Overview of Author Cocitation Analysis Procedures

The previous two chapters examined the two alternative approaches of retrieving cocitation counts using custom databases and cocitation frequency counts extraction systems. The cocitaion frequency counts are the inputs to the SAS or SPSS systems for multivariate statistical analysis. The primary purpose of this chapter is to overview several important steps in author cocitation analysis. ACA consists of the six major steps beginning with the selection of author sets for further analysis, then collection of cocitation frequency counts, statistical analysis of the cocitation frequency counts, and the validation and interpretation of statistical outputs.

An Introduction to Bibliometrics and Informetrics

Author cocitation analysis (ACA) is a branch of bibliometrics. Bibliometrics/informetrics is one of the older areas of library and information science. The terms bibliometrics, scientometrics, and informetrics are often used synonymously. This chapter briefly overviews bibliometrics, including basic concepts, scopes, and study areas of bibliometrics. The areas of study cover bibliometric distribution, citation and cocitation analyses, and library use studies. The study of bibliometric distribution led to the invention of Lotka’s law of scientific productivity, Bradford’s law of core scatter in journals, and Zipf’s law of word occurrence. The researchers in the citation and co-citation areas identify the pattern of how published documents are cited over time using many different approaches such as bibliometric coupling, document cocitation analysis, author cocitation analysis, and co-word analysis. This chapter also discusses assumptions, purposes, benefits, limitations, and criticism of ACA. The last section of this chapter includes discussions of several developments in informetrics and ACA. Since the late 1990s, a new subset of informetrics, webometrics/ cybermetrics, has become part of the main stream library and information science research area. In ACA, there had been a series of intense debates on the use of Pearson correlations coefficients, r, as a similarity measure along with several new developments in ACA visualization tools such as Pathfinder networks (Howard D. White, 2003b), AuthorLink (Lin, White, & Buzydlowski, 2003), and VxInsight (Boyack, Wylie, & Davidson, 2002).