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

Test for Homogeneity

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...

Correlation and Regression

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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...

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...

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...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

You might also read

Related Articles

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

Sort by

Same author

How often is "often"? Improving assessment of the externalizing spectrum using absolute frequency.

Psychological assessment·2025

Same author

Associations Among in-The-Moment Emotional Clarity, Emotion Regulation, and Psychopathology in Obsessive-Compulsive Disorder.

Depression and anxiety·2025

Same author

Scoring German Alternate Uses Items Applying Large Language Models.

Journal of Intelligence·2025

Same author

Risks and protective measures for synthetic relationships.

Nature human behaviour·2024

Same author

Worry Postponement From the Metacognitive Perspective: A Randomized Waitlist-Controlled Trial.

Clinical psychology in Europe·2024

Same author

The Manic Idea Creator? A Review and Meta-Analysis of the Relationship between Bipolar Disorder and Creative Cognitive Potential.

International journal of environmental research and public health·2023

Search research articles

Related Experiment Video

Updated: Jul 3, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

Understanding Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model

Marie Beisemann¹, Boris Forthmann², Philipp Doebler¹

¹Department of Statistics, TU Dortmund University.

Multivariate Behavioral Research

|February 13, 2024

Summary

This summary is machine-generated.

New Item Response Theory (IRT) models explain count data from tests and questionnaires. These models, based on the 2PCMPM, help understand how item and person characteristics influence test results.

Keywords:

2PCMPM Conway-Maxwell-Poisson distribution EM algorithm Item response theory count data item covariates person covariates

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

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

Related Experiment Videos

Last Updated: Jul 3, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

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

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

Area of Science:

Psychometrics
Educational Measurement
Psychology

Background:

Item Response Theory (IRT) methods for count data are less developed than for binary data.
Existing models like the Two-Parameter Conway-Maxwell-Poisson model (2PCMPM) offer item-specific parameters but lack explanatory power for covariates.
Understanding parameter variations is crucial for effective item development and selection.

Purpose of the Study:

To introduce novel explanatory count IRT models for analyzing count data.
To extend the 2PCMPM framework by incorporating item and person covariates.
To provide estimation methods and evaluate their statistical properties.

Main Methods:

Development of the Distributional Regression Test Model (DRTM) for item covariates.
Development of the Count Latent Regression Model (CLRM) for person covariates.
Simulation studies to assess the statistical properties of the proposed models.

Main Results:

The proposed DRTM and CLRM models effectively explain variations in item and person parameters.
Simulation results demonstrated satisfactory statistical properties of the estimation methods.
The models provide insights into the relationships between covariates and response patterns.

Conclusions:

The new 2PCMPM-based explanatory count IRT models advance the analysis of psychological and educational assessments.
These models offer valuable tools for understanding test constructs and improving item design.
The developed methods facilitate a deeper interpretation of item and person characteristics in count-based measurement.