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

321
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...
321
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

1.4K
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...
1.4K
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
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

561
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...
561
Experimental Designs01:16

Experimental Designs

18.5K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
18.5K
Correlation of Experimental Data01:23

Correlation of Experimental Data

521
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
521

You might also read

Related Articles

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

Sort by
Same author

Synaptic abnormalities of mice lacking toll-like receptor (TLR)-9.

Neuroscience·2016
Same author

On the Madness in His Method: R. B. Cattell's Contributions to Structural Equation Modeling.

Multivariate behavioral research·2016
Same author

Structural Equation Models of Factorial Invariance in Parallel Proportional Profiles and Oblique Confactor Problems.

Multivariate behavioral research·2016
Same author

Principles versus Principals of Structural Factor Analyses.

Multivariate behavioral research·2016
Same author

Prediction of somatic and non-somatic depressive symptoms between inpatient rehabilitation and follow-up.

Spinal cord·2009
Same author

Electrophysiological evidence of functional integration between the language and motor systems in the brain: a study of the speech Bereitschaftspotential.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2008
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

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

Structural Factor Analysis Experiments with Incomplete Data.

J J McArdle

    Multivariate Behavioral Research
    |January 9, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Structural equation models offer benefits and limitations for multivariate experiments with incomplete data. These methods provide new opportunities for advanced research in experimental design and latent variable analysis.

    More Related Videos

    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
    20:24

    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

    Published on: January 31, 2014

    17.3K
    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

    3.8K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    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
    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
    20:24

    Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

    Published on: January 31, 2014

    17.3K
    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

    3.8K

    Area of Science:

    • Statistics
    • Psychometrics
    • Experimental Design

    Background:

    • Multivariate experiments often encounter incomplete data, posing challenges for traditional analysis.
    • Structural equation modeling (SEM) provides a flexible framework for handling complex data structures.

    Purpose of the Study:

    • To present the benefits and limitations of SEM for multivariate experiments with incomplete data.
    • To illustrate SEM applications with various types of missing or omitted data in cognitive performance studies.

    Main Methods:

    • Analysis of latent variable path models using SEM.
    • Illustrative examples covering latent variables, omitted variables, and random/non-random missing data.
    • Power-based cost-benefit analyses for experimental design.

    Main Results:

    • SEM effectively handles different types of incomplete data in multivariate experiments.
    • Demonstrated utility in analyzing latent variable path models of cognitive performance.
    • Cost-benefit analyses offer guidance for experimental planning.

    Conclusions:

    • SEM offers valuable tools for addressing incomplete data in multivariate research.
    • These methods connect to classical experimental design, longitudinal, and genetic analyses.
    • SEM presents new avenues for future multivariate research and complex data analysis.