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

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

7.1K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.1K
Comparing Experimental Results: Student's t-Test01:09

Comparing Experimental Results: Student's t-Test

6.9K
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
6.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
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).
8.9K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.5K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.5K
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

939
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
939
Kendall's Tau Test01:16

Kendall's Tau Test

1.3K
Kendall's tau test, also known as the Kendall rank coefficient test, is a nonparametric method for assessing association between two variables. This test is particularly useful for identifying significant correlations when the distributions of the sample and population are unknown. Developed in 1938 by the British statistician Sir Maurice George Kendall, the tau coefficient (denoted as τ) serves as a rank correlation coefficient, with values ranging from -1 to +1.
A τ value of +1...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Publisher Correction: Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation.

Behavior research methods·2026
Same author

Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation.

Behavior research methods·2026
Same author

Machine learning for propensity score estimation: A systematic review and reporting guidelines.

Psychological methods·2025
Same author

Power and sample size for reversible linear mixed models with clustering and longitudinality: GLIMMPSE Version 3.

PloS one·2025
Same author

Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches.

Evaluation review·2025
Same author

Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices.

Educational and psychological measurement·2024
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: Apr 18, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.4K

Evaluating the Performance of a Regularized Differential Item Functioning Method for Testlet-Based Polytomous Items.

Jing Huang1, M David Miller1, Anne Corinne Huggins-Manley1

  • 1University of Florida, Gainesville, FL, USA.

Educational and Psychological Measurement
|June 3, 2025
PubMed
Summary
This summary is machine-generated.

This study found that the generalized partial credit model with lasso penalization (GPCMlasso) effectively detects differential item functioning (DIF) in polytomous items, even with testlet effects. Small testlet effects did not significantly impact the model's performance.

Keywords:
DIFGPCMlassopolytomoustestlet

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
09:00

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

1.3K
Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA
10:58

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

Published on: August 28, 2021

5.1K

Related Experiment Videos

Last Updated: Apr 18, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.4K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

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

1.3K
Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA
10:58

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

Published on: August 28, 2021

5.1K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) is crucial for fair assessments.
  • Testlets can influence item response theory (IRT) model performance.
  • Regularization methods offer potential for robust DIF detection.

Purpose of the Study:

  • To evaluate the generalized partial credit model with lasso penalization (GPCMlasso) for DIF detection in polytomous items under testlet conditions.
  • To investigate the impact of sample size, testlet effect magnitude, DIF magnitude, number of DIF items, and covariate types on GPCMlasso performance.

Main Methods:

  • A simulation study was conducted manipulating five key factors.
  • The GPCMlasso method was applied to detect DIF in polytomous items.
  • Model performance was assessed using false-positive rate (FPR) and true-positive rate (TPR).

Main Results:

  • The GPCMlasso method demonstrated effective control of FPR across all simulated conditions.
  • True-positive rate (TPR) was influenced by the manipulated factors.
  • Small testlet effects showed minimal impact on both FPR and TPR.

Conclusions:

  • The GPCMlasso method is effective for DIF detection in polytomous items within testlet-based assessments.
  • The study provides evidence supporting the robustness of GPCMlasso against moderate testlet effects.
  • Findings inform the application of advanced IRT methods in complex testing environments.