Thursday, April 29, 2010

IQs Corner Recent Literature of Interest 04-28-10

This weeks "recent literature of interest" is now available.  Click here for download.

Information regarding this feature, its basis, and the reasons for type of references included in each weekly installment can be found in a prior post.

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Wednesday, April 28, 2010

Dissertation Dish: WJ III Normative Update (NU) vs original WJ III norm scores

Normative comparison for the Woodcock-Johnson III: Tests of achievement in 15 and18 year olds by Cummings, Amber, Ed.S., Marshall University, 2009 , 27 pages; AAT 1481309

This study evaluates the use of the original and updated norms of the Woodcock Johnson-III in making educational decisions. The method of collection involved placing the raw score obtained from the updated norms into the original Compuscore program to see if there is a difference between the two scoring systems. The scores were then placed in a figure to see how much the scores varied from each other. Results of the study showed that there was a 1 to 3 point difference between specific skill areas, with some skill areas obtaining a 5 to 6 point difference. Suggestions are made for Practitioners when using the updated norms.

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Monday, April 26, 2010

iPost: Brain fitness for all

New resource from SHARP BRAINS

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Saturday, April 24, 2010

Research bytes 4-24-10: Broad factor 1.0 g loadings power issue; domain general mental resource mechanism

Matzke, D. Dolan, C., & Molenaar, D. (in press). The issue of power in the identification of “g” with lower-order factors.  Intelligence.

In higher order factor models, general intelligence (g) is often found to correlate perfectly with lower-order common factors, suggesting that g and some well-defined cognitive ability, such as working memory, may be identical. However, the results of studies that addressed the equivalence of g and lower-order factors are inconsistent. We suggest that this inconsistency may partly be attributable to the lack of statistical power to detect the distinctiveness of the two factors. The present study therefore investigated the power to reject the hypothesis that g and a lower-order factor are perfectly correlated using artificial datasets, based on realistic parameter values and on the results of selected publications. The results of the power analyses indicated that power was substantially influenced by the effect size and the number and the reliability of the indicators. The examination of published studies revealed that most case studies that reported a perfect correlation between g and a lower-order factor were underpowered, with power coefficients rarely exceeding 0.30. We conclude the paper by emphasizing the importance of considering power in the context of identifying g with lower-order factors.

Vergauwe, E., Barrouillet, P., & Camos, V. (2010). Do Mental Processes Share a Domain-General Resource? Psychological Science, 21(3), 384-390.


What determines success and failure in dual-task situations? Many theories propose that the extent to which two activities can be performed concurrently depends on the nature of the information involved in the activities. In particular, verbal and visuospatial activities are thought to be fueled by distinct resources, so that interference occurs between two verbal activities or two visuospatial activities, but little or no interference occurs between verbal and visuospatial activities. The current study examined trade-offs in four dual-task situations in which participants maintained verbal or visuospatial information while concurrently processing either verbal or visuospatial information. We manipulated the cognitive load of concurrent processing and assessed recall performance in each condition. Results revealed that both verbal and visuospatial recall performance decreased as a direct function of increasing cognitive load, regardless of the nature of the information concurrently processed. The observed trade-offs suggest strongly that verbal and visuospatial activities compete for a common domain-general pool of resources.

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Research Bytes 4-24-10: WISC-IV & TBI and WAIS-IV factor study research studies

Allen, D. N., Thaler, N. S., Donohue, B., & Mayfield, J. (2010). WISC-IV Profiles in Children With Traumatic Brain Injury: Similarities to and Differences From the WISC-III. Psychological Assessment, 22(1), 57-64.

The Wechsler Intelligence Scale for Children—Fourth Edition (WISC–IV; D. Wechsler, 2003a) is often utilized to assess children with traumatic brain injury (TBI), although little information is available regarding its psychometric properties in these children. The current study examined WISC–IV performance in a sample of 61 children with TBI. As compared to the standardization sample, results indicated that the TBI group exhibited relative deficits on all subtest and index scores, with the greatest deficits on the Processing Speed Index (PSI) and Coding subtest scores. However, the Perceptual Reasoning Index score was not uniquely sensitive to brain injury, and the Cognitive Processing Index score was less sensitive to TBI than the PSI score. Also, the PSI did not uniquely predict learning and memory abilities, as had been reported in previous studies of the Wechsler Intelligence Scale for Children—Third Edition (WISC–III; D. Wechsler, 1991). The present findings indicate substantive differences between the WISC–III and WISC–IV profiles of children with TBI.

Benson, N., Hulac, D. M., & Kranzler, J. H. (2010). Independent Examination of the Wechsler Adult Intelligence
Scale-Fourth Edition (WAIS-IV): What Does the WAIS-IV Measure? Psychological Assessment, 22(1),

Published empirical evidence for the Wechsler Adult Intelligence Scale—Fourth Edition (WAIS–IV) does not address some essential questions pertaining to the applied practice of intellectual assessment. In this study, the structure and cross-age invariance of the latest WAIS–IV revision were examined to (a) elucidate the nature of the constructs measured and (b) determine whether the same constructs are measured across ages. Results suggest that a Cattell–Horn–Carroll (CHC)–inspired structure provides a better description of test performance than the published scoring structure does. Broad CHC abilities measured by the WAIS–IV include crystallized ability (Gc), fluid reasoning (Gf), visual processing (Gv), short-term memory (Gsm), and processing speed (Gs), although some of these abilities are measured more comprehensively than are others. Additionally, the WAIS–IV provides a measure of quantitative reasoning (QR). Results also suggest a lack of cross-age invariance resulting from age-related differences in factor loadings. Formulas for calculating CHC indexes and suggestions for interpretation are provided.

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Journal of Psych Assessment: New editor focus on measurement

Kudos to Cecil Reynolds.  I like the new emphasis on basic measurement for the journal.

Reynolds, C. (2010).  Measurement and Assessment:  An Editorial View.  Psychological Assessment, 22(1), 1-4

If a thing exists, it can be measured. Measurement is a central component of assessment if we believe that fear, anxiety, intelligence, self-esteem, attention, and similar latent variables exist and are useful to us in developing an understanding of the human condition and leading us to ways to improve it. Much of what is published in Psychological Assessment deals with the development and the application of measurement devices of various sorts with the end goal of applications in assessment practice. What is submitted but not published largely deals with the same topics. As the new Editor writing the inaugural editorial, I am focusing on this topic for two major reasons. The first is that the most frequent reason why manuscripts are rejected in the peer-review process for Psychological Assessment and other high-quality journals devoted to clinical or neuropsychological assessment is inadequate attention to sound and high-quality measurement practices. The second reason is my surmise that measurement as a science is no longer taught with the rigor that characterized the earlier years of professional psychology. One of the tasks of Psychological Assessment is to promote a strong science of clinical assessment as practiced throughout professional psychology. To that end, I have attempted to pull together an eclectic group of Associate Editors and Consulting Editors. Our hope is to attract more and better manuscripts that deal with issues focusing on all aspects of clinical assessment

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iPost: The genetics of Williams Syndrome

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Friday, April 23, 2010

Is the Mozart effect dead?

Pietschniig, J., Voracek, M., & Formann, A. (in press).  Mozart effect–Shmozart effect: A meta-analysis, Intelligence.

Abstract (emphasis added by blogmaster)
The transient enhancement of performance on spatial tasks in standardized tests after exposure to the first movement “allegro con spirito” of the Mozart sonata for two pianos in D major (KV 448) is referred to as the Mozart effect since its first observation by Rauscher, Shaw, and Ky (1993). These findings turned out to be amazingly hard to replicate, thus leading to an abundance of conflicting results. Sixteen years after initial publication we conduct the so far largest, most comprehensive, and up-to-date meta-analysis (nearly 40 studies, over 3000 subjects), including a diversity of unpublished research papers to finally clarify the scientific record about whether or not a specific Mozart effect exists. We could show that the overall estimated effect is small in size (d = 0.37, 95% CI [0.23, 0.52]) for samples exposed to the Mozart sonata KV 448 and samples that had been exposed to a non-musical stimulus or no stimulus at all preceding spatial task performance. Additionally, calculation of effect sizes for samples exposed to any other musical stimulus and samples exposed to a non-musical stimulus or no stimulus at all yielded effects similar in strength (d = 0.38, 95% CI [0.13, 0.63]), whereas there was a negligible effect between the two music conditions (d = 0.15, 95% CI [0.02, 0.28]). Furthermore, formal tests yielded evidence for confounding publication bias, requiring downward correction of effects. The central finding of the present paper however, is certainly the noticeably higher overall effect in studies performed by Rauscher and colleagues than in studies performed by other researchers, indicating systematically moderating effects of lab affiliation. On the whole, there is little evidence left for a specific, performance-enhancing Mozart effect.

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Research Byte 4-23-10: Flynn effect and black/white IQ score differences

Another Flynn Effect related article, this time focused on the black/white race IQ gap.  This article will be included in the next update to the Flynn Effect on-line archive.....hopefully soon.

Rushton, J. P., & Jensen, A. R. (2010). The rise and fall of the Flynn Effect as a reason to expect a narrowing of the Black White IQ gap. Intelligence, 38(2), 213-219.

In this Editorial we correct the false claim that g loadings and inbreeding depression scores correlate with the secular gains in IQ. This claim has been used to render the logic of heritable g a “red herring” and an “absurdity” as an explanation of Black–White differences because secular gains are environmental in origin. In point of fact, while g loadings and inbreeding depression scores on the 11 subtests of the Wechsler Intelligence Scale for Children correlate significantly positively with Black–White differences (0.61 and 0.48, P < 0.001), they correlate significantly negatively (or not at all) with the secular gains (mean r = -0.33, P < 0.001; and 0.13, ns, respectively). Moreover, heritabilities calculated from twins also correlate with the g loadings (r = 0.99, P < 0.001 for the estimated true correlation), providing biological evidence for a true genetic g, as opposed to a mere statistical g. While the secular gains are on g-loaded tests (such as the Wechsler), they are negatively correlated with the most g-loaded components of those tests. Also, the tests lose their g loadedness over time with training, retesting, and familiarity. In an analysis of mathematics and reading scores from tests such as the NAEP and Coleman Report over the last 54 years, we show that there has been no narrowing of the gap in either IQ scores or in educational achievement. From 1954 to 2008, Black 17-year-olds have consistently scored at about the level of White 14-year-olds, yielding IQ equivalents of 85 for 1954, 82 for 1965, 70 for 1975, and 81 for 2008. We conclude that predictions about the Black–White IQ gap narrowing as a result of the secular rise are unsupported. The (mostly heritable) cause of the one is not the (mostly environmental) cause of the other. The Flynn Effect (the secular rise in IQ) is not a Jensen Effect (because it does not occur on g).

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Thursday, April 22, 2010

iPost: Research links SES level to prefrontal cortex brain development

Story at link below

NeuropathLrng: How poverty can affect children's developing brains:

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Wednesday, April 21, 2010

Beyond CHC: Pushing the edge of the CHC and WJ III envelope--the grand model

The grand unveiling!!!!!!! This is a follow-up to my "Beyond CHC: Pushing the edge of the CHC and WJ III envelop" project. I urge (require, demand?) you to read the prior explanation and view the PPT slides/PDF files previously posted. They provide the necessary background re: the nature of this project.

As a result of all the analysis summarized in the above information, today I'm taking a risk and publishing, without explanation (it would take pages and pages of text) the grand WJ III CHC+ interpretation scheme that I've iterated to. An image of the grand scheme is below.....simply to catch your attention. You can see a much larger and clearer version of the proposed/hypothesized interpretation system by clicking here (PDF file).

I do hope to explain the logic, rationale, data, hypotheses behind the components of this document over time...maybe through a series of blog posts. To wait until I have written everything will simply keep this baby from being born....and, I am anxious to receive feedback.

I hope it makes sense. Please be sure to read the material I mentioned above as it provides the proper context of this proposed interpretative framework. This framework is offered in the spirit of a hypothesized model that is "under construction" and needs further study with proper methods.

I would suggest channeling any discussion to the CHC listserv.

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IQ Test DNA Fingerprints: Comparison of WJ III/BAT III to WJ-R/BAT-R

Here is another of IQ's Corner "IQ Test CHC DNA Fingerprint" test comparison series.  This particular CHC fingerprint figure compares the CHC composition of the respective full scale total composite IQ scores from the WJ III/BAT III and the the earlier version of these batteries....the WJ-R/BAT-R.

Background information regarding the development, use and interpretation of this IQ global IQ score feature can be found at a prior post and in the IQ Test CHC DNA Fingerprint section on the blog side bar.  More can be found at IQ's Corner sister blog...the ICDP blog.

I now present a comparison of the R/III versions of the WJ/BAT batteries as I have seen psych reports where a subject had previously been administered the WJ-R and was later tested with the revised WJ III (in the case of Spanish-speaking individuals, I've seen the BAT-R and the BAT-III---click here for more background information on the Spanish version of the WJ III...the BAT III).  

In the case of the WJ-R/BAT-R, the full scale IQ composite is called the Broad Cognitive Ability (BCA) cluster.  The name was changed in the WJ III/BAT III to General Intellectual Ability (GIA) cluster.  The name change was not cosmetic.  The use of the term "general intellectual ability" in the newest WJ III/BAT III reflects the fact that this global IQ composite score is designed to be the best statistical estimate of the theoretical construct of general intelligence (g) via the use of differential test weights.

Using principal components analysis, a g-factor was extracted from the seven WJ III/BAT III Standard Cognitive battery tests (at each age level), g-factor weights calculated (by age---they shift slightly as a function of age), and the g-weights used to differentially weight the contribution of the seven tests to the composite GIA-Standard cluster score.  The same process was completed for the 14 test GIA-Extended cluster score.  This procedure is explained in detail in the WJ III/BAT III technical manuals/reports and is also briefly summarized in a free on-line Assessment Service Bulletin technical abstract.

In the case of the WJ-R/BAT-R, the respective 7-test BCA-Standard and 14-test BCA-Extended cluster scores are based on the simple arithmetic average of each set of scores, thus resulting in an equally weighted global IQ score.

Thus, differences between the global WJ-R/BAT-R and WJ III/BAT III IQ scores may occur as a function of the respective scores reflecting differential contributions of the broad Gf-Gc abilities as per the CHC theoretical model that underlies the batteries.

Below is the IQ Test CHC DNA Fingerprint comparison of the two respective editions of the WJ-R/BAT-R and WJ III/BAT III.  The weights presented for the WJ III/BAT III are the median (average) weights across all age groups.  The previously referenced ASB (see above) includes a table of the specific weights by age.

[double click on figure to enlarge]

Although the CHC composition of the respective global IQ scores did not change dramatically, there are enough differences by CHC ability to suggest that slightly different global IQ scores may be produced for the same individual depending on whether they took the WJ-R/BAT-R or the WJ III/BAT III (assuming proper administration, scoring, etc.).  Consistent with psychometric intelligence theory (aka., CHC theory), the WJ III/BAT III global IQ scores (GIA-Stnd; GIA-Ext) are more heavily weighted as per a subjects performance on the more g-loaded measures of Gf (fluid intelligence/reasoning), Gc (crystallized intelligence or comprehension-knowledge), and Glr (long-term storage and retrieval).  In contrast, abilities that are less cognitively demanding and more related to perceptual (Gv, Ga), speed (Gs), and short-term memory (Gsm) functioning contribute slightly less to an individuals WJ III/BAT III global IQ GIA score than was the case with the WJ-R/BAT-R.

If significant differences are found when comparing scores from the respective R/III editions of the WJ for an individual, examiners should review the Gf-Gc CHC test/cluster profiles to determine if some (or all) of the score differences might be related to the shift from an equally weighted global IQ score (WJ-R/BAT-R) to a differentially-weighted (WJ III/BAT III) global IQ score.  In theory, an individual could obtain very similar test-level scores on each battery, but because "all scores are not created equal" (in the estimation of general intelligence or g) in the case of the WJ III, a shift in the global GIA IQ scores may occur.

Other IQ Test CHC DNA Fingerprint comparisons can be found by clicking here.  More will be coming in the future.

[Conflict of interest note:  I am a co-author of the WJ III/BAT III]

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Tuesday, April 20, 2010

iPost: Rasch/Winsteps Online Course - April 30, 2010

Learn Rasch/IRT online

"Practical Rasch Measurement - Core Topics", the next Winsteps introductory month-long online study Course starts Friday, April 30, 2010: - the course is administered by

The current Winsteps version is - see for recent changes

Mike Linacre

WMF Human Cognitive Abilities Project Update 4-20-10: 22 new Carroll data sets

The free on-line WMF Human Cognitive Abilities (HCA) archive project was updated today. An overview of the project, with a direct link to the archive, can be found at the Woodcock-Muñoz Foundation web page (click on "Current Woodcock-Muñoz Foundation Human Cognitive Abilities Archive") . Also, an on-line PPT copy of a poster presentation I made at the 2008 (Dec) ISIR conference re: this project can be found by clicking here.

Today's update added the following 22 new data sets from John "Jack" Carroll's original collection.

  • **GUIL31, GUIL32A, GUIL41:     Guilford, J.P., Lacey, J.I. (Eds.) (1947).  Printed classification tests.  Army Air Force Aviation Psychology Program Research Reports, No. 5.  Washington, DC: U.S. Government Printing Office. [discussed or re-analyzed by Lohman (1979)]
  • HEMP21:     Hemphill, J.K., Griffiths, E., Frederiksen, N., Stice, G., Iannaccone, L., Coffield, W., & Carlton, S. (1961). Dimensions of administrative performance. New York and Princeton: Teachers College, Columbia University, & Educational Testing Service.
  • PICK01:     Pickens, J. D., Pollio, H. R. (1979). Patterns of figurative language competence in adult speakers. Psychological Research, 40, 299-313. 
  • PORT01:     Porter, E. L. H. (1938). Factors in the fluctuation of fifteen ambiguous phenomena. Psychological Record, 2, 231-253. 
  • **PRIC01:     Price, E. J. J. (1940). The nature of the practical factor (f). British Journal of Psychology, 30, 341-351. 
  • RICH01, RICH02:     Richards, T. W., & Nelson, V. L. (1939). Abilities of infants during the first eighteen months. Journal of Genetic Psychology, 55, 299-318. 
  • ** RIEB01, REIB02:     Rieben, l., & Mengal, P. (1977). Intelligence globale, creativite et operativite chez l'enfant: Analyse factorielle et analyse discriminante. [Global intelligence, creativity, and operativity in the child: Factorial and discriminant analysis.] Psychologie - Schweizerische Zeitschrift fur Psychologie und ihre Anwendungen, 36, 100-108. 
  • RIMO11:     Rimoldi, H. J. A. (1948). Study of some factors related to intelligence. Psychometrika, 13, 27-46. 
  • **ROBE11:     Robertson-Tchabo, e., & Arenberg, D. (1976). Age differences in cognition in healthy educated men: A factor analysis of experimental measures. Experimental Aging Research, 2, 75-89. 
  • ROND01, ROND02:     Rondal, J. A. (1978). Patterns of correlations for various language measures in mother-child interactions for normal and Down's syndrome children. Language & Speech, 21, 242-252.  
  • **ROSE01:     Rose, A. M. (1974). Human information processing: An assessment and research battery. Ann Arbor: Human Performance Center, Department of Psychology, University of Michigan. (Technical Report No. 46)
  • **ROSE11:      Rose, A. M. & Fernandes, K. (1977). An information processing approach to performance assessment: I. Experimental investigation of an information processing performance battery. Washington: American Institutes for Research, Technical Report No. 1. 
  • STAN01:     Stankov, L. (1978).  Fluid and crystalized intelligence and broad perceptual factors among 11 to 12 year olds.  Journal of Educational Psychology, 70, 324-334.
  • STAN21:     Stankov, L. (1983). Attention and intelligence. Journal of Educational Psychology, 75, 471-490.
  • STAN41:     Stankov, L., Horn, J. L., & Roy, T. (1980). On the relationship between Gf/Cg theory and Jensen's Level I/Level II theory. Journal of Educational Psychology, 72, 796-809. 
  • STAN51:    Stanovich, K. E. (1981). Relationships between word decoding speed, general name-retrieval ability, and reading progress in first-grade children. Journal of Educational Psychology, 73, 809-815. 
  • STAN61:    Stanovich, K. E., Cunningham, A. E., & Freman, D. J. (1984). Intelligence, cognitive skills, and early reading progress. Reading Research Quarterly, 29, 278-303.

Request for assistance: The HCA project needs help tracking down copies of old journal articles, dissertations, etc. for a number of datasets being archived. We have yet to locate copies of the original manuscripts for the data sets listed above that are designated with **. Help in locating copies of these MIA manuscripts would be appreciated.

  please visit the special "Requests for Assistance" section of this archive to view a more complete list of manuscripts that we are currently having trouble locating. If you have access to either a paper or e-copy of any of the designated "fugitive" documents, and would be willing to provide them to WMF to copy/scan (we would cover the costs), please contact Dr. Kevin McGrew at the email address listed at the site.

Please join the WMF HCA listserv to receive routine email updates regarding the WMF HCA project.

All posts regarding this project can be found here.

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Monday, April 19, 2010

FYI: Jeanne Chall Research Grant @ Harvard

Jeanne S. Chall Research Grant

Scholars in the field of reading research are encouraged to submit applications for the 2010-2011 Jeanne S. Chall Research Grant. The purpose of this grant is to provide a stipend for a scholar to spend a period of time (usually from 2-8 weeks) at the Harvard Graduate School of Education to conduct research utilizing the Jeanne S. Chall Collection on the Teaching of Reading housed in the Monroe C. Gutman Library’s Special Collections Department.  Additionally, the researcher will have access to other extensive reading resources available in Special Collections, Gutman Library and elsewhere at Harvard University. The Chall Collection consists of books and other materials related to the history of reading research and the teaching of reading, spanning both the 19th and 20th centuries. Most of the materials are dated from the 1950s through 1980s and include reading textbooks, curriculum sets, and scholarly works.

The research should focus on beginning reading, reading instruction, reading difficulty, or other related topics in the field.  Additionally, projects may be historical in nature, focus on textual analysis, or relate to the research and writing of Jeanne Chall.  The award will support travel to and from Cambridge and other expenses (up to a total of $2500). Applicants must hold a doctoral degree from an accredited institution of higher learning.   Please include a current resume and a project proposal not exceeding 500 words in length. The application deadline is May 14, 2010. 

Send to Edward Copenhagen, Special Collections Librarian, Harvard Graduate School of Education, Gutman Library, 6 Appian Way, Cambridge, MA 02138;

e-mail submission to:

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Dissertation Dish: Extension of CHC theory-SB5 and Bender Gestalt factor study

Structural extension of the Cattell-Horn-Carroll cross-battery approach to include measures of visual-motor integration by Brooks, Janell Hargrove, Ph.D., Georgia State University, 2009 , 117 pages; AAT 3401596


In spite of the long-standing tradition of including measures of visual-motor integration in psychological evaluations, visual-motor abilities have not been included in the Cattell-Horn-Carroll (CHC) theory of cognitive abilities or its complementary cross-battery approach to assessment. The purpose of this research was to identify the shared constructs of a popular test of visual-motor integration and a test of intellectual functioning, and to investigate how a test of visual-motor integration would be classified within the CHC model. A large normative sample of 3,015 participants that ranged in age from 5 to 97 years completed the Bender Visual-Motor Gestalt Test, Second Edition (Bender-Gestalt II; Brannigan & Decker, 2003) and the Stanford-Binet Intelligence Scale, Fifth Edition (SB5; Roid, 2003). Correlational analyses indicated positive moderate correlations across all age ranges between the Bender-Gestalt II Copy measure and the SB5 Nonverbal Visual-Spatial Processing subscale and between the Bender-Gestalt II Recall measure and the SB5 Nonverbal Visual-Spatial Processing and Nonverbal Working Memory subscales. Exploratory factor analyses revealed a three-factor model for four age groupings and four-factor model for one age grouping, suggesting factors which represent crystallized ability, fluid reasoning, and visual-motor ability. The results of this study suggest that the Bender-Gestalt II measures abilities that are not included in the SB5. Therefore, the Bender-Gestalt II would complement an intelligence test such as the SB5 in order to form a CHC Visual Processing ( Gv ) broad ability factor. These findings also address the need for further research to validate the constructs measured by newer versions of widely-used tests of cognitive ability.

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iPost: 50 neurology blogs

Neuromodulation: 50 blogs for neurology students

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Monday, April 12, 2010

Saturday, April 10, 2010

Research Briefs 4-10-10: Working memory, executive functioning and ID/MR

The cognitive neuroscience constructs of working memory and executive function (EF) are hot topics in all areas of cognition.  Although working memory measures are now showing up on most contemporary IQ tests, the measurement of executive functioning (EF) is not...and is typically measured by special purpose tests, tests that all suffer from a solid empirical foundation of research that clearly specifies the elements of, relations between, operational definitions of, etc. the components of EF.

I was thus excited to see the table of contents for the recent issue of the Journal of Intellectual Disability Research (see below), as it focuses primarily on working memory and EF research for people with ID/MR.  My excitement was short-lived as I found that, at least at my Universities library, access to articles in this journal are not allowed until one year after publication.  Darn.Re

Ball, S. L., Holland, A. J., Watson, P. C., & Huppert, F. A. (2010). Theoretical exploration of the neural bases of behavioural disinhibition, apathy and executive dysfunction in preclinical Alzheimer's disease in people with Down's syndrome: potential involvement of multiple frontal-subcortical neuronal circuits. Journal of Intellectual Disability Research, 54, 320-336.

Carretti, B., Belacchi, C., & Cornoldi, C. (2010). Difficulties in working memory updating in individuals with intellectual disability. Journal of Intellectual Disability Research, 54, 337-345.

Henry, L., Cornoldi, C., & Mahler, C. (2010). Special issues on 'working memory and executive functioning in individuals with intellectual disabilities'. Journal of Intellectual Disability Research, 54, 293-294.

Henry, L., & Winfield, J. (2010). Working memory and educational achievement in children with intellectual disabilities. Journal of Intellectual Disability Research, 54, 354-365.

Lanfranchi, S., Jerman, O., DalPont, E., Alberti, A., & Vianello, R. (2010). Executive function in adolescents with Down Syndrome. Journal of Intellectual Disability Research, 54, 308-319.

Mosse, E. K., & Jarrold, C. (2010). Searching for the Hebb effect in Down syndrome: evidence for a dissociation between verbal short-term memory and domain-general learning of serial order. Journal of Intellectual Disability Research, 54, 295-307.

Schuchardt, K., Gebhardt, M., & Maehler, C. (2010). Working memory functions in children with different degrees of intellectual disability. Journal of Intellectual Disability Research, 54, 346-353.

Willner, P., Bailey, R., Parry, R., & Dymond, S. (2010). Evaluation of executive functioning in people with intellectual disabilities. Journal of Intellectual Disability Research, 54, 366-379.

Willner, P., Bailey, R., Parry, R., & Dymond, S. (2010). Evaluation of the ability of people with intellectual disabilities to 'weigh up' information in two tests of financial reasoning. Journal of Intellectual Disability Research, 54, 380-391.

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Tiger Woods Masters Ability IQ scores within his "zone" (band): Real world example of SEM

After two rounds at the Masters, Tiger Woods first two scores are within expectations based on his prior 13 years of Round 1 and 2 scores.  Based on his prior 52 rounds (the first two rounds of each year over 13 prior years), I had previously calculated his "true" Masters Golf Ability (IQ) to be 70 with a standard error of measurement (SEM) of 3 points.  This means, based on psychometric theory and the importance of understanding the concept of SEM, going into this years tourney we could be 68% confident that his first two round scores would range somewhere between 67 and 73.  He shot a 68 and 70....well within his Masters Golf Ability 68% SEM.  You gotta love good stats and psychometrics!

If you are intrigued and want to learn more about SEM and its importance in psychological testing (using Masters golf performance as a real world example), click here to visit prior blog post and be sure to read the entire report that is mentioned (click on the report URL).

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Thursday, April 08, 2010

iPost: Applied implications of genetic/cognition research

Genetics and cognition: The impact for psychologists in applied settings.
By Carlier, Michèle; Roubertoux, Pierre
European Psychologist. Vol 15(1),2010, 49-57.
How genes contribute to cognition is a perennial question for psychologists and geneticists. In the early 21st century, familial studies, including twin studies, supported the theory that genetic variations contribute to differences in cognition, but have been of little practical use to clinical and educational practitioners as no individual predictions can be made using such data; heritability cannot predict the impact of environmental factors or intervention programs. With the sequencing of animal genomes and the development of molecular genetics, new methodologies have been developed: gene targeting (replacing a functional gene with a neutral gene by homologous recombination), transgenesis (overexpressing one gene or a set of genes from one species in another species), and genome-wide scans and quantitative trait loci mapping (a strategy for identifying chromosomal regions involved in complex traits). Association studies can be performed to find associations between allelic forms and variations in IQ. Genes linked to "normal" variations in cognition have been detected but for the moment such discoveries have had no direct applications in a clinical setting; the number of genes identified as being linked to intellectual impairment has increased rapidly. Links have been reported between chromosomal deletions and triplications and behavioral phenotypes. The identification of mechanisms involved in genetic diseases should have long-term consequences on educational and/or psychological support programs as well as on health care. Psychologists need to keep up to date on advances in research establishing relationships between genetics and intellectual disability and will thus be able to refer children with cognitive impairments to specialized care services. (PsycINFO Database Record (c) 2010 APA, all rights reserved)

Wednesday, April 07, 2010

Psychometric PS to Johnston v Florida (2010) denied appeal re: new WAIS-IV scores

This is a follow-up to my brief comments yesterday regarding the Johstone v Fl (2010) denied MR/ID appeal of two days ago.

As mentioned in the decision and my blog comment, the WAIS-III/WAIS-IV tests correlated .94 in a study reported in the WAIS-IV technical manual.  This is a very high correlation...but does NOT mean that the two tests should be expected to provide identical IQ scores.  I discuss these issues in a prior IAP AP101 report.

The tests have different norm dates and thus, the later version (WAIS-IV) would be expected to provide a lower score based on the Flynn effect.  More importantly, as reported in the IAP AP101 report, when one calculates the standard deviation of the difference score (see page 6 of that report) for a correlation of .94, the resulting value is 5.2 (round to 5 for ease of discussion).  This means that, on average, the WAIS-III/WAIS-IV (even if highly correlated at the .94 level) would in the general population be expected to display a range of difference scores from -5 to +5...or a range of 10 IQ 68% of the population.  Please review that prior report for further explanation and discussion.

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iPost: Free Article from 'Aging, Neuropsychology, and Cognition'

Subject: Free Article from 'Aging, Neuropsychology, and Cognition'

Psychology Press

Gain Eleven IQ Points in Ten Minutes

Read for free Thinking Aloud Improves Raven's Matrices Performance in Older Adults by Marc C. Fox and Neil Charness, a recent article from Aging, Neuropsychology, and Cognition.

The article examines the impact of age on reactivity to concurrent think-aloud verbal reports. Results revealed that thinking aloud improves older adult performance on Raven's Matrices tests, corresponding to a fluid intelligence increase of nearly one standard deviation.

Aging, Neuropsychology, and Cognition

Impact Factor 2008: 1.143 (©2009 Thomson Reuters, 2008 Journal Citation Reports®)

Edited by Linas A. Bieliauskas (University of Michigan Health System, USA) and Martin Sliwinski (Penn State University, USA), this journal publishes research on both the normal and dysfunctional aspects of cognitive development in adulthood and aging, and promotes the integration of theories, methods, and research findings between the fields of cognitive gerontology and neuropsychology.

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Saturday, April 03, 2010

Research Bytes 4-3-10: Gf, Gs, Gv, Ga, working memory, exec function, orthography plus more

Nettelbeck, T., & Burns, N. R. (2010). Processing speed, working memory and reasoning ability from childhood to old age. Personality and Individual Differences, 48(4), 379-384.

The study investigated whether theoretical causative relations among declining cognitive abilities during adulthood and old age conform to a literal reversal of improving cognitive development during childhood. Children aged 8–14 years (n = 240) and adults aged 18–87 (n = 238) completed the same battery of psychometric tests, which defined latent traits for processing speed, working memory, and reasoning ability. Speeded performance improved during childhood and slowed across the adult range. Childhood performance was well described by a developmental cascade, whereby increasing chronological age is accompanied by faster processing speed, which influences improved working memory, which in turn influences improving reasoning ability. However, although adult performance resembled a cascade with diminishing reasoning ability mediated by processing speed and working memory, this was not a mirror image of the cascade for children. The main difference with adults was a direct causal path between age and working memory. Post hoc analysis located this among adults aged 55 years and over. This suggests that, whereas childhood cognitive development is substantially mediated by processing speed, declining reasoning ability in old age is influenced by slower processing speed but also by age-related change(s) influencing working memory that are independent from processing speed.
Article Outline

1. Introduction
2. Method

2.1. Participants
2.2. Materials

2.2.1. Processing speed (PS)
2.2.2. Digit Symbol
2.2.3. Visual Matching
2.2.4. Inspection time (IT, Nettelbeck (2001))
2.2.5. Simple reaction time (RT)
2.2.6. Odd man out – decision time (DT, Frearson & Eysenck (1986))
2.2.7. Working memory (WM)
2.2.8. Picture Swaps (Stankov (2000))
2.2.9. Picture Recognition (modelled on the test from WJ – R)
2.2.10. Digit Span (modelled on the test from WAIS-IV)
2.2.11. Reasoning ability (RA)

2.3. Procedure
2.4. Statistical analyses

3. Results
4. Discussion

Wolbers, T., & Hegarty, M., (2010). What determines our navigational abilities? Trends in Cognitive Sciences, 14(2), 138-146
The ability to find one's way in our complex environments represents one of the most fundamental cognitive functions. Although involving basic perceptual and memory related processes, spatial navigation is particularly complex because it is a multisensory process in which information needs to be integrated and manipulated over time and space. Not surprisingly, humans differ widely in this ability, and recent animal and human work has begun to unveil the underlying mechanisms. Here, we consider three interdependent domains that have been related to navigational abilities: cognitive and perceptual factors, neural information processing and variability in brain microstructure. Together, the findings converge into an emerging model of how different factors interact to produce individual patterns of navigational performance.
Article Outline

Spatial navigation – a complex behavior with large individual differences
Variability in perceptual and cognitive processing
Variability in structure and function of critical brain circuits
Concluding remarks

Booth, J. N., Boyle, J. M. E., & Kelly, S. W. (2010). Do tasks make a difference? Accounting for heterogeneity of performance of children with reading difficulties on tasks of executive function: Findings from a meta-analysis. British Journal of Developmental Psychology, 28(1), 133-176.
Research studies have implicated executive functions in reading difficulties (RD). But while some studies have found children with RD to be impaired on tasks of executive function other studies report unimpaired performance. A meta-analysis was carried out to determine whether these discrepant findings can be accounted for by differences in the tasks of executive function that are utilized. A total of 48 studies comparing the performance on tasks of executive function of children with RD with their typically developing peers were included in the meta-analysis, yielding 180 effect sizes. An overall effect size of 0.57 (SE .03) was obtained, indicating that children with RD have impairments on tasks of executive function. However, effect sizes varied considerably suggesting that the impairment is not uniform. Moderator analysis revealed that task modality and IQ-achievement discrepancy definitions of RD influenced the magnitude of effect; however, the age and gender of participants and the nature of the RD did not have an influence. While the children's RD were associated with executive function impairments, variation in effect size is a product of the assessment task employed, underlying task demands, and definitional criteria.

Jordan, J. A., Wylie, J., & Mulhern, G. (2010). Phonological awareness and mathematical difficulty: A longitudinal perspective. British Journal of Developmental Psychology, 28(1), 89-107.

The present longitudinal study sought to investigate the impact of poor phonology on children's mathematical status. From a screening sample of 256 five-year-olds, 82 children were identified as either typically achieving (TA; N=31), having comorbid poor phonology and mathematical difficulties (PDMD; N=31), or having only poor phonology (phonological difficulty, PD; N=20). Children were assessed on eight components of informal and formal mathematics achievement at ages 5-7 years. PD children were found to have significant impairments in some, mainly formal, components of mathematics by age 7 compared to TA children. Analysis also revealed that, by age 7, approximately half of the PD children met the criteria for PDMD, while the remainder exhibited less severe deficits in some components of formal mathematics. Children's mathematical performance at age 5, however, did not predict which PD children were more likely to become PDMD at age 7, nor did they differ in terms of phonological awareness at age 5. However, those PD children who later became PDMD had lower scores on verbal and non-verbal tests of general ability.

Newton, E. J., Roberts, M. J., & Donlan, C. (2010). Deductive reasoning in children with specific language impairment. British Journal of Developmental Psychology, 28(1), 71-87.
The diagnosis of specific language impairment (SLI) requires non-verbal ability to be in the normal range, but little is known regarding the extent to which general reasoning skills are preserved during development. A total of 122 children were tested; 40 SLI, 42 age-matched controls, and 40 younger language-matched controls. Deductive reasoning tasks were given in both verbal and pictorial presentation types, namely the relational inference task and the reduced array selection task (RAST). Pictorial presentation facilitated all groups for all tasks equally. For the relational inference task, SLI performance was below both age and language matches. For the RAST, contextual information facilitated all groups equally. SLI performance was intermediate between age and language matches. It is concluded that the non-verbal versus verbal distinction is a complex one and that non-verbal reasoning can draw upon linguistic processes. It is also suggested that SLI reasoning depends upon precise task demands, here the need to sequence information in working memory, and the need for explicit reasoning with conditional rules. Reasoning processes may not be equivalent to normally developing children, even when tasks appear non-verbal.

Damian, M. F., & Bowers, J. S. (2010). Orthographic effects in rhyme monitoring tasks: Are they automatic? European Journal of Cognitive Psychology, 22(1), 106-116.

Over the last 30 years or so, various findings have been reported which suggest that the perception of spoken words may involve the automatic coactivation of orthographic properties. Here we assessed this possibility in auditory rhyme judgement tasks and replicated a classic finding reported by Seidenberg and Tanenhaus (1979), showing that orthographic similarity between stimuli facilitated responses on rhyming pairs, but had the opposite effect on nonrhyming pairs. However, Experiments 2 and 3 showed that manipulating the nature of the nonrhymes, or adding a large proportion of filler items, eliminated the effects of orthographic match or mismatch. These findings suggest the involvement of strategic factors in the emergence of orthographic effects in rhyme judgement tasks.
Keywords: Speech perception; Orthographic effects; Rhyme monitoring

Cowan, N. (2010). The Magical Mystery Four: How Is Working Memory Capacity Limited, and Why? Current Directions in Psychological Science, 19(1), 51-57.
Working memory storage capacity is important because cognitive tasks can be completed only with sufficient ability to hold information as it is processed. The ability to repeat information depends on task demands but can be distinguished from a more constant, underlying mechanism: a central memory store limited to 3 to 5 meaningful items for young adults. I discuss why this central limit is important, how it can be observed, how it differs among individuals, and why it may exist.

Hubbard, T. (2010). Auditory imagery:  Empircal findings.  Psychological Bulletin, 136(2), 302-329
The empirical literature on auditory imagery is reviewed. Data on (a) imagery for auditory features (pitch, timbre, loudness), (b) imagery for complex nonverbal auditory stimuli (musical contour, melody, harmony, tempo, notational audiation, environmental sounds), (c) imagery for verbal stimuli (speech, text, in dreams, interior monologue), (d) auditory imagery’s relationship to perception and memory (detection, encoding, recall, mnemonic properties, phonological loop), and (e) individual differences in auditory imagery (in vividness, musical ability and experience, synesthesia, musical hallucinosis, schizophrenia, amusia) are considered. It is concluded that auditory imagery (a) preserves many structural and temporal properties of auditory stimuli, (b) can facilitate auditory discrimination but interfere with auditory detection, (c) involves many of the same brain areas as auditory perception, (d) is often but not necessarily influenced by subvocalization, (e) involves semantically interpreted information and expectancies, (f) involves depictive components and descriptive components, (g) can function as a mnemonic but is distinct from rehearsal, and (h) is related to musical ability and experience (although the mechanisms of that relationship are not clear

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