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

15.3K
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...
15.3K
Multiple Bar Graph01:07

Multiple Bar Graph

10.5K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.5K
Bar Graph01:07

Bar Graph

23.7K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.7K
Two-Way ANOVA01:17

Two-Way ANOVA

3.5K
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...
3.5K
One-Way ANOVA01:18

One-Way ANOVA

14.3K
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...
14.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Twenty-five years of simulated demand: A bibliometric and systematic review of hypothetical drug purchase tasks.

Journal of the experimental analysis of behavior·2026
Same author

Alpha-EEG/EMG neurofeedback combined with binaural beats stimulation: A randomized placebo-controlled within-subject cross over study.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology·2026
Same author

Delay discounting violations vary by adolescent sociodemographics: Excluding nonsystematic data may bias conclusions.

Experimental and clinical psychopharmacology·2025
Same author

Exploring the mechanism of the age-related P3 anterior shift.

Brain research·2025
Same author

Time estimation variability in delay discounting: Model comparison and considerations.

Behavioural processes·2025
Same author

Operant demand for cigarettes varies by unit of purchase: Comparing purchasing of packs and individual cigarettes.

Experimental and clinical psychopharmacology·2025

Related Experiment Video

Updated: Mar 19, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.6K

Visual analysis of data in a multielement design.

James W Diller1, Robert J Barry1, Brett W Gelino1

  • 1Eastern Connecticut State University.

Journal of Applied Behavior Analysis
|June 10, 2016
PubMed
Summary

Board Certified Behavior Analysts

Area of Science:

  • Behavior analysis
  • Research methodology
  • Data interpretation

Background:

  • Experimental control is crucial in behavior analysis.
  • Visual analysis of graphed data is a common method for demonstrating experimental control.
  • Factors influencing judgments of experimental control require further investigation.

Purpose of the Study:

  • To examine how data variability, trend, and mean shift influence judgments of experimental control.
  • To assess the agreement among experts when evaluating experimental control under different data conditions.
  • To compare current agreement levels with previous research findings.

Main Methods:

  • Ninety Board Certified Behavior Analysts (BCBAs) and 19 editorial board members evaluated hypothetical data sets.
Keywords:
data analysisexperimental controlvariabilityvisual inspection

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.9K

Related Experiment Videos

Last Updated: Mar 19, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.6K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

17.2K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.9K
  • Data sets were presented in a multielement design with manipulated variability, trend, and mean shift.
  • Participants rated whether the data demonstrated experimental control.

    • Main Results:

    • Data variability, trend, and mean shift significantly interacted to influence participants' decisions about experimental control.
    • The level of agreement among participants varied across conditions.
    • Overall agreement was lower than reported in prior studies on visual data analysis.

    Conclusions:

    • Judgments of experimental control are complex and influenced by multiple data characteristics.
    • Expert agreement in visual analysis can be inconsistent, highlighting potential challenges in research and practice.
    • Further research is needed to understand the precise decision-making processes involved in evaluating experimental control.