  Brewe, Bruun, and Bearden first applied network analysis to understand
patterns of incorrect conceptual physics reasoning in multiple-choice
instruments introducing the Module Analysis for Multiple-Choice Responses
(MAMCR) algorithm. Wells {\it et al.} proposed an extension to the algorithm
which allowed the analysis of large datasets called Modified Module Analysis
(MMA). This method analyzed the network structure of the correlation matrix of
the responses to a multiple-choice instrument. Both MAMCR and MMA could only be
applied to networks of incorrect responses. In this study, an extension of MMA
is explored which allows the analysis of networks involving both correct and
incorrect responses. The extension analyzes the network structure of the
partial correlation matrix instead of the correlation matrix. The new
algorithm, called MMA-P, was applied to the FCI and recovered much of the
structure identified by MMA. The algorithm also identified sets of correct
answers requiring similar physical reasoning reported in previous studies.
Beyond groups of all correct and all incorrect responses, some groups of
responses which mixed correct and incorrect responses were also identified.
Some of these mixed response groups were produced when a correct response was
selected for incorrect reasons; some of the groups helped to explain the gender
unfairness previously reported for the FCI.
