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

198
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...
198
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
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:
5.8K
Factorial Design02:01

Factorial Design

13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

3.3K
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...
3.3K
One-Way ANOVA01:18

One-Way ANOVA

7.9K
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...
7.9K

You might also read

Related Articles

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

Sort by
Same author

Cumulant-Based Approaches for Testing the Assumption of Independent Errors in Non-Gaussian Parallel and Congeneric Measures.

Educational and psychological measurement·2026
Same author

Leveraging computerized adaptive testing for cost-effective evaluation of large language models in medical benchmarking.

NPJ digital medicine·2026
Same author

Does X at Time 1 Cause Y at Time 2? Longitudinal Causal Learning with Hidden Confounders.

Psychometrika·2026
Same author

Fine-grained evaluation of large language models in medicine using non-parametric cognitive diagnostic modeling.

Scientific reports·2026
Same author

Empowering Expert Judgment: A Data-Driven Decision Framework for Standard Setting in High-Dimensional and Data-Scarce Assessments.

Educational and psychological measurement·2026
Same author

Medical exam question difficulty prediction: An analysis of embedding representations, machine-learning approaches, and input feature impact.

Medical teacher·2025
Same journal

A Simple Approach for Differential Test Functioning Based on Sum Scores.

Educational and psychological measurement·2026
Same journal

Evaluating Factor Retention in Large Factor Analysis Models: A Simulation Study Comparing 15 Methods.

Educational and psychological measurement·2026
Same journal

Agreement and Alignment in Binary Rating Tasks: Strategic Convergence as an Equilibrium Outcome.

Educational and psychological measurement·2026
Same journal

Interactions Between Termination Criteria and Ability Estimators in Computerized Adaptive Testing.

Educational and psychological measurement·2026
Same journal

Identification and Diagnosis of Misreporting in Surveys.

Educational and psychological measurement·2026
Same journal

The Aggregated Latent Profile Index: Measuring Person Profile Differentiation Within a Bootstrap-Validated Latent Profile Space.

Educational and psychological measurement·2026
See all related articles

Related Experiment Video

Updated: Jul 5, 2025

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

6.9K

Evaluating Close Fit in Ordinal Factor Analysis Models With Multiply Imputed Data.

Dexin Shi1, Bo Zhang2, Ren Liu3

  • 1University of South Carolina, Columbia, USA.

Educational and Psychological Measurement
|January 22, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces methods for assessing ordinal factor analysis model fit using multiple imputation (MI) with fit indices like SRMR and RMSEA. The proposed techniques provide accurate estimates for missing data analysis.

Keywords:
RMSEASRMRmissing datamodel fitmultiple imputationordinal factor analysis model

More Related Videos

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
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

786

Related Experiment Videos

Last Updated: Jul 5, 2025

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

6.9K
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
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

786

Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Missing data is a common challenge in statistical modeling.
  • Multiple imputation (MI) is a recommended technique for handling missing data.
  • Fit indices for ordinal factor analysis with MI are not well-established.

Purpose of the Study:

  • To introduce methods for computing MI-based fit indices in ordinal factor analysis.
  • To assess the fit of ordinal factor analysis models using multiply imputed data.
  • To provide accurate point and interval estimates for SRMR and RMSEA.

Main Methods:

  • Utilized multiple imputation (MI) for handling missing data.
  • Developed procedures for computing standardized root mean squared residual (SRMR) and root mean square error of approximation (RMSEA) with MI data.
  • Constructed confidence intervals for the MI-based fit indices.

Main Results:

  • Proposed methods yielded accurate point and interval estimates for SRMR and RMSEA.
  • Accuracy improved with larger sample sizes, less missing data, more response categories, and higher misfit.
  • Simulation results supported the validity of the developed techniques.

Conclusions:

  • The introduced methods effectively assess ordinal factor analysis model fit with multiply imputed data.
  • These techniques offer reliable fit index estimation in the presence of missing data.
  • Recommendations for practical application and future research were discussed.