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

Factorial Design02:01

Factorial Design

13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
One-Way ANOVA01:18

One-Way ANOVA

7.9K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
7.9K
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

194
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...
194
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

490
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...
490
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

39
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...
39

You might also read

Related Articles

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

Sort by
Same author

Group Iterative Multiple Model Estimation Approaches in Clinical Science.

Annual review of clinical psychology·2026
Same author

Dynamic Fit Index Cutoffs for Time Series Network Models.

Multivariate behavioral research·2025
Same author

Association of PFAS and Metals with Cardiovascular Disease Risk: Exploring the Mediating Effect of Diet.

Environments (Basel, Switzerland)·2025
Same author

Automated machine learning for classification and regression: A tutorial for psychologists.

Behavior research methods·2025
Same author

Group-to-individual generalizability and individual-level inferences in cognitive neuroscience.

Neuroscience and biobehavioral reviews·2025
Same author

Common and uncommon risky drinking patterns in young adulthood uncovered by person-specific computational modeling.

Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors·2025
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

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

6.9K

A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA).

Kenneth A Bollen1,2, Kathleen M Gates3, Lan Luo3

  • 1Thurstone Psychometric Laboratory, Department of Psychology and Neuroscience, Department of Sociology, University of North Carolina at Chapel Hill, 235 E. Cameron Avenue, Chapel Hill, NC, 27599-3270, USA. bollen@unc.edu.

Psychometrika
|March 27, 2024
PubMed
Summary
This summary is machine-generated.

A new model implied instrumental variable (MIIV) approach to exploratory factor analysis (EFA) accurately identifies the number of factors and loadings. This method performs well even with complex models and smaller sample sizes, enhancing factor analysis reliability.

Keywords:
exploratory analysisexploratory factor analysislatent variablesmiivmodel implied instrumental variablesnumber of factors

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K
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

6.3K

Related Experiment Videos

Last Updated: Jun 29, 2025

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

6.9K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K
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

6.3K

Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • Factor analysis, pioneered by Spearman, has evolved significantly.
  • Distinctions between confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are established.
  • Existing EFA methods have limitations in handling complex factor structures and determining the number of factors.

Purpose of the Study:

  • To introduce a novel model implied instrumental variable (MIIV) approach for exploratory factor analysis (EFA).
  • To enhance EFA by incorporating features like measurement equation intercepts, correlated factors and errors, and robust standard error estimation.
  • To develop a procedure for determining the number of factors and simplifying structures by removing nonsignificant loadings.

Main Methods:

  • The proposed method is a model implied instrumental variable (MIIV) approach to exploratory factor analysis (EFA).
  • It allows for intercepts in measurement equations, correlated common factors, and correlated errors.
  • Includes overidentification tests and a procedure for determining the number of factors, with options for simpler structures.

Main Results:

  • Simulations demonstrate the MIIV-EFA procedure's effectiveness in recovering the correct number of factors.
  • The method successfully recovers primary and secondary loadings, even in complex models.
  • Accurate factor number identification is achieved with sample sizes of 100 or more; loadings are recovered with N=500.

Conclusions:

  • The MIIV-EFA approach offers a powerful and reliable method for exploratory factor analysis.
  • It addresses limitations of traditional EFA, particularly in determining the number of factors and estimating loadings.
  • The procedure shows strong performance across various model complexities and sample sizes, suggesting broad applicability.