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

Factorial Design02:01

Factorial Design

15.6K
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...
15.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

567
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...
567
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

323
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...
323
Structuralism01:26

Structuralism

4.0K
Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
4.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

397
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...
397
Applications of Stress01:04

Applications of Stress

763
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
763

You might also read

Related Articles

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

Sort by
Same author

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes.

Multivariate behavioral research·2026
Same author

Missing Data Sensitivity Analyses for Alcohol Research.

Alcohol, clinical & experimental research·2026
Same author

A Self-Guided App-Based Mindfulness Intervention for Racially and Ethnically Minoritized Individuals Who Experience Discrimination-Related Mental Health Symptoms: Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

To Disaggregate or Not to Disaggregate: A Focus on Covariates in Multilevel Models.

Multivariate behavioral research·2026
Same author

A factored regression approach to modeling latent variable interactions and nonlinear effects.

Psychological methods·2025
Same author

Building a simpler moderated nonlinear factor analysis model with Markov Chain Monte Carlo estimation.

Psychological methods·2024
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 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

Factored structural equation modeling in blimp.

Craig K Enders1, Brian T Keller2

  • 1Department of Psychology, University of California, Los Angeles.

Psychological Methods
|March 30, 2026
PubMed
Summary
This summary is machine-generated.

Factored structural equation modeling (FSEM) offers a flexible alternative for analyzing complex data. This method handles diverse variable types and structures using Bayesian imputation, simplifying advanced statistical modeling in social sciences.

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
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

6.9K

Related Experiment Videos

Last Updated: Mar 31, 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
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

6.9K

Area of Science:

  • Behavioral and social sciences
  • Statistical modeling
  • Psychometrics

Background:

  • Multivariate structural equation modeling (SEM) can be complex to implement.
  • Existing SEM approaches may struggle with diverse data types and structures.
  • Factored structural equation modeling (FSEM) presents a novel alternative.

Purpose of the Study:

  • Introduce factored structural equation modeling (FSEM).
  • Demonstrate FSEM implementation using Blimp software and the rblimp R package.
  • Provide a flexible framework for complex data analysis in social sciences.

Main Methods:

  • Reconceptualizes joint distributions using univariate/multivariate submodels.
  • Specifies models via regression equations.
  • Treats latent variables as missing data imputed via Bayesian data augmentation.
  • Accommodates continuous, binary, ordinal, nominal, count, and two-part variables.
  • Handles interactions, nonlinear effects, heteroscedasticity, and multilevel data.

Main Results:

  • FSEM seamlessly integrates diverse variable types and complex data structures.
  • The approach avoids violating distributional assumptions.
  • Illustrative models range from confirmatory factor analysis to dynamic, multilevel, and hybrid generalized-linear-structural equation models.
  • Provides Blimp syntax and real-data examples.

Conclusions:

  • FSEM offers a user-friendly and flexible framework for advanced statistical modeling.
  • The method is suitable for researchers in behavioral and social sciences.
  • Future research directions and limitations are discussed.