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

Instrument Calibration01:12

Instrument Calibration

1.0K
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...
1.0K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.9K
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...
4.9K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

5.3K
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...
5.3K
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

55.4K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
55.4K
Glassware Calibration01:11

Glassware Calibration

1.7K
Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...
1.7K
Constant Volume Calorimetry02:41

Constant Volume Calorimetry

31.4K
Calorimeters are useful to determine the heat released or absorbed by a chemical reaction. Coffee cup calorimeters are designed to operate at constant (atmospheric) pressure and are convenient to measure heat flow (or enthalpy change) accompanying processes that occur in solution at constant pressure. A different type of calorimeter that operates at constant volume, colloquially known as a bomb calorimeter, is used to measure the energy produced by reactions that yield large amounts of heat and...
31.4K

You might also read

Related Articles

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

Sort by
Same author

Differential item functioning detection across multiple groups.

The British journal of mathematical and statistical psychology·2025
Same author

A boosting method to select the random effects in linear mixed models.

Biometrics·2024
Same author

A Likelihood Approach to Item Response Theory Equating of Multiple Forms.

Applied psychological measurement·2023
Same author

Evaluating Equating Transformations in IRT Observed-Score and Kernel Equating Methods.

Applied psychological measurement·2023
Same author

Shrinkage estimation of the three-parameter logistic model.

The British journal of mathematical and statistical psychology·2021
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
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 14, 2026

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

8.9K

Multiple Equating of Separate IRT Calibrations.

Michela Battauz1

  • 1Department of Economics and Statistics, University of Udine, Udine, Italy. michela.battauz@uniud.it.

Psychometrika
|October 5, 2016
PubMed
Summary
This summary is machine-generated.

This study generalizes equating methods for multiple test forms, ensuring item response theory parameters are comparable across different scales. New methods simultaneously estimate coefficients for scale transformation, improving test score comparability.

Keywords:
HaebaraStocking–Lordequating coefficientsitem response theorylinkingmean-geometric meanmean-meanstandard errors

More Related Videos

Calibration Procedures for Orthogonal Superposition Rheology
08:43

Calibration Procedures for Orthogonal Superposition Rheology

Published on: November 18, 2020

2.5K
Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
08:40

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

Published on: January 20, 2022

4.9K

Related Experiment Videos

Last Updated: Mar 14, 2026

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

8.9K
Calibration Procedures for Orthogonal Superposition Rheology
08:43

Calibration Procedures for Orthogonal Superposition Rheology

Published on: November 18, 2020

2.5K
Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
08:40

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

Published on: January 20, 2022

4.9K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Item response theory (IRT) calibration on separate test forms results in non-comparable parameter estimates due to different measurement scales.
  • Test equating is crucial for converting item parameter estimates to a common scale and establishing comparable test scores.

Purpose of the Study:

  • To generalize existing equating methods (mean-geometric mean, mean-mean, Haebara, Stocking-Lord) to accommodate multiple test forms.
  • To develop methods for simultaneously estimating equating coefficients for scale transformation across several test forms.

Main Methods:

  • Generalization of four established equating methods for application to multiple test forms.
  • Simultaneous estimation of equating coefficients to transform parameters onto a base form's scale.
  • Derivation of asymptotic standard errors for the estimated equating coefficients.

Main Results:

  • The proposed methods provide a unified framework for multi-form equating.
  • Simultaneous estimation allows for consistent scale transformation of all forms relative to a base form.
  • Asymptotic standard errors are derived for evaluating the precision of equating coefficients.

Conclusions:

  • The generalized methods offer a robust approach to equating multiple test forms, enhancing score comparability.
  • Accurate equating is essential for fair and valid comparisons of student performance across different test administrations.
  • The study contributes advanced statistical techniques to the field of educational measurement and psychometrics.