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 Significance01:50

Statistical Significance

22.2K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
22.2K
Probability in Statistics01:14

Probability in Statistics

23.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
23.5K
Introduction to Statistics01:17

Introduction to Statistics

64.4K
The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
64.4K
Variability: Analysis01:11

Variability: Analysis

524
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
524
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K
Accountability and Responsibility of a Nurse II01:09

Accountability and Responsibility of a Nurse II

1.2K
Professional accountability in nursing is a multifaceted concept that encompasses professional ethics, legal standards, and employment expectations. This framework ensures that nurses maintain and elevate the quality of care while upholding the values of their profession. It compels them to treat patients, families, and colleagues with respect, compassion, and integrity.
For example, a nurse demonstrating respect and compassion might listen attentively to a patient's concerns, provide...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Relationships between Toenail, Urinary, and Drinking-water Fluoride Concentrations in a Pregnancy Cohort using Private Water Systems in the United States.

Biological trace element research·2026
Same author

The InterModel Vigorish as a Lens for Understanding (and Quantifying) the Value of Item Response Models for Dichotomously Coded Items.

Psychometrika·2026
Same author

Spower: A general-purpose Monte Carlo simulation power analysis program.

Behavior research methods·2025
Same author

Longitudinal disparities in psychological distress and mental health symptoms in Canadian lesbian, gay, bisexual, transgender and gender-diverse midlife and older adults: Findings from the Canadian Longitudinal Study on Aging (CLSA).

Aging & mental health·2025
Same author

Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset).

Journal of open psychology data·2025
Same author

Including Empirical Prior Information in the Reliable Change Index.

Applied psychological measurement·2025

Related Experiment Video

Updated: Feb 10, 2026

Chemical Reversion of Conventional Human Pluripotent Stem Cells to a Naïve-like State with Improved Multilineage Differentiation Potency
09:07

Chemical Reversion of Conventional Human Pluripotent Stem Cells to a Naïve-like State with Improved Multilineage Differentiation Potency

Published on: June 10, 2018

10.6K

It Might Not Make a Big DIF: Improved Differential Test Functioning Statistics That Account for Sampling Variability.

R Philip Chalmers1, Alyssa Counsell1, David B Flora1

  • 1York University, Toronto, Ontario, Canada.

Educational and Psychological Measurement
|May 26, 2018
PubMed
Summary

Differential test functioning (DTF) occurs when multiple test items show differential item functioning (DIF). This study introduces improved DTF statistics to accurately quantify these aggregate effects at the test level.

Keywords:
differential item functioningdifferential test functioningitem response theorymultiple imputation

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Measuring Neuromuscular Junction Functionality
10:40

Measuring Neuromuscular Junction Functionality

Published on: August 6, 2017

18.7K

Related Experiment Videos

Last Updated: Feb 10, 2026

Chemical Reversion of Conventional Human Pluripotent Stem Cells to a Naïve-like State with Improved Multilineage Differentiation Potency
09:07

Chemical Reversion of Conventional Human Pluripotent Stem Cells to a Naïve-like State with Improved Multilineage Differentiation Potency

Published on: June 10, 2018

10.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Measuring Neuromuscular Junction Functionality
10:40

Measuring Neuromuscular Junction Functionality

Published on: August 6, 2017

18.7K

Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Differential item functioning (DIF) can aggregate to impact overall test functioning.
  • Quantifying these aggregate effects at the test level, known as differential test functioning (DTF), is crucial but lacks optimal statistical methods.
  • Existing methods often rely on approximations and do not adequately account for sampling variability.

Purpose of the Study:

  • To propose and evaluate improved statistical methodology for detecting and understanding DTF.
  • To develop DTF statistics that properly account for sampling variability in item parameter estimates.
  • To avoid the need for provisional latent trait estimates in two-step approximations.

Main Methods:

  • Development of novel DTF statistics.
  • Examination of DTF statistic properties using two Monte Carlo simulation studies.
  • Application of the proposed methodology in an empirical analysis.
  • Inclusion of both dichotomous and polytomous item response theory (IRT) models.

Main Results:

  • The improved DTF statistics demonstrated optimal and consistent statistical properties in simulations.
  • Consistent Type I error rates were achieved, indicating reliable detection of DTF.
  • The empirical analysis confirmed the practical applicability of the proposed methodology.

Conclusions:

  • The proposed DTF statistics offer a statistically sound approach to quantifying test-level effects of DIF.
  • This methodology provides a more accurate and reliable way to assess overall test functioning.
  • The study suggests future research directions for advancing DTF analysis in psychometrics.