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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
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...
1.2K
Instrument Calibration01:12

Instrument Calibration

149
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
149
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
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...
2.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
38
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

3.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26

You might also read

Related Articles

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

Sort by
Same journal

Random Responding Detection in Two Alternative Forced Choice Tests: <i>l</i> <sub><i>z</i></sub> vs. Optimal Appropriateness Measurement.

Applied psychological measurement·2026
Same journal

General formulae for transforming Pearson's r to the scale of Cohen's d.

Applied psychological measurement·2026
Same journal

babebi: An R Package for Bayesian Estimation and Validation in Small-N Two-Rater Pre-Post Designs.

Applied psychological measurement·2026
Same journal

A Tool for Agreement and Alignment Analysis in Binary Rating Tasks: The R Package scindex.

Applied psychological measurement·2026
Same journal

The EM Algorithm and Its Variants in Cognitive Diagnostic Models: Comparing Their Propensity for Boundaries, Extremes, Convergence, and Suboptimal Solutions.

Applied psychological measurement·2026
Same journal

When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking.

Applied psychological measurement·2026

Related Experiment Video

Updated: May 30, 2025

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

6.5K

Compound Optimal Design for Online Item Calibration Under the Two-Parameter Logistic Model.

Lihong Song1, Wenyi Wang2

  • 1School of Education, Jiangxi Normal University, Nanchang, China.

Applied Psychological Measurement
|January 31, 2025
PubMed
Summary

This study introduces a compound optimal design for efficiently calibrating item parameters in computerized testing. This new method improves the accuracy of estimating item difficulty and discrimination parameters compared to existing designs.

Keywords:
D-optimal designcompound optimal designcomputerized adaptive testingonline calibrationtwo-parameter logistic model

More Related Videos

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.8K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K

Related Experiment Videos

Last Updated: May 30, 2025

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

6.5K
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.8K
Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.2K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Item response theory (IRT) models are crucial for analyzing test data.
  • Efficient calibration of item parameters is essential for accurate ability estimation in computerized adaptive testing (CAT).
  • Existing designs may not optimally balance the estimation of different item parameters.

Purpose of the Study:

  • To propose a novel compound optimal design for efficient simultaneous calibration of item difficulty and discrimination parameters.
  • To adaptively optimize the estimation of challenging parameters within the two-parameter logistic (2PL) model.
  • To evaluate the performance of the proposed design against D-optimal and random designs.

Main Methods:

  • Development of a compound optimal design incorporating two optimality criteria.
  • Utilizing acceptance probability to generate design points for optimizing item parameters.
  • Simulation studies and real data analysis to assess parameter recovery.

Main Results:

  • The compound optimal design demonstrated superior performance in recovering both discrimination and difficulty parameters.
  • Outperformed D-optimal and random designs in simulation and real data analyses.
  • Effectively balances the optimization of difficult-to-estimate parameters.

Conclusions:

  • The proposed compound optimal design offers a more efficient and accurate approach for item parameter calibration in IRT.
  • This method enhances the precision of item parameter estimation in computerized testing.
  • Provides a valuable tool for improving the quality of educational and psychological assessments.