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

Relationship Formation02:12

Relationship Formation

46.3K
What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
46.3K
Factors Influencing Attraction IV: Reciprocity01:28

Factors Influencing Attraction IV: Reciprocity

387
Reciprocity in attraction is fundamental to social and romantic relationships, shaping how individuals form and maintain connections. The psychological principle underlying this phenomenon is that people tend to like those who express liking toward them. Balance theory supports this tendency, suggesting that mutual attraction fosters psychological harmony, whereas one-sided affection leads to discomfort and cognitive dissonance.The Psychological Mechanisms Behind ReciprocityWhen individuals...
387
Relationship Growth01:27

Relationship Growth

284
Interpersonal relationships progress through stages, beginning with awareness and moving toward mutuality, where emotional connections deepen. While many relationships remain at moderate levels of mutuality, deeper connections form through self-disclosure, trust, and interdependence.Self-DisclosureSelf-disclosure involves revealing personal information, starting with surface-level details and gradually progressing to more intimate content. As trust grows, individuals feel more comfortable...
284
System of Forces and Couples01:16

System of Forces and Couples

829
In the analysis of structural systems, it is common to encounter members subjected to various forces and couple moments. Simplifying these systems can make the analysis more manageable and easier to understand. One approach to achieve this simplification is by moving a force to a point O that does not lie on its line of action and adding a couple with a moment equal to the moment of the force about point O.
The principle of transmissibility plays a crucial role in this process. According to...
829
Understanding Interpersonal Attraction01:25

Understanding Interpersonal Attraction

477
Interpersonal attraction is a fundamental psychological phenomenon influencing human relationships across various contexts. It refers to one person's positive feelings or interests toward another, serving as the foundation for friendships, romantic partnerships, familial bonds, and professional relationships. The nature of interpersonal attraction extends beyond romantic connections, shaping interactions in both short-term and long-term social engagements.Psychological Foundations of...
477
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

911
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
911

You might also read

Related Articles

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

Sort by
Same author

Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.

Journal of the American Heart Association·2026
Same author

An individual participant data meta-analysis of how physical activity relates to affective well-being in daily life.

Nature human behaviour·2026
Same author

A two-stage approach to account for measurement error when using empirical Bayes estimates of random slopes.

Psychological methods·2026
Same author

Molybdenum-based antioxidative nanomedicine fighting against retinal pigment epithelium degeneration.

Biomaterials·2026
Same author

Self-amplifying RNA therapy encoding CNTF with disulfiram co-delivery promotes optic nerve repair through microglial pyroptosis inhibition and RGC axonal regeneration.

Journal of nanobiotechnology·2026
Same author

Phosphate Removal by Surface-Modified Ceramsite Derived from the Synergistic Use of Multiple Solid Wastes.

Materials (Basel, Switzerland)·2026
Same journal

Modeling Individual Language Patterns and Psychological Constructs to Generate AI-Augmented Data for Scalable Psychological Assessment.

Assessment·2026
Same journal

The Psychometric Properties of the Perth Emotion Regulation Competency Inventory (PERCI) in Sexual and Gender Minority Adults: Minority Stress and Resilience Correlates of Positive and Negative Emotion Regulation Difficulties.

Assessment·2026
Same journal

Future Orientation Scale: A Psychometric Evaluation Across Health-Vulnerable Samples.

Assessment·2026
Same journal

Spurious Reliability Increase?: The Number of Response Options in the Likert-Type Scale Influences Only Internal Consistency, Not Criterion Validity.

Assessment·2026
Same journal

Measuring Moral Injury Outcome and Distress in High-Risk Populations in Germany: A Validation Study.

Assessment·2026
Same journal

Establishing Psychometric Validity of the PBSS-20 for Sexual Minority College Students.

Assessment·2026
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
15:01

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

16.0K

Methods for Quantifying Patterns of Dynamic Interactions in Dyads.

Kathleen M Gates1, Siwei Liu2

  • 11 University of North Carolina - Chapel Hill, NC, USA.

Assessment
|April 17, 2016
PubMed
Summary
This summary is machine-generated.

This study overviews analytic methods for time series data from multiple time points, focusing on dynamic systems approaches for quantifying dyadic relations. It guides researchers in selecting optimal techniques for diverse data types and research questions.

Keywords:
analytic methodsdyadic interactionsdynamic systemtime series analysis

More Related Videos

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

4.2K
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

3.5K

Related Experiment Videos

Last Updated: Mar 22, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
15:01

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

16.0K
Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

4.2K
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

3.5K

Area of Science:

  • Behavioral Science
  • Psychology
  • Data Science

Background:

  • Increasingly, individuals provide data across numerous time points from various sources like wearables and diaries.
  • This data, collected over time, is classified as time series data.
  • Dynamic systems perspectives are valuable for examining such data.

Purpose of the Study:

  • To provide a broad overview of current analytic methods for quantifying relations among dyads using time series data.
  • To guide practitioners, clinicians, and researchers in choosing optimal methods.
  • To highlight available software programs for each technique.

Main Methods:

  • Overview of analytic techniques within linear modeling frameworks.
  • Discussion of approaches incorporating measurement models.
  • Examination of methods for cyclical relations and nonlinear curve shapes.
  • Exploration of special topics like time-shifting models and individual heterogeneity.

Main Results:

  • A comprehensive review of diverse analytical methods for dyadic time series data is presented.
  • Methods range from linear models to advanced techniques accounting for time shifts and individual differences.
  • Guidance is provided for selecting appropriate analytical tools based on data characteristics and research questions.

Conclusions:

  • The study offers a wide array of analytical options for time series dyadic data.
  • It aims to empower researchers to select the most suitable method for their specific research context.
  • The presented methods are applicable to various dyadic relationships, including clinical and familial contexts.