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

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
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

2.0K
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
2.0K
Variability: Analysis01:11

Variability: Analysis

627
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
627
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.1K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.4K
Variation01:19

Variation

8.3K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.3K

You might also read

Related Articles

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

Sort by
Same author

A NEW MATRIX FOR THE ASSESSMENT OF FACTOR CONTRIBUTIONS.

Multivariate behavioral research·2016
Same author

Brief Report: An Additional Minimal Transformation To Orthonormality.

Multivariate behavioral research·2016
Same author

The Relationship Of Personality Structure To Patterns Of Adolescent Substance Use.

Multivariate behavioral research·2016
Same author

Interrelations Among Models For The Analysis Of Moment Structures.

Multivariate behavioral research·2016
Same author

Longitudinal Analysis Of The Role Of Peer Support, Adult Models, And Peer Subcultures In Beginning Adolescent Substance Use: An Application Of Setwise Canonical Correlation Methods.

Multivariate behavioral research·2016
Same author

Fit Indexes, Lagrange Multipliers, Constraint Changes and Incomplete Data in Structural Models.

Multivariate behavioral research·2016
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 26, 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

7.3K

Invariant Standardized Estimated Parameter Change for Model Modification in Covariance Structure Analysis.

C P Chou, P M Bentler

    Multivariate Behavioral Research
    |January 31, 2016
    PubMed
    Summary
    This summary is machine-generated.

    A new standardized estimated parameter change (SEPC) is introduced for covariance structure analysis, improving upon existing methods. This invariant SEPC enhances model modification by addressing limitations in current standardization techniques.

    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

    3.8K
    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
    09:00

    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

    Published on: August 16, 2024

    1.3K

    Related Experiment Videos

    Last Updated: Mar 26, 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

    7.3K
    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
    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
    09:00

    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

    Published on: August 16, 2024

    1.3K

    Area of Science:

    • Structural Equation Modeling
    • Covariance Structure Analysis
    • Multivariate Statistics

    Background:

    • The Estimated Parameter Change (EPC) is a criterion for model modification in covariance structure analysis.
    • Kaplan's standardized version (SEPC-K) has limitations, as it is not fully standardized and lacks invariance to variable scaling.

    Purpose of the Study:

    • To introduce a new, fully standardized SEPC that is invariant to the original metrics of measured and latent variables.
    • To present a Multivariate Estimated Parameter Change (MEPC) and its standardized version (SMEPC) for simultaneous parameter freeing.
    • To discuss the appropriate use of standardized solutions in covariance structure analysis.

    Main Methods:

    • Development of a novel SEPC metric ensuring scale invariance.
    • Introduction of MEPC for assessing simultaneous parameter changes.
    • Derivation of SMEPC for standardized assessment of multiple parameter changes.

    Main Results:

    • The proposed SEPC is invariant to variable scaling, offering a more reliable measure for model modification.
    • MEPC and SMEPC provide tools for evaluating simultaneous changes in multiple parameters.
    • The study highlights the inappropriate application of standardization in scale-specific models.

    Conclusions:

    • The new invariant SEPC offers a superior approach to model modification in covariance structure analysis.
    • MEPC and SMEPC extend the utility of parameter change estimation to multivariate contexts.
    • Careful consideration of standardization is crucial for accurate interpretation in structural modeling.