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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

152
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...
152
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

178
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...
178
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

907
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...
907
Multiple Regression01:25

Multiple Regression

3.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.4K
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.2K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.2K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

829
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
829

You might also read

Related Articles

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

Sort by
Same author

Cumulant-Based Approaches for Testing the Assumption of Independent Errors in Non-Gaussian Parallel and Congeneric Measures.

Educational and psychological measurement·2026
Same author

You must parcel carefully if you have to! Comparing eight item parceling strategies with the item-level model for bifactor predictive models.

Psychological methods·2026
Same author

Does X at Time 1 Cause Y at Time 2? Longitudinal Causal Learning with Hidden Confounders.

Psychometrika·2026
Same author

Testing the validity of instrumental variables in just-identified linear non-Gaussian models.

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

Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models.

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

Comparing Likert and Slider Response Formats in Clinical Assessment: Evidence From Measuring Depression Symptoms Using CES-D 8.

Assessment·2025

Related Experiment Video

Updated: Nov 19, 2025

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

7.1K

A comparison of full information maximum likelihood and multiple imputation in structural equation modeling with

Taehun Lee1, Dexin Shi2

  • 1Department of Psychology, Chung-Ang University.

Psychological Methods
|January 28, 2021
PubMed
Summary

Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI) yield similar results when data models are correct. When models are misspecified, MI estimates align better with complete data, while FIML better approximates specific fit indices.

More Related Videos

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

3.6K
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

6.6K

Related Experiment Videos

Last Updated: Nov 19, 2025

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

7.1K
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

3.6K
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

6.6K

Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Missing data is a common challenge in statistical analyses.
  • Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI) are two prominent methods for handling missing data.
  • Understanding their performance under various conditions is crucial for accurate research.

Purpose of the Study:

  • To compare the performance of FIML and MI missing data procedures.
  • To investigate their relative accuracy against complete data analyses.
  • To identify discrepancies when the statistical model is misspecified.

Main Methods:

  • Monte Carlo simulation studies were employed.
  • Researchers had access to original complete data for comparison.
  • Analyses varied sample size, missingness percentage, and model misfit.

Main Results:

  • FIML and MI produced equivalent results to each other and complete data when the model was correctly specified.
  • When the model was misspecified, MI parameter estimates, CFI, and TLI were closer to complete data estimates.
  • FIML chi-squares and RMSEA were closer to complete data estimates under model misspecification.

Conclusions:

  • The choice between FIML and MI can impact results, particularly with model misspecification.
  • Discrepancies arise from the interplay between imputation model parsimony and accuracy.
  • Further research is needed to explore practical and methodological implications.