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

494
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:
494
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

183
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
183
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

421
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...
421
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.5K
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:
6.5K
Sample Size Calculation01:19

Sample Size Calculation

6.0K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.9K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.9K

You might also read

Related Articles

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

Sort by
Same author

The Accuracy of Bayesian Model Fit Indices in Selecting Among Multidimensional Item Response Theory Models.

Educational and psychological measurement·2024
Same author

Social-Emotional Learning for Whom? Implications of a Universal SEL Program and Teacher Well-being for Teachers' Interactions with Students.

School mental health·2022
Same author

Email me back: Examining provider biases through email return and responsiveness.

Journal of counseling psychology·2022
Same author

A study of psychological pain in substance use disorder and its relationship to treatment outcome.

PloS one·2019
Same author

The Bayesian Multilevel Trifactor Item Response Theory Model.

Educational and psychological measurement·2019
Same author

A general Bayesian multilevel multidimensional IRT model for locally dependent data.

The British journal of mathematical and statistical psychology·2018
Same journal

A Simple Approach for Differential Test Functioning Based on Sum Scores.

Educational and psychological measurement·2026
Same journal

Evaluating Factor Retention in Large Factor Analysis Models: A Simulation Study Comparing 15 Methods.

Educational and psychological measurement·2026
Same journal

Agreement and Alignment in Binary Rating Tasks: Strategic Convergence as an Equilibrium Outcome.

Educational and psychological measurement·2026
Same journal

Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing.

Educational and psychological measurement·2026
Same journal

Identification and Diagnosis of Misreporting in Surveys.

Educational and psychological measurement·2026
Same journal

The Aggregated Latent Profile Index: Measuring Person Profile Differentiation Within a Bootstrap-Validated Latent Profile Space.

Educational and psychological measurement·2026
See all related articles

Related Experiment Video

Updated: Dec 16, 2025

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

2.8K

A General Bayesian Multidimensional Item Response Theory Model for Small and Large Samples.

Ken A Fujimoto1, Sabina R Neugebauer2

  • 1Loyola University Chicago, Chicago, IL, USA.

Educational and Psychological Measurement
|July 4, 2020
PubMed
Summary
This summary is machine-generated.

New Bayesian multidimensional item response theory (MIRT) models confirm complex data structures even with small samples. This flexible MIRT approach supports researchers in educational and psychological studies.

Keywords:
Bayesian IRTmultidimensional IRTnested-dimensionality structuresnonnested-dimensionality structures

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

983

Related Experiment Videos

Last Updated: Dec 16, 2025

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

2.8K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

983

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Statistics

Background:

  • Multidimensional item response theory (MIRT) models like bifactor, two-tier, and between-item dimensionality are essential for analyzing complex educational and psychological data.
  • These MIRT models are often highly parameterized, requiring large sample sizes, which limits their application in smaller-scale studies.

Purpose of the Study:

  • To develop a general Bayesian MIRT model applicable to complex dimensional structures in smaller sample sizes.
  • To provide a flexible modeling option for researchers facing limitations with traditional MIRT models.

Main Methods:

  • A general Bayesian multidimensional item response theory (MIRT) model was developed using adaptive informative priors.
  • Simulations were conducted to evaluate the model's performance with various complex structures (two-tier, bifactor, between-item) and sample sizes, including N=100.

Main Results:

  • The proposed Bayesian MIRT model successfully confirmed two-tier, bifactor, and between-item dimensional structures with sample sizes as small as 100.
  • The model demonstrated applicability and effectiveness for both small and large sample sizes.
  • Real data analysis with 121 individuals validated the simulation findings in a practical context.

Conclusions:

  • The developed Bayesian MIRT model offers a viable solution for analyzing complex dimensional structures in educational and psychological data, particularly when sample sizes are limited.
  • This approach expands the utility of MIRT models, enabling researchers to confirm hypothesized structures more effectively across diverse study scales.