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A research investigation had to meet all
of the following criteria to be included in the current review.
First, the study had to be explicitly designed as per the CHC (or
Gf-Gc) cognitive abilities framework. Second, the study must have
empirically investigated the relations between the primary CHC
cognitive variables (independent variables; IVs) and achievement
variables in reading and math (dependent variables; DVs). Third,
the research report had to report quantitative information (e.g.,
reporting of statistical
difference tests; regression
weights; effect
sizes) regarding the relevant strength of each CHC
IV and the respective achievement DV domain. Finally, the study had
to include markers from 5 or more of the 7
primary CHC cognitive
domains. Nineteen investigations met all criteria. Each
investigation was described (see Table 1) according to: type of
sample, age or grade range of samples , size of sample(s), number
of samples analyzed, cognitive assessment batteries providing the
IVs
, CHC strata of IVs, achievementDVs
, type of achievement DV, and type of analyses (see Table 1
notes for additional information). Inspection of the 19
investigations reflected a division between studies that analyzed
observable or manifestIVs
(and no full scale or g score) and those that analyzed
latentIVs
(with or without a latent g factor).
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The majority of investigations reported results
for more than one sample. For example, in Study 1 (McGrew, 1993),
15 subsamples (sample n range of 59-325) of the WJ-R norm
data were analyzed separately with multiple
regression methods across the ages of 5 to 19
years. However, from this point forward we refer to the number
of analyses and not the number of samples. This
is an important distinction given that many analyses used the same
norm subjects (e.g., WJ-R or WJ III standardization data) but
divided the same subject pool into different age groups, analyzed
the same set of subject data but employed different analysis
methods, or analyzed test vs. cluster scores with the same subject
pool (see second note in Table 1). Thus, many of the
samples were not independent—but the analyses were.
There were a total of 134 analyses—54.5 % were manifest
variable (MV; n=73) and 45.5 % were latent variable (LV;
n=61). Of the 134 analyses, 126 (94 %) were exclusive to
the WJ-R
or WJ III norm data. Given the preponderance of
WJ-R and WJ III studies, the coded age-ranges were driven, in large
part, by the age- ranges reported in these investigations. The
result was the division of the analyses into the age groups of 6-8,
9-13, and 14-19 years of age. For investigations that
analyzed a finer gradation of development (e.g., McGrew, 1993,
reported regression results for each year from ages 5 thru 19), the
appropriate subsamples (e.g., results for ages 5, 6, 7 and 8) were
treated as a single age group (ages 6-8). Certain studies (studies
7-10) were based on a single wide-range sample (e.g., Ganci, 2004,
samples of elementary reading disabled (RD) and non-reading
disabled (NRD) across ages 6-12) and thus, these single sets of
results were included in each of the broad three age groups for
which the single sample included subjects. This resulted in some
single wide-age samples being reported in 2 or 3 of the three major
age-grouped analyses.
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As reported in Table 1, studies were categorized
as analyzing either manifest or latent variables. Most
studies also used one of four types of research analysis
methods. A summary description and pictorial representation
of the four types of research models can be accessed by clicking
here.Below are references and links to the 19 research
studies included in Table 1.
Benson, N. (2009). Integrating psychometric and
information processing perspectives to clarify the process of
mathematical reasoning. Manuscript in
preparation.
Benson,
N. (2008). Cattell-horn-carroll cognitive
abilities and reading achievement. Journal of Psychoeducational
Assessment, 26(1), 27-41.
Flanagan, D. P. (2000). Wechsler-based CHC
cross-battery assessment and reading achievement: Strengthening the
validity of interpretations drawn from Wechsler test scores.
School Psychology Quarterly, 15(3), 295-329.
Floyd,
R. G., Keith, T. Z., Taub, G. E., & McGrew, K. S.
(2007). Cattell-Horn-Carroll cognitive abilities
and their effects on reading decoding skills: g has indirect
effects, more specific abilities have direct effects. School
Psychology Quarterly, 22(2), 200-233.
Ganci,
M. (2004). The diagnostic validity of a
developmental neuropsychological assessment (NEPSY) - Wechsler
Intelligence Scale for Children-third edition (WISC-III) based
cross battery assessment. Retrieved from ProQuest UMI
Dissertation Publishing (UMI Microform 3150999).
Hale,
J. B., Fiorello, C. A., Dumont, R., Willis, J. O., Rackley, C.,
& Elliott, C. (2008). Differential Ability
Scales-Second Edition (Neuro) psychological predictor of math
performance for typical children and children with math
disabilities. Psychology in the Schools, 45(9),
838-858.
Keith,
T. Z. (1999). Effects of general and specific
abilities on student achievement: Similarities and differences
across ethnic groups. School Psychology Quarterly, 14(3),
239-262.
McGrew, K. (2007). Prediction of WJ
III reading and math achievement by WJ III cognitive and language
tests. Unpublished data analysis available at IQs Corner
Blog.
McGrew, K. (2008).
Cattell-Horn-Carroll g and specific ability effects on reading
and math: Reanalysis of Phelps et al. (2007). Unpublished
data analysis available at IQs Corner Blog.
McGrew,
K. S. (1993). The relationship between the WJ-R
Gf-Gc cognitive clusters and reading achievement across the
lifespan. Journal of Psychoeducational Assessment, Monograph
Series: WJ R Monograph, 39-53.
McGrew,
K. S., & Hessler, G. L. (1995). The
relationship between the WJ-R Gf-Gc cognitive clusters and
mathematics acheivement across the life-span. Journal of
Psychoeducational Assessment, 13, 21-38.
Miller,
B. D. (2001). Using Cattell-Horn-Carroll
cross-battery assessment to predict reading achievement in learning
disabled middle school students. Retrieved from ProQuest UMI
Dissertation Publishing (UMI Microform 9997281).
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1. Introduction
