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

Response Surface Methodology01:16

Response Surface Methodology

769
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
769
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

5.6K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
5.6K
Dose Response Curve: Conventional Versus Nonmonotonic01:21

Dose Response Curve: Conventional Versus Nonmonotonic

187
The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response...
187
Ratio Level of Measurement00:54

Ratio Level of Measurement

22.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
22.1K
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

307
The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
307
The Response of Equilibria to the Conditions01:30

The Response of Equilibria to the Conditions

28
Named after the French chemist Henry Louis Le Chatelier, Le Chatelier's principle states that when a system at equilibrium is subjected to any change (like pressure, temperature, or concentration), the composition of the system adjusts in a way that counteracts the effect of this change, thereby attempting to restore the equilibrium.According to Le Chatelier's principle, for exothermic reactions, when the system's temperature is increased, the system will try to reduce the temperature. This...
28

You might also read

Related Articles

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

Sort by
Same author

Bayesian fine-mapping pinpoints candidate genes and pleiotropic loci of production traits from a chicken backcrossing scheme.

BMC genomics·2026
Same author

Genetically Determined Levels of Inflammation-Related Proteins and Functional Outcome After Ischemic Stroke: A Mendelian Randomization Study.

Journal of stroke·2026
Same author

Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Scientific reports·2026
Same author

Developing and validating a frailty score based on patient-reported outcome 3 months after stroke: A Riksstroke-based study.

PloS one·2026
Same author

Integrated Methylome-Transcriptome Analysis Reveals Epigenomic Remodeling and Rho GTPase-Linked Immune-Epithelial Crosstalk in Atopic Dermatitis.

Allergy·2026
Same author

Deep learning can automate chicken tibia-breaking strength quantification to improve animal welfare.

Poultry science·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
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 13, 2026

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.9K

Item Response Theory Observed-Score Kernel Equating.

Björn Andersson1,2, Marie Wiberg3

  • 1Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, No. 19 Xinjiekou Wai Street, Haidian District, 100875, Beijing, China. bjoern.andersson@bnu.edu.cn.

Psychometrika
|October 16, 2016
PubMed
Summary
This summary is machine-generated.

Item response theory (IRT) observed-score kernel equating provides accurate results for non-equivalent groups. This method shows low bias and small standard errors in equating standardized achievement tests.

Keywords:
NEAT designequipercentile equatingitem response theoryobserved-score equatingstandard errors

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Mar 13, 2026

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
06:57

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Published on: May 14, 2019

10.9K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Modeling

Background:

  • Equating is crucial for comparing scores from different test forms.
  • Traditional equating methods face challenges with non-equivalent groups and anchor tests.
  • Item response theory (IRT) offers advanced statistical frameworks for test equating.

Purpose of the Study:

  • Introduce IRT observed-score kernel equating for non-equivalent groups with anchor test designs.
  • Evaluate the performance of this new equating method.
  • Compare it with existing IRT equating techniques.

Main Methods:

  • Developed IRT observed-score kernel equating within a multivariate framework.
  • Derived asymptotic covariance matrices for equating functions.
  • Applied the method using two-parameter and three-parameter logistic models.
  • Utilized simulated data and real data from a standardized achievement test.

Main Results:

  • IRT observed-score kernel equating demonstrated low equating bias across various settings.
  • The method yielded small standard errors, indicating precision.
  • Performance was consistent under different IRT models and data conditions.

Conclusions:

  • IRT observed-score kernel equating is a viable and effective method for non-equivalent group designs.
  • It offers advantages in terms of accuracy and precision compared to traditional methods.
  • This approach enhances the reliability of score comparisons in standardized testing.