
  • A concrete applied example was used.
  • A reanalysis of a meta-analysis on ability-interview correlations was conducted.

Main Results:

  • Predictor intercorrelations can deviate substantially from population values when predictors are part of a selection composite.
  • The compensatory nature of composites, where individuals with low scores on one predictor need high scores on another, distorts predictor intercorrelations.

Conclusions:

  • Range restriction in selection contexts can lead to significant distortions in estimated predictor intercorrelations.
  • These findings have implications for understanding predictor relationships and developing effective selection strategies.