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

Multiple Bar Graph01:07

Multiple Bar Graph

8.5K
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...
8.5K
Time-Series Graph00:54

Time-Series Graph

4.7K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.7K
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

14
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
14
Bar Graph01:07

Bar Graph

20.5K
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...
20.5K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.6K
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

24
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
24

You might also read

Related Articles

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

Sort by
Same author

Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based Measures.

Behavior modification·2025
Same author

Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.

Journal of the experimental analysis of behavior·2025
Same author

The Family Game to support parents with intellectual disability in managing challenging behaviours: A replication.

Journal of applied research in intellectual disabilities : JARID·2024
Same author

Brief Report: Virtual Reality to Raise Awareness About Autism.

Journal of autism and developmental disorders·2023
Same author

Tutorial: Artificial neural networks to analyze single-case experimental designs.

Psychological methods·2022
Same author

Agreement between visual inspection and objective analysis methods: A replication and extension.

Journal of applied behavior analysis·2022

Related Experiment Video

Updated: Oct 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

789

Machine learning to analyze single-case graphs: A comparison to visual inspection.

Marc J Lanovaz1,2, Kieva Hranchuk3

  • 1École de psychoéducation, Université de Montréal.

Journal of Applied Behavior Analysis
|July 15, 2021
PubMed
Summary

Machine learning methods offer improved reliability for analyzing single-case graphs compared to traditional visual inspection. This approach balances error rates and statistical power, aiding behavior analysts in data interpretation.

Keywords:
AB designartificial intelligencemachine learningn-of-1 trialsingle-case designvisual analysis

More Related Videos

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.6K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.7K

Related Experiment Videos

Last Updated: Oct 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

789
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.6K
A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
08:22

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

Published on: August 31, 2018

6.7K

Area of Science:

  • Behavior Analysis
  • Data Science
  • Machine Learning

Background:

  • Visual inspection is standard for analyzing single-case graphs in behavior analysis.
  • Previous studies show inconsistent reliability of visual inspection methods.
  • Objective and reliable graph analysis is crucial for evidence-based practice.

Purpose of the Study:

  • To compare the reliability of visual inspection with machine learning for analyzing single-case graphs.
  • To evaluate Type I error rates and statistical power of different analysis methods.
  • To assess the consistency of machine learning models across various graph characteristics.

Main Methods:

  • Simulated 1,024 AB graphs with varying characteristics (phase points, autocorrelation, trend, variability, effect size).
  • Five expert visual raters analyzed the graphs.
  • Compared visual inspection results with a dual-criteria method and two machine learning models.
  • Evaluated agreement rates, Type I error, and power.

Main Results:

  • Visual inspection showed only 75% average agreement among expert raters.
  • Machine learning models demonstrated a superior balance between Type I error rate and power.
  • Machine learning provided more consistent results across diverse graph parameters.

Conclusions:

  • Machine learning shows promise for enhancing the accuracy and consistency of single-case graph analysis.
  • This approach may help reduce errors in data interpretation for researchers and practitioners.
  • Further research and replications are needed to validate these findings.