Informant Accuracy in Social Network Data

This paper examines the problem of informant accuracy in the production of social network data, through the use of a self-monitoring network, which allows a comparison between cognitive network data and informants' interactive behavior.

Abstract

This paper examines problem of informant accuracy in production of social network data, through use of a self-monitoring network. This allows a comparison between cognitive network data and informants' interactive behavior. Against expectations, it turns out that informants are extremely inaccurate. In other words, informants' reports of their behavior bear little resemblance to their behavior. If an informant claimed to have communicated with some person the most then, in fact, he communicated with that person between first and fourth most frequently only 52% of time. The implications of our findings for sociometric and network analysis are: (1) Attempts to filter out noise in a sociometric network matrix by using sophisticated software are likely to be unproductive. This is because such manipulations assume a much lower level of noise than actually occurs. (2) Due to low level of informant accuracy, theories of social structure built upon presently available network data...