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

Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

797
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
797
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

73
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...
73
Classifying Matter by Composition03:35

Classifying Matter by Composition

72.3K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
72.3K
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

622
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...
622
Classification of Systems-II01:31

Classification of Systems-II

195
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
195
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88

You might also read

Related Articles

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

Sort by
Same author

Organizational culture and turnover intention among Generation Z in Korea: Associations with job satisfaction and organizational commitment.

Frontiers in psychology·2026
Same author

Utilizing Carbonated Reclaimed Water as Concrete Mixing Water: Improved CO<sub>2</sub> Uptake and Compressive Strength.

Materials (Basel, Switzerland)·2026
Same author

A Comparison of LTA Models with and Without Residual Correlation in Estimating Transition Probabilities.

Educational and psychological measurement·2025
Same author

The Impact of Imposing Equality Constraints on Residual Variances Across Classes in Regression Mixture Models.

Frontiers in psychology·2022
Same author

Multiple Group Analysis in Multilevel Data Across Within-Level Groups: A Comparison of Multilevel Factor Mixture Modeling and Multilevel Multiple-Indicators Multiple-Causes Modeling.

Educational and psychological measurement·2021
Same author

Adequate Sample Sizes for a Three-Level Growth Model.

Frontiers in psychology·2021

Related Experiment Video

Updated: Aug 8, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.8K

Evaluating the Quality of Classification in Mixture Model Simulations.

Yoona Jang1, Sehee Hong1

  • 1Korea University, Seoul, Republic of Korea.

Educational and Psychological Measurement
|March 3, 2023
PubMed
Summary

Including covariates in latent class models did not improve classification accuracy. Models without covariates better predicted the number of classes, supporting the three-step approach for robust latent class analysis.

Keywords:
Monte Carlo simulationeffects of covariateslatent class analysisquality of classificationsample size

More Related Videos

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

9.0K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K

Related Experiment Videos

Last Updated: Aug 8, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.8K
Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts
10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

9.0K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K

Area of Science:

  • Statistics
  • Psychometrics
  • Social Sciences

Background:

  • Latent class analysis (LCA) is a statistical method used to identify underlying subgroups within a population.
  • The inclusion of covariates in LCA models is a common practice, but its impact on classification quality requires careful examination.
  • The three-step approach is a popular method for estimating LCA models, particularly when dealing with covariates.

Purpose of the Study:

  • To evaluate the impact of covariate inclusion on the classification quality of basic latent class models.
  • To compare the performance of latent class models with and without covariates in predicting the number of latent classes.
  • To assess the overall classification quality of the three-step approach under varying conditions.

Main Methods:

  • Monte Carlo simulations were employed to generate data for latent class models.
  • Models with and without covariates were compared to assess their predictive accuracy for the number of classes.
  • Classification quality was evaluated under different scenarios of covariate effects, sample sizes, and indicator quality.

Main Results:

  • Latent class models that did not include covariates demonstrated superior prediction of the number of latent classes.
  • The three-step approach exhibited classification quality exceeding 70% across various simulation conditions.
  • Covariate inclusion did not consistently enhance the classification accuracy of the latent class models.

Conclusions:

  • The findings suggest that models without covariates may offer better class enumeration in latent class analysis.
  • The three-step approach for latent class analysis demonstrates reliable classification quality, even with potential covariate influences.
  • Applied researchers should carefully consider the practical implications of evaluating classification quality when using latent class models.