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

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Types of Selection01:46

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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...
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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

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: Jun 3, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Designing Pareto-optimal selection systems: formalizing the decisions required for selection system development.

Wilfried De Corte1, Paul R Sackett, Filip Lievens

  • 1Department of Data Analysis, Ghent University, Ghent, Belgium. wilfried.decorte@ugent.be

The Journal of Applied Psychology
|April 6, 2011
PubMed
Summary

This study introduces a novel analytic method for designing optimal selection systems for diverse applicant groups. It aids practitioners in making key decisions to reduce adverse impact and improve fairness.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

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

Related Experiment Videos

Last Updated: Jun 3, 2026

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

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:

  • Decision Science
  • Organizational Psychology
  • Statistical Modeling

Background:

  • Selection systems often face challenges with diverse applicant pools.
  • Existing methods may not adequately address complex design decisions.
  • Reducing adverse impact is a critical goal in personnel selection.

Purpose of the Study:

  • To present an analytic method for designing Pareto-optimal selection systems for mixed candidate populations.
  • To provide a framework for addressing six key selection design issues.
  • To facilitate research on strategies for mitigating adverse impact.

Main Methods:

  • Development of an analytic approach for Pareto-optimal selection system design.
  • Integration of decision-making on predictor subsets, rules, staging, sequencing, weighting, and retention.
  • Application in both applied and research contexts.

Main Results:

  • The proposed method offers a systematic way to optimize selection systems.
  • It provides practical guidance for selection practitioners on critical design choices.
  • The method enables empirical study of adverse impact reduction strategies.

Conclusions:

  • The analytic method is valuable for applied selection and research.
  • It addresses a gap in current selection system design tools.
  • It supports the development of fairer and more effective selection processes.