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

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...
Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a decrease in the...
One-Way ANOVA01:18

One-Way ANOVA

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...
Factors Affecting Illness01:18

Factors Affecting Illness

When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
For instance, risk factors are connected to illness, disability,...
Two-Way ANOVA01:17

Two-Way ANOVA

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 means for...
Compacting Factor test01:22

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...

You might also read

Related Articles

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

Sort by
Same author

[Psychometric properties of the Addenbrooke's Cognitive Examination III (ACE-III) for the detection of dementia].

Revista medica de Chile·2021
Same author

The mediating role of self-esteem on the relationship between perceived discrimination and mental health in South American immigrants in Chile.

Psychiatry research·2018
Same author

Mean structure analysis from an IRT approach: an application in the context of organizational psychology.

Psicothema·2012
See all related articles

Related Experiment Video

Updated: May 24, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

[Factor analysis of ipsative data: a simulation study].

Carmen Ximénez Gómez1, Carlos Calderón Carvajal

  • 1Facultad de Psicología, Universidad Autónoma de Madrid, 28049 Madrid, Spain. carmen.ximenez@uam.es

Psicothema
|March 17, 2012
PubMed
Summary

Researchers must exercise caution when performing factor analysis on ipsative data due to a singular covariance matrix. Incorrect model specification, especially with fewer factors, poses significant risks for reliable results in factor analysis.

More Related Videos

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

Related Experiment Videos

Last Updated: May 24, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
09:00

Advancing Dyslexia Assessment in Children Through Computerized Testing

Published on: August 16, 2024

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

Area of Science:

  • Psychometrics
  • Statistical analysis

Context:

  • Factor analysis is a common statistical technique used to identify underlying structures in data.
  • Ipsative data, derived from forced-choice responses, presents unique challenges for standard factor analysis due to its inherent constraints.

Purpose:

  • To review optimal conditions for conducting factor analysis on ipsative data.
  • To present findings from a simulation study examining factor analysis of ipsative data under various conditions.
  • To provide guidance for researchers using ipsative data in factor analysis.

Summary:

  • Classical factor analysis methods are unsuitable for ipsative data because they result in a singular covariance matrix.
  • A simulation study evaluated the impact of sample size, model complexity, and model specification (correct vs. incorrect) on factor analysis of ipsative data.
  • Results indicate a need for careful application of factor analysis with ipsative data, especially when model misspecification is suspected or fewer factors are involved.

Impact:

  • Highlights the limitations of standard factor analysis with ipsative data.
  • Offers empirical evidence on the performance of factor analysis under different conditions with ipsative data.
  • Advises researchers on potential pitfalls and best practices when analyzing ipsative datasets to ensure valid findings.