Diagonal Values in ACA

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Author(s): Sean Eom (Southeast Missouri State University, USA)
Copyright: 2009
Pages: 31
Source title: Author Cocitation Analysis: Quantitative Methods for Mapping the Intellectual Structure of an Academic Discipline
Source Author(s)/Editor(s): Sean B. Eom (Southeast Missouri State University, USA)
DOI: 10.4018/978-1-59904-738-6.ch004

Keywords: Information Science Reference / Library & Information Science / Library Information Systems / Library Science

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Abstract

Diagonal values in the cocitation frequency counts matrix are a fundamental issue in ACA study. Diagonal values are the co-citation frequency counts between the author himself/herself excluding self-citation. Retrieving exact values of diagonal values in the co-citation matrix requires a manual and time consuming procedure. For that reasons, ACA researchers suggested many different approaches to create, not retrieving the real values, the diagonal cells in the cocitation matrix. They include the mean cocitation count, missing values, zeroes, highest off-diagonal counts, adjusted off-diagonal values, and the number of times cocited with himself/herself. The majority of ACA researchers seem to prefer to use either the adjusted value approach by adding three highest off-diagonal values and divided by two or the missing value approach. This chapter empirically examines the impact of these different approaches on the ACA outcomes. Based on the results of this study, if the pure cocitation counts are not used, the next best alternatives are as follows. They are the missing value approaches, mean cocitation value approach, and the highest off-diagonal value approaches in the order of the highest total variance explained.

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