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

Time-Series Graph00:54

Time-Series Graph

4.5K
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...
4.5K
Quantitative Analysis01:12

Quantitative Analysis

561
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
561
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

262
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
262
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

497
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
497
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

162
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
162

You might also read

Related Articles

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

Sort by
Same author

Medical Appointments are Associated With Same-Day Worry and Sleep Difficulties Among Cancer Survivors.

Psycho-oncology·2026
Same author

Dynamical Cardiovascular Synchrony in Patient-caregiver Dyads Affected by Cancer: An Application of the Coupled Linear Oscillator Model.

Biopsychosocial science and medicine·2026
Same author

A mixed methods evaluation of a pilot open trial of a mentor-guided digital intervention for youth anxiety.

PLOS digital health·2026
Same author

Identifying Time-Variant Predictors of Interest in Completing Brief Digital Mental Health Interventions Among Adult Survivors of Cancer: Ecological Momentary Assessment Study.

JMIR mHealth and uHealth·2025
Same author

Within-Person Changes in Daily Ovarian Hormone Levels Influence Genetic Effects on Emotional Eating in Women.

The International journal of eating disorders·2025
Same author

Anxiety Symptom Severity and Implicit and Explicit Self-As-Anxious Associations in a Large Online Sample of U.S. Adults: Trends From 2011 to 2022.

Clinical psychological science : a journal of the Association for Psychological Science·2025

Related Experiment Video

Updated: Aug 31, 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

10.8K

Stability and spread: A novel method for quantifying transitions within multivariate binary time series data.

Katharine E Daniel1, Robert G Moulder2, Bethany A Teachman2

  • 1Department of Psychology, University of Virginia, P.O. Box 400400, Charlottesville, VA, 22904, USA. ked4fd@virginia.edu.

Behavior Research Methods
|August 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to measure stability and spread in complex binary time series data. The findings show these metrics uniquely capture switching behavior and can be reliably applied to various datasets.

Keywords:
Emotion regulationHigh dimensionalityMultivariate binary time seriesSwitchingTransition matrix

More Related Videos

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Related Experiment Videos

Last Updated: Aug 31, 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

10.8K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.1K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.5K

Area of Science:

  • Quantitative Psychology
  • Computational Statistics
  • Behavioral Data Science

Background:

  • Existing methods for analyzing binary time series data struggle with high-dimensional and sparse matrices.
  • Quantifying dynamic transitions and behavioral switching in complex datasets remains a challenge.

Purpose of the Study:

  • To introduce a novel method for quantifying transitions in multivariate binary time series data.
  • To define and validate metrics of stability and spread for analyzing complex time series.
  • To assess the applicability and utility of the proposed method in simulations and real-world data.

Main Methods:

  • Developed a method using sliding transition matrices to derive stability and spread metrics.
  • Defined stability as the normalized trace of a transition matrix.
  • Defined spread as the density of non-zero transitions within a transition matrix.

Main Results:

  • 1728 simulations indicated that stability and spread capture unique information and are moderately inversely associated.
  • The method is reliable for time series with as few as nine observations, using five consecutive observations and four variables per transition matrix.
  • In ecological momentary assessment data, stability and spread of emotion regulation strategies predicted subsequent affect, with stability also predicting anxiety.

Conclusions:

  • The novel method effectively quantifies stability and spread in multivariate binary time series.
  • These metrics offer unique insights into switching behavior and have potential applications in psychological and behavioral research.
  • The method demonstrates reliability and applicability to sparse, high-dimensional data, even with limited observations.