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

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
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...
4.4K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

565
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...
565
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

7.0K
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:
7.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
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).
8.8K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.2K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.4K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.4K

You might also read

Related Articles

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

Sort by
Same author

Clusters of Trauma Types as Measured by the Life Events Checklist for DSM-5.

International journal of stress management·2022
Same author

Comparing patients and families perceptions of satisfaction and predictors of overall satisfaction in the emergency department.

PloS one·2019
Same author

Assessing Statistical Anxiety Among Online and Traditional Students.

Frontiers in psychology·2019
Same author

Challenges in measuring ACGME competencies: considerations for milestones.

International journal of emergency medicine·2019
Same author

Invariance of the Construct of Posttraumatic Stress Disorder: A Systematic Review.

Journal of traumatic stress·2019
Same author

Development and initial psychometric validation of the Brief-Caffeine Expectancy Questionnaire (B-CaffEQ).

Psychological assessment·2018

Related Experiment Video

Updated: Mar 30, 2026

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

7.3K

Comparing interval estimates for small sample ordinal CFA models.

Prathiba Natesan1

  • 1Department of Psychology, University of North Texas Denton, TX, USA.

Frontiers in Psychology
|November 19, 2015
PubMed
Summary
This summary is machine-generated.

Bayesian methods provide more accurate interval estimates for factor correlations in ordinal structural equation models compared to non-Bayesian approaches like robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS), especially with small sample sizes.

Keywords:
BayesianMarkov chain Monte Carloconfidence intervalsconfirmatory factor analysisordinal data analysissimulationstructural equation modeling

More Related Videos

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

1.3K
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

3.8K

Related Experiment Videos

Last Updated: Mar 30, 2026

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

7.3K
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

1.3K
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

3.8K

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) are recommended for ordinal structural equation models.
  • Previous studies noted standard error underestimation by these methods, but interval estimate bias and coverage remained unexamined.

Purpose of the Study:

  • To compare Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis (CFA) models.
  • To evaluate interval estimate bias and coverage, particularly in small sample data.

Main Methods:

  • Simulated ordinal CFA models across six sample sizes, three factor correlations, and two distributions (normal, skewed).
  • Investigated two Bayesian prior specifications (informative, less informative) and compared with RML and AGLS methods.
  • Analyzed interval estimate coverage, bias, standard errors, and convergence rates.

Main Results:

  • Non-Bayesian methods (RML, AGLS) commonly showed undercoverage and underestimated standard errors, potentially inflating Type-I errors.
  • Non-Bayesian intervals exhibited more positive bias. Some non-Bayesian methods yielded non-converging or inadmissible solutions with small samples and non-normal data.
  • Bayesian methods demonstrated more accurate point estimates, better standard error estimation, and credible interval coverage closer to nominal levels, reflecting statistical uncertainty.

Conclusions:

  • Analyzing interval estimate coverage and bias is crucial, as ignoring them can be misleading.
  • Bayesian approaches offer advantages in accuracy and reliability for factor correlation estimation in ordinal CFA, especially under challenging data conditions.
  • Editors and policymakers should emphasize the inclusion of interval estimates in research reporting.