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

Ordinal Level of Measurement

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

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 from...

Two-Way ANOVA

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...

One-Way ANOVA: Unequal Sample Sizes

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

One-Way ANOVA

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...

You might also read

Related Articles

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

Sort by

Same author

Evaluating local structural-after-measurement and traditional approaches for the estimation of complex nonlinear effects among latent variables.

Psychological methods·2026

Same author

Exploratory structural equation modeling and the curse of dimensionality.

Behavior research methods·2026

Same author

Measuring Chinese Personality in 8 Minutes: A Short Measure of the Five-Factor Model of Personality.

Journal of personality assessment·2025

Same author

Rethinking causal inference for recurring exposures: The incremental propensity score approach with lavaan.

Behavior research methods·2025

Same author

A tutorial for understanding SEM using R: Where do all the numbers come from?

The British journal of mathematical and statistical psychology·2025

Same author

Profiles of mentalizing in individuals with antisocial behavior: comparing state- and trait-mentalizing.

Psychotherapy research : journal of the Society for Psychotherapy Research·2025

Search research articles

Related Experiment Video

Updated: Jul 14, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Mixture multigroup structural equation modelling for ordinal data.

Andres F Perez Alonso^1,2, Jeroen K Vermunt¹, Yves Rosseel³

¹Tilburg University, Tilburg, The Netherlands.

The British Journal of Mathematical and Statistical Psychology

|July 13, 2026

Summary

This study extends Mixture Multigroup SEM (MMG-SEM) for ordinal data using a stepwise approach. The new method improves measurement model parameter recovery compared to standard MMG-SEM, especially with fewer response categories.

Keywords:

mixture modelling ordinal data structural equation modelling structural relations

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jul 14, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Social Sciences
Psychometrics
Statistics

Background:

Social scientists use Structural Equation Modelling (SEM) to compare groups based on latent variable (LV) relations.
Measurement invariance testing is crucial before group comparisons, as LVs are indirectly measured.
Mixture Multigroup SEM (MMG-SEM) clusters groups by structural relations, accounting for measurement invariance.

Purpose of the Study:

To extend MMG-SEM to accommodate ordinal data, addressing limitations of existing methods assuming continuous indicators.
To introduce a stepwise Structural-After-Measurement estimation approach for handling ordinal data in MMG-SEM.
To compare the performance of the new MMG-SEM for ordinal data against the standard ML-based MMG-SEM.

Main Methods:

Implemented a two-step approach: multigroup categorical confirmatory factor analysis (MG-CCFA) with diagonally weighted least squares (DWLS) for the measurement model.
Utilized maximum likelihood (ML) estimation in the second step for clustering groups and estimating structural relations.
Conducted two simulation studies to compare the proposed method with ML-based MMG-SEM regarding parameter recovery and model selection.

Main Results:

The DWLS estimation in the first step showed better recovery of measurement model parameters, particularly with fewer response categories.
Both the proposed method and the standard ML-based MMG-SEM performed similarly in recovering clusters and structural relations.
Model selection performance was comparable between the two approaches.

Conclusions:

The extended MMG-SEM effectively handles ordinal data, offering improved measurement model estimation.
The stepwise approach provides a viable alternative for researchers working with ordinal survey data in group comparisons.
The method enhances the applicability of MMG-SEM in social science research where ordinal variables are common.