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

70
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...
70
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

6.2K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
6.2K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.1K
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...
2.1K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

729
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
729
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.3K
Experimental Designs01:16

Experimental Designs

11.3K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.3K

You might also read

Related Articles

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

Sort by
Same author

Author Correction: A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources.

Nature communications·2023
Same author

Synthesis and functionalization of scalable and versatile 2D protein films via amyloid-like aggregation.

Nature protocols·2023
Same author

PROCESS Trial: Effect of Duloxetine Premedication for Postherpetic Neuralgia Within 72 Hours of Herpes Zoster Reactivation-A Randomized Controlled Trial.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2023
Same author

Polymerized PEI-modified lignin polyphenolic materials by acid hydrolysis-phase separation for removal of Cr (VI) from industrial wastewater.

International journal of biological macromolecules·2023
Same author

IDO-1 impairs antitumor immunity of natural killer cells in triple-negative breast cancer via up-regulation of HLA-G.

Breast cancer (Tokyo, Japan)·2023
Same author

A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources.

Nature communications·2023
Same journal

Predicting Multilevel Growth Trajectories Using a Random-Effect Diagnostic Classification Model.

Psychometrika·2026
Same journal

Regularized Joint Maximum Likelihood Estimation for Exploratory Multidimensional Item Response Theory Models.

Psychometrika·2026
Same journal

Capturing Heterogeneity in Levels, Variability, and Couplings across Persons and Time with a Hierarchical Time-Varying Coefficient Formulation of the Multivariate Normal.

Psychometrika·2026
Same journal

BAYESIAN MIXED MULTIDIMENSIONAL SCALING FOR AUDITORY PROCESSING.

Psychometrika·2026
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: May 4, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

5.4K

Optimal Bayesian Adaptive Design for Test-Item Calibration.

Wim J van der Linden1, Hao Ren

  • 1CTB/McGraw-Hill, 20 Ryan Ranch Road, Monterey, CA, 93940, USA, wim_vanderlinden@ctb.com.

Psychometrika
|January 11, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal adaptive design for test-item calibration using Bayesian criteria. It efficiently calibrates items by adapting to examinees and item parameters, outperforming other methods.

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
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

840

Related Experiment Videos

Last Updated: May 4, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

5.4K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
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

840

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Traditional test-item calibration can be inefficient.
  • Adaptive testing requires dynamic item selection.
  • Bayesian methods offer a framework for optimal design.

Purpose of the Study:

  • To present an optimal adaptive design for test-item calibration.
  • To utilize Bayesian optimality criteria for enhanced calibration.
  • To adapt item selection based on examinee and item parameter distributions.

Main Methods:

  • Developed an optimal adaptive design using Bayesian optimality criteria.
  • Implemented a Markov Chain Monte Carlo (MCMC) scheme for parameter estimation.
  • Alternating sampling from posterior distributions of examinee abilities and item parameters.

Main Results:

  • Demonstrated the feasibility of the MCMC implementation for operational item calibration.
  • D-optimality criterion showed faster calibration compared to A-optimality and random assignment.
  • The adaptive design effectively utilizes posterior distribution information.

Conclusions:

  • The proposed Bayesian adaptive design is feasible and efficient for operational item calibration.
  • D-optimality offers a superior criterion for faster item calibration.
  • This approach enhances the precision and speed of test development.