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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

228
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...
228
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

84
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
84
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
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).
2.6K
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

587
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
587

You might also read

Related Articles

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

Sort by
Same author

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same author

Autonomy and Relatedness in Mother-Adolescent Interactions: An Investigation Using Exploratory Graph Analysis.

Family process·2026
Same author

Dynamic network models reveal personalized patterns of well-being in young adult daily lives.

Scientific reports·2025
Same author

Developmental changes in youth affect: A within-person approach.

Emotion (Washington, D.C.)·2025
Same author

Dimensionality Assessment in Forced-Choice Questionnaires: First Steps Toward an Exploratory Framework.

Educational and psychological measurement·2025
Same author

A Systematic Evaluation of Wording Effects Modeling Under the Exploratory Structural Equation Modeling Framework.

Multivariate behavioral research·2025

Related Experiment Video

Updated: Jul 26, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.

Alexander P Christensen1, Luis Eduardo Garrido2, Kiero Guerra-Peña2

  • 1Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, 37203, USA. alexpaulchristensen@gmail.com.

Behavior Research Methods
|June 16, 2023
PubMed
Summary

Exploratory graph analysis (EGA) methods using GLASSO with Fast-greedy, Louvain, or Walktrap algorithms accurately identify the number of factors in psychological data. These network psychometric approaches outperform traditional factor analysis in simulations.

Keywords:
Community detectionDimension reductionNetwork psychometrics

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

Related Experiment Videos

Last Updated: Jul 26, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.2K

Area of Science:

  • Psychometrics
  • Network Science
  • Data Analysis

Background:

  • Determining the correct number of factors is crucial for psychological measurement.
  • Exploratory Graph Analysis (EGA), based on network psychometrics, has emerged as a robust alternative to traditional factor analysis.
  • Previous research suggests EGA's effectiveness, but optimal methods require further investigation.

Purpose of the Study:

  • To compare the performance of various sparsity induction methods and community detection algorithms within the EGA framework.
  • To evaluate these methods for identifying unidimensional structures in psychological data.
  • To assess performance across continuous and polytomous data types.

Main Methods:

  • A Monte Carlo simulation was employed.
  • Methods included zero-order correlation, GLASSO, and non-regularized partial correlation for sparsity induction.
  • Community detection algorithms such as Fast-greedy, Louvain, and Walktrap were utilized.

Main Results:

  • The combination of GLASSO with Fast-greedy, Louvain, and Walktrap algorithms demonstrated high accuracy and low bias.
  • These method-algorithm pairings consistently outperformed other tested combinations.
  • Performance was evaluated across various simulation conditions for both continuous and polytomous data.

Conclusions:

  • GLASSO paired with Fast-greedy, Louvain, or Walktrap algorithms represents a superior approach for factor number determination in psychological measurement.
  • These network-based methods offer a powerful and accurate alternative to traditional factor analytic techniques.
  • Further research into community detection algorithms within EGA is warranted for advancing psychological measurement.