Correction for Range Restriction: Lessons from 20 Scenarios

Abstract

Data are often available only for a preselected range-restricted sample in many applied settings. This creates the potential for drawing incorrect inferences and making poor decisions. This is because most inferences and decisions concern the population from which the sample was drawn. Despite these problems, researchers must try to determine statistical values as if the sample were not range-restricted. Although methods for correcting the effects of range restriction have been available for more than a century, often they are not applied or applied incorrectly.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 21, 2021
Accession Number
AD1135820

Entities

People

  • Malcolm J. Ree
  • Thomas R. Carretta

Organizations

  • 711th Human Performance Wing

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Applied Psychology
  • Business Administration
  • Computer Programming
  • Computers
  • Data Science
  • Employment
  • Enlisted Personnel
  • Factor Analysis
  • Flight Training
  • Governments
  • Human Resources
  • Information Science
  • Intervals
  • Job Analysis
  • Literature Surveys
  • Management Personnel
  • Motor Skills
  • Programming Languages
  • Psychology
  • Reliability
  • Standards
  • Students

Readers

  • Climatology
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms