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

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

384
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
384
Crossover Experiments01:16

Crossover Experiments

4.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
4.7K
Group Design02:01

Group Design

11.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
11.0K
One-Way ANOVA01:18

One-Way ANOVA

14.5K
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...
14.5K
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
What is an ANOVA?01:16

What is an ANOVA?

11.2K
The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...
11.2K

You might also read

Related Articles

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

Sort by
Same author

Optical and mechanical design of a telescope for lunar spectral irradiance measurements from a high-altitude aircraft.

The Review of scientific instruments·2020
Same author

Precise methane absorption measurements in the 1.64 μm spectral region for the MERLIN mission.

Journal of geophysical research. Atmospheres : JGR·2016
Same author

Multitrait-Multimethod Comparisons Across Populations: A Confirmatory Factor Analytic Approach.

Multivariate behavioral research·2016
Same author

Multiheterodyne spectroscopy with optical frequency combs generated from a continuous-wave laser.

Optics letters·2014
Same author

Toxicity and exposure of an adenovirus containing human interferon alpha-2b following intracystic administration in cynomolgus monkeys.

Gene therapy·2011
Same author

Individual differences in cognitive abilities.

Annual review of psychology·2010
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 26, 2026

Author Spotlight: Repetitive Transcranial Magnetic Stimulation Combined with Movement Observation in Cerebral Palsy
07:20

Author Spotlight: Repetitive Transcranial Magnetic Stimulation Combined with Movement Observation in Cerebral Palsy

Published on: August 9, 2024

2.1K

On Using Analysis Of Covariance In Repeated Measures Designs.

H D Delaney, S E Maxwell

    Multivariate Behavioral Research
    |January 24, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Analysis of covariance (ANCOVA) with repeated measures designs requires careful consideration of within-subject factors and covariate interactions. This study details ANCOVA application for complex designs, offering guidance for accurate analysis.

    More Related Videos

    Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
    07:40

    Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

    Published on: May 31, 2021

    4.3K
    A Within-Subject Experimental Design using an Object Location Task in Rats
    09:28

    A Within-Subject Experimental Design using an Object Location Task in Rats

    Published on: May 6, 2021

    5.4K

    Related Experiment Videos

    Last Updated: Mar 26, 2026

    Author Spotlight: Repetitive Transcranial Magnetic Stimulation Combined with Movement Observation in Cerebral Palsy
    07:20

    Author Spotlight: Repetitive Transcranial Magnetic Stimulation Combined with Movement Observation in Cerebral Palsy

    Published on: August 9, 2024

    2.1K
    Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
    07:40

    Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

    Published on: May 31, 2021

    4.3K
    A Within-Subject Experimental Design using an Object Location Task in Rats
    09:28

    A Within-Subject Experimental Design using an Object Location Task in Rats

    Published on: May 6, 2021

    5.4K

    Area of Science:

    • Statistics
    • Psychology
    • Biostatistics

    Background:

    • Repeated measures designs are common in scientific research, involving measurements taken over time or under different conditions.
    • Analysis of covariance (ANCOVA) is a statistical technique used to control for the effects of extraneous variables (covariates).
    • Integrating ANCOVA with multivariate approaches for repeated measures designs presents unique analytical challenges.

    Purpose of the Study:

    • To examine the application of ANCOVA within a multivariate framework for repeated measures designs.
    • To address key considerations for the accurate use of ANCOVA in designs with between- and within-subject factors.
    • To provide guidance on issues affecting the validity of statistical tests and the interpretation of results.

    Main Methods:

    • The study considers designs with one dependent variable and a single covariate observation per subject.
    • It focuses on the interplay between ANCOVA, multivariate analysis, and repeated measures.
    • Methodological issues concerning the main effect of within-subject factors, interaction effects, and change score reliability are detailed.

    Main Results:

    • Specific considerations for ANCOVA in repeated measures are detailed, including the validity of within-subject factor tests.
    • The desirability of interactions between within-subject factors and pre-measure scores is discussed.
    • Tables offering upper bounds on covariate-change score correlations are provided, alongside numerical examples.

    Conclusions:

    • Proper application of ANCOVA in repeated measures designs is crucial for valid statistical inference.
    • Understanding the nuances of covariate interactions and change score reliability enhances analytical rigor.
    • The study provides practical tools and insights for researchers utilizing complex repeated measures analyses.