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

2.1K
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...
2.1K
Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
193
Differential Leveling01:12

Differential Leveling

308
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
308
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Deconvolution01:20

Deconvolution

247
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
247
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Few and Different: Detecting Examinees With Preknowledge Using Extended Isolation Forests.

Applied psychological measurement·2025
Same author

Outlier Detection Using t-test in Rasch IRT Equating under NEAT Design.

Applied psychological measurement·2022
Same author

Application of Sampling Variance of Item Response Theory Parameter Estimates in Detecting Outliers in Common Item Equating.

Applied psychological measurement·2022
Same author

A Seed Usage Issue on Using catR for Simulation and the Solution.

Applied psychological measurement·2020
Same author

Evaluating Robust Scale Transformation Methods With Multiple Outlying Common Items Under IRT True Score Equating.

Applied psychological measurement·2020
Same author

On a New Algorithm for Removing Repeating Patterns in Similarity Analysis.

Educational and psychological measurement·2020
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
Same journal

csemGT: An R Package for Estimating Raw-Score Conditional Standard Errors of Measurement in Generalizability Theory.

Applied psychological measurement·2026
Same journal

Confirmatory Factor Analysis with Adaptive Quadrature Estimator Using Four Link Functions.

Applied psychological measurement·2026
Same journal

Automatic Item Generation Measurement Models Respecting the Stochastic Sampling Space for Cross-Classified and Two-Level Sampling of Subjects and Incidentals.

Applied psychological measurement·2026
Same journal

Multistage Testing for Cognitive Diagnosis Based on Skill-Space Partitioning.

Applied psychological measurement·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Using Deep Learning to Choose Optimal Smoothing Values for Equating.

Chunyan Liu1, Zhongmin Cui2

  • 1Psychometrics and Data Analysis, National Board of Medical Examiners, Philadelphia, PA, USA.

Applied Psychological Measurement
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

This study automated test score equating using deep learning. A convolutional neural network achieved 71% agreement with human experts in selecting optimal smoothing values for equating test forms.

Keywords:
automationconvolutional neural networkcubic splinedeep learningequatingsmoothing

More Related Videos

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

960
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Related Experiment Videos

Last Updated: Sep 9, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

960
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

Area of Science:

  • Psychometrics
  • Machine Learning
  • Educational Measurement

Background:

  • Alternate test forms are used to maintain test score integrity.
  • Equating adjusts scores between different test forms due to difficulty variations.
  • Smoothing methods are applied during equating to minimize sampling errors.

Purpose of the Study:

  • To automate the selection of optimal smoothing values in test equating.
  • To evaluate the efficacy of deep learning, specifically convolutional neural networks (CNNs), for this task.
  • To compare the performance of a CNN with human expert judgment in choosing smoothing parameters.

Main Methods:

  • A convolutional neural network was trained on human-classified postsmoothing plots.
  • The trained CNN was used to determine optimal smoothing values for empirical testing data.
  • The CNN's choices were compared against human expert selections.

Main Results:

  • The deep learning model achieved a 71% agreement rate with human experts.
  • This indicates a high degree of concordance between the automated method and manual selection.

Conclusions:

  • Deep learning offers a viable automated approach for selecting optimal smoothing values in test equating.
  • This automation has the potential to improve the efficiency and consistency of the equating process.