Implications of 20 Years of CHC Cognitive-Achievement Research: Back-to-the-Future and Beyond CHC

Implications of 20 Years of CHC
Cognitive-Achievement Research:
Back-to-the-Future and Beyond CHC

Kevin S. McGrew PhD

Woodcock-Muñoz Foundation

Staying current with “IQ McGrew”

(@iqmobile)

ICDP Blog

Introduction and Context
Dr. Woodcock’s legacy & impact on my career and this paper

My WJ data sandbox

The Journey (2002now)

Back-to-the-future Beyond CHC

General
General Intelligence (g)

Quantitative Comp - Long-Term Processing
Reading & Fluid Short-Term Visual Auditory
Broad Knowledge Knowledge Storage &
Writing (Grw) Reasoning (Gf) Memory (Gsm) Processing (Gv) Processing (Ga) Speed (Gs)
(Gq) (Gc) Retrieval (Glr)

Narrow Mathematical Reading General verbal Memory span Associative Visualization Phonetic coding Perceptual speed
Induction (I)
knowledge (KM) decoding (RD) information (K0) (MS) memory (MA) * (Vz) (PC) (P)

Mathematical Reading Language General Speeded Speech sound
Working memory Meaningful Rate of test-
achievement comprehension development sequential discrimination
capacity (MW) memory (MM) * rotation (SR) taking (R9)
(A3) (RC) (LD) reasoning (RG) (US)

Resistance to
Reading speed Lexical Quantitative Free-recall Closure speed Number facility
auditory stimulus
(RS) knowledge (VL) reasoning (RQ) memory (M6) * (CS) (N)
distortion (UR)

Memory for Reading
Spelling ability Listening ability Ideational Flexibility of
sound patterns speed/fluency
(SG) (LS) fluency (FI) ** closure (CF)
(UM) (RS)

Maintaining & Writing
English usage Communication Associational Visual memory
judging rhythm speed/fluency
(EU) ability (CM) fluency (FA) ** (MV)
(U8) (WS)
General
Musical discrim.
Writing ability Grammatical Expressional Spatial scanning Speed +
& judgment (U1
(WA) sensitivity (MY) fluency (FE) ** (SS)
U9)

Sens. to probs.
Writing speed Serial perceptual Absolute pitch
/altern. Sol.
(WS) integration (PI) (UP)
fluency (SP) **

Acquired Knowledge + Memory Originality
Length Sound
* Learning /creativity (FO)
estimation (LE) localization (UL)
Efficiency **
** Retrieval
Fluency Naming facility Perceptual
(NA)** illusions (IL)

Functional groupings
Word Fluency Perceptual
(FW) ** alternations (PN)
Conceptual groupings
Figural Fluency Imagery (IM)
+ = additional CHC abilities in groupings (FF) **
in Part 2 of model
Domain-Independent General Sensory-Motor Domain
Figural flexibility
Figure 1. CHC v2.0 model based on Schneider and McGrew (2012) Capacities + (FX) ** Specific Abilities (Sensory) +

General
General Intelligence (g)

Domain Reaction & Tactile Abilities
Specific Know. Psychomotor Olfactory Kinesthetic Psychomotor
Broad Decision Speed
(Gkn) Speed (Gps) Abilities (Go) (Gh) Abilities (Gk) Abilities (Gp)
(Gt)

Narrow Simple reaction Speed of limb Olfactory Static strength
? ? ?
time (R1) movement (R3) memory (OM) (P3)

Choice reaction Writing speed Multilimb
time (R2) (fluency) WS coordination (P6)

Semantic
Speed of Finger dexterity
processing speed
articulation (PT) (P2)
(R4)

Mental Movement time Manual
comparison (MT)
speed (R7) dexterity (P1)

Inspection time Arm-hand
(IT) steadiness (P7)

General Speed +
Control
precision (P8)

Aiming (A1)

Acquired
Knowledge +
Gross body
equilibrium (P4)

Motor
Functional groupings

Sensory-Motor Domain Specific Abilities +
Conceptual groupings

+ = additional CHC abilities in groupings
in Part I of model

Figure 1 (continued). CHC v2.0 model based on Schneider and McGrew (2012)

CHC COGACH Relations: What We Know Today

•Almost all available CHC-designed COGACH research is limited to the WJ
Battery

•The primary action in CHC COGACH relations is at the narrow ability level

• There is a future for “intelligent” intelligence testing, even in the current
response-to-intervention (RTI) environment

General
Intelligence (g)

Comp - Long-Term Processing
Fluid Short-Term Visual Auditory
Knowledge Storage &
Reasoning (Gf) Memory (Gsm) Processing (Gv) Processing (Ga) Speed (Gs)
(Gc) Retrieval (Glr)

Language Naming facility
Working memory Perceptual speed
M development
capacity (MW (NA) (P)
(LD)
a R
Associative
t
Listening ability
(LS) memory (MA) d
h g
General verbal
information (K0)
Ach. Domain General Cognitive Abilities

A A
c Lexical Memory span Meaningful Phonetic coding c
knowledge (VL) (MS) memory (MM) (PC)
h h
Speech sound
i discrimination i
(US)
e Resistance to
e
Rdg. Domain Specific auditory stimulus
v Cognitive Abilities distortion (UR)
v
e e
m Visualization Number facility
m
Quantitative
(Vz) (N)
e
reasoning (RQ) e
n General
sequential
Speeded n
rotation (SR)
t
reasoning (RG)
t
Math. Domain Specific
Visual memory
Induction (I) Cognitive Abilities (MV)

[Developmental (age-based) differences are not captured by this abridged summary. See McGrew & Wendling (2010) for this information]

Established narrow CHCrdg./math ach. relations abridged summary

Clarification of Ability Construct Terminology

Ability

“as used to describe an attribute of individuals, ability refers to the
possible variations over individuals in the liminal levels of task difficulty
(or in derived measurements based on such liminal levels) at which, on
any given occasion in which all conditions appear favorable, individuals
perform successfully on a defined class of tasks” (p. 8, italics in original).

“every ability is defined in terms of some kind of performance, or potential
for performance (p. 4).”

Cognitive Abilities

Abilities on tasks “in which correct or appropriate processing of mental
information is critical to successful performance” (p. 10; italics in original).

Achievement abilities

“refers to the degree of learning in some procedure intended to produce
learning, such as an informal or informal course of instruction, or a
period of self study of a topic, or practice of a skill” (p. 17). As noted by
Carroll (1993)

What is “aptitude”

Aptitude
(Defined in this paper—narrow sense, not
broader Richard Snow definition)

Aptitude is defined as the
combination, amalgam or
complex of specific cognitive
abilities, that when
combined, best predict a
specific achievement domain

Abilities
Achievement Abilities Cognitive Abilities

General
Intelligence (g)

Quantitative Comp - Long-Term Processing
Reading & Knowledge Fluid Short-Term Visual Auditory
Knowledge Storage &
Writing (Grw) (Gc) Reasoning (Gf) Memory (Gsm) Processing (Gv) Processing (Ga) Speed (Gs)
(Gq) Retrieval (Glr)

Rdg
Apt
Math
Apt

Etc. Etc. Etc. Etc. Etc. Etc. Etc. Etc. Etc.

Vertical columns represent abilities, factors or latent traits (primarily
Ach. domain- factor-analysis derived internal structural validity constructs)
general apt.
Horizontal arrow rows represent aptitudes (primarily multiple
Ach. domain- regression derived external [predictive] validity constructs)
specific apt.

Conceptual distinction between Abilities: Cognitive abilities, achievement abilities, and aptitudes

Selective Referral-Focused Assessment (RFSA)

Kaufman’s “Intelligent” Intelligence testing

Intelligent
“RFSA”
CHC Cog-
Ach
CHC-based Research
batteries Synthesis

CHC
Theory

Two illustrative CHC general selective referral-focused assessment (SRFA) scenarios: BRS problems for ages 6 to 8 yrs

The evolution of differential
Scholastic Aptitude Clusters (SAPTs)

Developmentally
sensitive CHC-
WJ III Pred. designed SAPTs
Ach. GIA
WJ-R option
SAPTs

WJ
SAPTs

Developmentally-Sensitive CHC-
Consistent Scholastic Aptitude Clusters

(McGrew, 1986, 1994)

ITD - Developmentally-Sensitive CHC-

Run final MR
Backward model at each
Run MR deletion of age and smooth
models tests from MR regression
across entire model. Inspect coefficients by
school-age each step age
Select WJ III WJ III norm results noting
tests based sample “bridesmaid”
CHC on first step predictors
COG>ACH for initial
res. predictor
synthesis pool

Standardized regression coefficient

Vis-Aud Learning (Glr-MA)

Verbal Comp. (Gc-LD/VL)

Age group (in years)


Visual Matching (Gs-P)

Number Matrices (Gf-RQ)

Analysis-Synthesis (Gf-RG)

Numbers Reversed(Gsm-WM)
Analysis-Synthesis (Gf-RG)

Number Matrices (Gf-RQ)
Numbers Reversed(Gsm-WM)


Age 5 6 7 8 9 10 11 12 13 14 15 16 17 18
GIA-Std. 32 39 44 46 53 56 50 60 64 56 53 65 53 47
MR-Apt. 46 42 47 53 56 61 62 63 71 72 64 77 64 66
Difference 6 3 3 7 3 5 12 3 13 12 11 12 11 19

Smoothed standardized regression coefficients of best set of WJ III cognitive test predictors of WJ III Math Reasoning
(MR) cluster from ages 6 thru 18. Table is % of MR variance accounted for by GIA-Std and MR Aptitude as constructed
and weighted per the figure.


Verbal Comp. (Gf-LD/VL)


Vis-Aud Learning (Glr-MA)
Sound Awareness (Ga-PC/Gsm-WM)

Sound Blending (Ga-PC) Numbers Reversed (Gsm-Wm)
Numbers Reversed (Gsm-Wm)

Sound Blending (Ga-PC)


Sound Awareness (Ga-PC/Gsm-WM) Vis-Aud Learning (Glr-MA)


Age 5 6 7 8 9 10 11 12 13 14 15 16 17 18
GIA-Std. 33 40 42 39 41 50 43 35 43 48 48 48 59 45
BRS-Apt. 50 49 50 48 45 56 48 43 50 54 52 52 63 52
Difference 17 9 8 9 4 6 5 6 7 6 4 4 4 7

Smoothed standardized regression coefficients of best set of WJ III cognitive test predictors of WJ III Basic Reading
Skills (BRS) cluster from ages 6 thru 18. Table is percent of BRS variance accounted for by GIA-Std and BRS Aptitude as
constructed and weighted per the figure.


ITD: “Intelligent” Test Design Principles

ITD: SAPTs are better predictors of achievement than g-based composites

ITD: SAPTs require a mixture of domain-general and domain-
specific CHC cognitive abilities

• Test developers should utilize the extant CHC COGACH
relations literature when selecting the initial pool of tests to
include in the prediction models

ITD: SRFA requires 3-way thinking. 3-way interaction of CHC
abilities X achievement domains X age (developmental status).


ITD: “Intelligent” Test Design Principles

ITD: Developmental trends are critically important in aptitude-
achievement comparisons

• Test developers should provide age-based developmental
weighting of the tests in the different CHC-consistent SAPTs

•Those who implement an aptitude-achievement
consistency/concordance SLD model must be cautious and
not use a "one size fits all" approach when determining
which CHC COG abilities should be examined for the
aptitude portion of the consistency model


Group vs individual centered focus
(McGrew & Flanagan, 1998)

• Group-based statistical results may not
translate perfectly to all individuals

• “Intelligent” testing is required

• “We are the instrument”

CHC-Consistent Scholastic Aptitude Clusters SRFA Strategy
WJ III example in basic reading skills (BRS) and math reasoning (MR)

Optimal developmentally weighted
linear combination of WJ III tests
General
Intelligence (g)

Comp - Long-Term Processing
Fluid Short-Term Visual Auditory
Knowledge Storage &
Reasoning (Gf) Memory (Gsm) Processing (Gv) Processing (Ga) Speed (Gs)
(Gc) Retrieval (Glr)

Snd Blending
Verbal Numbers Vis.-Aud. (PC) Visual Matching
Comprehension Reversed Learning
(P)
WJ III Basic Rdg. Skills Aptitude
(LD) (MW) (MA) Snd Awareness
(PC;Gsm-WM)

Analysis-
Verbal Synthesis Numbers
Comprehension (RG) Reversed Visual Matching WJ III Math Reason. Aptitude
(LD) (MW) (P)
Number Matrices
(RQ)

Examine PSW within aptitude clusters (and as suggested by other tests
administered and other non-test information) to determine additional selective
follow-up assessment in narrow ability domains

CHC COGACH relations research & SRFA
provides opportunity to engage in “intelligent”
testing (ala, A. Kaufman)

“ Tests do not think for themselves, nor do they
directly communicate with patients. Like a
stethoscope, a blood pressure gauge, or an MRI
scan, a psychological test is a dumb tool, and the
worth of the tool cannot be separated from the
sophistication of the clinician who draws inferences
from it and then communicates with patients and
professionals”

Meyer et al. (2001). Psychological testing and psychological assessment. American Psychologist,

Beyond CHC # 2: WJ III Productive Exploratory Rabbit
Hole (circa 2009-2010) Experience

Data Sets

•WJ III norm data
•WJ III+ other batteries
(WISC-R; WAIS-III/WMS-III/KAIT)
•WAIS-IV subtest correlations

Methods
•Cluster analysis
•Multidimensional scaling analysis (MDS) – 2D and 3D
•Standard and Carroll EFA+CFA exploratory factor analysis
•Model-generation CFA (SEM)
•CHC cognitive causal SEM models

Beyond CHC: Linear minds living in a non-linear world

“A fundamental limitation of any theory built on a rectilinear system of
factors it that it is not of a form that well describes natural phenomena. It
is thus unlikely to be fully adequate. It is a system that can accurately
describe rectangular structures built by humans…but not the rounded and
irregular structures of mother nature. The phenomena of nature are not
usually well described by the linear equations of a Catesian coordinate
system….The equations that describe the out structure and convolutions of
brains must be
parabolas, cycloids, cissoids, spirals, foliums, exponentials, hyperboles, and
the like (p. 84). (Horn & Noll, 1997)

Beyond CHC #1: CHC + Information
Processing Causal SEM Models

CHC
g+ specific COG<>ACH
SEM res.
CHC COC- abilities
COG>ACH (person
ACH reg
WJ-R SEM res. fit?)
studies
WJ III +
IP/CPM
Gf-Gc/
models
CHC
theory


Independent Variables Dependent Variable
(IV)) – Cog. (DV) – Ach.
(Note: Residuals and significant correlations between residuals are omitted from the

TeCog. Test 1
Cog TeAch. Test 5
TeCog. Test 2 LV1 Ach
LV3
TeAch. Test 6
TeCog. Test 3
diagram for readability purposes

TeCog. Test 4
TeAch. Test 3
Cog Ach
TeCog. Test 5
LV2 g LV2
TeAch. Test 4

TeCog. Test 6 Cog
LV3
TeCog. Test 7
TeAch. Test 1
Ach
TeCog. Test 8 Cog LV1
LV nth TeAch. Test 2
TeCog. Test nth

Visual Matching
Cognitive efficiency
Mem.for Sent.
Indirect effect Mem. Span
Decision Speed Gs (MS)
Direct effect Mem.for Words
Cross Out

Aud. Wrk. Mem.
Wrk. Mem.
(WM)
Num. Reversed

Block Rotation

Spat. Relations Verbal Comp.
Gv
Pic. Recognition
Oral Comp.
Gc
Mem.for Names Gen. Info.

Ages 6-8
Ret. Fluency g
Anal.-Synth.
Glr
DR Vis-Aud.Lrg.
Conc. Form.
Gf
Vis.-Aud. Lrg.
Numerical Reas.
Sound Blending
.27
Inc. Words Ga Word Word Attack
Attack

Sound Patterns
Effects
Direct Indirect Total
Plausible CHC/IP COGWord Attack causal model in WJ III norm data (ages 6-8) Gs 0.19 0.40 0.59
MS 0.00 0.34 0.34
Chi-square =1016.5. df=239 WM 0.00 0.54 0.54
GFI=.93; AGFI=.91; PGFI=.74 g 0.36 0.23 0.59
RMSEA=.055 (.051-.058) Ga 0.27 0.00 0.29

Stankov, Boyle and Cattell (1995) who stated, within the
context of research on human intelligence“

“while we acknowledge the principle of parsimony and endorse it
whenever applicable, the evidence points to relative complexity
rather than simplicity. Insistence on parsimony at all costs can
lead to bad science” (p. 16).

Beyond CHC #1: Develop SEM “person fit” indices ?

Indirect effect Visual Matching
Cognitive efficiency
Mem.for Sent.
Direct effect Gs Mem. Span
Decision Speed (MS)
Mem.for Words
Cross Out
Chi-square =1016.5. df=239
GFI=.93; AGFI=.91; PGFI=.74 Aud. Wrk. Mem.
RMSEA=.055 (.051-.058) Wrk. Mem.
(WM)
Num. Reversed

Block Rotation

Spat. Relations Verbal Comp.
Gv
Pic. Recognition
Oral Comp.
Gc
Mem.for Names Gen. Info.

Ages 6-8
Ret. Fluency g
Anal.-Synth.
Glr
DR Vis-Aud.Lrg.
Conc. Form.
Gf
Vis.-Aud. Lrg.
Numerical Reas.
Sound Blending

Ga .27 Word Word Attack
Inc. Words
Attack

Sound Patterns

A challenge to the LISRELites, AMOSites,
MPLUSites in the room

Build it an they shall come.

Beyond CHC #1: CHC + Information Processing
Causal SEM Models

Example:

Beyond CHC #2: Cognitive-Aptitude-
Achievement Trait Complexes (CAATC’s)

Cog-Apt-
Ach Trait
Beyond Complexes
Jöreskog (CAATC)
Psych trait syndrome New SLD
complex
model ideas
Third theory &
method SLD research
models
WJ/ (apt-ach
WJ-R consistency)
SAPTs

Beyond CHC #2: Cognitive-Aptitude-
Achievement Trait Complexes (CAATC’s)

Aptitude
for Acd. Domain
Academic
Domain
Cognitive
Abilities
Degree of
cohesion

Cognitive-Aptitude-Achievement
Trait Complex

Beyond CHC: Jöreskog syndrome

American psychology, and mainstream quantitative school
psychology, have expressed little interest in non-confirmatory statistical
methodological lens (e.g., exploratory cluster analysis; MDS) in favor of
what I call Jöreskog syndrome—an almost blind allegiance and belief in
structural equation modeling confirmatory factor analysis (SEM-CFA)
methods as the only way to see the “true light” of the structure of
intelligence and intelligence tests

Beyond CHC: Jöreskog syndrome

The law of the instrument

“Give a small boy a hammer, and he will find
that everything he encounters needs pounding”

Important Reminder: All statistical methods, such
as factor analysis (EFA or CFA) have limitations and
constraints.

It only provides evidence of structural/internal validity
and typically nothing about
external, developmental, heritability, neurocognitive
validity evidence

Need to examine other sources of evidence and use
other methods – looking/thinking outside the factor
analysis box

2
Notes on WJ-R Derived Scholastic
Aptitude Clusters (SAPTs)
C
GRWAPT = Gc(LD/VL) + Gs(P) + Ga(PC) + Glr(VAL)
or Gsm-MS

(RAPT and WLAPT nearly overlapped in figure.
Given their high degree of overlap, they were
GA (PC)
1 GLR (MA) combined into a single GRWAPT in the figure)
GV (MV/CS)
MAPT = Gc(LD/VL) + Gs(P) + Gf(I) + Gf(RG)

-WJ-R SAPTs each comprised of 4 tests with equal
GC (LD/VL) weightings (.25)

BCA GSM (MS) -Bold font designates shared test CHC ability
EXT GRWAPT content in GRWAPT and MAPT
0 A B
WJ-R CHC factor clusters
MAPT
GF (I/RG) BRDG WJ-R broad achievement lcusters

WJ-R Broad Cognitive Ability &
BWLANG
Scholastic Aptitude Clusters

-1 BMATH Note: Measures closer to the center are
more cognitively complex. The distance
between points represents the inter-
relations between variables. Highly-related
GS (P) variables are spatially closer-have less
distance between their circles.

D
-2 Figure 9. Guttman radex MDS
-2 -1 0 1 2 analysis summary of WJ-R cognitive,
aptitude, and achievement measures
A  B = Visual-figural/numeric/quantitative  Auditory-linguistic/language dimension across all ages in WJ-R norm sample
C  D = Cognitive operations/processes Acquired knowledge /product dimension

2

C Math (Gq) cognitive-aptitude-
achievement trait complex

r =.55
GA (PC)
1 GLR (MA) Reading/Writing (Grw)
GV (MV/CS)
cognitive-aptitude-

GC (LD/VL)

BCA GSM (MS)
Notes on WJ-R Derived Scholastic
EXT GRWAPT Aptitude Clusters (SAPTs)
0 A B
GRWAPT = Gc(LD/VL) + Gs(P) + Ga(PC) + Glr(VAL)
MAPT or Gsm-MS
GF (I/RG) BRDG
(RAPT and WLAPT nearly overlapped in figure.
BWLANG Given their high degree of overlap, they were
combined into a single GRWAPT in the figure)

-1 BMATH MAPT = Gc(LD/VL) + Gs(P) + Gf(I) + Gf(RG)

-WJ-R SAPTs each comprised of 4 tests with equal
weightings (.25)
GS (P) Angle = approximately 57o
-Bold font designates shared test CHC ability
r = approximately .55 content in GRWAPT and MAPT

D
-2 WJ-R CHC factor clusters
-2 -1 0 1 2
WJ-R broad achievement lcusters
AB = Visual-figural/numeric/quantitative Auditory-linguistic/language dimension
WJ-R Broad Cognitive Ability &
CD = Cognitive operations/processes Acquired knowledge /product dimension Scholastic Aptitude Clusters

Figure 10. WJ III based reading and math cognitive-aptitude-achievement trait complexes (CAATC)

Cognitive-aptitude-achievement trait complexes

Cognitive-aptitude-achievement trait complex (CAATC)

A constellation or combination of related cognitive, aptitude, and achievement traits
that, when combined together in a functional fashion, facilitate or impede the
acquisition of academic learning

Cognitive-aptitude-achievement trait complexes

CAATCs emphasize the
constellation or combination of
elements that are related and
are combined together in a
functional fashion

Imply a form of a centrally
inward directed force that pulls
elements together much like
magnetism

Cohesion defined

Cohesion appears the most appropriate term for
this form of multiple element bonding. Cohesion
is defined, as per the Shorter English Oxford
Dictionary (Brown, 2002), as “the action or
condition of sticking together or cohering; a
tendency to remain united” (Brown, 2002, p. 444).

Element bonding and stickiness are also conveyed
in the APA Dictionary of Psychology
(VandenBos, 2007) definition of cohesion as “the
unity or solidarity of a group, as indicated by the
strength of the bonds that link group members to
the group as a whole” (p. 192).

Beyond CHC: Comparison of current PSW
and CAATC SLD models

Cognitive / Academic
Strengths

Cognitive
Strength
Discrepant/
Discordant
Discrepant/ Discrepant/
Discordant Discordant
Aptitude
Academic for Acd. Domain
Domain
Cognitive
Academic Cognitive Abilities
weakness weakness Degree of
cohesion
Consistent/
Concordant Cognitive-Aptitude-Achievement
Trait Complex
Common Components of Third-Method
Approaches to SLD Identification
Dashed shapes designate academic domain related cognitive abilities.
(adapted from Flanagan & Alfonso, 2011)
Suggested re-conceptualization of academic and cognitive weaknesses
(and possible SLD identification model) based on cognitive-aptitude-
achievement trait complexes (CAATC)

2

C Math (Gq) cognitive-aptitude-

r =.55
GA (PC)
1 GLR (MA) Reading/Writing (Grw)
GV (MV/CS)
cognitive-aptitude-

GC (LD/VL)

BCA GSM (MS)
EXT GRWAPT
0 A B
MAPT
GF (I/RG) BRDG Aptitude
BWLANG Domain
Cognitive
-1 BMATH
Abilities
Degree of
cohesion

GS (P) Cognitive-Aptitude-Achievement
Angle = approximately 57o
Trait Complex
r = approximately .55

D
-2
-2 -1 0 1

Beyond CHC: Potential benefit of CAATC
based SLD models

The identification of CAATC taxon’s that better
approximate “nature carved at the joints” (Meehl, 1973,
as quoted and explained by Greenspan, 2006, in the
context of MR/ID diagnosis).

Such a development would be consistent with Reynolds
and Lakin’s (1987) plea, 25 years ago, for disability
identification methods that better represent dispositional
taxon’s rather than classes or categories based on specific
cutting scores which are grounded in “administrative
conveniences with boundaries created out of political and
economic considerations” (p. 342).

Beyond CHC: Proposed CAATC based SLD model
(early ideas)

• Evaluating the degree of cohesion within a
Cognitive / Academic CAATC is integral and critical first step
Strengths

• The stronger the within-CAATC cohesion, the
more confidence one could place in the
Discrepant/ identification of a CAATC as possibly indicative
Discordant of a SLD

Aptitude • If the CAATC demonstrates very weak
Domain cohesion, the hypothesis of a possible SLD
Cognitive should receive less consideration
Abilities
Degree of
cohesion
• PSW-based SLD identification would be based
Cognitive-Aptitude-Achievement first on the identification of a weakness in a
Trait Complex
cohesive specific CAATC which is then
determined to be significantly discrepant from
Dashed shapes designate academic domain related
cognitive abilities.
relative strengths in other cognitive and
achievement domains

Beyond CHC: Proposed CAATC based SLD model
(early ideas)

Quantifying degree of cohesion is likely possible via
use of Euclidean Geometry metrics

For example, Mahalanobis
distance measure which can
quantify the cohesion between
CAATC measures as well as
distance from the centroid of a
CAATC exist (see Schneider, 2012)

Beyond CHC #3: Optimizing Cognitive
Complexity of CHC measures

Optimizing
cognitive
MDS and complexity
“cognitive of CHC
complexity” measures
CHC
findings
COG>ACH
rels. “Narrow
is better”
First CHC
IQ
batteries
focused on
broad
stratum


CHC factor breadth Cognitive complexity

2

SNDISC
PHNAWR GIA-EXT and three-test broad clusters
1 GA Two-test broad clusters

Two-test narrow clusters
GV3
ASMEM AUDMS

GV PHNAW3

GLR GC
VISUAL
GIA-EXT GSM
RDGCMP
0
GF
GF3 RDGBR WRKMEM
MTHREA

RDGBS
NUMREA
MTHBR

-1
PERSPD
MTHCAL

GS

-2
-2 -1 0 1 2

MDS radex model based cognitive complexity analysis of primary WJ III clusters


According to Lohman (2011), those tests closer to the
center of an MDS radex model are more cognitively
complex, and this is due to five possible factor:

• Larger number of cognitive component processes

• Accumulation of speed component differences

• More important component processes (e.g., inference)

• Increased demands of attentional control and working
memory

• More demands on adaptive functions (assembly, control,
and monitoring).

Beyond CHC #3: Optimizing Cognitive Complexity—Implications
for Test Battery Design and Assessment Strategies

•The push to feature broad CHC clusters in contemporary IQ
batteries (or in XBA assessments) fails to recognize the
importance of cognitive complexity

•Developing factorially complex measures is one way to
achieve cognitive complexity (e.g., KABC-II, DAS-II, Wechslers)

•ITD: It is proposed that within-CHC domain cognitive
complexity should be an important ITD


As per Brunswick Symmetry and BIS Model: Need to pay more attention
to matching the predictor-criteria space on the dimension of cognitive
complexity (e.g., levels of aggregation)

Beyond CHC #3: Cognitive Complexity and
CHC COGACH relations

McGrew & Wendling’s (2010) “narrow is better” may need revision to…

“Within CHC-domain cognitively complexity is better”


Possible implication for use of the WJ III Battery:
ITD: Broad+narrow hybrid example to optimize ach. prediction

“Front end” featured clusters

• Fluid Reasoning (Gf)
• Comprehension-Knowledge (Gc)
• Long-term Retrieval (Glr)
• Working Memory (Gsm-MW)
• Phonemic Awareness 3 (Ga-PC)
• Perceptual Speed (Gs-P)
• Visualization (not clear winner)

Then, if broad Gsm, Ga, Gs, Gv measures are desired..supplemental
testing as per administration of

• Gs (Decision Speed)
• Gsm (Memory for Words)
• Gv (Picture Recognition)


ITD: IQ test batteries of the future might better be based on a
hybrid (broad+narrow) partially inverted CHC model that
deliberately incorporates within-CHC domain cognitive complexity
into the test/cluster design process and battery configuration or
suggested testing sequence

Concluding Comments
Proximal Implications
“Intelligent” selective-referral focused assessments (SRFA)

• Types of Strategies

• General SRFA
• Scholastic Aptitude Cluster-based SRFA

• Important considerations

• Recognize domain-general and domain-specific CHC COG-ACH relations
• Recognize 3-way COC x ACH x Age interaction
• Recognize importance of cognitive complexity in SRFA
• Narrow may not necessarily be better as a general rule
• Use broad+narrow inverted CHC hybrid approach to assessment
• Cautious use of CHC COG-ACH relations findings with non-WJ III batteries

Concluding Comments
Proximal Implications

Develop Developmentally-Sensitive CHC-based Scholastic Aptitude
Clusters (ITD)

• The research knowledge and statistical and computer software
technology exists

• e.g., WJ III GIA; WJ III Predicted Achievement

Investigate and validate more “dynamic/interacting” CHC
COGACH SEM models

Use more “Intelligent Test Design” (ITD) principles when revising
old test batteries or developing new test batteries

Concluding Comments
More Distal Implications
Develop SEM “person fit” statistics for possible diagnostic and
instructional purposes

Pursue research into the validity and utility of identifying cognitive-
aptitude-achievement trait complexes (CAATCs)

• Identify and validate CAATCs

• Develop metrics for operationalizing CAATCs

• Ability domain cohesion metrics

• Investigate validity and utility of CAATC based SLD models
for understanding learning and identifying learning problems

Concluding Comments
More Distal Implications
Use more “Intelligent Test Design” (ITD) principles when revising
old test batteries or developing new test batteries

Incorporate suggested “Intelligent Test Design” (ITD) principles into
current “best practice” test development principles when
developing new test batteries

• Broad+narrow inverted CHC hybrid approach (ITD)

Concluding Comments
Enduring Implications
Intelligence researchers and test developers need to embrace a
wider diversity of validated theories, models, and data analytic
methodological lenses to counter Jöreskog syndrome.

”If I have seen
farther, it is by standing
on the shoulders of
giants”
As stated by Isaac Newton
in a letter to Robert
Hooke in 1676:

Concluding Comments
Enduring Implications
Exploratory research methods need to be used more frequently by
intelligence researchers

Many a scientific adventurer sails the uncharted seas and sets his
course for a certain objective only to find unknown land and
unsuspected ports in strange parts. To reach such harbors, he
must ship and sail, do and dare; he must quest and question.
These chance discoveries are called “accidental” but there is
nothing fortuitous about them, for laggards drift by a haven that
may be a heaven. They pass by ports of opportunity. Only the
determined sailor, who is not afraid to seek, to work, to try, who
is inquisitive and alert to find, will come back to his home port
with discovery in his cargo (p. 177)

Implications of 20 Years of CHC Cognitive-Achievement Research: Back-to-the-Future and Beyond CHC

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