weaknesses. Perhaps most important, the predictive
properties of actuarial models tend to be optimized
within the sample of development, with no guarantee
that these properties will apply to novel settings or sam-
ples (generalizability). For this reason, the precise
numerical probability estimates, or bright-line classifi-
cation cut scores, that tend to be used in actuarial pre-
diction are in crucial need of cross-validation and
replication prior to use.
Second, some actuarial techniques may have lim-
ited clinical applicability, in that decision makers may
be concerned about violence in a context (e.g., immi-
nent violence) that is incongruent with existing actu-
arial protocols constructed with a specific set of
conditions (e.g., a long-term follow-up period). Third,
actuarial approaches tend to ignore low base rate fac-
tors that failed to enter nomothetically derived statis-
tical equations because of their rarity or their
case-specific nature, even if they may be important
in individual cases. Under the strictest actuarial
approaches, any extraneous information not contained
on the instrument cannot be considered. Fourth, some
actuarial models have been criticized for not being
helpful in terms of risk management, treatment, or
risk reduction more broadly because they tend to
focus on static risk factors as opposed to dynamic
(changeable) risk factors that may be better suited to
treatment efforts.Structured Professional Judgment
To contend with these weaknesses, a more recently
developed risk assessment approach, termed structured
professional judgment, has been forwarded. Like most
actuarial approaches, the SPJ approach specifies a
fixed set of operationally defined risk factors with
explicit coding procedures. The purpose of this struc-
ture is to facilitate both interrater reliability and com-
prehensive domain coverage, or content validity. It has
three primary differences compared with most actuar-
ial approaches. First, SPJ approaches use logical or
rational item selection as opposed to empirical item
selection procedures to select risk factors. This process
involves extensive consultation of the scientific and
professional literature to select risk factors with broad
support across contexts. In theory, this approach fos-
ters generalizability as well as comprehensiveness of
the set of risk factors.
Second, SPJ approaches do not require algorithmic
combinations of risk factors to derive risk esti-
mates,and hence they are not actuarial. There are fourprimary reasons why SPJ approaches do not adopt
algorithmic item combinatory procedures. (1) Such
procedures are susceptible to degradation of predic-
tive accuracy across contexts, meaning that a cutoff
score in one sample cannot be assumed to apply to
another context. (2) While combination rules promote
consistency, they may do so at the expense of individ-
ual relevance; that is, certain risk factors will be more
relevant for one person’s violent risk than for
another’s risk, and a risk assessment process should
be able to account for this differential individual rele-
vance. (3) Decisions based on fixed algorithmic pro-
cedures presume that the future is fixed as well; if
circumstances change, the actuarial estimate may be
invalid. (4) There may be cases with only a few risk
factors present, but their salience compels a conclu-
sion of high risk.
SPJ approaches attempt to optimize the relevance
of nomothetically derived risk factors to the individual—
which, whether for legal or clinical purposes, is the
level of decision making. Final decisions of low, mod-
erate, or high risk are formed by decision makers after
consideration of the number and relevance of risk fac-
tors present in the case and the intensity and urgency
of any necessary intervention or management strate-
gies to mitigate risk. The SPJ model does not provide
estimated numerical probability levels of future vio-
lence for the individual case, because it is assumed
that it is not actually possible to do so given the prob-
lems with lack of stability of such procedures, as
reviewed above. Furthermore, actuarially derived
numerical probability estimates are group-based esti-
mates(i.e., 53 of 100 persons in X risk group were
violent); their applicability to what an individual who
was not in this groupmight do in future is tenuous.
Critics of the SPJ approach have argued that it low-
ers reliability and validity through the allowance of dis-
cretion at the variable integration phase of decision
making. Though this is a controversial aspect of SPJ,
research to date suggests that the reliability and predic-
tive validity of the SPJ approach are at least compara-
ble with the reliability and predictive validity of the
actuarial approach—and in some studies, exceed them.
Researchers continue to study the strengths and
limits of both actuarial and SPJ approaches to risk
assessment. Both have promise, and both have limita-
tions. The field would benefit from research on how to
increase individual relevance, treatment relevance,
and cross-validated generalizability of actuarial pro-
cedures. In terms of SPJ research, questions in need of
research include whether additional structure can be700 ———Risk Assessment ApproachesR-Cutler (Encyc)-45463.qxd 11/18/2007 12:43 PM Page 700