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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

37.0K
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...
37.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

561
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...
561
Factorial Design02:01

Factorial Design

15.4K
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...
15.4K
Nominal Level of Measurement00:56

Nominal Level of Measurement

41.9K
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. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
41.9K
Ratio Level of Measurement00:54

Ratio Level of Measurement

22.4K
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.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
22.4K
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

You might also read

Related Articles

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

Sort by
Same author

Genetics of major depressive disorder in a homogeneous population with uniform phenotyping.

Molecular psychiatry·2026
Same author

Evaluating differences in latent means across studies: Extending meta-analytic confirmatory factor analysis with the analysis of means.

Research synthesis methods·2026
Same author

Can we include dichotomous variables in meta-analytic structural equation modeling? Mind the prevalence.

Behavior research methods·2026
Same author

Six ways to handle dependent effect sizes in meta-analytic structural equation modeling: Is there a gold standard?

Research synthesis methods·2026
Same author

Causation Between Smoking Quantity and Depressive Symptoms in Young Adults: Evidence From Novel Cross-Lagged Twin Models.

medRxiv : the preprint server for health sciences·2025
Same author

The Power to Resolve Cultural Transmission and Sibling Interaction Using Polygenic Scores.

Behavior genetics·2025
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Using Two-Level Factor Analysis to Test for Cluster Bias in Ordinal Data.

Suzanne Jak1, Frans J Oort2, Conor V Dolan3

  • 1a Department of Social Sciences-Methodology and Statistics , Utrecht University.

Multivariate Behavioral Research
|January 7, 2016
PubMed
Summary
This summary is machine-generated.

The study found that the unscaled likelihood ratio test (LRT) and Wald test are reliable for detecting cluster bias in two-level data, especially with large bias. The scaled LRT yielded untrustworthy results with ordinal data.

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

7.3K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Related Experiment Videos

Last Updated: Mar 27, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
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
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Area of Science:

  • Statistics
  • Educational Psychology
  • Psychometrics

Background:

  • Cluster bias can affect statistical analyses in hierarchical data.
  • Measurement invariance testing is crucial for valid comparisons across groups.
  • Assessing the performance of bias detection methods is essential for robust research.

Purpose of the Study:

  • To evaluate the true positive and false positive rates of cluster bias tests.
  • To compare the likelihood ratio test (LRT) and Wald test performance with ordinal data.
  • To provide guidance on selecting appropriate statistical methods for cluster bias detection.

Main Methods:

  • A simulation study was conducted using two-level data with ordinal outcomes.
  • The performance of the scaled and unscaled LRT and the Wald test was examined.
  • True positive rates (empirical power) and false positive rates were calculated.

Main Results:

  • The scaled LRT, which adjusts for nonnormality, produced untrustworthy results.
  • The unscaled LRT and Wald test demonstrated acceptable false positive rates.
  • Both the unscaled LRT and Wald test showed good empirical power when cluster bias was substantial.

Conclusions:

  • The unscaled LRT and Wald test are recommended for detecting cluster bias in two-level ordinal data.
  • Researchers should exercise caution when using scaled LRT versions that account for nonnormality.
  • These findings have implications for analyzing hierarchical data in fields like education research.