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

Survival Tree01:19

Survival Tree

131
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
131
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

224
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
224
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

247
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...
247
Multiple Regression01:25

Multiple Regression

3.1K
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...
3.1K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

305
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
305
Regression Analysis01:11

Regression Analysis

6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K

You might also read

Related Articles

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

Sort by
Same author

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same author

Designing Learning Intervention Studies: Identifiability of Heterogeneous Hidden Markov Models.

Psychometrika·2025
Same author

Restricted Latent Class Models for Nominal Response Data: Identifiability and Estimation.

Psychometrika·2023
Same author

Improving our understanding of predictive bias in testing.

The Journal of applied psychology·2023
Same author

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy.

The British journal of mathematical and statistical psychology·2023
Same author

Identifiability of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis.

Psychometrika·2023
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

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

Bias and precision in true-score estimation.

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

Polychoric correlations under the assumption of elliptical latent traits.

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

Regularized reduced rank regression for mixed predictor and response variables.

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

A multiple-choice SDT model for cognitive diagnosis models.

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

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Video

Updated: Aug 15, 2025

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

6.4K

Extending exploratory diagnostic classification models: Inferring the effect of covariates.

Hulya Duygu Yigit1, Steven Andrew Culpepper2

  • 1University of Illinois Urbana-Champaign, Champaign, Illinois, USA.

The British Journal of Mathematical and Statistical Psychology
|January 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for diagnostic models, incorporating student background knowledge to better understand skill mastery. These advancements aid in evaluating educational interventions and improving learning assessments.

Keywords:
Bayesian statisticsMarkov chain Monte Carlo (MCMC) methodscovariatesvariable selection algorithm

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K

Related Experiment Videos

Last Updated: Aug 15, 2025

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

6.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.3K

Area of Science:

  • Educational Measurement and Statistics
  • Cognitive Science
  • Bayesian Modeling

Background:

  • Diagnostic models are crucial for formative assessments, classifying student knowledge via attributes.
  • Student learning context and background knowledge significantly influence skill mastery.
  • Current methods primarily incorporate covariates into confirmatory diagnostic models (restricted latent class models).

Purpose of the Study:

  • To develop novel methods for integrating student covariates into exploratory restricted latent class models (RLCMs).
  • To jointly infer latent structure and assess covariate effects on performance and skill mastery.
  • To provide a flexible framework for analyzing the impact of background knowledge on attribute mastery.

Main Methods:

  • A novel Bayesian formulation for exploratory RLCMs with covariates.
  • Implementation of a Markov chain Monte Carlo (MCMC) algorithm using Metropolis-within-Gibbs for posterior distribution approximation.
  • Monte Carlo simulations to evaluate the accuracy and performance of the proposed methods.

Main Results:

  • The proposed methods accurately estimate model parameters and covariate effects.
  • The application demonstrates the utility of the methods in examining student background knowledge in probability.
  • The study validates the effectiveness of incorporating covariates in exploratory diagnostic models.

Conclusions:

  • The developed Bayesian approach offers a robust framework for including covariates in exploratory RLCMs.
  • These methods enhance the understanding of how student background influences skill acquisition.
  • The findings support the use of advanced diagnostic models for personalized education and intervention evaluation.