Golden, R. M. (1998).
Knowledge digraph contribution analysis of protocol data.
Discourse Processes, 25,
179-210.
ABSTRACT
A knowledge digraph
defines a set of semantic (or syntactic)
associative relationships among propositions in a text
(e.g., Graesser and Clark (1985) conceptual graph structures and
the causal network analysis of Trabasso \& van den Broek, 1985).
This paper introduces
the Knowledge Digraph Contribution (KDC)
data analysis methodology for quantitatively measuring
the degree to which a given knowledge digraph can account for
the occurrence of specific sequences of propositions in
recall, summarization, talk-aloud, and question-answering
protocol data. KDC data analysis provides statistical tests
for selecting the knowledge digraph which "best-fits" a given
data set. KDC data analysis also allows one to test hypotheses
about the relative contributions of each member in a set of knowledge
digraphs. The validity of specific knowledge digraph representational
assumptions may be evaluated by comparing human protocol data with
protocol data generated by sampling from the KDC distribution.
Specific concrete examples involving the use of actual human recall protocol
data are used to illustrate the KDC data analysis methodology.
The limitations of the KDC approach are also briefly discussed.
Semi-final post script draft (possibly missing figures)
Golden's Text Comprehension and Memory Publications