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

5.3K
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...
5.3K
Instrument Calibration01:12

Instrument Calibration

1.2K
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...
1.2K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.7K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
5.7K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.6K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.6K

You might also read

Related Articles

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

Sort by
Same author

A new Q-matrix validation method based on signal detection theory.

The British journal of mathematical and statistical psychology·2024
Same author

Item selection methods with exposure and time control for computerized classification test.

The British journal of mathematical and statistical psychology·2022
Same author

Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration.

Psychometrika·2021
Same author

Maximum information per time unit designs for continuous online item calibration.

The British journal of mathematical and statistical psychology·2020
Same author

Optimal Online Calibration Designs for Item Replenishment in Adaptive Testing.

Psychometrika·2019
Same author

Developing new online calibration methods for multidimensional computerized adaptive testing.

The British journal of mathematical and statistical psychology·2017
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
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 29, 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

6.4K

A New Online Calibration Method for Multidimensional Computerized Adaptive Testing.

Ping Chen1, Chun Wang2

  • 1National Innovation Center for Assessment of Basic Education Quality, Beijing Normal University, No. 19, Xin Jie Kou Wai Street, Hai Dian District, Beijing, 100875 , China. pchen@bnu.edu.cn.

Psychometrika
|November 27, 2015
PubMed
Summary
This summary is machine-generated.

A new method called full functional MLE-M-Method A improves item calibration in multidimensional computerized adaptive testing (MCAT). It corrects errors in ability estimates, leading to more accurate results than the original M-Method A.

Keywords:
full functional maximum likelihood estimatormultidimensional computerized adaptive testingmultidimensional two-parameter logistic modelnew itemonline calibrationoperational item

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Related Experiment Videos

Last Updated: Mar 29, 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

6.4K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Multidimensional computerized adaptive testing (MCAT) relies on accurate item calibration for effective assessment.
  • The existing Multidimensional-Method A (M-Method A) for online calibration assumes perfect person parameter estimates, which can lead to calibration errors due to measurement inaccuracies.
  • Improving the precision of item calibration is crucial for the validity and reliability of MCAT results.

Purpose of the Study:

  • To introduce a novel online calibration method for MCAT that addresses the limitations of M-Method A.
  • To enhance the accuracy of item parameter estimation by accounting for errors in person ability estimates.
  • To propose and evaluate the full functional MLE-M-Method A (FFMLE-M-Method A) as a superior alternative.

Main Methods:

  • Development of the full functional MLE-M-Method A (FFMLE-M-Method A) by integrating full functional Maximum Likelihood Estimation (MLE) with M-Method A.
  • Incorporation of two distinct correction schemes within the FFMLE-M-Method A framework.
  • Conducting a simulation study to compare the performance of FFMLE-M-Method A against the original M-Method A.

Main Results:

  • The proposed FFMLE-M-Method A demonstrated superior accuracy in item parameter estimation compared to the original M-Method A across various simulated conditions.
  • The method effectively mitigated the adverse effects of measurement errors in person ability estimates on item calibration.
  • Simulation results consistently favored FFMLE-M-Method A, indicating improved precision and reliability.

Conclusions:

  • FFMLE-M-Method A offers a significant advancement in online item calibration for MCAT, particularly when person parameter estimates contain non-ignorable errors.
  • The integration of full functional MLE provides a robust approach to correct for estimation errors, enhancing the overall quality of MCAT.
  • This new method holds promise for improving the efficiency and effectiveness of adaptive testing systems.