Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists

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This is the current version of my previous "Beyond CHC theory" module. It presents my current thinking [based on extensive exploratory and confirmatory analysis of multiple data sets (esp. the WJ III …

This is the current version of my previous "Beyond CHC theory" module. It presents my current thinking [based on extensive exploratory and confirmatory analysis of multiple data sets (esp. the WJ III norm data and WJ III joint cross-battery data sets) plus the integration of contemporary cognitive, neurocognitive, intelligence and neuropsychological research] re: potential future evolutions of the Cattell-Horn-Carroll (CHC) model of human cognitive abilities. This current presentation was last presented at the CNN (neuropsych) conference the first week of October, 2010, in Fremantle Australia

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  • 1. Pushing the edge of the contemporary cognitive (CHC) theory: New directions for psychologists
    Kevin S. McGrew, PhD
    Woodcock-Muñoz Foundation
    16th Annual APS College of Clinical Neuropsychologists Conference
    From East to West: New directions in Neuropsychology
    30 September - 2 October 2010
    Notre Dame University, Fremantle, Western Australia
  • 2. Or…..what an inquisitive applied intelligence scholar/psychometrician constructed/discovered from playing almost a decade in his data, literature, and theoretical sandbox
  • 3. “Intelligent” testing and interpretation
    requires…knowing thy instruments
    Neuropsych. interpretation
    Error variance (reliability)
    External criterion relevance
    Uniqueness (specificity)
    g loading
    Degree of cognitive complexity
    CHC Ability factor classifications
    Degree of cultural loading
    Degree of linguistic demand
    Metric scale
    Information processing & stimulus/response characteristics
    Ability domain cohesion
  • 4. “ If you give a monkey a stradivarius violin and you get bad music……..you don’t blame the violin”
    McGrew (circa 1986)
  • 5. Three things (or major steps) completed that have resulted in the intelligence model(s) to be presented today
  • 6. Things 1 and 2:
    Will be covered quickly to provide context and background for primary content of today – Thing 3
  • 7. Psychometric vs. neuropsychological conception/model assessment gap
    “It is notable that there is a gap between neuropsychological measures and evolving conceptualizations of intelligence. That is, for as seemingly related as the instruments and concepts are, they have strikingly different historical backgrounds.”
    (Hoelzle, 2008)
  • 8. Psychometric vs. neuropsychological assessment gap: Select reasons why (Hoelzle, 2008)
    • Singular concept of intelligence (g) has hadminimal clinical utilityin neuropsychological assessment
    • 9. NP assessment has been traditionally non-theoretical---popular models of intelligence and cognitive abilities have been derived via statistical procedures
    • 10. NP measures traditionally selected on ability to differentiate between neurological and normal conditions---psychometric frameworks derived with factor analytic techniques to synthesize theories that were similarly derived
  • Verticalfactor analysis (trait) model
    Gf
    Gc
    Glr
    G..
    Gsm
    Gv
    etc
    Attn
    Psychometric approaches have had primary (but not sole) focus/goal on internal/structural validity within each construct domain --- Vertical models
  • 11. Horizontalmultiple regression (aptitude/functional/pragmatic) model
    Criterion DVs
    Gf
    Gc
    Glr
    G..
    Gsm
    Gv
    etc
    Attn
    TBI ?
    Brain Area/function
    Neuropsychological approaches have had primary (but not sole) focus/goal on external/predictive (Dx) validity – Horizontal models
    Result has been many NP measures are mixture measures of multiple CHC domain abilities (which abilities and in what amount [weighting] best predict criterion variables?)
  • 12. My primary goal
    Present a different (yet compatible value-added) psychometric theory of intelligence perspective for thinking about testing cognitive abilities
  • 13. Importance Of Classification
    Taxonomies In All Sciences
    Classification is arguably one of the most central and generic of all our conceptual exercises…without classification, there could be no advanced conceptualization, reasoning, language, data analysis, or for that matter, social science research (K.D. Bailey, 1994).
    A specialized science of classification of empirical entities known astaxonomy(Bailey, 1994; Prentky, 1994) is ubiquitous in all fields of study because it guides our search for information or truth.
  • 14. ?
    Reliable variance
    (reliability)
    Error variance
    -individual/situational variables (e.g., distractibility)
    -item variables (e.g., item sampling and item gradients;
    test floor and ceiling)
    -examiner variables (e.g., rapport, scoring and
    administration errors)
    -testing environment variables (e.g., noise, comfort)
    Unique abilitiesnot shared
    in common with other CHC factor indicators (specificity)
    We have been searching for an empirically/theoretically-based cognitive taxonomyto interpret the reliable variance of cognitive tests
  • 15. The Cattell-Horn-Carroll (CHC) theory of cognitive
    abilities is the contemporary consensus
    psychometric model of the structure of human intelligence
    The CHC Timeline Project (and detailed information re: CHC theory/model)can be found at IQ’s Corner blog
    www.iqscorner.com
  • 16. g
    T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    PMA1
    PMA2
    PMA3
    PMA4
    PMA1
    PMA2
    PMA3
    PMA4
    PMA1
    PMA2
    PMA3
    PMA4
    PMA1
    PMA2
    PMA3
    PMA4
    …etc
    (1b) Thurston’s Multiple Factor (Primary Mental Abilities) Model
    …etc
    (1a) Spearman’s general Factor model
    G1
    G2
    G3
    …etc
    …etc
    …etc
    g ?
    …etc
    G1
    G2
    G3
    …etc
    …etc
    (1e) Consensus Cattell-Horn-Carroll Hierarchical Three-Stratum Model
    Arrows from g to each test
    (rectangle) have been omitted for readability
    Stratum III
    g
    G1
    Stratum II
    G2
    …etc
    Stratum I
    …etc
    …etc
    (1d) Carroll’s Schmid-Leiman Hierarchical Three-Stratum Model
    (1c) Cattell-Horn Gf-Gc Hierarchical Model
    Stratum III
    Note: Circles represent
    latent factors. Squares represent manifest measures (tests; T1..). Single-headed path arrows designate factor loadings. Double headed arrows designate latent factor correlations
    Stratum II
    Stratum I
    Figure 1: Major stages in the evolution of psychometric theories from Spearman’s g to Cattell-Horn-Carroll (CHC) theory
  • 17. CHC theory has entered the mainstream neuropsychological assessment literature
  • 18. A landmark event in understanding the structure of human cognitive abilities - 1993
  • 19. THE SCOPE OF CARROLL’S FACTOR ANALYTIC REVIEW
    Reviewed factor analytic research of the past 50-60 years
    Includes nearly all of the more important and classic factor analytic investigations
    Started with 1,500 references
    Final pool of 461 data sets that meet specific criteria
    Reanalyzed all or nearly all of the data sets
    Used exploratory methods in order to “let the data speak for themselves”
  • 20. The verdict is unanimous re: the importance of Carroll’s (1993) work
    Richard Snow (1993):
    “John Carroll has done a magnificent thing. He has reviewed and reanalyzed the world’s literature on individual differences in cognitive abilities…no one else could have done it… it defines the taxonomy of cognitive differential psychology for many years to come.”
    Burns (1994):
    Carroll’s book “is simply the finest work of research and scholarship I have read and is destined to be the classic study and referencework on human abilities for decades to come” (p. 35).
     
    John Horn (1998):
    A “tour de force summary and integration” that is the “definitive foundation for current theory” (p. 58).  Horn compared Carroll’s summary to “Mendelyev’s first presentation of a periodic table of elements in chemistry” (p. 58). 
    Arthur Jensen (2004):
    “…on my first reading this tome, in 1993, I was reminded of the conductor Hans von Bülow’s exclamation on first reading the full orchestral score of Wagner’s Die Meistersinger, ‘‘It’s impossible, but there it is!’’
    “Carroll’s magnum opus thus distills and synthesizes the results of a century of factor analyses of mental tests. It is virtually the grand finale of the era of psychometric description and taxonomy of human cognitive abilities. It is unlikely that his monumental feat will ever be attempted again by anyone, or that it could be much improved on. It will long be the key reference point and a solid foundation for the explanatory era of differential psychology that we now see burgeoning in genetics and the brain sciences” (p. 5).
  • 21. Contemporary psychometric research has converged on
    the Cattell-Horn-Carroll (CHC) theory of cognitive abilities as the consensusworking taxonomy of human intelligence
    McGrew, K. (2009). Editorial: CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research, Intelligence, 37, 1-10.
  • 22. T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    PMA1
    PMA2
    PMA3
    PMA4
    g ?
    …etc
    G1
    G2
    G3
    …etc
    …etc
    (1e) Consensus Cattell-Horn-Carroll Hierarchical Three-Stratum Model
    CHC as the consensus psychometric model of intelligence
    Because the Carroll model is largely consistent with the model originally proposed by Cattell (1971), McGrew (2009) has proposed an integration of the two models which he calls the Cattell-Horn-Carroll (C-H-C) Integration model….Because of the inclusiveness of this model, it is becoming the standard typology for human ability. It is certainly the culmination of exploratory factor analysis.
    The Science of Intelligence
    (Doug Detterman, 2010; book manuscript in preparation)
  • 23. T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    PMA1
    PMA2
    PMA3
    PMA4
    g ?
    …etc
    G1
    G2
    G3
    …etc
    …etc
    (1e) Consensus Cattell-Horn-Carroll Hierarchical Three-Stratum Model
    CHC as the consensus psychometric model of intelligence
    “The Cattell–Horn–Carroll (CHC) theory of cognitive abilities is the best validated model of human cognitive abilities”
    [Ackerman, P. L. & Lohman D. F. (2006).  Individual differences in cognitive functions.  In P. A. Alexander, P. Winne (Eds.), Handbook of educational psychology, 2nd edition (pp. 139-161).  Mahwah, NJ: Erlbaum.]
  • 24. T2
    T3
    T4
    T5
    T6
    T7
    T8
    T9
    T1
    T12
    T10
    T11
    PMA1
    PMA2
    PMA3
    PMA4
    g ?
    …etc
    G1
    G2
    G3
    …etc
    …etc
    (1e) Consensus Cattell-Horn-Carroll Hierarchical Three-Stratum Model
    CHC as the consensus psychometric model of intelligence
    A significant number of Australian intelligence scholars have framed (and/or continue to frame) their research as per the extended Gf-Gc (aka. CHC) model of intelligence. Many have made foundational contributions to building the model.
    N. R. Burns
    T. Nettlebeck
    L. Stankov
    R. Roberts
    S. Bowden
  • 25. Importance Of Classification
    Taxonomies In All Sciences
    Classification is arguably one of the most central and generic of all our conceptual exercises…without classification, there could be no advanced conceptualization, reasoning, language, data analysis, or for that matter, social science research (K.D. Bailey, 1994).
    A specialized science of classification of empirical entities known astaxonomy(Bailey, 1994; Prentky, 1994) is ubiquitous in all fields of study because it guides our search for information or truth.
  • 26. Gf
    Broad
    RG
    RP
    Narrow
    I
    RQ
    RE
    CHC theory classifies abilities according to three levels or strata
    g
    All CHC narrow abilities and their definitions can be found at www.IAPsych.com
    General
    RG = Gen Sequential (deductive) Reasoning
    I = Induction
    RQ = Quantitative Reasoning
    RP = Piagetian Reasoning
    RE = Speed of Reasoning
  • 27.
  • 28. ...most disciplines have a common set of terms and definitions (i.e., a standard nomenclature) that facilitates communication among professionals and guards against misinterpretations. In chemistry, this standard nomenclature is reflected in the ‘Table of Periodic Elements’. Carroll (1993a) has provided an analogous table for intelligence…..
    (Flanagan & McGrew, 1998)
  • 29. g
    Gv
    Gf
    Glr
    Gs
    Gsm
    Gc
    Ga
    Reliable variance
    (reliability)
    Error variance
    -individual/situational variables (e.g., distractibility)
    -item variables (e.g., item sampling and item gradients;
    test floor and ceiling)
    -examiner variables (e.g., rapport, scoring and
    administration errors)
    -testing environment variables (e.g., noise, comfort)
    Unique abilitiesnot shared
    in common with other CHC factor indicators (specificity)
    CHC Theory is the best available empirically and theoretically sound cognitive ability taxonomy available today
  • 30. This is where the field of psychometric intellectual
    assessment is at..and a bandwagon has formed
    g
    Gf
    Gc
    Induction
    (I)
    Lang.
    Develpmt
    (LD) (LD)
    General Seq.
    Reasoning
    (RG)
    Quantitative
    Reasoning
    (RQ)
    Listening
    Ability
    (LS)
    General
    Information
    (K0)
    Lexical
    Knowledge
    (VL)
    Speed of
    Reasoning
    (RE)
    Primary ability
    Reliable variance
    (reliability)
    Secondary ability
    Error variance
    -individual/situational variables (e.g., distractibility)
    -item variables (e.g., item sampling and item gradients;
    test floor and ceiling)
    -examiner variables (e.g., rapport, scoring and
    administration errors)
    -testing environment variables (e.g., noise, comfort)
    Unique abilitiesnot shared
    in common with other CHC factor indicators (specificity)
  • 31. Published WJ III CHC model (McGrew & Woodcock, 2001
    CFA analysis of 50+ cognitive and achievement tests
  • 32. Starting point
    Ages 6-adult CFA Broad CHC Model in WJ III Technical Manual
    (McGrew & Woodcock, 2001)
    g
    .55
    .91
    .88
    .73
    .87
    .88
    .79
    .82
    .93
    Gs
    Gsm
    Gv
    Gc
    Gq
    Ga
    Glr
    Gf
    Grw
    First order measurement model omitted for readability purposes
  • 33. Deconstruction: The validated/published WJ III CHC structure was “torn down”
    Psychologists need a healthy degree of positive skepticism
  • 34. Reconstruction: New structural models specified based on insights from large variety of statistical analysis of the WJ III norm data since 2001.
  • 35. Stage (Thing 2) approach
    Theoretical considerations (Berlin BIS model; dual-processing cognitive models; etc.) also served as guides during exploratory model specification.
    Important caution: The final models demonstrated near identical model fit statistics (e.g., some equivalent models). Also, the large amount of exploratory model specification employed has the potential to capitalize on "random chance factors"- thus rendering statistical model evaluation comparisons useless.
    Thegoal of these analyses were to "push the edge of the envelope" of the WJ three data via SEM-based model generation procedures. Thelaw of parsimony was deliberately discarded.
    Cross validation of proposed final models in independent samples is needed.
  • 36. Variety of Exploratory Data Analyses with Variety of Datasets
    Data Sets
    • WJ III norm data
    • 37. WJ III+ other batteries
    (WISC-R; WAIS-III/WMS-III/KAIT)
    • WAIS-IV subtest correlations
    Methods
    • Cluster analysis
    • 38. Multidimensional scaling analysis (MDS) – 2D and 3D
    • 39. Standard and Carroll EFA+CFA exploratory factor analysis
    • 40. Model-generation CFA (SEM)
    • 41. CHC cognitive causal SEM models
  • Cluster analysis of WJ III and WJ III + other batteries (joint analysis) + other batteries alone (WAIS-IV)
  • 42. Cluster Analysis
    Cluster analysis is an set of exploratory (structure discovering) data analysis tools for solving classification problems.  Sometimes it has been called a “poor mans” factor analysis. Its object is to sort cases (people, things, events, tests, etc) into groups, or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters.  Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type.
    CA often helps confirm EFA results and similar to MDS (multidimensional scaling), can spatially represent the degree of similarity of tests measuring a common dimension (dimension cohesion). Its hierarchical sequential structure is often useful in suggesting higher-order dimensions/factors.
  • 43. Cluster Analysis
    The strength of cluster analysis (discovering structure in data with more relaxed statistical assumptions and mathematics than data reduction methods such as exploratory factor analysis) is also one of its major limitations. CA will find groups or clusters in random data. The algorithms are designed to find any structure, even if structure is not present. As a result, the later clusters in a hierarchical approach are often “necessary evils or by products”--CA must end with one grand cluster. Thus, often in CA a point is reached where the further collapsing of meaningful groupings ceases to make substantive sense. It is important to recognize this in the resultant cluster dendogram.
    Also, given the above, tests (objects, etc.) that share little in common with other measures need to be assigned to some grouping and cluster. Thus, often “loner” type tests will appear in very meaningful clusters but will not be consistent with the underlying interpretation of the grouping/cluster. Sometimes this suggests new insights regarding the test. Other times these “I’ve got to be grouped with some cluster somewhere in the process” tests are best ignored and should not interpreted as discounting the strong communality of a grouping or clustering of tests
  • 44. AS
    CF
    SA
    UD
    Gf (language-based)
    AP
    Clusters beyond this point not easily interpretable – see limitations of CA method
    NS
    Complex lang. processing/
    reasoning?
    QC
    NM
    Gf
    CAL
    RQ
    MF
    Gf (numeric-based)
    RDF
    WF
    ? = no apparent current CHC ability classification
    Red font = CHC factors
    Blue font = possible new abilities at different strata to consider
    PSC
    RV
    Gs (achievement)
    ED
    ?
    LW
    SP
    RC
    WS
    SOS
    Orthographic
    processing?
    Grw (words,
    sent, con. disc)
    WA
    PV
    VC
    Grw
    GI
    Grw (phonemes)
    AK
    OC
    LD/VL
    STR
    DRS
    K0
    RPN
    ?
    REF
    Gc
    DS
    LS
    PC
    VM
    NA/R4 (RAN?)
    CO
    Gs (cognitive)
    VCL
    R9
    PR
    BR
    Ppr
    SPR
    Pc
    SNP
    PLN
    MV
    NR
    Gv
    Cluster analysis (Wards method) of 50 WJ III cognitive and achievement tests (ages 6-18; NU norms)
    Kevin McGrew
    11-13-09
    AWM
    SR/Vz
    MS
    ?
    MW
    SR/Vz+
    IW
    SB
    MW
    AA
    Gsm
    DRV
    MS
    VAL
    Temporal Processing or Tracking /
    Aud. Sequential Processing
    MN
    PC
    DRM
    Ga
    Glr-MA
    Distances
    2.5
    0.5
    1.0
    1.5
    2.0
    0.0
  • 45. Cluster analysis (Ward method) for all WJ3 tests across all ages (K. McGrew 12-7-03)
    VISCLO
    MV
    Gv (content facet—visual/figural)
    PICREC
    SNDPTV
    SPAREL
    SR/Vz
    BLKROT
    SR/Vz
    PLAN
    RPCNAM
    NA
    RETFLU
    AUDATN
    Ga
    SNDBLN
    PC
    INCWRD
    Ga+Gsm (content facet–auditory)
    MEMWRD
    System 2 (controlled) cognitive processing
    MS
    MEMSEN
    AWKMEM
    Gsm
    NUMREV
    SPELL
    LWIDNT
    [ phoneme/grapheme knowledge ]
    WRDATK
    SPLSND
    Grw (content facet –
    Language; read or written)
    WRTSMP
    EDIT
    SNDAWR
    V
    RDGVOC
    PSGCMP
    RC
    ORLCMP
    VERBANL
    LS
    Gc (content facet – words
    & connected discourse)
    STYREC
    ORALVOC
    GENINF
    LD/VL
    PICVOC
    AKHUM
    AKSOC
    AKSCI
    K0
    CALC
    QCCONC
    A3/KM
    APPROB
    NUMSER
    Gq (content facet – numerical)
    NUMMAT
    RQ
    ANLSYN
    UNDDIR
    Gf
    CONFRM
    (Shading designates stratum II abilities)
    VAL
    (content facet – figural/visual)
    MEMNAM
    MA
    WRTFLU
    g (stratum III)
    RDGFL
    Gs (ach/content)
    MTHFLU
    CRSOUT
    Gs System 1 (automatic) cognitive processing
    VISMAT
    PAIRCN
    P
    DECSPD
    Gs (cog/process)
    Distances
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    © Institute for Applied Psychometrics llc 12-07-03
  • 46. WAIS-IV test Cluster Tree (Wards method)
    of WAIS-IV subtest intercorrelations
    Verbal know & comp (Gc)
    IN
    CO
    VC
    Level (unspeeded) cognitive abilities
    SI
    Short-term & working memory (Gsm)
    LN
    DS
    AR
    Fluid Reasoning (Gf)
    FW
    MR
    Visual-Spatial Proc.(Gv)
    BD
    VP
    General Intelligence (g) as per WAIS-IV
    ?
    PCM
    CD
    Processing Speed (Gs)
    (rate cognitive abilities)
    SS
    CA
    0.0
    0.5
    1.0
    1.5
    Distances
  • 47. PS
    PCSS
    Gv
    BLKROTWR
    SR
    BDSS
    SR/VZ
    OS
    SPAREL
    CONFRM
    UNDDIR
    RQ
    Thinking Abilities /
    Controlled Processing
    NUREAWR
    Gq/Gf
    QNTCON
    APPROB
    A3
    ARITHSS
    CALC
    MA
    MEMNAMWR
    Glr
    VAL
    ORLCMP
    ANLSYN
    Gf/MW?
    AWKMEM
    MW
    NUMREV
    DSSS
    PLAN
    COMPSS
    VL
    SIMSS
    LD
    Gc
    VOCSS
    Acquired Knowledge
    INFOSS
    VRBCMP
    ACKNOWWR
    GENINF
    STYREC
    NA/R4/RAN
    RETFLU
    Cog Fluency/ Efficiency (speeded)
    RPCNAM
    Gs
    DECSPD
    SSSS
    VISMAT
    P/R9
    CODSS
    Cognitive Efficiency /
    Automatic Processing
    MS
    MEMSENWR
    Succ Proc/ Gsm / Temp Tracking ?
    MEMWRD
    PC
    INCWRD
    BLND
    Cog Fluency/ Efficiency (unspeeded)
    US/UR
    AUDATN
    SNDPATWR
    PICREC
    VISCLOWR
    McGrew-Evans WJ III/WISC-III Cluster Analysis Interpretation Worksheet
    Phelps WJ III Technical Manual
    Validity sample – n=150 grade 3-5
    © Institute for Applied Psychometrics, llc 02-05-02
  • 48. WJR Visual Matching
    P (Gs)
    WJR Cross Out
    RG
    WJR Analysis-Synthesis
    Gf
    KAIT Logical Steps
    I
    KAIT Mystery Codes
    WJR Concept Formation
    CS
    WJR Visual Closure
    Gv
    WISC3 Object Assembly
    MV
    WJR Picture Recognition
    KAIT Memory for Block Designs
    PC (Ga)
    WJR Incomplete Words
    WJR Sound Blending
    LD
    KAIT Double Meanings
    KAIT Famous Faces
    WJR Picture Vocabulary
    Gc
    VL
    WJR Oral Vocabulary
    KAIT Definitions
    Gc/Grw
    WJR Letter-Word ID
    WJR-Reading Vocabulary
    WJR Visual-Auditory Learning
    WJR Memory for Names
    MA
    KAIT Rebus Learning
    KAIT Rebus Learning-Delayed
    Glr
    KAIT Auditory Comprehension-Delayed
    LS/MM
    Gy
    KAIT Auditory Comprehension
    WJR Memory for Sentences
    MS (Gsm)
    WJR Memory for Words
    Distances
    0
    1
    2
    3
    4
    Flanagan & McGrew (1998) WJ-R/KAIT joint cluster analysis
    © Institute for Applied Psychometrics llc 8-24-03
  • 49. Late Career Carroll-EFA+CFA Method (e.g., Carroll, 2003) of WJ III
  • 50. Traditional EFA of WJ III at
    various age levels
  • 51. CHC CFA (SEM) of WJ III and WJ III + other batteries (joint analysis)
  • 52. Gregg/Hoy College Sample-WJ III + WAIS-III + WMS-III
    (LD/Non-LD; n=200)
    (McGrew et al., 2001)
    © Institute for Applied Psychometrics, llc 02-05-02
  • 53. © Institute for Applied Psychometrics, llc 02-05-02
  • 54. SEM Causal Information Processing Models of WJ III
  • 55. Note: Ovals represent
    latent factors. Rectangles represent manifest measures (tests). Single-headed arrows to tests from ovals designate factor loadings. Single headed arrows between ovals represent causal paths (effects). Test and factor residuals omitted for readability purposes.
    Visual Matching
    Mem for Sentences
    .78
    .78
    Decision Speed
    .49
    .66
    MS
    Gs
    Mem for Words
    .75
    .81
    Cross Out
    .80
    .27
    Aud Working Mem
    .72
    MW
    Numbers Reversed
    .72
    Block Rotation
    .07
    .53
    .07
    Verbal Comp
    Spatial Relations
    Picture Recognition
    .69
    Gv
    .94
    .47
    .82
    Oral Comp
    Memory for Names
    .74
    Gc
    .83
    .88
    General Information
    .87
    .59
    Retrieval Fluency
    Analysis-Synthesis
    .50
    .92
    g
    Glr
    .74
    Del Recall-VAL
    .69
    .95
    Concept Formation
    .72
    .78
    Gf
    76 % of g variance explained
    .77
    .78
    Vis-Aud Lrng (VAL)
    Numerical Reas
    Sound Blending
    Incomplete Words
    .74
    .58
    Ga
    Figure 4: WJ III CHC information processing  g causal model (ages 14-19)
    .40
    Sound Patterns
    Cognitive Efficiency
  • 56. Guttman’s Radex Theory
    Ability tests can be classified by:
    • Degree of cognitive complexity
    • 57. Differences in kind of content
    • 58. Differences in type of processes
    UsesMDS (multidimensional scaling)
  • 59. Example of MDS (Radex Model)
    The closer a test is to the center of the figure, the more it is related to the underlying general dimension of the battery. Also, the center represents the most cognitively complex (i.e., have the largest number of performance components) tests.
    Tests that group together are interpreted as sharing common stimulus content or cognitive processing characteristics
  • 60. MDS (Guttman Radex model) of WAIS-IV subtest intercorrelations
    3
    Short-term memory /working memory (Gsm)
    1
    Processing speed (Gs)
    LN
    DS
    CD
    Verbal know &
    comp (Gc)
    VC
    CO
    Dimension-2
    Fluid
    reasoning (Gf)
    AR
    MR
    CA
    SS
    SI
    IN
    FW
    BD
    VP
    -1
    PCM
    Visual-spatial processing (Gv)
    -3
    -3
    -1
    1
    3
    Dimension-1
  • 61. MDS (Guttman Radex model) of WAIS-IV subtest intercorrelations
    It is a common practice in MDS analysis to visually partition the MDS spatial configuration into broader dimensions and consider interpretation at a higher-order level.
    The current WAIS-IV MDS revealed the following hypothesized higher-order structure
    Note – similar to hand rotation of factors in early days of EFA, K. McGrew took the cross-hair lines and hand rotated them (simultaneosly) until a meaningful pattern emerged. The four-broad dimensions are interpreted as being very similar to the four cognitive domains of Woodcock’s Cognitive Performance Model (CPM) – see next two slides
    Short-term memory /working memory (Gsm) – Cognitive Efficiency unspeeded/memory
    3
    Verbal know &
    comp (Gc) – Acquired Knowledge or “Product” dominant abilities?
    1
    LN
    DS
    CD
    VC
    AR
    CA
    CO
    MR
    SS
    Processing speed (Gs) -
    Cognitive Efficiency speeded
    SI
    IN
    FW
    BD
    VP
    -1
    PCM
    Fluid Reasoning (Gf) and Visual-spatial processing (Gv) –Thinking or “Process” dominant abilities?
    -3
    -3
    -1
    1
    3
  • 62.
    • Thinking abilities
    • 63. Process-dominant “level” abilities
    • 64. Visual-spatial/figural (low linguistic) stimuli (Gv,Gf,Glr)
    • 65. Controlled cognitive processing
    • 66. Acquired knowledge abilities
    • 67. Product -dominant “level” abilities
    • 68. Language (aud-linquistic) and symbolic stimuli (Ga,Gc,Grw,Gq)
    • 69. Controlled cognitive processing
    2
    WJ III Radex Model
    IW
    1
    Broad CHC factor
    ability font key legend
    (based on CFA studies)
    drm
    mn
    SB
    PV
    drs
    MS
    OC
    STR
    BR
    Gf
    SNP
    Gsm
    GI
    NM
    AK
    drv
    RV
    PSC
    val
    glr
    VC
    WA
    LW
    DIM(2)
    ED
    SA
    0
    SOS
    AP
    REF
    CF
    SPL
    Ga
    WS
    QC
    AS
    RDF
    UD
    NS
    Gv
    CAL
    WF
    RPN
    AWM
    SPR
    Gs
    MW
    MF
    NR
    Cognitive efficiency (speeded-Gs) rate/fluency abilities
    • Automatic cognitive processing
    Gc
    VCL
    VM
    CO
    Grw
    -1
    Gq
    PC
    DS
    AA
    PR
    PLN
    Cognitive efficiency (unspeeded-Gsm) abilities
    • Automatic cognitive processing
    -2
    The grand “big picture model” --- probably requires a subsequent 3-D MDS analysis to see clearly….more to come
    -3
    -2
    -1
    0
    1
    2
    DIM(1)
  • 70. Gv
    2
    PL
    BR
    PR
    More
    visual-spatial
    & figural
    Gf
    SR
    DIM(3)
    VC
    Gq
    AS
    MN
    SPV
    VAL
    CF
    NM
    AP
    CO
    NS
    DS
    CNC
    CA
    PC
    VM
    Gsm
    AA
    More auditory
    & linguistic
    SA
    RDF
    -3
    MF
    WF
    BL
    NR
    RF
    AWM
    IW
    More process-
    dominant
    MW
    RPN
    DIM(1)
    2
    More System 2
    (controlled) cognitive
    processes
    Red font = Gs
    DIM(2)
    Note – all Gc and
    Grw unspeeded tests
    are omitted and are
    located within
    dashed area in center
    0
    0
    Blue font = Ga
    More System 1
    (automatic) cognitive
    processes
    More product-
    dominant
    2
    -2
    WJ III 3-D MDS Model
    © Institute for Applied Psychometrics llc 12-07-03
  • 71. “Intelligent” testing and interpretation
    requires…knowing thy instruments
    Neuropsych. interpretation
    Error variance (reliability)
    External criterion relevance
    Uniqueness (specificity)
    g loading
    Degree of cognitive complexity
    CHC Ability factor classifications
    Degree of cultural loading
    Degree of linguistic demand
    Metric scale
    Information processing & stimulus/response characteristics
    Ability domain cohesion
  • 72. Food for thought: Are the MDS quadrants or partitions reflecting content “facets” or a combination of content“facets and “operations” as per the BIS model of intelligence….see next slide
  • 73. BIS: Berlin Model of
    Intelligence Structure
    Gs
    Gsm + Glr (level abilities)
    Carroll’s Gy
    Glr(fluency abilities)
    Gf
    Note difference in term in different versions:
    Processing capacity defined as complex reasoning
  • 74. Unveiling of preliminary new models in WJ III norm data
  • 75. Alternative Model 1
    g
    .39
    Gs
    (Cognitive speed)
    .82
    .88
    .71
    .87
    .86
    .79
    .84
    1.0
    .64
    .55
    .62
    .59
    .36
    .49
    Gs (Grw)
    .54
    Gsm
    Gv
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .62
    Gc
    Gq
    Ga
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 76. Alternative Model 2
    .86
    .99
    .93
    g
    .36
    1.0
    Cognitive efficiency
    (More automatic & effortless)
    Cog. knowledge domains/systems
    (product/content abilities)
    Lang/linguistic./symbolic abilities
    Cognitive operations
    (process/operations/analytic/rule-based abilities) figural-spatial, lower-linguistic abilities
    Gs
    (Cognitive speed)
    .89
    .76
    .91
    .85
    1.0
    .83
    .82
    .64
    .52
    .60
    .58
    .41
    .52
    Gs (Grw)
    .58
    Gsm
    Gv
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .67
    Gc
    Gq
    Ga
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 77.
  • 78.
  • 79.
  • 80. Close inspection of the evidence suggests that generic dual-system theory is currently oversimplified andmisleading
    We might be better off talking about type 1 and type 2 processes since all theories seem to contrast fast, automatic, or unconscious processes with those that are slow, effortful, and conscious (Samuels 2006). Such terminology does not commit use to a two-system view. However, it would then be helpful to have some clear basis for this distinction
    My suggestion is that type 2 processes are those that require access to a single, capacity-limited central working memory, while type 1 processes do notrequire such access. This implies that the core features of type 2 processes are that they are slow, sequential, and capacity limited. The last feature implies also that their functioning will correlate with individual differences in cognitive capacity and be disrupted by concurrent working memory load. Depending upon what else is assumed about working memory, there may be a rationale for describing such type 2 processes as registering in consciousness and having properties associated with executive processes and intentional, higher-order control.
  • 81. Alternative Model 2b
    .93
    .99
    1.0
    .86
    g
    .36
    1.0
    Type I cognitive processing
    (Cognitive efficiency):
    More automatic & effortless
    Cog. knowledge domains/systems
    (product/content abilities)
    Lang/linguistic./symbolic abilities
    Type II cognitive processing:
    More cognitively controlled & deliberate
    Cognitive operations
    (process/operations/analytic/rule-based abilities) figural-spatial, lower-linguistic abilities
    Gs
    (Cognitive speed)
    .89
    .76
    .91
    .85
    1.0
    .83
    .82
    .64
    .50
    .60
    .58
    .41
    .52
    Gs (Grw)
    .58
    Gsm
    Gv
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .67
    Gc
    Gq
    Ga
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 82. Alternative Model 3
    .95
    .99
    .94
    .21
    g
    Auditory temporal (serial) Proc.
    Visual/figural (parallel?) Proc.
    Cog. knowledge
    domains/systems
    Gs
    (Cognitive speed)
    .89
    .77
    .91
    .85
    1.0
    .82
    .86
    .90
    .63
    .45
    .60
    .54
    .48
    .65
    Gs (Grw)
    .64
    Gsm
    Ga
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .73
    Gc
    Gq
    Gv
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 83. Alternative Model 3b
    1.0
    .94
    .21
    Cognitive operations
    (process/operations/
    analytic/rule-based abilities)
    g
    .95
    1.0
    Auditory temporal (serial) Proc.
    Visual/figural (parallel?) Proc.
    Cog. knowledge
    domains/systems
    Gs
    (Cognitive speed)
    .89
    .77
    .91
    .85
    1.0
    .82
    .86
    .90
    .63
    .45
    .60
    .54
    .48
    .66
    Gs (Grw)
    .64
    Gsm
    Ga
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .74
    Gc
    Gq
    Gv
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 84. Pushing the edge of the envelope of CHC theory and the WJ III measurement model: Part IIThe first-order measurement model and implications for interpretation of WJ III tests
  • 85. Glr and Gsm measurement models were similar to
    those originally reported by McGrew & Woodcock (2001)
  • 86. See next slide for other
    indicators
    Vis. Clos. (.41)
    Blk. Rot. (.52)
    Spat. Rel. (.66)
    Pic. Rec (.43)
    Planning (.43)
    Alternative Models: WJ III Measurement model for speed factors
    Gq
    Gv
    .54
    Gs(Gq)
    .36
    .62
    Gs(Gv)
    GGs
    (Cog Spd)
    .64
    .55
    Gs(Gc)
    .59
    Gc
    .49
    Gs(Grw)
    See next slide for other
    indicators
    Grw
    .62
    Wrd. Atk. (.78) Edit. (.78)
    Psg. Cmp.* (.55) Wrt. Smp, (.76)
    Rdg. Voc.* (.34) Spelling (.86)
    LWrdID (.89)
    * Dual loading on Gc on next slide
  • 87. Alternative Models: WJ III Measurement model for possible new Gf factor structure
    Calculation (.75)
    Gq
    .34
    .51
    .27
    Gf (RQ)
    .66
    .17
    Gf
    .99
    Gf *
    .70
    Gc
    Gen. Info .(.89)
    Acd. Knw. (.89)
    Orl. Cmp. (.77)
    Psg. Cmp. (.30) (.55-Grw)
    Rdg. Voc. (.54) (.34-Grw)
    Mem. Sen. (.36) (.38- Gsm)
    Story Rec. (.29) (.39-Glr)
    Sound Awareness and Understanding Directions did not load on any other factors
    Gf * = complex language-based working memory and reasoning?
  • 88. Iteration 1:
    CHC-based
    Intelligence model of WJ III battery
    Kevin McGrew
    8-18-2010
    See handouts for clear copy
  • 89. Gf (RQ)
    .66
    Gf
    .99
    Gf *
    Hmmmm…???
  • 90. It is time to look at some non-CHC/Gf-Gc research on reasoning (Gf): Alternative lenses
  • 91. The distinction between inductive and deductive reasoning (i.e., CHC/Gf-Gc Carroll-type model) may be outdated (Wilhelm, 2005)
    Most established reasoning tests confound the direction of inference with deductive and inductive reasoning task
    (Whilhelm, 2005)
  • 92. Whilhelm tested Gf model’s as per CHC (I, RQ, RG) and BIS (verbal, quant, figural) structures, and various model interactions. The following was the best fitting model
  • 93.
  • 94. CFA using dual indicators (split-half—odd/even item sets) for each test:
    Conclusion: WJ III RG, I, RQ tests are highly correlated but do measure different aspects of Gf
  • 95. WJ III CHC Gf model
    Fit for this and prior model (prior slide) more-or-less equivalent
  • 96. Lets add in more CHC domain indicators
  • 97. f8
    r12
    2
    8
    .
    PICVOCER
    .
    7
    PicVoc
    4
    r13
    4
    f1
    PICVOCOR
    9
    .
    r0
    .
    9
    4
    ANLSYNER
    6
    9
    .
    r14
    GENINFOR
    AnlSyn
    7
    GenInf
    9
    .
    9
    5
    .
    r1
    4
    9
    ANLSYNOR
    .
    r15
    GENINFER
    f9
    Gc
    f2
    r16
    .
    0
    9
    9
    8
    .
    r2
    ACKNOWOR
    .
    CONFRMER
    9
    5
    AcdKnw
    .
    9
    1
    ConFrm
    r17
    ACKNOWER
    r3
    5
    9
    f10
    .
    .
    CONFRMOR
    7
    r18
    7
    .
    1
    9
    2
    .
    1
    ORLVOCER
    OrlVoc
    .
    9
    2
    f3
    r19
    r4
    Gf
    ORLVOCOR
    .
    9
    4
    NUMSERER
    f11
    r20
    NumSer
    5
    1
    8
    .
    2
    VERBANLER
    .
    .
    r5
    9
    VrbAnl
    9
    4
    NUMSEROR
    9
    .
    r21
    VERBANLOR
    .
    9
    0
    3
    7
    f4
    .
    f12
    Gf
    .
    9
    r6
    5
    f14
    NUMMATER
    (lang)
    r22
    NumMat
    9
    8
    .
    SNDAWRER
    r7
    SndAwr
    f13
    4
    9
    f5
    NUMMATOR
    .
    r23
    SNDAWROR
    .
    8
    8
    r8
    .
    r24
    6
    APPROBER
    5
    AppPrb
    0
    9
    UNDDIRER
    .
    Gq
    r9
    r25
    UndDir
    APPROBOR
    f6
    UNDDIROR
    3
    9
    r10
    .
    .
    8
    8
    CALCER
    f7
    f15
    Calc
    r11
    CALCOR
    7
    8
    .
    Gf sub-abilities differentiated by content/stimulus features (Wilhelm model)
    f16
    .
    5
    f18
    8
    Gf
    (vis)
    2
    .
    9
    3
    .
    3
    8
    8
    .
    .
    9
    2
    f19
    .
    9
    g
    9
    3
    9
    .
    .
    9
    4
    8
    7
    .
    Gf
    .
    9
    6
    .
    (qnt)
    8
    9
    .
    5
    9
    6
    3
    .
    f17
    .
    9
    7
    6
    9
    .
    .
    9
    6
    Although some fit stats are slightly better for this model (when compared to
    model on prior slide) using practical criteria they are more-or-less equivalent
  • 98. 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
  • 99. Additional support for differentiation of Gf by type of content or stimulus features
    3
    Note which tests are near the center: More cognitively complex
    • Snd Awareness
    • 100. Under. Directions.
    #/quant.
    Lang (aud-verbal)
    1
    Calc
    ApPrb
    AcdKn
    NumMat
    PicVoc
    GenInf
    NumSer
    SndAwr
    Reasoning (procedural/Gf)-------Recall (declarative/Gc/Gq))
    OrlVoc
    UndDir
    AnlSyn
    VrbAnl
    -1
    Visual-figural
    ConFrm
    -3
    -3
    -1
    1
    3
    Language (verbal/aud.)--------Nonverbal (#’s,visual)
    Guttman Radex MDS model of WJ III Gf, Gc, and Gq test indicators
  • 101. Thing 3 – attempt to integrate Thing 1 and Thing 2 with neuropsychological
    assessment models
  • 102. The First Commandment of Neuropsychological Assessment
    "If one writes a book on neuropsychological assessment, thou shall not write a book that is less than 3 inches thick or less than 3 lbs in weight“ (McGrew, August 13, 2010)
  • 103. Lets look at the pieces one by one – blow them up
  • 104. Arm-chair factor analysis of neuropsych. assessment domains [and CHC construct mapping] (K. McGrew; 8-18-10) [I of 3]
    g
    Gf
    Gc
    Grw
    Gq
  • 105. Arm-chair factor analysis of neuropsych. assessment domains [and CHC construct mapping] (K. McGrew; 8-18-10) [I of 3]
    Gv
    Ga
    Gsm
    Glr
  • 106. Arm-chair factor analysis of neuropsych. assessment domains [and CHC construct mapping] (K. McGrew; 8-18-10) [I of 3]
    Gs
    Gsm
    AC
    ??
    Gp
    Gps
    Go
    Gh
    Gk
  • 107. Hypothesized (“working”) CHC-based intelligence model (iteration 2)
    Kevin McGrew (8-26-2010)
  • 108. Mapping of current CHC domains with hypothesized new CHC-based intelligence model
    Kevin McGrew
    8-18-2010
    Lets look at the pieces one by one – blow them up
    Motor functions (including motor speed) - Expressive across domains?
  • 109.
  • 110. Empirical examples of Gkn domains
    From Carroll (1993)
  • 111. Empirical examples of Gkn domains
    Ackerman et al. research group
  • 112.
  • 113.
  • 114. Gp and Gps across domains
    Motor functions (including motor speed) - Expressive across domains?
  • 115.   
    .
    The somatosensory system
    The cortical homunculus was discovered by Wilder Penfield
  • 116. Olfactory abilities/functioning (Go)
  • 117. Olfactory abilities/functioning (Go): Possible
    sub-abilities mentioned in the literature
    • Olfactory memory (OM)
    • 118. Odor-evoked memories
    • 119. Episodic odor memory
    • 120. Olfactory store in working memory
    • 121. Olfactory sensitivity (OS) /detection
    • 122. Odor specific abilities (O1, O2, O3, O4)
    • 123. Odor identification/recognition/detection
    /discrimination
    • Olfactory thresholds (and reaction time)
    • 124. Olfactory acuity
    • 125. Semantic odor networks/odor naming
    • 126. Olfactory imagery
    • 127. Odor discrimination
    • 128. Odor awareness
    • 129. Sexual role of odors
    • 130. Ecological odor sensitivity
  • Olfactory abilities/functioning (Go): Dx importance
    (Doty, 2001)
  • 131.
  • 132. This is research/work in progress: Suggested research that needs to be explored and integrated. Go from here to……………..
  • 133.
  • 134. “Intelligent” testing and interpretation
    requires…knowing thy instruments
    Neuropsych. interpretation
    Error variance (reliability)
    External criterion relevance
    Uniqueness (specificity)
    g loading
    Degree of cognitive complexity
    CHC Ability factor classifications
    Degree of cultural loading
    Degree of linguistic demand
    Metric scale
    Information processing & stimulus/response characteristics
    Ability domain cohesion
  • 135. This is NOT a model of human functioning – it is a “working” heuristic of Kevin McGrew’s current hypothesized thinking (iteration 3?) regarding the important dimensions that may be important in the development and interpretation of measures of human abilities …………. (not a Guilford SOI model where all cells are believed to exist)
    Content/stimulus dimension
    Language (aud.-verb.)
    Numerical/quant.
    Somatasensory
    Visual-figural
    Olfactory
    ?: Is the low-how cog. complexity continuum simply a continuous representation of the Type 1/I processing distinction ?
    Cognitive knowledge
    domains/systems
    Cognitive operations
    Type II
    Processing
    Cognitive control
    High
    Abilty domain dimension
    Cognitive efficiency
    Sensory functions
    Low
    Type I
    Processing
    Motor functions
    Cognitve complexity
    dimension
    Note: CHC taxonomy is embedded in the ability domain dimension (see prior slides)
  • 136. g – speed ?
    Stratum III
    (General)
    Stratum II
    (Broad)
    Stratum I
    (Narrow)
    Gp
    Broad Psycho-
    Motor Ability
    Gt
    Broad Decision
    Speed
    Gs
    Broad Cognitive
    Speed
    Gps
    Broad Psycho-
    Motor Speed
    RT
    Reaction
    Time
    R9
    Rate-of-test
    Taking *
    P
    Perceptual
    Speed *
    MT
    Movement
    Time
    [ Narrow P abilities suggested by Ackerman et al. (2002) ]
    * Carroll classified P and R9 as narrow abilities
    under Gs/Gv and Gt, respectively
    ** Classified as speed and level (Gf) ability by Carroll
    *** Classified as a speed and level (Gc) ability by Carroll
    Also classified under Grw by the current author
    **** Classified as Psychomotor Ability by Carroll. Also
    classified under Grw by current author
    Figure 2: Hypothesized speed hierarchy based on integration of Carroll (1993) speed abilities with recent research
    (Ackerman, Beier & Boyle, 2002; O’Connor & Burns, 2003; McGrew & Woodcock, 2001; Roberts & Stankov,
    1998; Stankov, 2000; Stankov & Roberts, 1997)
    Integrate proposed g-speed hierarchy (McGrew & Evans, 2004; McGrew, 2005)
  • 158. Alternative Model 2b
    g – speed ?
    .93
    .99
    1.0
    .86
    g
    .36
    1.0
    Type I cognitive processing
    (Cognitive efficiency):
    More automatic & effortless
    Cog. knowledge domains/systems
    (product/content abilities)
    Lang/linguistic./symbolic abilities
    Type II cognitive processing:
    More cognitively controlled & deliberate
    Cognitive operations
    (process/operations/analytic/rule-based abilities) figural-spatial, lower-linguistic abilities
    Gs
    (Cognitive speed)
    .89
    .76
    .91
    .85
    1.0
    .83
    .82
    .64
    .50
    .60
    .58
    .41
    .52
    Gs (Grw)
    .58
    Gsm
    Gv
    Gs (Gv)
    Gs (Gq)
    Gs (Gc)
    .67
    Gc
    Gq
    Ga
    Glr
    Gf
    Grw
    First order measurement model and other lower-order latent factors (below smallest oval latent factors) omitted for readability purposes. Thicker path arrow with bold font 1.0 parameter designates path that had to be constrained (fixed) to 1.0
  • 159. f8
    r12
    2
    8
    .
    PICVOCER
    .
    7
    PicVoc
    4
    r13
    4
    f1
    PICVOCOR
    9
    .
    r0
    .
    9
    4
    ANLSYNER
    6
    9
    .
    r14
    GENINFOR
    AnlSyn
    7
    GenInf
    9
    .
    9
    5
    .
    r1
    4
    9
    ANLSYNOR
    .
    r15
    GENINFER
    f9
    Gc
    f2
    r16
    .
    0
    9
    9
    8
    .
    r2
    ACKNOWOR
    .
    CONFRMER
    9
    5
    AcdKnw
    .
    9
    1
    ConFrm
    r17
    ACKNOWER
    r3
    5
    9
    f10
    .
    .
    CONFRMOR
    7
    r18
    7
    .
    1
    9
    2
    .
    1
    ORLVOCER
    OrlVoc
    .
    9
    2
    f3
    r19
    r4
    Gf
    ORLVOCOR
    .
    9
    4
    NUMSERER
    f11
    r20
    NumSer
    5
    1
    8
    .
    2
    VERBANLER
    .
    .
    r5
    9
    VrbAnl
    9
    4
    NUMSEROR
    9
    .
    r21
    VERBANLOR
    .
    9
    0
    3
    7
    f4
    .
    f12
    Gf
    .
    9
    r6
    5
    f14
    NUMMATER
    (lang)
    r22
    NumMat
    9
    8
    .
    SNDAWRER
    r7
    SndAwr
    f13
    4
    9
    f5
    NUMMATOR
    .
    r23
    SNDAWROR
    .
    8
    8
    r8
    .
    r24
    6
    APPROBER
    5
    AppPrb
    0
    9
    UNDDIRER
    .
    Gq
    r9
    r25
    UndDir
    APPROBOR
    f6
    UNDDIROR
    3
    9
    r10
    .
    .
    8
    8
    CALCER
    f7
    f15
    Calc
    r11
    CALCOR
    7
    8
    .
    Gf sub-abilities differentiated by content/stimulus features (Wilhelm model)
    Additional dual-indicator modeling of WJ III data in other domains (e.g., Gsm, Glr, Ga, Gv, Gq, Grw)
    f16
    .
    5
    f18
    8
    Gf
    (vis)
    2
    .
    9
    3
    .
    3
    8
    8
    .
    .
    9
    2
    f19
    .
    9
    g
    9
    3
    9
    .
    .
    9
    4
    8
    7
    .
    Gf
    .
    9
    6
    .
    (qnt)
    8
    9
    .
    5
    9
    6
    3
    .
    f17
    .
    9
    7
    6
    9
    .
    .
    9
    6
  • 160. Reconcile and integrate Johnson & Bouchard VPR (Verbal-Perceptual-Image Rotation) psychometric model of intelligence with working CHC model
  • 161. Integrate and conceptualize working model within information processing models
  • 162. Note: Ovals represent
    latent factors. Rectangles represent manifest measures (tests). Single-headed arrows to tests from ovals designate factor loadings. Single headed arrows between ovals represent causal paths (effects). Test and factor residuals omitted for readability purposes.
    Visual Matching
    Mem for Sentences
    .78
    .78
    Decision Speed
    .49
    .66
    MS
    Gs
    Mem for Words
    .75
    .81
    Cross Out
    .80
    .27
    Aud Working Mem
    .72
    MW
    Numbers Reversed
    .72
    Block Rotation
    .07
    .53
    .07
    Verbal Comp
    Spatial Relations
    Picture Recognition
    .69
    Gv
    .94
    .47
    .82
    Oral Comp
    Memory for Names
    .74
    Gc
    .83
    .88
    General Information
    .87
    .59
    Retrieval Fluency
    Analysis-Synthesis
    .50
    .92
    g
    Glr
    .74
    Del Recall-VAL
    .69
    .95
    Concept Formation
    .72
    .78
    Gf
    76 % of g variance explained
    .77
    .78
    Vis-Aud Lrng (VAL)
    Numerical Reas
    Sound Blending
    Incomplete Words
    .74
    .58
    Ga
    Figure 4: WJ III CHC information processing  g causal model (ages 14-19)
    .40
    Sound Patterns
    Cognitive Efficiency
  • 163.
  • 164. And…..the state-of-the art research being conducted on working memory
    I particularly favor the models and research of:
    Conway, Engle and Kane group– Human working memory lab – Princeton, NJ.
    • Controlled executive attention model
    Torkel Klingberg group - Karolinska Institute-Stockholm Brain Institute
  • 165. Integrate and conceptualize working model within dual-processing neuro-cognitive research and models
  • 166. Integrate working model with Haier and colleagues parieto-frontal integration theory (P-FIT)
  • 167. P-FIT model
  • 168. P-FIT model researchers are mapping brain areas to CHC domain constructs
  • 169. Gc
    Gv
  • 170. Timescales of temporal processing
    (Mauk & Buonomano, 2004)
    Humans process
    temporal information over scales of at least 10-12 orders of magnitude that have been categorized into 3-4 major timescale groups
  • 171. Research suggests common dopamine link (e.g., dopaminergic disorders)
    Mental Timing Research:
    Has been implicated as important in human learning and understanding a variety of clinical disorders. Examples include:
    • Parkinson’s
    • 172. Huntington’s
    • 173. Schizophrenia
    • 174. ADHD
    • 175. Reading development and disorders (dyslexia/reading disabilities)
    • 176. Speech and language development and related disorders
    • 177. Analogy – auditory processing of Morse code
    • 178. Musical abilities and performance
    • 179. Motor timing disorders
    • 180. Aspergers???
    (See IQ Brain
    Clock EWOK
    for research)
  • 181. Integrate working model with temporal g (brain clock) research
    Temporal information processing models (Creelman, 1962; Gibbon, 1991; Rammsayer & Ulrich, 2001; Treisman et al., 1990; see Grondin, 2001 for review) are based on the central assumption ofneural oscilliations(note – same central feature of Jensen’s neural efficiency theory of g) as a major determinant of timing performance.
    The higher the frequency (higher speed) of neural oscillations the finer the temporal resolution of the internal clock = greater timing accuracy (Rammsayer & Brandler; 2007)
  • 182. Temporal g ?
    Analyses suggested a unitary timing mechanism, referred to as temporal g.
    Performance on temporal information processing provided a more valid predictor of psychometric g than traditional reaction time measures
    r (with psychometric g) = .56 (temporal g) vs .34 (reaction time g)
    Findings suggest that temporal resolution capacity of the brain (as assessed with psychophysical temporal tasks) reflects aspects of neural efficiency associated with general intelligence.
    Rammsayer & Brandler (2007)
  • 183. Two primary mental timing circuits
    (Buhusi & Meck, 2005; Lewis & Miall, 2006)
    Automatictiming system
    • Works in the millisecond range
    • 184. Discrete-event (discontinuous) timing,
    esp. movement/motor tasks
    • Involves the cerebellum
    Cognitively-controlled timing system
    • Continuous-event timing
    • 185. Requires attention and involvement of
    working memory
    • Involves the basal ganglia and related cortical structures
    It is the “constellation of task characteristics that dictate which timing “circuits” of brain “systems”are invoked in a particular task performance (Lewis & Miall, 2006)
  • 186. In conclusion.....
  • 187.
  • 188. “Intelligent” testing and interpretation
    requires…knowing thy instruments
    Neuropsych. interpretation
    Error variance (reliability)
    External criterion relevance
    Uniqueness (specificity)
    g loading
    Degree of cognitive complexity
    CHC Ability factor classifications
    Degree of cultural loading
    Degree of linguistic demand
    Metric scale
    Information processing & stimulus/response characteristics
    Ability domain cohesion