Gifted Issues Discussion homepage Forums Identification, Testing & Assessment WISC-V Extended Norms data collection in progress

Is it possible to use the table in the link to arrive at extended scores if you know your scaled scores (but not raw)? It seems to make sense for the subtests (table 2) but I'm unclear about the composites. Is the left column in table 3 ("sum of scaled scores") referring to the sum of the original scaled scores of the subtests that make up that composite, or the sum of the extended scaled scores derived in table 2? I'm just curious if it is worth it to go back to the neuropsychologist and ask for an addendum to our report.

It is not possible to use the ExNorms tables to arrive at extended scores if you do not have raw scores for the relevant subtests. The sum of scaled scores used for each composite is derived from the extended scaled scores. If you used the original scaled scores, you would end up with the same non-extended composites as you had originally on the standard norms.

It may be worth pursuing an addendum if your child
a) has more than one scaled score of 19 (or possibly 18, if it is an older child)
b) was a preteen at the time of testing (with some older exceptions).

In a few cases with more extreme extended scale scores, the ExNorms will make a difference for an older child, closer to the age-limits of the test (the norms end just before the 17th birthday). I have had one adolescent where the Extended Norms resulted in a modest, but significant, increase in one composite.

Can you talk a little bit about what extended norm values mean? I think I have a good understanding about the opinions out there on what scores up to 160 may represent, but beyond that, do we have enough data to categorize students by scores? Is a sample of 108 children large enough to create meaningful information? What is the goal or expected outcome of creating extended norms? How can we use this information? Thank you for your thoughts.

The rationale behind extended norms is that there is as much diversity in the top 1% of the population as there is between the 50th %ile and the 99th %ile, but much less data--and ExNorms are an attempt to "spread the curve" in that last percentile (or fraction thereof).

Do we have enough data to make meaningful distinctions (from 108 students)? Well, more data would, of course, be better, but 108 is certainly a whole lot better than what exists in the standard norms. There were 2,200 students in 11 age groups in the original standardization pool, which means about 200 students per age bracket--which you could think of as about three students-worth in the entire pool, at all age levels, representing everyone of IQ 145+ (or, if you like, 0.07 of a person representing everyone in the data set of 160+). I think it's fair to say that 108 is a much better sample size than 3 (let alone 0.07).

We readily acknowledge that there is a significant functional difference (including in educational needs) between someone of IQ 100 and someone of IQ 130. The ExNorms are in service to the idea that a similar functional difference exists between the individuals of IQ 160 and 190. (BTW, having assessed individuals with IQs below the floor of 40, at the opposite extreme, I can attest to analogously significant functional differences between someone assessed in the 40s and someone who is not formally assessable, such as adolescents for whom one can obtain standardized-ish data only using infant measures.)

Thank you, aeh. I understand what you are saying, but I still have two main questions.
1)Is the data good enough to be better than no data? Are most professionals in this area pretty confident of that? Can we say the extended norms are valid and reliable?

2)If we differentiate between 160 and 175 and 190, what is the utility of this and what do those numbers mean for a child in terms of an educational pathway (this may be a question best answered with specific data for a specific individual, but just in general)?

Thanks!

1. I think the data is better than no data, and I do think that most professionals would take it into thoughtful consideration. I would treat them as valid and reliable, insofar as testing data generally is. For example, the psychometrics are not substantially different in quality than, say, the linking data for predicted achievement between the WISC and the WIAT, or the KABC and the KTEA (not as good at that between the WJ Cog and Ach, which are truly co-normed), which are the basis for the kind of aptitude-achievement discrepancy analyses used in several learning disability diagnostic models.

2. I would agree that this is probably easier to discuss in the particular than in the general...so here's an anecdotal example, using SBLM data (which is, of course, based on a completely different quantitative model of IQ, but just for the sake of discussion...). A sibling group was assessed with scores in the GT range, with a range of scores on the same instrument upwards of 40 points. An illustration of the functional difference in educational needs, using a simple metric: the lowest scoring individual finished K-12 education with four years acceleration, while the highest scoring person reached that milestone accelerated eight years.

Here's another example, with better group data: some years ago, Lubinski & Benbow published research compiling a selection of outcomes for a cohort of students scoring in the top one in 10,000, which includes some comparisons to the top 1%.
http://www.appstate.edu/~webbrm/jap2001.pdf

Of course, in making educational decisions, more than IQ ought to be considered, but it certainly suggests some starting points for discussion. In the first example, while one can propose a situation in which the most-accelerated person might possibly have been maintainable with fewer skips, it is extremely unlikely that the least-accelerated person could have handled four more years of acceleration.

Thank you, aeh! I read the study you linked. This motivated me to go down the rabbit hole of pubmed. I think I’ve now discovered more questions than answers 😆

Can anyone comment on what value the extended norms might have where a child had a very spikey profile even without them (ie multiple 19s in one area and the rest of their testing more uniformly in the 120 sort of range)?

It's intriguing to know that one or more of those 19s might be 20/21/22 etc. I like the idea of more specific data. But what further conclusions might we draw? How specifically might we use this data?

Mainly, I think it would give a bit more specificity to the magnitude (not so much the nature, since the lows are sufficiently different from the highs even on standard norms) of the spikiness. So it would likely provide the same kind of information as for a more evenly-developed child for whom extended norms were relevant. Just a matter of degree. For spiky profiles in particular, it would highlight the asynchronies even more (and all the sequelae of same).

Thank you AEH! Yes and I guess for a child who was "somewhat" spikey with standard norms you might find they were actually "extremely" spikey...