A new stochastic path-length tree methodology for constructing communication networks

Network analysis has become a popular method for identifying the communication structure in a system where positional and relational aspects are important. In this paper, a maximum likelihood based methodology is presented that allows for the analysis of binary sociometric data. This methodology provides a network representation via estimated path-length or additive trees that indicate the distance between all pairs of members. The methodology is distinguished from traditional hierarchical clustering based procedures by its direct consideration of the asymmetry in a typical communication process, the simultaneous representation of structural characteristics (e.g., clique membership, clique cohesiveness), and the identification of the specialized communication roles of each member (e.g., opinion leader, liaison). A penalty function algorithm is developed and its performance is investigated via a Monte Carlo analysis with synthetic data. An application examining information flows among managers is presented. Finally, directions for future research are suggested.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29291/1/0000352.pd

A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/ n ” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze “pick any/ n ” choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45743/1/11336_2005_Article_BF02294452.pd