An overall statistic for testing symmetry in social interactions
Related Experiment Video
Updated: Jun 28, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats
Published on: January 18, 2013
David Leiva1, Antonio Solanas, Lluís Salafranca
1Facultad de Psicología, Universidad de Barcelona, Barcelona, Spain. dleivaur@ub.edu
Abstract:
The present work focuses on the skew-symmetry index as a measure of social reciprocity. This index is based on the correspondence between the amount of behaviour that individuals address toward their partners and what they receive in return. Although the skew-symmetry index enables researchers to describe social groups, statistical inferential tests are required. This study proposes an overall statistical technique for testing symmetry in experimental conditions, calculating the skew-symmetry statistic (Phi) at group level. Sampling distributions for the skew-symmetry statistic were estimated by means of a Monte Carlo simulation to allow researchers to make statistical decisions. Furthermore, this study will allow researchers to choose the optimal experimental conditions for carrying out their research, as the power of the statistical test was estimated. This statistical test could be used in experimental social psychology studies in which researchers may control the group size and the number of interactions within dyads.
Related Concept Videos
Test for Homogeneity
Sign Test for Matched Pairs
To conduct the sign test, we first calculate the differences in value between...
Causes of Similarity-Dissimilarity Effect
Relationship Formation
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
McNemar's Test

