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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

523
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...
523
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

4.0K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
4.0K
Outliers and Influential Points01:08

Outliers and Influential Points

6.5K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.5K
Significance Testing: Overview01:04

Significance Testing: Overview

12.9K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
12.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
Fisher's Exact Test01:08

Fisher's Exact Test

1.3K
Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of...
1.3K

You might also read

Related Articles

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

Sort by
Same author

The Effects of Personalized Feedback About ALDH2*2, Alcohol Use, and Associated Health Risks on Drinking Intention and Consumption: The Role of Self-Efficacy and Perceived Threat.

Alcohol, clinical & experimental research·2026
Same authorSame journal

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

Multivariate behavioral research·2026
Same author

From barriers to benefits: A personalized sleep intervention enhances sleep duration and emotional health in chronic short sleepers.

British journal of psychology (London, England : 1953)·2026
Same author

A two-stage approach to account for measurement error when using empirical Bayes estimates of random slopes.

Psychological methods·2026
Same author

Multi-Group Multidimensional Classification Accuracy Analysis (MMCAA): A General Framework for Evaluating the Practical Impact of Partial Invariance.

Multivariate behavioral research·2026
Same author

How plausible is my model? Assessing model plausibility of structural equation models using Bayesian posterior probabilities (BPP).

Behavior research methods·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

Exploring the Use of Multiple Imputation for Handling Missing Covariates in Meta-Regression with Dependent Effect Sizes.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 3, 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

semfindr: An R Package for Identifying Influential Cases in Structural Equation Modeling.

Shu Fai Cheung1, Mark H C Lai2

  • 1Department of Psychology, University of Macau, Taipa, Macao SAR, China.

Multivariate Behavioral Research
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces semfindr, an R package for identifying influential cases in structural equation modeling (SEM). It simplifies sensitivity analysis for robust research findings.

Keywords:
Structural equation modelinginfluential casesoutlierssensitivity analysis

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
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.6K

Related Experiment Videos

Last Updated: Mar 3, 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
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.6K

Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Computational Statistics

Background:

  • Sensitivity analysis is crucial for assessing the robustness of structural equation modeling (SEM) findings.
  • Assessing case influence on parameter estimates and model fit is a key aspect of SEM sensitivity analysis.
  • Current methods for identifying influential cases in SEM are often limited or inappropriately applied.

Purpose of the Study:

  • To develop an accessible R package, semfindr, for identifying influential cases in SEM.
  • To provide efficient and comprehensive tools for sensitivity analysis in SEM.
  • To facilitate the appropriate assessment of case influence and improve the robustness of SEM findings.

Main Methods:

  • Development of the 'semfindr' R package utilizing the leave-one-out (LOO) method.
  • Implementation of computational efficiency by separating refitting and influence computation steps.
  • Inclusion of plotting functions for effective visualization of case influence in complex SEM.

Main Results:

  • The semfindr package enables efficient identification of influential cases in SEM.
  • The package supports multiple-group models and handles missing data.
  • semfindr provides publication-ready results and plots for case influence assessment.

Conclusions:

  • semfindr offers a user-friendly and computationally efficient solution for influential case identification in SEM.
  • The package enhances the quality and reliability of SEM sensitivity analyses.
  • semfindr facilitates better understanding and reporting of influential cases in SEM research.