The pairwise approximate spatiotemporal symmetry algorithm: A method for segmenting time series pairs.

Methods that measure the association between two intensively measured time series are of interest to researchers studying the symmetry of behaviors during social interaction. Such methods have historically focused on aggregating the amount of symmetry across all measurement occasions. However, it is rarely expected that symmetry is present at all measurement occasions. The current method, the pairwise approximate spatiotemporal symmetry (PASS) algorithm, is an approach that may be used to determine which measurement occasions in pairwise time series are indicative of symmetry and which are not. This process thus divides time series into symmetric and nonsymmetric segments. The PASS algorithm is demonstrated here on representative simulated data and naturalistic psychotherapy data. Results suggest that the PASS algorithm has the potential to extract meaningful symmetry segments from human signals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Gossip, sabotage, and friendship network dataset.

This article describes the data reported in the paper "Being in the know: Social network analysis of gossip and friendship on college campuses" (Yucel et al. 2021). Data were collected from a Men's and Women's collegiate crew team members from a small liberal arts college. Participants (N = 44) reported information about how often they gossip about members of the team (positively, negatively), who they have had hooked-up with on the team, who they consider to be friends with on the team, whether they have to sabotaged or been sabotaged by any teammates, their well-being and feelings of loneliness. This data brief provides detailed information about data preparation and participants responses to all survey items.

Being in the Know : Social Network Analysis of Gossip and Friendship on a College Campus.

Gossip (evaluative talk about others) is ubiquitous. Gossip allows important rules to be clarified and reinforced, and it allows individuals to keep track of their social networks while strengthening their bonds to the group. The purpose of this study is to decipher the nature of gossip and how it relates to friendship connections. To measure how gossip relates to friendship, participants from men's and women's collegiate competitive rowing (crew) teams (N = 44) noted their friendship connections and their tendencies to gossip about each of their teammates. Using social network analysis, we found that the crew members' friend group connectedness significantly correlated with their positive and negative gossip network involvement. Higher connectedness among friends was associated with less involvement in spreading negative gossip and/or being a target of negative gossip. More central connectedness to the friend group was associated with more involvement in spreading positive gossip and/or being a target of positive gossip. These results suggest that the spread of both positive and negative gossip may influence and be influenced by friendship connections in a social network.

Constrained Fourth Order Latent Differential Equation Reduces Parameter Estimation Bias for Damped Linear Oscillator Models.

Second order linear differential equations can be used as models for regulation since under a range of parameter values they can account for return to equilibrium as well as potential oscillations in regulated variables. One method that can estimate parameters of these equations from intensive time series data is the method of Latent Differential Equations (LDE). However, the LDE method can exhibit bias in its parameters if the dimension of the time delay embedding and thus the width of the convolution kernel is not chosen wisely. This article presents a simulation study showing that a constrained fourth order Latent Differential Equation (FOLDE) model for the second order system almost completely eliminates bias as long as the width of the convolution kernel is less than two thirds the period of oscillations in the data. The FOLDE model adds two degrees of freedom over the standard LDE model but significantly improves model fit.