by Jaret Hodges, Ph.D.
Identifying students as twice exceptional is hard work. A big reason why this is the case is due to the fact that this group of students constitutes a nonlinearly separable one. In other words, percentages, thresholds, and checklists don’t cut it. These students would need a set of tests nuanced and complex enough to appropriately identify them – tests that can separate the exceptionalities and account for something like dyslexia on a verbal intelligence measure. Let’s imagine that such a test could exist, a test that can perfectly identify students with exceptionalities as gifted. Getting there would still be a tremendous challenge for academics and practitioners due to one not-so-small problem: small area estimation.
Small area estimation is a statistical term that refers to the difficulty of making accurate and meaningful decisions about a given group due to their small size. Statistically speaking, the smaller the group, the harder it becomes to make meaningful conclusions about its characteristics, regardless of the supposed “perfectness” of a test. The best way to describe this is that you need enough people in a group to have the mean/median/mode of that group tell you something useful. Three thousand students and their average test performance on the STAAR can tell you something about that group of students. The average of three students does not tell you much. This is the fundamental issue with coming up with a useful test. Even a perfectly designed assessment needs a large enough group to learn what “twice exceptionality” looks like in the real world. But for most school districts, twice exceptional students make up a small fraction of the population (i.e., a small area). In other words, educators do not encounter enough examples to interpret the patterns they see with any kind of confidence.
And so we have a paradox: even if the ideal test existed, big testing companies wouldn’t have the sample size required to calibrate it. They would not be able to norm it. School districts most certainly wouldn’t have the numbers to calibrate it to their school district. Small numbers act like an amplifier for statistical noise. Unusual patterns can look typical and typical patterns might look unusual. The fewer students you have, the harder it is to know whether a child’s profile is due to twice exceptionality or just plain old ordinary statistical variation. In practice, this statistical reality adds another layer of difficulty to an already complex identification process.
This approach does not get to the real heart of the challenge of identifying twice-exceptional students and small-area estimation. The term twice exceptional is a wide umbrella under which a variety of different combinations of differences and abilities exist. The small area becomes even smaller. The perfect test that works on identifying the mathematically gifted child with dyscalculia will probably not work on the verbally gifted child with dyslexia. That does not mean that identification of twice exceptional students is impossible. It just means that a test or checklist is likely not the best strategy to identify these students.
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