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

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).
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
Test for Homogeneity01:23

Test for Homogeneity

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 be stated as...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

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...
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...

You might also read

Related Articles

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

Sort by
Same author

Barriers and facilitators to intracerebral haemorrhage platform trial recruitment: a survey of stroke clinicians.

Cerebrovascular diseases (Basel, Switzerland)·2026
Same author

Rates, risks and routes to reduce vascular dementia (R4VaD), a UK-wide multicentre prospective observational cohort study of cognition after stroke: baseline data and statistical analysis plan (ISRCTN18274006).

Cerebrovascular diseases extra·2026
Same author

Effect of tranexamic acid for acute spontaneous intracerebral haemorrhage: a systematic review and individual patient data meta-analysis.

Journal of neurology, neurosurgery, and psychiatry·2026
Same author

Early Versus Delayed Anticoagulation in Acute Ischemic Stroke According to Atrial Fibrillation Subtype and Time of Diagnosis: Subgroup Analysis of the OPTIMAS Randomized Controlled Trial.

Stroke·2026
Same author

Early versus delayed anticoagulation in acute ischemic stroke with atrial fibrillation according to infarct volume and location: A prespecified subgroup analysis of the OPTIMAS randomized controlled trial.

International journal of stroke : official journal of the International Stroke Society·2026
Same author

Biomarkers for advancing diagnosis and prognosis in stroke.

The Lancet. Neurology·2026

Related Experiment Video

Updated: May 16, 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

Testing for differential item functioning within the EQ-5D.

David K Whynes1, Nikola Sprigg2, James Selby3

  • 1School of Economics (DKW), University of Nottingham, Nottingham, UK

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|November 28, 2012
PubMed
Summary

Differential item functioning (DIF) was assessed in the EQ-5D quality-of-life instrument. Regional differences significantly impacted EQ-5D scores, while proxy vs. patient reporting showed minimal impact.

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Development of a Virtual Reality Assessment of Everyday Living Skills
10:32

Development of a Virtual Reality Assessment of Everyday Living Skills

Published on: April 23, 2014

Related Experiment Videos

Last Updated: May 16, 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

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Development of a Virtual Reality Assessment of Everyday Living Skills
10:32

Development of a Virtual Reality Assessment of Everyday Living Skills

Published on: April 23, 2014

Area of Science:

  • Health Services Research
  • Psychometrics
  • Clinical Trials

Background:

  • The EQ-5D is a widely used health-related quality-of-life instrument.
  • Differential Item Functioning (DIF) can bias instrument scores when subgroups respond differently.

Purpose of the Study:

  • To investigate DIF in the EQ-5D instrument within an acute stroke clinical trial.
  • To examine DIF related to geographical region and patient-proxy reporting.

Main Methods:

  • Analysis of 1462 patient records from a large clinical trial (ISRCTN 99414122).
  • Mapping clinical outcome measures (modified Rankin Scale, Barthel Index, Zung Depression Scale) to EQ-5D index scores.
  • Inclusion of dummy variables for proxy responses and treatment region (UK, Asia, rest of world).

Main Results:

  • Proxies reported more health problems than patients for similar clinical states, but this did not significantly alter mean EQ-5D index scores.
  • Significant divergence in reported problem severity distributions by geographical region, leading to different EQ-5D index scores.
  • Mean EQ-5D index scores were significantly higher for UK responses compared to Asia and the rest of the world.

Conclusions:

  • Geographical region is a significant factor contributing to DIF in the EQ-5D instrument.
  • While proxy reporting showed some differences, it did not substantially impact overall EQ-5D index scores in this cohort.
  • Findings highlight the need to consider regional variations when interpreting EQ-5D scores in international clinical research.