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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

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Related Experiment Video

Updated: May 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

ADA perceived disability claims: a decision-tree analysis.

William R Draper1, Carolyn E Hawley, Brian T McMahon

  • 1Department of Rehabilitation Counseling, Virginia Commonwealth University, PO Box 980330, Richmond, VA, 23298, USA.

Journal of Occupational Rehabilitation
|August 10, 2013
PubMed
Summary
This summary is machine-generated.

Workplace disability claims under the Americans with Disabilities Act (ADA) show that hiring issues, not just discharge, significantly impact merit outcomes for those regarded as disabled. Industry type and demographics also play a role.

Related Experiment Videos

Last Updated: May 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Employment Law
  • Social Science Research
  • Data Analytics

Background:

  • Workplace discrimination claims are analyzed using data from the U.S. Equal Employment Opportunity Commission (EEOC).
  • Focus is on claims filed under the "regarded as disabled" provision of the Americans with Disabilities Act (ADA).

Purpose of the Study:

  • To investigate interactions between predictor variables in perceived disability claims.
  • To understand factors influencing merit outcomes in ADA "regarded as disabled" allegations.

Main Methods:

  • Retrospective analysis of over two million EEOC charges from July 1992 to December 2008.
  • Chi Squared Automatic Interaction Detection (CHAID) used to identify variable interactions.
  • Examined relationships between issue, industry, age, and race with merit outcomes.

Main Results:

  • Issue and industry type are key predictors of merit outcomes in perceived disability claims.
  • Demographic factors like age and race add complexity to outcome predictions.
  • Hiring grievances had a significantly higher merit outcome compared to discharge grievances, despite discharge being more frequent.

Conclusions:

  • The study identifies key factors influencing the success of "regarded as disabled" workplace discrimination claims.
  • Findings highlight the importance of issue type (e.g., hiring vs. discharge) in ADA claim outcomes.
  • Results suggest a nuanced understanding of disability discrimination is needed, considering various interacting factors.