Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Test for Homogeneity

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...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

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

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

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 data...

Introduction to Nonparametric Statistics

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...

Wilcoxon Rank-Sum Test

Wilcoxon Rank-Sum Test

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:

Significance Testing: Overview

Significance Testing: Overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...

You might also read

Related Articles

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

Sort by

Same author

Ultra-Fast Implementation of Multivariate GWAS in Genomic SEM Using Flexible Analytic Estimation.

bioRxiv : the preprint server for biology·2026

Same author

Moment-to-Moment Coordination of Parent-Infant Visual Attention: An Actor-Partner Interdependence Framework Within Dynamic Structural Equation Modeling.

Developmental science·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

Nested model comparisons between common factors and composites.

Psychological methods·2026

Same author

Calculating and Interpreting Maximal Reliability in Bifactor Models.

Multivariate behavioral research·2026

Same author

Improving the Measurement of the Big Five via Alternative Formats for the BFI-2.

Journal of personality assessment·2025

Search research articles

Related Experiment Video

Updated: May 22, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The performance of robust test statistics with categorical data.

Victoria Savalei¹, Mijke Rhemtulla

¹University of British Columbia, Vancouver, Canada. v.savalei@ubc.ca

The British Journal of Mathematical and Statistical Psychology

|May 10, 2012

Summary

This study evaluated structural equation model test statistics for categorical data. The unweighted least squares (cat-ULS) estimator

Area of Science:

Psychometrics
Statistical Modeling
Behavioral Sciences

Background:

Structural Equation Modeling (SEM) is widely used in various scientific disciplines.
Appropriate statistical test statistics are crucial for accurate analysis of categorical data in SEM.
Previous research indicates potential advantages of the unweighted least squares (cat-ULS) estimator over diagonally weighted least squares (cat-DWLS).

Purpose of the Study:

To evaluate the performance of five SEM test statistics for categorical data.
To compare Type I error rates and statistical power across different conditions.
To identify the most effective test statistic and estimator for categorical SEM.

Main Methods:

A simulation study was conducted to assess SEM test statistics.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Related Experiment Videos

Last Updated: May 22, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

Published on: March 22, 2022

Evaluated statistics associated with both cat-DWLS and cat-ULS estimators.

Considered variations in model size, sample size, number of categories, and threshold distributions.

Main Results:

The mean- and variance-adjusted test statistic for the cat-ULS estimator demonstrated superior overall performance.
A new version of the cat-ULS statistic, without a degrees-of-freedom adjustment, is recommended.
The cat-ULS estimator is generally recommended over cat-DWLS, especially for small to medium sample sizes.

Conclusions:

The mean- and variance-adjusted test statistic for the cat-ULS estimator is the preferred choice for categorical SEM.
The updated cat-ULS statistic without degrees-of-freedom adjustment offers improved performance.
Researchers should consider using the cat-ULS estimator, particularly when dealing with limited sample sizes.