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

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

457
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
457
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Harmonic Mean01:09

Harmonic Mean

3.1K
The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
3.1K
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
27

You might also read

Related Articles

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

Sort by
Same author

Correction: Ferris et al. (2025) Chaining Differential Reinforcement of Compliance and Functional Communication Training to Treat Challenging Behavior Maintained by Negative Reinforcement. <i>Behavioral Sciences</i>, <i>15</i>(7), 891.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Downshifts in synthesized alternative reinforcement and resurgence.

Journal of the experimental analysis of behavior·2026
Same author

Contingency discrimination training and resurgence: Effects of reduced extinction session durations.

Journal of the experimental analysis of behavior·2025
Same author

Alternative-reinforcer magnitude effects on resurgence across successive relapse tests in mice.

Journal of the experimental analysis of behavior·2025
Same author

On the Evidence for Interactive Effects During and Following Synthesized Contingency Assessments.

Behavioral interventions : theory & practice in residential & community-based clinical programs·2025
Same author

Chaining Differential Reinforcement of Compliance and Functional Communication Training to Treat Challenging Behavior Maintained by Negative Reinforcement.

Behavioral sciences (Basel, Switzerland)·2025
Same journal

An Eye-Tracking Study on Text Accessibility and Comprehension in University Students.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

The Relationship Between Physical Activity, Social Support, and Life Satisfaction Among Female College Students: A Variable- and Person-Centered Analysis.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Shifting the Blame: How Narrative Framing, Coercive Strategies, and Rape Myth Acceptance Distort Perceptions of Sexual Assault and Fuel Victim Blame.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

An AI Perspective on Counseling Supervision.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Symbolic Participation or Substantial Learning Behavior? A PSM-Based Comparison Between Honors and Non-Honors Undergraduates from Two Top Elite Universities in China.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Literacy Profiles in Twice-Exceptional Preadolescents with Intellectual Giftedness and Dyslexia.

Behavioral sciences (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K

Identifying Cyclical Patterns of Behavior Using a Moving-Average, Data-Smoothing Manipulation.

Billie J Retzlaff1, Andrew R Craig2, Todd M Owen3

  • 1Intermediate School District 917, Rosemount, MN 55068, USA.

Behavioral Sciences (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

Identifying cyclical patterns in destructive behavior is crucial for prediction and understanding biological processes. Data smoothing offers a novel method to reveal these patterns, improving clinical analysis.

Keywords:
behavior cyclesdata analysisdata smoothingmoving averagevisual analysis

More Related Videos

Visualizing Motion Patterns in Acupuncture Manipulation
08:18

Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

8.7K
A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

5.9K

Related Experiment Videos

Last Updated: Jun 3, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Visualizing Motion Patterns in Acupuncture Manipulation
08:18

Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

8.7K
A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

5.9K

Area of Science:

  • Behavioral science
  • Data analysis
  • Clinical psychology

Background:

  • Destructive behavior can exhibit predictable cyclical patterns.
  • Identifying these patterns is vital for prediction and understanding underlying biological mechanisms.
  • Traditional visual analysis methods struggle to detect cyclical behavior patterns.

Purpose of the Study:

  • To introduce data smoothing as a method for identifying cyclical patterns in destructive behavior.
  • To demonstrate the utility of data smoothing in clinical case examples.
  • To highlight the importance of analyzing smoothed data across various time windows.

Main Methods:

  • Data smoothing involves averaging data across specified time windows (e.g., 3, 5, or 7 days).
  • This technique reduces data variability, enhancing the visibility of cyclical patterns.
  • The method was applied to daily destructive behavior occurrences in two clinical cases.

Main Results:

  • Data smoothing successfully identified cyclical patterns in destructive behavior.
  • Analysis across different smoothing windows proved essential for pattern detection.
  • The method proved effective in cases where behavior varied independently of programmed contingencies.

Conclusions:

  • Data smoothing is a valuable tool for identifying cyclical patterns in destructive behavior.
  • Clinicians should consider using data smoothing when behavior variability is not explained by external factors.
  • This approach can aid in predicting behavior and identifying potential biological correlates.