One never notices what has been done; one can only see
what remains to be done. . . .
Marie Curi (1894)
Space
does not allow a thorough discussion of all potential implications of contemporary CHC
theory. As a result, only three major points will be offered for consideration.
First,
the structural research of the past decade demonstrates the dynamic and unfolding nature
of the CHC taxonomy. Additional research is needed to better elucidate the structure of abilities
in the broad domains of Gkn, Gk, Gh, and Go. In addition, Carroll’s primary focus
on identifying
an overall structural hierarchy necessitated a deliberate ignoring of datasets with small number of
variables within a single broad domain (Carroll, 1994). The current author believes that
more
focused “mining” within each broad stratum II domain is rich with possible new discoveries,
and
will be forthcoming soon. The parochial Ga, Gv, Gs factor research reviewed
in this chapter
illustrates how studies with a molar focus on a small number of variables within a single broad
CHC domain can provide valuable insights into the structure and relations of narrow abilities
within a broad domain. With the foundational CHC structure serving as a working map,
researchers can return to previously ignored or recently published datasets, armed with both
exploratory and confirmatory factor analytic tools, to seek a better understanding of the narrow
stratum I abilities. In turn, test developers and users of tests of intelligence need to continue to
develop and embrace tools and procedures grounded in the best contemporary psychometric
theory (viz., CHC theory; see recommendations by McGrew, 1997, McGrew & Flanagan, 1998;
Flanagan et al., 2000).
Second,
CHC theory needs to move beyond the mere description and cataloguing of human
abilities to provide multi-lens explanatory models that will produce more prescriptive hypothesis
(e.g., aptitude-treatment-interactions). A particularly important area of research is CHC-
grounded investigations of the causal relations between basic information processing abilities
(e.g., processing speed and working memory--“behind g”) and higher-order cognitive
abilities
(e.g., Gf, g, language, reading, etc.). The recent research in this area by a cadre of
prominent
researchers (Ackerman, Beier & Boyle, 2002; Ardila, 2003; Baddeley, 2003; Bayliss, Jarrold,
Gunn, & Baddeley, 2003; Cocchini,Logie, Sala, Macpherson, Baddeley, 2002; Conway, Cowan,
Bunting, Therriault, & Minkoff, 2002; Daneman, & Merikle, 1996; Fry & Hale, 2000; Lohman,
2001; Kyllonen, 1996; Miyake, Friedman, Rettinger, Shah & Hegarty,2001; Oberauer, Süß,
Wilhelm & Wittman, 2003; Paas, Renkl & Sweller, 2003; Paas, Tuovinen, Tabbers, Van Gerven,
2003) has produced promising models for understanding the dynamic interplay of cognitive
abilities during cognitive and academic performance.
Additionally,
a better understanding of human abilities most likely will require an equal emphasis
on investigations of both the content and processes underlying performance on diverse
cognitive
tasks. The content “faceted” hierarchical Berlin Intelligence Structure Model (BIS)
(Beauducel,
Brocke, Liepmann, 2001; Süß, Oberauer, Wittman, Wilhelm, & Schulze, 2002) is a promising
lens
from which to view CHC theory. “Older” and lesser used multivariate statistical procedures,
such as multidimensional scaling (MDS), need to be pulled from psychometricians’ closets to
allow for the simultaneous examination of the content (facets), processes, and processing
complexity. [Note.
For example, in an unpublished MDS analyses of 50 different cognitive and
achievement tests from the WJ III battery, the current author identified, in addition to the primary
broad
CHC abilities (e.g., Gv, Gf, Gc, etc.), three other dimensions (possibly reflecting intermediate
stratum
abilities?) by which to organize and view the diverse array of CHC measures: (1) Visual-
spatial/figuralßàAuditory Linguistic; (2) Process
dominantßàProduct dominant; (3) Automatic
ProcessesßàControlled Process. ] In addition, the promising “beyond g” (g+specific abilities)
research should continue and be extended to additional domains of human performance. The
evidence is convincing that a number of lower-stratum CHC abilities make important
contributions to understanding academic and cognitive performance, above and beyond the
effect of g.
Finally,
it is time for the CHC taxonomy to go “back to the future” and revisit the original
conceptualization of “aptitude,” as updated most recently by Richard Snow and colleagues
(Corono et al., 2002). Contrary to many current erroneous assumptions, “aptitude” is
not the
same as with “ability” or “intelligence.” According to Snow and colleagues, aptitude
is more
aligned with the concepts of readiness, suitability, susceptibility, and proneness, all which suggest
a “predisposition to respond in a way that fits, or does not fit, a particular situation or class
of
situations. The common thread is potentiality—a latent quality that enables the development or
production, given specified conditions, of some more advanced performance” (Corno et al., 2002,
p. 3). Aptitudes represent the multivariate repertoire of a learner’s degree of readiness
(propensities) to learn and to perform well in general and domain- specific learning settings.
As
such, a person’s aptitudes must include, along with cognitive and achievement abilities, affective
and conative characteristics. Intelligence scholars and applied assessment personal are urged
to investigate the contemporary theoretical and empirical research that has married cognitive
constructs (CHC and cognitive information processing) with affective and conative traits in the
form of aptitude trait complexes. Snow et al.’s aptitude model (Corno, Cronbach, Kupermintz,
Lohman, Mandinach, Porteus, & Talbert, 2002; Snow, Corno & Jackson 1996; Snow, 2001) and
Ackerman and colleagues Intelligence-as-Process, Personality, Interests and Intelligence-as-
Knowledge (PPIK) model should be required reading for all involved in understanding and
measuring human performance (Ackerman, 1996; Ackerman & Beier, 2003; Ackerman, Bowen,
Beier & Kanfer, 2001). The CHC taxonomy is the obvious cognitive cornerstone of a model of
human aptitude.
Yes.
These are indeed exciting times in the ongoing quest to describe, understand, predict,
explain and measure human intelligence and performance.
[Note.
In the area of school learning, McGrew, Johnson, Cosio and Evans (2004) recently presented a
research synthesis based comprehensive taxonomy (Essential Student Academic Facilitators--ESAF)
for
organizing and understanding the conative and affective components of academic aptitude. The model
includes the broad domains of Motivational Orientation (e.g., intrinsic motivation, academic
goal
orientation, etc.), Interests and Attitudes (e.g., academic interests, attitudes, values), Self-Beliefs
(e.g.,
academic self-efficacy, self- concept, and ability conception; etc.), Social/Interpersonal Abilities
(e.g.,
prosocial and problem behaviors; social goal setting, etc.), and Self-Regulation (e.g., planning,
activation,
monitoring, contral and regulation, and reaction/reflection strategies).]
1. 1. Introduction
