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

Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...

You might also read

Related Articles

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

Sort by
Same author

Nested model comparisons between common factors and composites.

Psychological methods·2026
Same author

Calculating and Interpreting Maximal Reliability in Bifactor Models.

Multivariate behavioral research·2026
Same author

Improving the Measurement of the Big Five via Alternative Formats for the BFI-2.

Journal of personality assessment·2025
Same author

Reading the Mind in the Eyes Test Scores Demonstrate Poor Structural Properties in Nine Large Non-Clinical Samples.

Assessment·2025
Same author

Evaluating statistical fit of confirmatory bifactor models: Updated recommendations and a review of current practice.

Psychological methods·2025
Same author

Fit indices are insensitive to multiple minor violations of perfect simple structure in confirmatory factor analysis.

Psychological methods·2025
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: Jul 4, 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

Constrained versus unconstrained estimation in structural equation modeling.

Victoria Savalei1, Stanislav Kolenikov

  • 1Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, Canada. v.savalei@ubc.ca

Psychological Methods
|June 19, 2008
PubMed
Summary
This summary is machine-generated.

Reference mixture distributions for difference tests are rarely appropriate, especially when dealing with constrained estimation in structural equation modeling. Unconstrained estimation is often simpler and more informative for researchers.

More Related Videos

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Related Experiment Videos

Last Updated: Jul 4, 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

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Reference mixture distributions are used for difference tests with boundary parameters.
  • Previous reviews have not fully addressed the conditions under which these distributions are necessary.
  • Constrained difference tests are a key context for the relevance of mixture distributions.

Purpose of the Study:

  • To clarify when reference mixture distributions for difference tests are needed.
  • To evaluate methods for assessing model fit under constrained estimation.
  • To compare constrained and unconstrained estimation in structural equation modeling.

Main Methods:

  • Analysis of the necessity of mixture distributions for difference tests.
  • Evaluation of global model fit assessment techniques under constrained estimation.
  • Discussion of the pros and cons of constrained versus unconstrained estimation.

Main Results:

  • Mixture distributions for difference tests are primarily relevant only for constrained difference tests.
  • No perfect methods exist for assessing global model fit under constrained estimation.
  • The conditional approach of releasing degrees of freedom is the most sound method for fit assessment.
  • Unconstrained estimation is simpler and provides more information on model misfit.
  • Researchers face practical difficulties in appropriately conducting constrained difference tests, particularly regarding Heywood cases.

Conclusions:

  • Reference mixture distributions for difference tests are rarely appropriate in practice.
  • Unconstrained estimation is generally preferred due to its simplicity and informativeness.
  • Careful consideration of model fit assessment is crucial, especially under constrained estimation.