Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...

You might also read

Related Articles

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

Sort by

Same author

Detecting Test Speededness Using Responses and/or Response Times: Change Point Analysis Approaches Based on Schwarz Information Criterion.

Psychometrika·2026

Same author

Using multilabel classification neural network to detect intersectional DIF with small sample sizes.

The British journal of mathematical and statistical psychology·2026

Same author

A multi-strategy cognitive diagnosis model based on response times and fixation counts.

Behavior research methods·2026

Same author

A Diagnostic Facet Status Model (DFSM) for Extracting Instructionally Useful Information from Diagnostic Assessment.

Psychometrika·2026

Same author

A Novel Method for Detecting Intersectional DIF: Multilevel Random Item Effects Model with Regularized Gaussian Variational Estimation.

Psychometrika·2025

Same author

Constructing a binary prediction model with incomplete data: Variable selection to balance fairness and precision.

Psychological methods·2025

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

Search research articles

Related Experiment Video

Updated: Jan 13, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Calibrating Multidimensional Assessments With Structural Missingness: An Application of a Multiple-Group Higher-Order

Yale Quan¹, Chun Wang¹

¹College of Education, University of Washington, Seattle, WA, USA.

Applied Psychological Measurement

|January 12, 2026

Summary

This summary is machine-generated.

This study introduces a novel application of multiple group hierarchical ordered item response theory (HO-IRT) models for educational assessment. The findings demonstrate that using a non-representative anchor test can still yield accurate scores for complex educational constructs.

Keywords:

Hierarchical models calibration item response theory missing data multiple group

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

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

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: Jan 13, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

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

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

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

Educational Measurement
Psychometrics
Item Response Theory

Background:

Educational constructs are increasingly complex, requiring measurement at both general and subdomain levels.
Current methods often necessitate large item banks or report scores separately, limiting practical assessment.
Simultaneous reporting of general and subdomain scores is desirable but challenging.

Purpose of the Study:

To propose and evaluate a multiple group hierarchical ordered item response theory (HO-IRT) model with structural missingness for simultaneous score reporting.
To investigate a novel application scenario using a NEAT (North, East, South, West) design with both representative and non-representative anchor tests.
To explore the parameter recovery of HO-IRT models when using a non-representative anchor test.

Main Methods:

Utilized a multiple group HO-IRT model with structural missingness.
Employed a NEAT design with both representative and non-representative anchor tests.
Conducted Monte Carlo simulations to assess parameter recovery and Root Mean Square Error (RMSE).
Addressed missing data using a full-information maximum likelihood approach.

Main Results:

The study demonstrated that a non-representative anchor test can yield comparable RMSE to a representative anchor test.
Parameter recovery was found to be robust even with a moderate correlation between higher- and lower-order factors.
The proposed HO-IRT model effectively controls assessment length while reporting both general and subdomain scores.

Conclusions:

Multiple group HO-IRT models offer a viable solution for simultaneously reporting general and subdomain scores for complex educational constructs.
The use of non-representative anchor tests in a NEAT design is a practical alternative when construct definitions evolve.
This approach enhances efficiency in educational measurement without compromising score accuracy.