Tuesday, January 30, 2007

WJ III EWOK (Evolving Web of Knowledge) revised

I'm pleased to announce an update/revision of the WJ III Evolving Web of Knowledge (EWOK), which was first announced in March of 2006. Click here to visit the original description and post. Click on the other link to go directly to the new WJ III EWOK.

Feedback is always welcome.

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Monday, January 29, 2007

Austism, in my language-powerful video

Thanks to Mind Hacks for the link to the very powerful video apparently created by a young woman with autism...a video where she "translates" from her world of environmental interaction to the more "typical" world of speech and perception imposed upon her by the rest of the world. Very powerful.

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Recent literature of interest 1-29-07

This weeks (actually the last three weeks...I'm trying to get caught up) recent literature of interest can be found by clicking here.

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NCLB Reading First controversies

I'm not tracking the politics of NCLB and Reading First with any diligence and count on readers to alert me to some of the political controversies that have arisen (click here for prior FYI post about possible problems revealed in an audit of Reading First).

Today someone sent me a copy of an article (Reading for Profit) in the Chronicle of Higher Education. A copy of the article can be read by clicking here. I have no comment as I've not kept up with the issues, studied the charges and counter-charges, etc. All I can say is that most academic-scholars who are involved in commercial products (like myself---coauthor of the WJ III) need to stay extra-vigilant with regard to potential conflicts of interest in the current high dollar stakes of NCLB-driven local/state educational policy decisions.

It is within this ethical spirit that readers need to be aware that some folks see a fundamental tension between norm-referenced testing (like the WJ III battery I coauther) and CBM measures (which are mentioned in the article). Thus, folks may think I'm posting this FYI as I want to see the CBM camp have less influence. This is not my motive as, individuals who have heard me present, know that I find norm-referenced and CBM approaches as complimentary...not mutually exclusive. They perform different functions and are both needed to better educate high risk students and students with disabilities.

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Saturday, January 27, 2007

Wots the word? "Whaz up" with bad stats in dyslexia study?

I recently ran across the following article in the journal Dyslexia, an article that suggested that measures of word reading (WJ III Letter-Word Identification) and pseudoword reading (WJ III Word Attack test) are measuring the same construct. As reported in the abstract below, the authors report a very high correlation of .94 between these measures in a sample of 71 subjects (ages 6 to 9). As highlighted in the abstract conclusion, the authors suggest that measures of pseudoword reading (Word Attack) are of dubious value, as they measure little different from tests of simple word reading (Letter-Word Identification).

Unfortunately, a close inspection of the research methods suggests a serious problem with the analysis. The authors correlated raw scores for achievement measures. It is well known that raw scores (as well as other developmental based scores such as the WJ III W-scores) are developmental in nature...that is..the scores increase systematically with age due to simply maturation and the influence of moving through successive grades. Developmental scores across a wide-age range cannot be correlated without first removing the confound of the age/maturation-based developmental variance shared by the correlated measures. The authors did NOT do this. They do report "standardizing" the scores from each measure in the study, but this sounds like they standardized the scores across the entire age range...a process that retains the developmental/age-based variance. [The authors should have standardized the metric for each measure BY AGE. Although the age subsamples make this a bit tricky. Ideally, the absence of standard scores, the authors should have partialled out the influence of chronological age, saved the residuals, and then correlated the residuals].

The proper way to conduct correlations with developmental scores (raw scores; W-scores) across a wide age-range, is to first remove the developmental/age variance either by using age-based standard scores or by partialling out the developmental age variance from the developmental scores, and then correlating the residual variance scores.

To make this point, I took the WJ III norm data [conflict of interest disclosure - I'm a coauthor of the WJ III) and selected all norm subjects from ages 6 yo 9. I first correlated (inappropriately) the W-scores, which is similar to the authors correlation raw scores. The correlation was .85. Although not as high as the .94 reported by the authors, it still is at the high end. Then, I eliminated the problem of the "third variable" confound (age/developmental variance shared by both Letter-Word ID and Word Attack) by correlating, for the same subjects, the age-based standard scores (Mean=100; SD=15). The correlation dropped to .72.

It is clear that the incorrect correlation of raw scores reported in the article produced a spuriously high correlation of .94, a value that suggests the two measures of reading shared 88% common variance (.94 squared). In a more nationally representative sample this inappropriate correlation is .85, which suggests .72% common variance. However, the accurate result is the .72 52 % shared variance correlation, a value that indicates . 52 % shared variance suggests a much different conclusion than does th 88% incorrect reported value. Although word reading (Letter-Word ID) and pseudoword reading (Word Attack) tests are significantly correlated (.72), they only share approximately 50% common variance, a finding that suggests that they are related measures, but are measures that still provide measurement of unique aspects of the reading process.

The rest of the results reported in the article are tainted by the same problem and should be ignored. I've posted scatterplots for the W-score and SS correlations I calculated for those who want to see these plots (click here).

  • Thomson, D., Crewther, D. & Crewther, S (2006). Wots that Werd? Pseudowords (non-words) may be a Misleading Measure of Phonological Skills in Young Learner Readers. Dyslexia, 12, 289-299. (click here to view)

Abstract (italics added by blogmaster)
  • Pseudoword (non-word) reading tasks are a commonly used measure of phonological processing across diverse fields of reading research. However, whether pseudoword reading gives any more information about phonological processing in young learner readers than does the reading of real words has seldom been considered. Here we show that pseudoword and real word reading are so strongly correlated (r=0.94) in the first 4 years of school as to be representative of the same construct. Two of the subskills of phonological processing, phonological awareness and rapid automatic naming also predict almost identical amounts of variance in pseudoword and real word reading. A divergence in the correlations between word and pseudoword reading and phonological awareness and rapid naming only emerges in the fourth year, while a significant correlation between phonological awareness and rapid automatic naming is evident only in the first year of schooling. Thus these results suggest that, at least in young children learning to read, care should be taken when using pseudoword reading to measure either phonological processing ability or phonological awareness as this may misinform the choice of therapy for a child showing symptoms of dyslexia.
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Friday, January 26, 2007

Butter's annual neuropsychology conference

The 17th Annual Nelson Butter's West Coast Neuropsychology Conference announcements are now circulating. The conference is scheduled March 22-25, 2007, in warm an sunny San Diego. The title is "Advances in neuropsychological assessment and treatment of school-age children with cognitive deficits."

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Quantoids corner-bifactor and second-order FA comparisons-Guest post by Matthew Reynolds

The following is a guest blog post by Matthew Reynolds, one of Tim Keith's Doctoral Student in Educational Psychology (School Psychology & Quantitative Methods) at the University of Texas at Austin, Department of Educational Psychology.

This is an excellent post by a future quantoid to be reckoned with in the field of school/educational psychology research. Kudos to Dr. Tim Keith for suggesting that one of his doctoral students make a quest blog post. This is the first such doctoral student virtual scholar post. If there are other professors who would like to entertain the idea of doctoral students being assigned articles to review and prepare for guest posts on IQ's Corner, then drop me an email.... iap@earthlink.net
  • Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189-225. (click to view)

Although not directly related to intelligence, this article compares two confirmatory factor analytic (CFA) models frequently used in psychometric research of intelligence: bifactor and second-order models. Chen et al. (2006) describe the bifactor model as having a general factor that accounts for the communality in all items and domain specific factors that account for influences above and beyond the general factor. The second-order model is described as having interrelated first-order factors with a general factor that accounts for those relations.

Study 1 compared the two models by applying the factor structure to a quality of life measurement from the AIDS Time-Oriented Health Outcome Study. Study 2 was a Monte Carlo study investigating whether there was enough power to detect differences in the bifactor and second-order model. Previous research had suggested that it was empirically impossible to distinguish between the two in typical samples used in social science research (i.e. Mulaik & Quartetti, 1997).

Results from Study 1:

  • Bifactor and second-order factor models were imposed on a 17 item health-care related quality of life survey. The models had a general overall quality of life factor and four domain specific factors. The four domain-specific factors included cognition, vitality, mental health, and disease worry.
  • The results from the bifactor model suggested that the mental health factor did not provide unique information above and beyond the general factor. Therefore, the model was re-specified without a mental health factor.
  • The second-order factor model was specified with four first-order factors and a general quality of life factor that accounted for the relations among the first-order factors. The residual variance for the mental health factor, however, was statistically significant suggesting that there was some unique contribution of this factor (although the general factor accounted for 91.4% of the variance in that factor). Note this finding was different from the bifactor model. In the bifactor model the mental health factor did not provide unique information. Therefore, to be consistent with the bifactor model the authors also re-specified the second-order model so that only three factors, and the subtests related to mental health factor loaded directly on the second-order factor.
  • The results comparing the two different models showed that both the bifactor and second-order factor models provided adequate fit. Because the second-order model is a more constrained version of the bi-factor model, the likelihood ratio test (i.e., chi-squared difference test) was used to compare the fit of the models. The second-order model fit worse than did the bifactor suggesting that the constraints applied to the bifactor model to get to the second-order model were too restrictive. Also, a power analysis suggested that there was adequate power to detect the difference.
  • Next, the authors used these models to predict social functioning. Both models resulted in almost identical standardized estimates. This finding was rather reassuring in regards to the interpretability of the ability factors.

Study 2:

  • The findings suggested that even with a sample size of 200 there appears to be enough power to detect differences between the bifactor and second-order models.


Discussion:

  • The authors concluded that the bifactor model offers several advantages over the second-order model. One advantage was that it identified three factors instead of four. I am not quite convinced that this is necessarily an advantage. Two, they noted that researchers may miss potential non-significant first-order factor variances when looking at their results. I thought this was a good point by the authors; however, I also have had the same concern about using bifactor models. For example, a not-so-careful researcher may not consider the non-significant domain specific factor loadings as well as a non-significant domain specific factor variance.
  • The second advantage was that the bifactor model fit better. That is, the relations between the general factor and the items could not be fully mediated by the first-order factors.
  • Third, they stated that the bi-actor model is easier to interpret when predicting external criteria because the domain factors are represented as common factors in bifactor models whereas they are residualized factors in the higher-order model. Although true, I think the point is rather minor.
  • Last, and perhaps most importantly, they conclude that BOTH models are useful in research. I agree completely with this point as CFA models should be consistent with theoretical models.
  • In general, the article provides great information for those interested in hierarchical factor analysis, and it is provided in a straightforward manner. I think that the advantages of the bifactor model were a bit overstated. I do agree that it is useful to examine both models in research, especially since the second-order model can be derived from the bifactor model.
  • In my own research, one weakness of the bifactor model has been related to empirical under-identification. I believe that perhaps it runs into some of the same difficulties as the multi-method multi-trait models in that they are over-parameterized. A recent study that used the bifactor model to test for method effects also found that the bifactor model may fit well even when it is an incorrect model (Maydeu-Olivares & Coffman, 2006).
  • In terms of research in psychometric intelligence, the interpretation of the two models is slightly different as well. For example, in a bifactor model all of the effects of the general factor are direct. In intelligence research it seems to me that the contemporary theories are more consistent with the higher order model in which the general factor explains the interrelations of the broad abilities and its relation to test performance is mediated through the broad abilities.
  • To make this more germane to intelligence researchers I have included some output of analyses that I performed using the Holzinger & Swineford correlation matrix reported in their 1937 study. Shown are the specifications, the models with standardized loadings, and the unstandardized loadings, variances, and the total effects shown separately. Just as a warning, the models are not in publication form, but suffice for a demonstration. I hope these models help to clarify how the second-order model is in fact a more constrained version of the bifactor model. See Yung, Thissen, and McLeod (1999) for a more technical account.
  • Last, as an aside, I thought I would share the last two sentences from the Holzinger & Swineford 1937 article in Psychometrika. In this article the authors introduced the bifactor model:
  • The Bi-factor analysis illustrated above is not only very simple, but the calculation is relatively easy as compared with other methods. The total time for computation, done by one person, was less than ten hours for the present example.”
  • I just ran a bifactor model in Amos 5, and other than setting the model up, the actual computational time took 0.29 seconds. You have to appreciate all of the time and patience that researchers have put in over the years to get us where we are today!
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IQs Corner Headlines 1-27-07

All the news thats fit for IQ's Corner readers:

This is the 19th installment of IQs Corner Headlines from the Brain and Mind Blogsphere

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Ravens test and Flynn Effect webcast

Thanks to Dr. Steve Hughes, Director of Research, The TOVA Company, and Assistant Professor of Pediatrics, University of Minnesota Medical School, for the follow-up to a prior IQ's Corner post that announced the webcast of a presentation by Dr. John Raven on the topic "The Raven Progressive Matrices and the Flynn Effect: Review and Recent Research."

Dr. Hughes just dropped me an email altering me to the fact that the entire 90 minute webcast is now archived and available for viewing via the University of Minnesota BREEZE technology server (click here to view).

In addition, it appears that Dr. Raven will be back in Minnesota in March for a two day seminar that expands on some of this material, and covers more territory on fostering high-level competencies, effective educational environments and how to change the education system to actually produce such outcomes. When I know more, I will make a post with specific information.

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Dyanamic data visualzation - stats in motion

Thanks to Werner Wittmann for bringing to my attention an absolutely AMAZING dynamic data visualization presentation by Hans Rosling (at TED). As Werner described in his email to me, and I concur, it is a "brilliant example of data visualization. Looh how Hans visualizes between and within group differerences and integrates the time dimension...absolutely breath taking."



Hans
Rosling is head of the Division of International Health at KI and leads
courses on global health in both undergraduate and postgraduate programmes.


He is founder of non profit gapminder.



/>







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Tuesday, January 23, 2007

Procrastinators clock - nifty idea

Thanks to the Download Squad for the FYI tip re: the interesing "procrastinators clock" software program. An interesting possible aid for those who are chronic procrastinators.

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Integrating the mind - book

FYI. I haven't check out this book yet, but the topic is of considerable interest to me......domain-general vs domain-general cognitive processes. So many books...so little time.

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On the road - bloggin' lite


I'm on the road for business starting today (1-23-07)) and will return late Thursday. Blog posts may be minimal. Check out the flurry of activity at IQs Corner and the IQ Brain Clock yesterday I shall return.


IQs Corner Headlines 1-22-07

All the news thats fit for IQ's Corner readers:

This is the 18th installment of IQs Corner Headlines from the Brain and Mind Blogsphere

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Monday, January 22, 2007

Asperger's and executive functioning

I ran across an interesting small-sample (but well controlled with subject matching) study in the recent issue of Neuropsychologia re: possible impairments in executive processes/function (EF) in adults with Asperger's Syndrome. The article presents a nice summary (in table form) of prior matched-control studies that have examined the performance of individuals with Asperger's on many classic executive function measures (e.g., Wisconsin Cart Sort Test; Delis-Kaplan).

The most important finding from this study is the possibility that specific EF deficits (viz., response initiation and intentionality, in particular the ability to engage and disengage actions in the service of overarching goals),may be associated with Asperger's, but this may not have emerged in prior research that has used traditional EF measures. IN particular, the authors identify two less frequently used EF measures (Behavioral Assessment of Dysexecutive Syndrome, BADS; Hayling Test) as being potentially important for clinicians to evaluate for possible diagnostic use.

  • Hill, E. Bird, C. (2006) Executive processes in Asperger syndrome: Patterns of performance in a multiple case series Neuropsychologia,44, 2822–2835 (click here to view)
Abstract
  • Mixed evidence exists for executive dysfunction in autism spectrum disorders (ASD). This may be because of the nature of the tasks used, the heterogeneity of participants, and difficulties with recruiting appropriate control groups. A comprehensive battery of ‘executive’ tests was administered to 22 individuals with Asperger syndrome and 22 well-matched controls. Performance was analysed both between groups and on an individual basis to identify outliers in both the ASD and control groups. There were no differences between the groups on all ‘classical’ tests of executive function. However, differences were found on newer tests of executive function. Specifically, deficits in planning, abstract problem solving and especially multitasking. On the tests that discriminated the groups, all of the ASD individuals except one were identified as significantly impaired (i.e. below the 5th percentile of the control mean) on at least one executive measure. This study provides evidence for significant executive dysfunction in Asperger syndrome. Greatest dysfunction appeared in response initiation and intentionality at the highest level—the ability to engage and disengage actions in the service of overarching goals. These deficits are best observed through using more recent, ecologically valid tests of executive dysfunction. Moreover, performance on these measures correlated with autistic symptomatology.

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Neural mechanisms of dyslexia summary


[double click on image to enlarge]

As I've written before, I always look forward to skimming the contents of Current Directions in Psychological Science for brief, contemporary “taking stock” summaries of an area of psychological research, typically by prominent researchers in the domain of inquiry.

The latest issue did not disappoint. There is a nice summary of the general consensus of the neurological research on severe reading disorders (dyslexia) by the Shawyitz team. The reference, abstract, and URL link to pdf is below. Enjoy.

  • Shaywitz, S., Mody, M. & Shaywitz, B. (2007). Neural Mechanisms in Dyslexia. Current Directions in Psychological Science, 15 (6), 278-281. (click here to view)

Abstract
  • Within the last two decades, evidence from many laboratories has converged to indicate the cognitive basis for dyslexia: Dyslexia is a disorder within the language system and, more specifically, within a particular subcomponent of that system, phonological processing. Converging evidence from a number of laboratories using functional brain imaging indicates that there is a disruption of left-hemisphere posterior neural systems in child and adult dyslexic readers when they perform reading tasks. The discovery of a disruption in the neural systems serving reading has significant implications for the acceptance of dyslexia as a valid disorder—a necessary condition for its identification and treatment. Brainimaging findings provide, for the first time, convincing, irrefutable evidence that what has been considered a hidden disability is ‘‘real,’’ and these findings have practical implications for the provision of accommodations, a critical component of management for older children and young adults attending postsecondary and graduate programs. The utilization of advances in neuroscience to inform educational policy and practices provides an exciting example of translational science being used for the public good.
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Saturday, January 20, 2007