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
Stereotype Content Model02:16

Stereotype Content Model

15.4K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.4K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

8.1K
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...
8.1K
Induced-fit Model01:13

Induced-fit Model

88.9K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
88.9K
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

5.5K
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...
5.5K
Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

95.0K
Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
95.0K

You might also read

Related Articles

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

Sort by
Same author

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same author

Identifying Weekly Physical and Physiological Profiles in Professional Basketball Using Heart Rate Variability and Training Load Clustering.

Research quarterly for exercise and sport·2026
Same author

Resistance to doxorubicin-induced proteinuria and proteolytic activation of ENaC in 129S2/SvPas mice.

Physiological reports·2025
Same author

Data of the Swiss common breeding bird monitoring program.

Ecology·2025
Same author

Functional characterization of a novel bark-specific multi-product monoterpene synthase from the rubber tree (Hevea brasiliensis).

Functional plant biology : FPB·2025
Same author

Fluoroscopy-guided celiac plexus block - Trans-Aortic approach.

Interventional pain medicine·2025

Related Experiment Video

Updated: Jan 21, 2026

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K

Model-based goodness-of-fit tests for the ordered stereotype model.

Daniel Fernández1, Ivy Liu2, Richard Arnold2

  • 1Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, CIBERSAM, Barcelona, Spain.

Statistical Methods in Medical Research
|July 31, 2019
PubMed
Summary

This study introduces two novel goodness-of-fit tests for the ordered stereotype model, enhancing statistical analysis for ordinal data. These tests improve model assessment by evaluating group effects within the data structure.

Keywords:
Goodness of fitHosmer–Lemeshow testordered stereotype modelordinal datauneven spacing

More Related Videos

Author Spotlight: A Battery of Highly Reproducible Behavioral Tests to Validate an Angelman Syndrome Murine Model
11:05

Author Spotlight: A Battery of Highly Reproducible Behavioral Tests to Validate an Angelman Syndrome Murine Model

Published on: October 20, 2023

5.7K
Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

10.1K

Related Experiment Videos

Last Updated: Jan 21, 2026

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

7.7K
Author Spotlight: A Battery of Highly Reproducible Behavioral Tests to Validate an Angelman Syndrome Murine Model
11:05

Author Spotlight: A Battery of Highly Reproducible Behavioral Tests to Validate an Angelman Syndrome Murine Model

Published on: October 20, 2023

5.7K
Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

10.1K

Area of Science:

  • Statistics
  • Biostatistics
  • Ordinal Regression Analysis

Background:

  • Ordinal response variables are common in many scientific fields.
  • The ordered stereotype model offers flexibility in modeling ordinal data by allowing uneven category spacing.
  • Existing goodness-of-fit tests may not fully capture the nuances of this model.

Purpose of the Study:

  • To develop and present two new model-based goodness-of-fit tests for the ordered stereotype model.
  • To adapt the Lipsitz test methodology for assessing the fit of ordered stereotype models.
  • To evaluate the performance of the proposed tests across various data scenarios.

Main Methods:

  • The proposed tests are based on the Lipsitz test, partitioning subjects into G groups.
  • An alternative model is constructed by adding group effects to the null model.
  • Two variations of the test are presented: one using the ordered stereotype model structure and another using an ordinary linear model.
  • The tests leverage the data-driven uneven spacing of ordinal response categories inherent to the ordered stereotype model.

Main Results:

  • The performance of the two new goodness-of-fit tests was demonstrated under diverse simulated scenarios.
  • The application of the tests in three real-world examples was presented.
  • The study provides enhanced tools for validating the ordered stereotype model in statistical analyses.

Conclusions:

  • The developed goodness-of-fit tests offer a valuable addition to the statistical toolkit for analyzing ordinal data.
  • These tests provide a robust method for assessing the fit of the ordered stereotype model, considering its unique feature of data-driven category spacing.
  • The findings support the utility of these tests in various applied research settings involving ordinal outcomes.