  Methods for clustering people into construals--social affinity groups of
individuals who share similarities in how they organize their outlooks on a
collection of issues--have recently gained traction. Relational Class Analysis
(RCA) is currently the most commonly used method for construal clustering. RCA
has been applied to identify affinity groups in social spheres as varied as
politics, musical preferences, and attitudes towards science. In this study, we
highlight limitations in RCA's ability to accurately identify the number and
underlying structure of construals. These limitations stem from RCA's
mathematical underpinnings and its insensitivity to the bipolar structure of
the survey items, which require respondents to place themselves in a support or
rejection space and then express the intensity of their support or rejection.
We develop an alternative method, which we call Bipolar Class Analysis (BCA),
that aims to address this foundational limitation. BCA conceptualizes people's
attitudinal positions as moving along support/rejection semispaces and assesses
similarity in opinion organization by taking into account position switches
across these semispaces. We conduct extensive simulation analyses, with data
organized around different construals, to demonstrate that BCA clusters
individuals more accurately than RCA and other available alternatives. We also
replicate previous analyses to show that BCA leads to substantively different
empirical results than those produced by RCA in its original and later
versions, and by Correlational Clustering Analysis (CCA), a method that has
been proposed as an alternative to RCA.
