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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

7.2K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
7.2K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

480
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...
480
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.5K
Factorial Design02:01

Factorial Design

13.7K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.7K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

578
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
578
Survival Tree01:19

Survival Tree

388
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...
388

You might also read

Related Articles

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

Sort by
Same author

Universal pluralism: Appreciating and exploring both similarity and difference across people and cultures.

The American psychologist·2026
Same author

Longitudinal associations of suffering with subsequent multidimensional well-being in the Global Flourishing Study.

Communications medicine·2026
Same author

Continuous blood pressure variability within young, healthy adults: a test-retest study to assess relative and absolute reliability.

American journal of physiology. Heart and circulatory physiology·2026
Same author

Swedish Well-Being: The rising importance of age among demographic, personality, and social relationship factors.

SSM - population health·2026
Same author

A cross-national study of demographic variation and childhood predictors of traumatic distress.

Communications medicine·2026
Same author

A longitudinal outcome-wide assessment of the association of life balance with flourishing: a 2-year cross-national analysis of 22 countries in the global flourishing study.

Scientific reports·2026
Same journal

Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R.

Psychological methods·2026
Same journal

Heterogeneous variance models with Gaussian processes.

Psychological methods·2026
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Regularizing threshold priors with sparse response patterns in Bayesian factor analysis with categorical indicators.

R Noah Padgett1, Grant B Morgan2, Tim Lomas3

  • 1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University.

Psychological Methods
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach to address issues with item response theory (IRT) threshold estimation. A novel prior specification improves estimation efficiency and credible interval coverage for sparse data.

More Related Videos

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.7K
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

2.9K

Related Experiment Videos

Last Updated: Jan 17, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.7K
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

2.9K

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Item Response Theory (IRT)

Background:

  • Ordered response items are common in psychological and educational measurement.
  • Infrequently endorsed or unendorsed response categories can cause estimation problems in IRT, specifically non-existent threshold estimates.
  • Maximum likelihood estimation (MLE) struggles with sparse data in IRT models.

Purpose of the Study:

  • To propose and evaluate a Bayesian estimation approach for IRT threshold estimation when response categories are sparsely endorsed.
  • To develop a novel method for specifying threshold priors that is more intuitive and communicable.
  • To demonstrate the statistical efficiency and reliability of the proposed Bayesian method.

Main Methods:

  • A Bayesian estimation framework was employed to handle threshold estimation issues.
  • A new prior specification was developed, conceptualizing threshold priors as priors on response category probabilities while maintaining ordering constraints.
  • The proposed method was evaluated using simulated data, Monte Carlo simulations, and a multigroup item-factor analysis.

Main Results:

  • The proposed Bayesian approach effectively addresses the non-existence of threshold estimates in IRT.
  • The novel prior specification demonstrated comparable statistical efficiency to existing threshold priors.
  • Informative threshold priors were found to be necessary for efficient posterior sampling and reliable credible interval coverage.

Conclusions:

  • A Bayesian estimation strategy with a carefully constructed threshold prior is essential for robust IRT analysis with sparse data.
  • The proposed induced-prior specification offers a communicable and statistically efficient alternative for threshold estimation.
  • The findings underscore the importance of informative priors in Bayesian IRT for ensuring accurate parameter estimation and inference.