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

459
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...
459
Two-Way ANOVA01:17

Two-Way ANOVA

3.3K
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...
3.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

216
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...
216
Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

31.7K
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...
31.7K
One-Way ANOVA01:18

One-Way ANOVA

11.7K
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...
11.7K

You might also read

Related Articles

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

Sort by
Same author

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Psychometrika·2026
Same author

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Psychometrika·2024
Same author

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?

The British journal of mathematical and statistical psychology·2023
Same author

Replies to comments on "Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023).

The British journal of mathematical and statistical psychology·2023
Same author

A systematic framework for defining R-squared measures in mediation analysis.

Psychological methods·2023
Same author

A Realistic Evaluation of Methods for Handling Missing Data When There is a Mixture of MCAR, MAR, and MNAR Mechanisms in the Same Dataset.

Multivariate behavioral research·2023

Related Experiment Video

Updated: Jan 2, 2026

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

6.7K

A Two-level Moderated Latent Variable Model with Single Level Data.

Hongyun Liu1, Ke-Hai Yuan2, Fang Liu3

  • 1Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University.

Multivariate Behavioral Research
|November 30, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a two-level moderated latent variable (2MLV) model for more accurate moderation analysis. The new Bayesian approach offers improved interaction effect estimation and reliable moderation effect testing compared to existing methods.

Keywords:
Bayesian estimationModeration effecteffect sizelatent variables

More Related Videos

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

9.6K
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

7.2K

Related Experiment Videos

Last Updated: Jan 2, 2026

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

6.7K
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

9.6K
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

7.2K

Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Existing two-level regression models offer improved moderation analysis.
  • Single-level models for moderation analysis have limitations in accuracy and reliability.

Purpose of the Study:

  • To extend the two-level regression model to a two-level moderated latent variable (2MLV) model.
  • To utilize a Bayesian approach for estimating and testing moderation effects within the 2MLV framework.
  • To develop a more interpretable measure of effect size for moderation analysis.

Main Methods:

  • Development of the two-level moderated latent variable (2MLV) model.
  • Application of a Bayesian approach for parameter estimation and hypothesis testing.
  • Monte Carlo simulations to compare the 2MLV model with product-indicator (PI) and latent variable interaction (LVI) approaches.

Main Results:

  • The 2MLV model provides more accurate estimates of interaction effects than PI and LVI methods.
  • Credibility intervals from the 2MLV model demonstrate coverage rates closer to the nominal 95%.
  • The 2MLV model offers more reliable Type I error control for moderation effect testing, particularly under heteroscedasticity.

Conclusions:

  • The 2MLV model, estimated via a Bayesian approach, enhances the accuracy and reliability of moderation analysis.
  • The developed effect size measure provides a direct and interpretable quantification of a moderator's influence.
  • The 2MLV model represents a significant advancement for statistical modeling in moderation analysis.