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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

325
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...
325
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

26.7K
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...
26.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

99
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...
99
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

808
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
808
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

783
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
783
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.2K
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).
3.2K

You might also read

Related Articles

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

Sort by
Same author

Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables.

The British journal of mathematical and statistical psychology·2024
Same author

A unified model-implied instrumental variable approach for structural equation modeling with mixed variables.

Psychometrika·2021
See all related articles

Related Experiment Video

Updated: Oct 5, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K

Frequentist Model Averaging in Structure Equation Model With Ordinal Data.

Shaobo Jin1

  • 1Department of Statistics, Uppsala University, Uppsala, Sweden. shaobo.jin@statistik.uu.se.

Psychometrika
|January 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new frequentist model averaging method for structural equation modeling (SEM) with ordinal data. It addresses limitations of previous methods, providing more accurate statistical inference by accounting for model selection uncertainty.

Keywords:
confidence intervalgoodness-of-fit testlocal asymptotic frameworkmean squared errormodel selection uncertaintypseudo maximum likelihood

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

6.4K

Related Experiment Videos

Last Updated: Oct 5, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

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

Published on: September 27, 2019

7.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

6.4K

Area of Science:

  • Statistics
  • Psychometrics
  • Econometrics

Background:

  • Structural Equation Modeling (SEM) often involves selecting a single best-fitting model from candidates.
  • Inference based on a selected model ignores model selection uncertainty, leading to overly optimistic results.
  • Existing frequentist model averaging methods for SEM are primarily designed for continuous data.

Purpose of the Study:

  • To adapt frequentist model averaging for SEM with ordinal data.
  • To address the limitations of applying continuous data methods to ordinal SEM.
  • To develop valid statistical inference techniques that account for model selection uncertainty in ordinal SEM.

Main Methods:

  • Proving consistency and asymptotic normality of polychoric correlation estimators under a local asymptotic framework.
  • Developing a novel frequentist model averaging estimator for ordinal SEM.
  • Deriving valid confidence intervals and goodness-of-fit test statistics for the proposed estimator.

Main Results:

  • Demonstrated that existing frequentist model averaging results are not directly applicable to ordinal SEM.
  • Established theoretical properties (consistency, asymptotic normality) of polychoric correlation estimators in the local asymptotic framework.
  • Proposed a new model averaging estimator and valid inferential tools specifically for ordinal data.

Conclusions:

  • The proposed frequentist model averaging approach provides a statistically sound method for SEM with ordinal data.
  • This method effectively handles model selection uncertainty, offering a compromise between selecting a single model and using the full model.
  • The developed techniques enhance the accuracy and reliability of statistical inference in ordinal SEM.