It was with sadness and surprise I read the article by Julian Vasquez Helig at this link: (> http://cloakinginequity.com/
A simple public request for information validated our hunch: this data set was disproportionately Black and disproportionately older. The numbers were not disputable. Through the hard work and dogged efforts of our colleague Brian Crowell we obtained PAR data from other districts: San Francisco, Oakland, Los Angeles, San Jose. In every case, the data showed senior teachers and teachers of color being put into PAR disproportionately. With all the noise around teacher evaluations and school accountability, much has been drowned out. Some brave voices try to speak through the din about budget cuts, increasingly anxious and malnourished students, senseless curriculum purporting to be rigorous. But no one is brave enough to tell the stories of these thousands of teachers: after a full life devoted to teaching often our must vulnerable students, they are thrown out of their profession, humiliated among their colleagues, described as "inadequate", and forced to retire on a smaller retirement than they planned for, or perhaps none at all. All this is accomplished with the full complicity of their union, and with the support of "progressive" voices in educational policy. So what about Mr.Vasqez-Helig's claim that the PAR data from Los Angeles does not show disparate impact?
I looked over his analysis with another math teacher. We found his use of mathematical analysis baffling.
Mr. Vasquez-Helig does some data analysis that has to do with age in the first case and with wage level in the second. But neither of these addresses the issue of disparate impact. He in fact explicitly admits that his analysis has not addressed either of these two areas:
"The average age of White teachers is about 55. I don’t know the average age of teachers in LAUSD population, so this is just a “composition” analysis. I can’t calculate relative “composition” or “risk”. Which means that I cannot say from only a PAR sample whether older teachers are being targeted for PAR. You would need data for all teachers in LA Unified for that calculation to answer that question or make suppositions about the possibility."
"Again, I cannot say from only a PAR sample whether teachers in higher pay scales are being targeted for PAR, I would need data for all teachers in LA Unified for that calculation and to make suppositions about the possibility." His third analysis, the chi-square test, does show signinficant results, meaning that the hypothesis being tested (that race is a non-factor in determining PAR placement) is rejected by the data. The value of his chi-square statistic is 77.43. He states that this value has a p-value less than .00001. He also states that p-values less than .05 are significant, meaning that his p-value of less than .00001 is in fact significant. The chi-square test value is a value that is calculated from a set of data, in this case data for PAR and non-PAR teachers of various races in LAUSD. Small chi-square values (close to zero) indicate that the data is as-to-be-expected based upon a given hypothesis. Large chi-square values indicate abnormal data. The further the value is away from zero, the smaller the probability (p-value) that the event would happen by chance if the hypothesis were valid. To re-iterate, the author says that p-values below .05 are significant and that this data gives a p-value below .00001. Thus his analysis reinforces the notion that PAR program placement is not neutral with respect to race.
Thus his data analysis seems to be in agreement with the notion of disparate impact with respect to race.
It is not clear to us why he claims otherwise. My concluding thoughts really come as requests for those who write sites with titles like "Cloaking Inequity":
1. Please use mathematics correctly.
2. Please get to know some of these PAR teachers. Get to know what their classrooms are like, their personal pain, and their students. If you are concerned about inequity in the classroom watch carefully for inequitable practices directed at teachers that are cloaked as something else. Masha AlbrechtMath Teacher, Berkeley High
I looked over his analysis with another math teacher. We found his use of mathematical analysis baffling.
Mr. Vasquez-Helig does some data analysis that has to do with age in the first case and with wage level in the second. But neither of these addresses the issue of disparate impact. He in fact explicitly admits that his analysis has not addressed either of these two areas:
"The average age of White teachers is about 55. I don’t know the average age of teachers in LAUSD population, so this is just a “composition” analysis. I can’t calculate relative “composition” or “risk”. Which means that I cannot say from only a PAR sample whether older teachers are being targeted for PAR. You would need data for all teachers in LA Unified for that calculation to answer that question or make suppositions about the possibility."
"Again, I cannot say from only a PAR sample whether teachers in higher pay scales are being targeted for PAR, I would need data for all teachers in LA Unified for that calculation and to make suppositions about the possibility." His third analysis, the chi-square test, does show signinficant results, meaning that the hypothesis being tested (that race is a non-factor in determining PAR placement) is rejected by the data. The value of his chi-square statistic is 77.43. He states that this value has a p-value less than .00001. He also states that p-values less than .05 are significant, meaning that his p-value of less than .00001 is in fact significant. The chi-square test value is a value that is calculated from a set of data, in this case data for PAR and non-PAR teachers of various races in LAUSD. Small chi-square values (close to zero) indicate that the data is as-to-be-expected based upon a given hypothesis. Large chi-square values indicate abnormal data. The further the value is away from zero, the smaller the probability (p-value) that the event would happen by chance if the hypothesis were valid. To re-iterate, the author says that p-values below .05 are significant and that this data gives a p-value below .00001. Thus his analysis reinforces the notion that PAR program placement is not neutral with respect to race.
Thus his data analysis seems to be in agreement with the notion of disparate impact with respect to race.
It is not clear to us why he claims otherwise. My concluding thoughts really come as requests for those who write sites with titles like "Cloaking Inequity":
1. Please use mathematics correctly.
2. Please get to know some of these PAR teachers. Get to know what their classrooms are like, their personal pain, and their students. If you are concerned about inequity in the classroom watch carefully for inequitable practices directed at teachers that are cloaked as something else. Masha AlbrechtMath Teacher, Berkeley High
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