c for Splus under windows platform
Hello all, I'm looking for a c compiler which can work for Splus in windows. At least gcc port for windows cygwin doesn't work, and I don't have watcom c. Anyone has such experience before? Thanks very much, Gang = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Herman Rubin <[EMAIL PROTECTED]> wrote: > In article <[EMAIL PROTECTED]>, > J. Williams <[EMAIL PROTECTED]> wrote: >>Francis Galton explained it in 1885. Possibly, the Mass. Dept. of >>Education missed it! Or, could it be that the same gang who brought >>us the exit poll data during the November election were helping them >>out? :-) >>I am wondering why they did not have a set of objective standards for >>ALL students to meet. > There are only two ways this can be done. One is by having > the standards so low as to be useless, and the other is by > not allowing the students who cannot do it to get to that > grade, regardless of age. The second is, at this time, > Politically Incorrect. And what alternative do you propose? Sending the underachievers to work in the fields as soon as signs of promise fail to manifest? [...] > The biggest factor in the performance of schools is in the > native ability of students; but again it is Politically > Incorrect to even hint that this differs between schools. It may be "politically incorrect" to say so. But does that support the proposition in any way shape or form? So go on, "hint"; get up on a beer-barrel and "hint" that the "fit" are languishing from the ignominious condition of having to suffer the presence of the "unfit". You'll have plenty of company: Pride is greedier even than mere Avarice. -- R. Bloom = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Robert J. MacG. Dawson <[EMAIL PROTECTED]> wrote: > > (2) _Why_ were even the best schools expected to improve, with targets > that seem to have been overambitious?? I would hazard a guess that it > might be due to an inappropriate overgeneralization of the philosophy - > appropriate, in an eduational context, for individual students - that > progress should constantly be being made. Continual Growth is the state religion. If you're not "growing" you're falling behind. In business, there is no such thing anymore as a "reasonable return" . Nowadays, there is in it's stead the mantra of a reasonable rate of growth of the rate of return. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Sample size for acceptance sampling
All - I have a problem which I am hoping someone can help me solve. (No, this is not a problem from class, but a real-life problem from industry.) A product has a specification of <= 1 defect/10 cm^2. The product has an overall surface area of X cm^2. While this specification was not obtained based on an analysis of the alpha & beta risks for acceptance sampling, the specification itself implies an alpha & beta level. Is there a simple way to calculate how the alpha and beta risks would be affected by a change in sample from 10 cm^2 to 5 cm^2? Specifically, if someone accidentally sampled 5 cm^2 and found 1 defect, what could you say about the status of the product? Thanks. - Eric Scharin = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Paul R Swank wrote: > > Robert: > > Why would you expect a strong correlation here? You're talking about tests done a >year apart with some new kids in each school and some kids who have moved on. Simply because there seems to be general consensus that there are such things as "good schools" and "poor schools". This may be partially because some schools have catchment areas in which parents are better educated/more suppportive/able to afford breakfast for their kids and others aren't, partially because schools in some areas have better funding, partially because some schools have better teachers, or for a host of other reasons. > Is regression toward the mean causing all of the noted results. Probably not. But it >is quite conceivable that it could be partially responsible for the results. It would certainly contribute - but I think it would be a minor contribution, in the presence of goals as stated. -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
On Wed, 10 Jan 2001 21:32:43 GMT, Gene Gallagher <[EMAIL PROTECTED]> wrote: > The Massachusetts Dept. of Education committed what appears to be a > howling statistical blunder yesterday. It would be funny if not for the > millions of dollars, thousands of hours of work, and thousands of > students' lives that could be affected. > < snip, much detail > > I find this really disturbing. I am not a big fan of standardized > testing, but if the state is going to spend millions of dollars > implementing a state-wide testing program, then the evaluation process > must be statistically valid. This evaluation plan, falling prey to the > regression fallacy, could not have been reviewed by a competent > statistician. > > I hate to be completely negative about this. I'm assuming that > psychologists and others involved in repeated testing must have > solutions to this test-retest problem. The proper starting point for a comparison for a school should be the estimate of the "true score" for the school: the regressed-predicted value under circumstances of no-change. "No-change" at the bottom would be satisfied by becoming only a little better; no-change at the top would be met by becoming only a little worse. If you are dumping money into the whole system, then you might hope to (expect to?) bias the changes into a positive direction. I thought it was curious that the "schools in the highest two categories were expected to increase their average MCAS scores by 1 to 2 points, while schools in the lowest two categories were expected to improve their scores by 4-7 points." That sounds rational in form. It appears to me that their model might have the correct form, but the numbers surprised them. That is: It looks as if someone was taking into account regression of a couple of points, then hoping for a gain of 4 or 5 points. That (probably) under-estimated the regression-to-the-mean, and over-estimated how much a school could achieve by freshly swearing to good intentions. What is needed -- in addition to self-serving excuses -- is an external source of validation. And it should validate in cases that are not predicted by regression to the mean. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Number of classes.
I asked Minitab support how they did it. Here is their answer: Date: Fri, 26 Sep 1997 15:07:50 -0400 To: [EMAIL PROTECTED] From: Tech Support Subject: number of bars in MINITAB histogram Jonathan, I finally found an answer for you. Here's the algorithm. There are upper and lower bounds on the number of bars. Lower bound = Round( (16.0*N)**(1.0/3.0) + 0.5 ) Upper bound = Lower bound + Round(0.5*N) After you find the bounds, MINITAB will always try to get as close to the lower bound as it can. Then we have a "nice numbers" algorithm that finds interval midpoints, given the constraints on the number of intervals. But there is special code for date/time data and for highly granular data (e.g., all 1's and 2's). Find the largest integer p such that each data value can be written (within fuzz) as an integer times 10**p. Let BinWidth = 10**p. Let BinCount = 1 + Round( ( range of data ) / BinWidth ) If BinCount is <= 10, then let the bin midpoints run from the data min to the data max in increments of BinWidth. Otherwise, use the "nice numbers" algorithm. Hope this helps. Andy Haines Minitab, Inc. At 11:01 PM 1/4/01 -0500, you wrote: >To determine the number of classes for a histogram, Excel uses square root >of the number of observations. Is it also true for the number of >observations greater than 200, say, for 2000?. Does the MINITAB use the same >for determining the number of classes for a histogram? >Any help would be appreciated. > > > > > >= >Instructions for joining and leaving this list and remarks about >the problem of INAPPROPRIATE MESSAGES are available at > http://jse.stat.ncsu.edu/ >= > > ___ --- | \ Jon Cryer, Professor [EMAIL PROTECTED] ( ) Dept. of Statistics www.stat.uiowa.edu/~jcryer \\_University and Actuarial Science office 319-335-0819 \ * \of Iowa The University of Iowa dept. 319-335-0706 \/Hawkeyes Iowa City, IA 52242 FAX319-335-3017 |__ ) --- V "It ain't so much the things we don't know that get us into trouble. It's the things we do know that just ain't so." --Artemus Ward = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Robert: Why would you expect a strong correlation here? You're talking about tests done a year apart with some new kids in each school and some kids who have moved on. Is regression toward the mean causing all of the noted results. Probably not. But it is quite conceivable that it could be partially responsible for the results. At 03:21 PM 1/11/01 -0400, you wrote: > > >Paul R Swank wrote: >> >> Regression toward the mean occurs when the pretest is used to form the groups, which it appears is the case here. > > Of course it "occurs": - but remember that the magnitude depends on >r^2. In the case where there is strong correlation between the pretest >and the posttest, we do not expect regression to the mean to be >particularly significant. > > Now, it is generally acknowledged that there are some schools which >_consistently_ perform better than others. (If that were not the case, >nobody would be much surprised by any one school failing to meet its >goal!) Year-over-year variation for one school is presumably much less >than between-school variation. > > Therefore, I would not expect regression to the mean to be sufficient >to explain the observed outcome (in which "practically no" top schools >met expectations); and I conclude that the goals may well have been >otherwise unreasonable. Indeed, requiring every school to improve its >average by at least two points every year is not maintainable in the >long run, and only justifiable in the short term if there is reason to >believe that *all* schools are underperforming. > > -Robert Dawson > > >= >Instructions for joining and leaving this list and remarks about >the problem of INAPPROPRIATE MESSAGES are available at > http://jse.stat.ncsu.edu/ >= > Paul R. Swank, PhD. Professor & Advanced Quantitative Methodologist UT-Houston School of Nursing Center for Nursing Research Phone (713)500-2031 Fax (713) 500-2033 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
In article <[EMAIL PROTECTED]>, J. Williams <[EMAIL PROTECTED]> wrote: >Francis Galton explained it in 1885. Possibly, the Mass. Dept. of >Education missed it! Or, could it be that the same gang who brought >us the exit poll data during the November election were helping them >out? :-) >I am wondering why they did not have a set of objective standards for >ALL students to meet. There are only two ways this can be done. One is by having the standards so low as to be useless, and the other is by not allowing the students who cannot do it to get to that grade, regardless of age. The second is, at this time, Politically Incorrect. When a given school is already "good" it naturally can't >"improve" more than schools on the bottom of the achievement ladder. It can, by changing curriculum and speeding things up. This is also not Politically Correct. >It seems they really should have prepared a better public announcement >of results. Rather than "knocking" the high achieving schools, they >should praise them justifiably. Then, noting the improvement in the >large urban schools would seem positive as well. The biggest factor in the performance of schools is in the native ability of students; but again it is Politically Incorrect to even hint that this differs between schools. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399 [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Paul R Swank wrote: > > Regression toward the mean occurs when the pretest is used to form the groups, which >it appears is the case here. Of course it "occurs": - but remember that the magnitude depends on r^2. In the case where there is strong correlation between the pretest and the posttest, we do not expect regression to the mean to be particularly significant. Now, it is generally acknowledged that there are some schools which _consistently_ perform better than others. (If that were not the case, nobody would be much surprised by any one school failing to meet its goal!) Year-over-year variation for one school is presumably much less than between-school variation. Therefore, I would not expect regression to the mean to be sufficient to explain the observed outcome (in which "practically no" top schools met expectations); and I conclude that the goals may well have been otherwise unreasonable. Indeed, requiring every school to improve its average by at least two points every year is not maintainable in the long run, and only justifiable in the short term if there is reason to believe that *all* schools are underperforming. -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
MCAS
Dear Gene, I share your feelings about MCAS (fortunately my daughter finished high school before it came into effect, but that's no consolation for the thousands of other kids who are supposed to be deprived of their high school diplomas), and had some similar reactions to the article. On the statistical issue, there are two ways that this could be done that have different implications. If you form categories based on the actual score in the previous year, then you have a classic regression to the mean situation. The magnitude of the regression to the mean effect could be estimated knowing variances and sample sizes at the various schools; it is even possible that allowing for a few points smaller required advance in the higher-scoring school might adjust for that effect (although I would be surprised!). Another approach would be to define groups by some other variable related to but distinct from outcomes, e.g. inner city versus suburbs, percent minority, or percent in poverty. Although the groups will differ in their baseline values, there is no regression to the mean effect there. However, either of these analyses begs the question of the potential for improvement in different schools. An excellent school may already be doing everything it should be doing and have no way to improve, while a low-scoring school may have a lot of possible avenues to improvement. There may also be issues about the appropriateness of the educational strategies that might be adopted. Somebody might argue that at a school already doing well at MCAS, improvements could only be obtained by teaching to the MCAS at the cost of less investment in AP exams. (I personally have little confidence in the educational incentives of the MCAS at any level; I am just laying out possible arguments.) Presumably some arguments could also be advanced to the opposite effect, as well. Best regards Alan Zaslavsky P.S. I see now that Robert Dawson had already made some similar comments, which I hadn't gotten to yet when I wrote this. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
i went to some of the sites given in the urls ... and, quite frankly, it is kind of difficult to really get a feel for what has transpired ... and how targets were set ... and how goals were assessed regardless of whether we like this kind of an approach for accountability ... or not ... we all have to admit that there are a host of problems with it ... many of these are simply political and policy oriented (in fact, these might be the largest of the problems ... when the legislature starts enacting regulations without a real good understanding of the methodological problems) ... some are measurement related ... and yes, some are statistical in nature we do have components of this total process 1. there are tests that are developed/used/administered/scored ... in 4th and 8th and 10th grades ... these are NOT the same tests of course ... so, one is never sure what it means to compare "results" from say the 8th grade to the 4th grade ... etc. 2. then we have the problem that one year ... we have the data on the 4th graders THAT year ... but, the next year we have data on the 4th graders for THAT year ... these are not the same students ... so any direct comparison of the scores ... 4th to 4th ... or 8th to 8th or 10th to 10th ... are not totally comparable ... so, ANY difference in the scores ... up or down ... cannot be necessarily attributed to improvement or lack of improvement ... the changes could be related and in fact, totally accounted for because there are small changes in the abilities of the 4th graders one year compared to another year ... (or many other reasons) 3. as i said before, we also have the problem of using aggregated performance ... either averages of schools and/or averages for districts ... when we line them up and then assign these quality names of very high, high, etc. there is a necessary disconnect between the real goals of education ... that is, helping individual kids learn .. and the way schools or districts are being evaluated ... when averages are being used ... 4. i would like to know how on earth ... these 'standards' for "dictated" improvement targets were derived ... did these have anything to do with real data ... or an analysis of data ... or, were just roundtabled and agreed to? we have to know this to be able to see if there is any connection between policy targets and statistical problems 5. we have to try to separate relative standing data from actual test score gain information ... and we don't know how or if the ones setting the standards and making decisions ... know anything about this problem so, to summarize ... there are many many issues and problems with implementing any system whereby you are trying to evaluate the performance of schools and districts ... and, perhaps the least important of these is a statistical one ... set in the context of political policy matters ... that a legislature works with ... and "legislates" targets and practices without really understanding the full gamut of difficulties when doing so unfortunately, in approaches like this, one makes an assumption that if a school ... or district ... gets better (by whatever measure) ... that this means that individual students get better too ... and we all know of course that this is NOT NECESSARILY TRUE ... and in fact we know more than that ... we know that it is NOT true ... in many cases sure, it is important to make some judgements about how schools and districts are doing ... especially if each gets large sums of money from taxpayers ... but, the real issue is how we work with students ... and how each and every one of them do ... how each kid improves or not ... and, all these approaches to evaluating schools and districts ... fail to keep that in mind ... thus, in the final analysis, all of these systems are fundamentally flawed ... (though they still may be useful) = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
At 11:31 PM 1/10/01 -0500, Bob Hayden wrote: regression to the mean applies to relative position ... NOT raw scores let's say we give a test called a final exam at the beginning of a course ... and assume for a moment that there is some spread ... though the mean necessarily would be rather low ... then, we give an alternate form of this final exam at the end of the course ... where again, there is reasonable spread but, obviously, the mean has gone up alot ... now, EVERYONE'S SCORES GO UP ... so everyone improves ... and it is not that the low scores (BECAUSE of regression) will improve more and the better scoring students on the pretest will improve less) that is NOT what regression to the mean is all about ... so, it depends on how these tests are scored and reported ... if the scores are reported on something like a percentile score basis ... then there is necessarily a problem ... but, if the scores are reported on some scale that reflect that 10th grade scores are higher than 8th grade scores ... and 8th grade scores are necessarily higher than 4th grade scores ... that is, the scores reflect an ever increasing general level of knowledge ... then regression to the mean is not the bugaboo that the "letter" makes it out to be now, the post said: The effectiveness of school districts is being assessed using average student MCAS scores. Based on the 1998 MCAS scores, districts were placed in one of 6 categories: very high, high, moderate, low, very low, or critically low. Schools were given improvement targets based on the 1998 scores, with schools in the highest two categories were expected to increase their average MCAS scores by 1 to 2 points, while schools in the lowest two categories were expected to improve their scores by 4-7 points = there are a number of ? that this paragraph brings to mind: 1. how are categories of very high, etc. ... translated into 1 to 2 points ... or 4 to 7 points? i don't see any particular connection of one to the other 2. we have a problem here of course that the scores in a district are averages ... not scores for individual kids in 4th, 8th, and 10th grades 3. what does passing mean in this context? 4. let's say there are 50 districts ... and, for last year ... using 4th grade as an example ... we line up from highest mean for a district down to lowest mean for a district then, in the adjacent column, we put what those same districts got as means on the tests for the 4th grade this year we would expect this correlation to be very high ... for two reasons ... first, means are being used and second, from year to year ... school district's population does not change much ... so if one district has on average, a lower scoring group of 4th grade students that is what is going to be the case next year thus, given this ... we would NOT expect there to be much regression to the mean ... since the r between these two variables i bet is very high 5. but, whatever the case is in #4 ... what does this have to do with CHANGE IN MEAN SCORES? or changes in the top group of at least 1/2 points and in the low groups changes of 4/7 points? the lack of r between the two years of 4th grade means on these test just means that their relative positions change with the higher ones not looking as relatively high ... and the low ones not looking quite so relatively low BUT, your position could change up or down relatively speaking regardless of whether your mean test performance went up or down ... or stayed the same bottom line: we need alot more information about exactly what was done ... and how improvement goals were defined in the first place ... before we can make any reasonable inference that regression to the mean would have anything to do with better districts being bad mouthed and poorer performing districts being praised = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Regression toward the mean occurs when the pretest is used to form the groups, which it appears is the case here. At 08:31 AM 1/11/01 -0400, you wrote: > > >Gene Gallagher wrote: >> >> Those familiar with "regression to the mean" know what's coming next. >> The poor schools, many in urban centers like Boston, met their > ^ >> improvement "targets," while most of the state's top school districts >> failed to meet their improvement targets. > > Wait one... Regression to the mean occurs because of the _random_ >component in the first measurement. Being in an urban center is not part >of the random component - those schools' grades didn't improve because >some of them woke up one day and found that their school had moved to a >wealthier district. > If the effect of nonrandom components such as this is large enough >(as I can well believe) to justify the generalization highlighted above, >and if there was a strong pattern of poor-performing schools meeting >their >targets and better-performing schools not doing so, we are looking at >something else - what, I'll suggest later > > >> The Globe article describes how superintendents of high performing >> school districts were outraged with their failing grades, while the >> superintendent of the Boston school district was all too pleased with >> the evaluation that many of his low-performing schools had improved: >> >> [Brookline High School, for example, with 18 National Merit Scholarship >> finalists and the highest SAT scores in years, missed its test-score >> target - a characterization blasted by Brookline Schools Superintendent >> James F. Walsh, who dismissed the report. > > There *is* a problem here,but it's not (entirely) regression >to the mean. If I recall correctly, Brookline High School is >internationally >known as an excellent school, on the basis of decades of excellent >teaching. >If it couldn't meet its target, it's not because its presence among the >top >schools was a fluke in the first measurement - it's probably because the >targets for the top schools were unrealistic. > > Was there any justification for the assumption voiced by the Boston >superintendant that the top-performing schools were in fact not >performing at >their capacity and would be "smug" if they assumed that their present >per- >formance was acceptable? The targets described seem to imply that no >school >in the entire state - not one - was performing satisfactorily, even the >top >ones. Perhaps this was felt to be true, or perhaps it was politically >more >acceptable to say "you all need to pull your socks up" than to say "the >following schools need to pull their socks up; the rest of you, steady >as she goes." > > As a reductio ad absurdum, if this policy were followed repeatedly, >it would be mathematically impossible for any school to meet its target > every year. That - and not regression to the mean - is the problem >here, I >think. > > -Robert Dawson > > >= >Instructions for joining and leaving this list and remarks about >the problem of INAPPROPRIATE MESSAGES are available at > http://jse.stat.ncsu.edu/ >= > Paul R. Swank, PhD. Professor & Advanced Quantitative Methodologist UT-Houston School of Nursing Center for Nursing Research Phone (713)500-2031 Fax (713) 500-2033 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Francis Galton explained it in 1885. Possibly, the Mass. Dept. of Education missed it! Or, could it be that the same gang who brought us the exit poll data during the November election were helping them out? :-) I am wondering why they did not have a set of objective standards for ALL students to meet. Of course, it is nice to reward academically weaker districts for "improving," but the real issue may not be "improvement," rather it might be attainment at a specific level for all schools as a minimum target. A sliding scale depicting "improvement" means little if the schools in question are producting students who fall behind in math, reading comprehension, etc. Rewarding urban schools for improving probably is a good idea, but that should not mean entering a zero sum game with the "good" schools. When a given school is already "good" it naturally can't "improve" more than schools on the bottom of the achievement ladder. It seems they really should have prepared a better public announcement of results. Rather than "knocking" the high achieving schools, they should praise them justifiably. Then, noting the improvement in the large urban schools would seem positive as well. On Wed, 10 Jan 2001 21:32:43 GMT, Gene Gallagher <[EMAIL PROTECTED]> wrote: >The Massachusetts Dept. of Education committed what appears to be a >howling statistical blunder yesterday. It would be funny if not for the >millions of dollars, thousands of hours of work, and thousands of >students' lives that could be affected. > >Massachusetts has implemented a state-wide mandatory student testing >program, called the MCAS. Students in the 4th, 8th and 10th grades are >being tested and next year 12th grade students must pass the MCAS to >graduate. > >The effectiveness of school districts is being assessed using average >student MCAS scores. Based on the 1998 MCAS scores, districts were >placed in one of 6 categories: very high, high, moderate, low, very low, >or critically low. Schools were given improvement targets based on the >1998 scores, with schools in the highest two categories were expected to >increase their average MCAS scores by 1 to 2 points, while schools in >the lowest two categories were expected to improve their scores by 4-7 >points (http://www.doe.mass.edu/ata/ratings00/rateguide00.pdf). > >Based on the average of 1999 and 2000 scores, each district was >evaluated yesterday on whether they had met their goals. The report was >posted on the MA Dept. of education web site: >http://www.doe.mass.edu/news/news.asp?id=174 > >Those familiar with "regression to the mean" know what's coming next. >The poor schools, many in urban centers like Boston, met their >improvement "targets," while most of the state's top school districts >failed to meet their improvement targets. > >The Boston Globe carried the report card and the response as a >front-page story today: >http://www.boston.com/dailyglobe2/010/metro/Some_top_scoring_schools_fau >lted+.shtml > >The Globe article describes how superintendents of high performing >school districts were outraged with their failing grades, while the >superintendent of the Boston school district was all too pleased with >the evaluation that many of his low-performing schools had improved: > >[Brookline High School, for example, with 18 National Merit Scholarship >finalists and the highest SAT scores in years, missed its test-score >target - a characterization blasted by Brookline Schools Superintendent >James F. Walsh, who dismissed the report. > >"This is not only not helpful, it's bizarre," Walsh said. ''To call >Brookline, Newton, Medfield, Weston, Wayland, Wellesley as failing to >improve means so little, it's not helpful. It becomes absurd when you're >using this formula the way they're using it.'' > >Boston School Superintendent Thomas W. Payzant, whose district had 52 of >113 schools meet or exceed expectations, was more blunt: "For the >high-flying schools, I say they have a responsibility to not be smug >about the level they have reached and continue to aspire to do better."] > > > >Freedman, Pisani & Purvis (1998, Statistics 3rd edition) describe the >fallacy involved: >"In virtually all test-retest situations, the bottom group on the first >test will on average show some improvement on the second test and the >top group will on average fall back. This is the regression effect. >Thinking that the regression effect must be due to something important, >..., is the regression fallacy." > >I find this really disturbing. I am not a big fan of standardized >testing, but if the state is going to spend millions of dollars >implementing a state-wide testing program, then the evaluation process >must be statistically valid. This evaluation plan, falling prey to the >regression fallacy, could not have been reviewed by a competent >statistician. > >I hate to be completely negative about this. I'm assuming that >psychologists and others involved in repeated testing must hav
Re: MA MCAS statistical fallacy
A couple additional thoghts I didn't get around to before leaving for my 8:30 lecture: (1) The clearest way of looking at the stats side of things is probably that one would expect a high enough r^2 between schools' performances in one year and in the next that regression to the mean would be a rather minor phenomenon. (2) _Why_ were even the best schools expected to improve, with targets that seem to have been overambitious?? I would hazard a guess that it might be due to an inappropriate overgeneralization of the philosophy - appropriate, in an eduational context, for individual students - that progress should constantly be being made. Getting further off-topic, we see the same thing in economics, where the standard model for the Western economies is one of constant growth, and our institutions seem unable to adapt to slight shrinkage - or even slower-than-usual growth - without pain all round. Our culture seems to concentrate on the idea that the moment an institution stops growing it starts to die, and does not seem to put much effort into maintaining "mature" institutions for which the constant-growth paradigm is no longer appropriate. Maybe this cultural neoteny is still appropriate and advantageous at this stage in history - I don't know. -Robert = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: MA MCAS statistical fallacy
Gene Gallagher wrote: > > Those familiar with "regression to the mean" know what's coming next. > The poor schools, many in urban centers like Boston, met their ^ > improvement "targets," while most of the state's top school districts > failed to meet their improvement targets. Wait one... Regression to the mean occurs because of the _random_ component in the first measurement. Being in an urban center is not part of the random component - those schools' grades didn't improve because some of them woke up one day and found that their school had moved to a wealthier district. If the effect of nonrandom components such as this is large enough (as I can well believe) to justify the generalization highlighted above, and if there was a strong pattern of poor-performing schools meeting their targets and better-performing schools not doing so, we are looking at something else - what, I'll suggest later > The Globe article describes how superintendents of high performing > school districts were outraged with their failing grades, while the > superintendent of the Boston school district was all too pleased with > the evaluation that many of his low-performing schools had improved: > > [Brookline High School, for example, with 18 National Merit Scholarship > finalists and the highest SAT scores in years, missed its test-score > target - a characterization blasted by Brookline Schools Superintendent > James F. Walsh, who dismissed the report. There *is* a problem here,but it's not (entirely) regression to the mean. If I recall correctly, Brookline High School is internationally known as an excellent school, on the basis of decades of excellent teaching. If it couldn't meet its target, it's not because its presence among the top schools was a fluke in the first measurement - it's probably because the targets for the top schools were unrealistic. Was there any justification for the assumption voiced by the Boston superintendant that the top-performing schools were in fact not performing at their capacity and would be "smug" if they assumed that their present per- formance was acceptable? The targets described seem to imply that no school in the entire state - not one - was performing satisfactorily, even the top ones. Perhaps this was felt to be true, or perhaps it was politically more acceptable to say "you all need to pull your socks up" than to say "the following schools need to pull their socks up; the rest of you, steady as she goes." As a reductio ad absurdum, if this policy were followed repeatedly, it would be mathematically impossible for any school to meet its target every year. That - and not regression to the mean - is the problem here, I think. -Robert Dawson = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
? re influence of cor. coeff.
Dear Colleagues; I derived the following equation using MINITAB: pKi = - 10.9 + 3.77 Tu + 1.89 Tm + 5.55 Av - 6.35 Ap + 37.0 Km - 71.6 Ks Predictor Coef StDev TP VIF Constant -10.894 2.798 -3.890.005 Tu3.7654 0.6885 5.470.000 37.6 Tm1.8904 0.4559 4.150.003 21.0 Av 5.550 1.100 5.050.0002187.1 Ap-6.353 1.146 -5.540.0002641.9 Km37.034 8.609 4.300.003 34.7 Ks-71.61 14.21 -5.040.000 85.5 n = 15 SD = 0.5804 R-Sq = 89.6% F = 11.51 Since the variance inflation factors are so large I generated a Pearson correlation maxtix: Tu Tm Av Ap Km Tm0.261 Av0.7790.405 Ap0.7720.4300.999 Km -0.1730.525 -0.436 -0.415 Ks -0.0360.368 -0.474 -0.4630.931 As you see Ap and Av are colinear. So I thought to see what would happen if either of these two variables were omitted from the equation. When the data were regressed without Ap I obtained pKi = - 5.23 + 1.67 Tu + 0.284 Tm - 0.473 Av + 6.8 Km - 17.6 Ks Predictor Coef StDev TP VIF Constant -5.228 5.404 -0.970.359 Tu 1.672 1.194 1.400.195 26.3 Tm0.2837 0.7302 0.390.707 12.5 Av -0.4735 0.3596 -1.320.220 54.3 Km 6.79 13.82 0.490.635 20.8 Ks-17.63 21.49 -0.820.433 45.4 n = 15 S = 1.204 R-Sq = 49.7% F = 1.78 whereas is Av is omitted I obtain pKi = - 7.37 + 2.32 Tu + 0.694 Tm - 0.645 Ap + 11.3 Km - 29.9 Ks Predictor Coef StDev TP VIF Constant -7.366 5.223 -1.410.192 Tu 2.324 1.208 1.920.086 31.2 Tm0.6935 0.7505 0.920.380 15.3 Ap -0.6447 0.3480 -1.850.097 65.6 Km 11.25 13.36 0.840.421 22.5 Ks-29.94 22.30 -1.340.212 56.6 n = 15 S = 1.119 R-Sq = 56.6% F = 2.34 My question is: why is there such a decline in the statistical quality of the regression equation when one of the two colinear variables (Av or Ap) is omitted?? Since Ap and Av are colinear, I expected that removing one of them from the 6-variable equation would have been compensated for by a change in the coefficient preceding the other, accompanied by only a negligible change in statistical quality of the 5-variable equations compared to the 6-variable equation. Obvious this was _not_ the case. Responders should contact me _directly_ at [EMAIL PROTECTED] because I rarely log on to this usegroup. Thanks in advance to all responders, S. Shapiro [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
