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

Survival Tree01:19

Survival Tree

142
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
142
Factorial Design02:01

Factorial Design

13.2K
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...
13.2K
Residual Plots01:07

Residual Plots

5.0K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.0K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.2K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
7.2K
Bar Graph01:07

Bar Graph

17.2K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
17.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

266
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
266

You might also read

Related Articles

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

Sort by
Same author

Consistent Factor Score Regression: A Better Alternative for Uncorrected Factor Score Regression?

Educational and psychological measurement·2026
Same author

Rethinking causal inference for recurring exposures: The incremental propensity score approach with lavaan.

Behavior research methods·2025
Same author

A tutorial for understanding SEM using R: Where do all the numbers come from?

The British journal of mathematical and statistical psychology·2025
Same author

Structural after measurement (SAM) approaches for accommodating latent quadratic and interaction effects.

Behavior research methods·2025
Same author

A note on using random starting values in small sample SEM.

Behavior research methods·2025
Same author

Mixture multigroup structural equation modeling: A novel method for comparing structural relations across many groups.

Psychological methods·2024
Same journal

A Kurtosis-Adjusted Bias Correction for the Standardized Mean Difference: Extending Hedges' <i>g</i> to Nonnormal Populations.

Educational and psychological measurement·2026
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
See all related articles

Related Experiment Video

Updated: Aug 31, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K

Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data.

Tim Cosemans1, Yves Rosseel2, Sarah Gelper1

  • 1Eindhoven University of Technology, The Netherlands.

Educational and Psychological Measurement
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

Exploratory graph analysis (EGA) accurately identifies latent variables, outperforming traditional methods for both continuous and binary data. For binary data, Pearson correlations are recommended over tetrachoric correlations for factor retention.

Keywords:
binary dataexploratory factor analysisexploratory graph analysisfactor retentionsimulation

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.0K

Related Experiment Videos

Last Updated: Aug 31, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.5K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.0K

Area of Science:

  • Social Sciences
  • Psychometrics
  • Statistical Modeling

Background:

  • Exploratory Graph Analysis (EGA) is a method for identifying latent variables.
  • Methodological choices, such as factor retention, can impact EGA results.
  • Limited research exists on factor retention criteria for binary data.

Purpose of the Study:

  • Compare the performance of EGA with traditional factor retention criteria.
  • Evaluate these methods using both continuous and binary data.
  • Investigate the accuracy of factor retention for binary data.

Main Methods:

  • Simulations were conducted with varying sample sizes, communalities, interfactor correlations, skewness, and correlation measures.
  • EGA was compared against traditional factor retention criteria.
  • Both continuous and binary data were utilized.

Main Results:

  • EGA demonstrated superior performance over traditional criteria in most scenarios, showing less bias and higher accuracy.
  • For binary data, Pearson correlations were found to be more accurate for factor retention than tetrachoric correlations.
  • Simulation results highlight the robustness of EGA across various data conditions.

Conclusions:

  • EGA is a reliable method for latent variable discovery, particularly outperforming traditional criteria.
  • The findings challenge conventional approaches to factor retention with binary data.
  • Researchers should consider Pearson correlations for binary data analysis in factor retention decisions.