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

Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.0K
Bonferroni Test01:10

Bonferroni Test

2.8K
The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
2.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

261
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...
261
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

1.7K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
1.7K
Test for Homogeneity01:23

Test for Homogeneity

2.0K
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...
2.0K
Fisher's Exact Test01:08

Fisher's Exact Test

658
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
658

You might also read

Related Articles

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

Sort by
Same author

Refining effect size measures and classification for differential item functioning: Toward unified guidelines across methods.

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

Enhancing Psychometric Analysis with Interactive SIA Modules.

Psychometrika·2026
Same author

Assessing quality of selection procedures: Lower bound of false positive rate as a function of inter-rater reliability.

The British journal of mathematical and statistical psychology·2024
Same author

On the Developmental Trajectories of Relational Concepts Among Children and Adolescents With Intellectual Disability of Undifferentiated Etiology.

American journal on intellectual and developmental disabilities·2020
Same author

Efficient Standard Errors in Item Response Theory Models for Short Tests.

Educational and psychological measurement·2020
Same author

Does the Development of Syntax Comprehension Show a Premature Asymptote Among Persons With Down Syndrome? A Cross-Sectional Analysis.

American journal on intellectual and developmental disabilities·2019

Related Experiment Video

Updated: Jul 29, 2025

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

831

Combining Item Purification and Multiple Comparison Adjustment Methods in Detection of Differential Item Functioning.

Adéla Hladká1,2, Patrícia Martinková1,3, David Magis4

  • 1Institute of Computer Science of the Czech Academy of Sciences.

Multivariate Behavioral Research
|May 23, 2023
PubMed
Summary

This study introduces a new iterative algorithm that combines item purification and multiple comparison adjustment to improve differential item functioning (DIF) detection. The proposed method enhances the accuracy of identifying DIF items in psychometric analysis.

Keywords:
Differential item functioningitem purificationmultiple comparison adjustments

More Related Videos

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

5.8K
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

7.0K

Related Experiment Videos

Last Updated: Jul 29, 2025

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

831
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

5.8K
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

7.0K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) detection methods often assume some items are DIF-free for item purification.
  • Controlling for multiple comparisons is crucial in DIF analysis to prevent false positives.
  • The interplay between item purification and multiple comparison adjustment in DIF detection is not fully understood.

Purpose of the Study:

  • To investigate the impact of combining item purification and multiple comparison adjustment on DIF detection.
  • To propose a novel iterative algorithm that integrates both item purification and multiple comparison adjustment.
  • To evaluate the performance of the proposed algorithm using simulation studies and a real data example.

Main Methods:

  • Development of an iterative algorithm that simultaneously performs item purification and adjusts for multiple comparisons.
  • Simulation study to assess the properties and effectiveness of the proposed algorithm compared to existing methods.
  • Application of the algorithm to a real data set to demonstrate its practical utility in DIF detection.

Main Results:

  • The combined approach of item purification and multiple comparison adjustment significantly impacts DIF item detection.
  • The newly proposed iterative algorithm demonstrates favorable properties in simulation studies.
  • The method is effectively demonstrated on a real-world data example, showing its practical applicability.

Conclusions:

  • Integrating item purification with multiple comparison adjustment offers a more robust approach to DIF detection.
  • The proposed iterative algorithm provides a valuable tool for researchers seeking accurate DIF identification.
  • This method enhances the reliability of psychometric analyses by improving the control of Type I errors in DIF detection.