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

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

4.4K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
4.4K
Introduction to R01:11

Introduction to R

621
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
621
Response Surface Methodology01:16

Response Surface Methodology

269
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
269
Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

5.9K
To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
5.9K
Interpreting R Charts01:22

Interpreting R Charts

118
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
118
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

404
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
404

You might also read

Related Articles

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

Sort by
Same author

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

Behavior research methods·2025
Same author

Modeling Emotional Arousal With Turbulence Measured by EEG.

Psychophysiology·2025
Same author

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

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 author

Knee flexion of saxophone players anticipates tonal context of music.

NPJ science of learning·2023
Same author

Cholinergic-related pupil activity reflects level of emotionality during motor performance.

The European journal of neuroscience·2023
Same journal

Proficiency order invariance of MLE, MAP, EAP, and WLE in item response theory.

The British journal of mathematical and statistical psychology·2026
Same journal

Bias and precision in true-score estimation.

The British journal of mathematical and statistical psychology·2026
Same journal

Polychoric correlations under the assumption of elliptical latent traits.

The British journal of mathematical and statistical psychology·2026
Same journal

Regularized reduced rank regression for mixed predictor and response variables.

The British journal of mathematical and statistical psychology·2026
Same journal

A multiple-choice SDT model for cognitive diagnosis models.

The British journal of mathematical and statistical psychology·2026
Same journal

Modular item response and structural equation modelling via measurement and uncertainty preserving parametric modelling.

The British journal of mathematical and statistical psychology·2026
See all related articles

Related Experiment Video

Updated: Sep 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

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

Yves Rosseel1, Marc Vidal2

  • 1Department of Data Analysis, Ghent University, Ghent, Belgium.

The British Journal of Mathematical and Statistical Psychology
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

This tutorial demystifies structural equation modeling (SEM) by showing how to manually compute key results like parameter estimates and fit measures using R. It makes complex SEM calculations accessible for researchers.

Keywords:
R codeconfirmatory factor analysisstructural equation modelingtutorial

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

726
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

6.4K

Related Experiment Videos

Last Updated: Sep 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

726
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

6.4K

Area of Science:

  • Statistics
  • Quantitative Psychology
  • Data Science

Background:

  • Structural Equation Modeling (SEM) is perceived as complex.
  • Understanding SEM's underlying computations in software output is challenging.
  • Open-source SEM tools in R exist but their source code can be overwhelming.

Purpose of the Study:

  • To provide an accessible introduction to the basic computations behind standard SEM analyses.
  • To clarify how SEM software output numbers are computed.
  • To enhance reader understanding of SEM's internal workings.

Main Methods:

  • Manual reproduction of key SEM results using simple R scripts.
  • Utilizing two well-known example datasets for demonstration.
  • Focusing on clarity and conceptual understanding over computational efficiency.

Main Results:

  • Demonstrated manual computation of parameter estimates.
  • Showcased reproduction of standard errors.
  • Illustrated the calculation of SEM fit measures.

Conclusions:

  • Readers can gain a better grasp of SEM "under the hood" through manual computation.
  • This tutorial facilitates applying SEM computational concepts in independent research.
  • Demystifies SEM calculations, making the method more approachable for researchers.