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
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
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

486
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
486
Response Surface Methodology01:16

Response Surface Methodology

822
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
822
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

677
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
677
Regression Analysis01:11

Regression Analysis

8.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.9K

You might also read

Related Articles

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

Sort by
Same author

Adventitious Error and Its Implications for Testing Relations Between Variables and for Composite Measurement Outcomes.

Psychometrika·2026
Same author

What does a <i>Z</i>-curve analysis tell us?

Cognition & emotion·2026
Same author

Depression and anxiety mediate the relationship between COVID-19 stay-at-home orders and tobacco and marijuana use.

PloS one·2025
Same author

Understanding measurement precision from a regression perspective.

Psychological methods·2025
Same author

On a general theoretical framework of reliability.

The British journal of mathematical and statistical psychology·2024
Same author

Uses of uncertain statistical power: Designing future studies, not evaluating completed studies.

Psychological methods·2024
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

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

Sensitivity Analysis in Structural Equation Models: Cases and Their Influence.

Jolynn Pek1, Robert C MacCallum1

  • 1a University of North Carolina at Chapel Hill.

Multivariate Behavioral Research
|January 8, 2016
PubMed
Summary
This summary is machine-generated.

Detecting influential cases in structural equation models (SEM) is crucial. This study introduces methods to identify "good" and "bad" cases, improving model interpretation and reliability.

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
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K

Related Experiment Videos

Last Updated: Mar 27, 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
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
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K

Area of Science:

  • Statistics
  • Social Sciences
  • Psychometrics

Background:

  • Outlier and influential case detection is standard in linear regression.
  • Case diagnostics in structural equation models (SEM) are less understood and applied.
  • Case diagnostics reveal data subset uncertainties and highlight unusual data points.

Purpose of the Study:

  • To present and illustrate measures of case influence for SEM.
  • To emphasize the practical application and interpretation of case diagnostics in SEM.
  • To highlight the distinction between outliers and influential cases, and introduce "good" and "bad" cases.

Main Methods:

  • Application of several case influence measures within SEM.
  • Empirical illustration using a common factor model (verbal/visual ability).
  • Empirical illustration using a general SEM (industrialization and democracy).

Main Results:

  • Cases can uniquely influence different aspects of SEM results.
  • Distinction between outliers and influential cases is critical.
  • Identification of "good" (improving fit) and "bad" (worsening fit) influential cases.

Conclusions:

  • Detecting influential cases is vital for robust SEM.
  • Recommendations for applying case influence measures in SEM are provided.
  • Understanding case influence enhances the reliability of SEM findings.