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

15.4K
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...
15.4K
Two-Way ANOVA01:17

Two-Way ANOVA

3.6K
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...
3.6K
One-Way ANOVA01:18

One-Way ANOVA

14.6K
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...
14.6K
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
Wechsler's Contribution to Measures of Intelligence01:23

Wechsler's Contribution to Measures of Intelligence

2.3K
David Wechsler, a psychologist who worked with World War I veterans, developed a significant IQ test in 1939 called the Wechsler-Bellevue Intelligence Scale. This test was innovative because it combined several subtests that measured both verbal and nonverbal skills, reflecting Wechsler's belief that intelligence is a global capacity involving purposeful action, rational thinking, and effective interaction with the environment. This test later evolved into the Wechsler Adult Intelligence...
2.3K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.0K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.0K

You might also read

Related Articles

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

Sort by
Same author

The Prevalence and Factors Associated with Workforce Attrition and Intention-to-Leave Among Healthcare Workers in New Zealand: A Systematic Literature Review and Meta-Analysis.

Journal of the Royal Society of New Zealand·2026
Same author

Preliminary validation of a 10-item version of the Depression, Anxiety and Stress Scale in a mild traumatic brain injury sample.

Brain impairment : a multidisciplinary journal of the Australian Society for the Study of Brain Impairment·2025
Same author

Identifying the Bridges Between Post Concussion Symptoms and Psychological Distress in Mild Traumatic Brain Injury Using Network Analysis.

The Journal of head trauma rehabilitation·2025
Same author

Rasch analysis of the depression anxiety stress scales-21 (DASS-21) in a mild traumatic brain injury sample.

Brain injury·2024
Same author

Photocurrent-driven transient symmetry breaking in the Weyl semimetal TaAs.

Nature materials·2021
Same author

Longitudinal Flow Decorrelations in Xe+Xe Collisions at sqrt[s_{NN}]=5.44  TeV with the ATLAS Detector.

Physical review letters·2021

Related Experiment Video

Updated: Mar 27, 2026

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

Factor Analysis of the WAIS-R Using the Factor Replication Procedure, FACTOREP.

R J Siegert, M D Patten, A J Taylor

    Multivariate Behavioral Research
    |January 14, 2016
    PubMed
    Summary

    The Wechsler Adult Intelligence Scale-Revised (WAIS-R) likely has two distinct factors, not three. A new analysis using FACTOREP suggests previous findings of three factors were influenced by a general factor.

    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

    7.3K
    Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
    10:39

    Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

    Published on: August 29, 2025

    1.3K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    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
    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
    Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
    10:39

    Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

    Published on: August 29, 2025

    1.3K

    Area of Science:

    • Psychometrics
    • Cognitive Psychology

    Background:

    • The factor structure of the Wechsler Adult Intelligence Scale-Revised (WAIS-R) is debated.
    • Previous studies using coefficient of congruence suggested three factors.

    Purpose of the Study:

    • To re-evaluate the factor structure of the WAIS-R.
    • To address claims that previous findings were artifacts of a general factor.

    Main Methods:

    • Utilized identical data from a previous study.
    • Employed the FACTOREP factor comparison technique.
    • FACTOREP was used to reduce the influence of error variance and the general factor.

    Main Results:

    • The analysis demonstrated two strong factors within the WAIS-R.
    • Little evidence was found to support the existence of a third factor.

    Conclusions:

    • The WAIS-R factor structure is best represented by two factors.
    • The previous conclusion of three factors may have been an artifact.