The first challenge we can call an input problem. Developmental disabilities are defined statistically for the most part. In statistics
This will start what I hope will be a series on formal learning in individualized services. Because I sometimes read, and all the writing these days is about big data (100.5%.) There are corners of the system (almost vacant of service providers and probably of families) that hope that collecting outcomes data will lead to better services. I think that hope should live on, but that people understand that the benefit will be much less direct than in other sections of the economy.
To give a quick overview of "big data" and its benefits, now that so much of what we interact with generates information and the capacity to store and analyze it so much vaster than it had been, that humans have grown much much abler to discern patterns that had escaped us in the past. The opportunity to make change comes when we are able to see those patterns in context. Using statistics, we can find what factors affect the patterns we are concerned with the most. The term of art for the factor most relatable to changing a pattern is "the big coefficient." Terms of art in statistics are still pretty arcane and prosaic.
From my spot on the spectrum, individualized services ought to include the search for, identification and exploitation of patterns along with respect, protection and kindness. And math, particularly statistics, are the handiest tools we have with which to do that. And the rest of this series, if and when it emerges, will be about why I think professional caregivers should do math. But there are reasons to question whether big data can have the same impact in this field that it already has in medicine, marketing, science, politics or engineering.
One reason why big data will have trouble helping us help the people we serve might be called an input problem. Just to clarify nomenclature, to the left is what is called a "normal distribution." Of any given naturally occurring trait, there is a central tendency where any random individual is most likely to fall. That is called the mean and can be pictured as a line through the highest part of the curve. Where the curve flattens out to the left and right is called "the tails." I bring this up just because the word "tails" can give either the sense of disparaging or adorable and I wouldn't want to be thought to mean either.
But most disabilities are defined at least in part by a trait being found in an individual to occur in the tail of the distribution. You can imagine a stone dropped in still water. Where the stone strikes, you get the most information and further along in the eddy you get less. Not only are the people we serve rare, but it is easier using statistics to learn about commoner individuals than about rarer ones. Which is just to say that coca-cola will still know more about refreshment-seekers than DDS will about people with developmental disabilities even after the latter starts really trying.
The other problem we can call an output problem. When KFC wants people to eat more chicken, it is easy to find factors that correlate with the sought behavior. If the state wants fewer people to be poor, it is relatively easy to use large data sets to figure out which factors have the profoundest impact (largest coefficient) on poverty and proliferate them. But the Lanterman Act and those of us who serve it, wants people to live the lives they choose, not to behave according to a standard. And that makes it much harder to find the large coefficient independent variables.
So now I hope to write upcoming posts about why measurement and math belong in the complex of tools states and their agents use in pursuit of our mission. But I hope this post set some boundaries on how much we can hope to accomplish this way.
But most disabilities are defined at least in part by a trait being found in an individual to occur in the tail of the distribution. You can imagine a stone dropped in still water. Where the stone strikes, you get the most information and further along in the eddy you get less. Not only are the people we serve rare, but it is easier using statistics to learn about commoner individuals than about rarer ones. Which is just to say that coca-cola will still know more about refreshment-seekers than DDS will about people with developmental disabilities even after the latter starts really trying.
The other problem we can call an output problem. When KFC wants people to eat more chicken, it is easy to find factors that correlate with the sought behavior. If the state wants fewer people to be poor, it is relatively easy to use large data sets to figure out which factors have the profoundest impact (largest coefficient) on poverty and proliferate them. But the Lanterman Act and those of us who serve it, wants people to live the lives they choose, not to behave according to a standard. And that makes it much harder to find the large coefficient independent variables.
So now I hope to write upcoming posts about why measurement and math belong in the complex of tools states and their agents use in pursuit of our mission. But I hope this post set some boundaries on how much we can hope to accomplish this way.