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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

580
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...
580
One-Way ANOVA01:18

One-Way ANOVA

11.2K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
11.2K
Two-Way ANOVA01:17

Two-Way ANOVA

2.5K
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...
2.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
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:
5.8K
Multiple Regression01:25

Multiple Regression

3.3K
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...
3.3K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.1K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Revisiting reliability and measurement precision: Towards a unified perspective.

The British journal of mathematical and statistical psychology·2026
Same author

Day-level latent classes of alcohol and cannabis use motives: Associations by combined use, level of use, and consequences.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors·2026
Same author

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same author

Serum acylcarnitine to amide ratio as a predictive biomarker for the progression of pulmonary disease caused by <i>Mycobacterium avium</i> complex.

ERJ open research·2026
Same author

Alcohol use and <i>APOE ε</i>4 interaction with cognitive domains among American adults from diverse racial/ethnic groups: A HABS-HD study.

Alzheimer's & dementia. Behavior & socioeconomics of aging·2026
Same author

Prediction of low birth weight using machine learning-based analysis of environmental and maternal risk factors: insights from the Korean CHildren's ENvironmental health study (Ko-CHENS).

Environmental research·2026

Related Experiment Video

Updated: Apr 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

6.1K

A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data

Yan Huo1, Jimmy de la Torre, Eun-Young Mun

  • 1Graduate School of Education, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA, yan.huo@gmail.com.

Psychometrika
|October 1, 2014
PubMed
Summary

A new hierarchical item response theory model and Markov chain Monte Carlo algorithm effectively analyze large, multi-site alcohol intervention studies with missing data. This approach ensures accurate scoring and parameter recovery for integrative data analysis.

More Related Videos

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

2.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

5.8K

Related Experiment Videos

Last Updated: Apr 23, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

6.1K
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

2.1K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

5.8K

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Analysis

Background:

  • Integrative Data Analysis (IDA) combines data from multiple studies, presenting challenges in scoring and missing data handling.
  • Existing methods for multidimensional item response theory (IRT) often constrain item parameters, limiting covariance matrix estimation.
  • A hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model is needed for complex, multi-group analyses.

Purpose of the Study:

  • To propose and validate a hierarchical 2PL-MUIRT model for large-scale IDA with multiple groups.
  • To develop a flexible Markov chain Monte Carlo (MCMC) algorithm for estimating model parameters, including group-specific means and covariance structures.
  • To address challenges of common scoring metrics and missing data inherent in IDA.

Main Methods:

  • Developed a novel MCMC algorithm for a hierarchical 2PL-MUIRT model accommodating multiple groups.
  • Adapted the MCMC algorithm to directly estimate the correlation matrix for the anchor group without item parameter constraints.
  • Validated the algorithm and calibration procedure through simulation studies and application to real-world IDA data.

Main Results:

  • Simulation studies demonstrated adequate recovery of model parameters and accurate estimation of latent trait scores.
  • Application to real data (69 items, 20 studies, 22,608 participants) showed good model fit and high correlations between MCMC and original scores.
  • An additional simulation confirmed the robustness of the MCMC procedures with high proportions of missing data.

Conclusions:

  • The proposed Bayesian hierarchical IRT model and MCMC algorithm are effective for analyzing complex IDA and multi-site studies.
  • The method provides a flexible and robust framework for estimating parameters and latent traits across diverse groups.
  • This approach has significant potential for wide implementation in applied research, particularly for large-scale collaborative projects.