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This Web site will help you view some of the important data that the U.S. Department of Education collects from the 50 states, the District of Columbia, and Puerto Rico. Please visit the About page for important background information on the data contained on this site before starting to explore the data.

There's no mystery or proprietary secret among academics or statisticians and data geeks as to how to construct simple comparisons of school demographics using available data.  It's really not that hard. It doesn't require bold assumptions, nor does it require complex statistical models. Sometimes, all that's needed to shed light on a situation is a simple descriptive summary of the relevant data.  Below is a "how to" (albeit sketchy) with links to data for doing your own exploring of charter and traditional public school demographics, by grade level and location.

I've written before about KIPP attrition in response to reports that had been released studying it.  When reports conclude that KIPP does not have high attrition, they tout it on their websites.  When reports concluded that they do have high attrition, KIPP responds with a rebuttal. The problem with most of these reports is that the data they give us has already been analyzed and then turned into percentages, which are only relative measures.  This is why I finally got around to navigating the New York START data system to find the actual raw data for myself which I could then compare to KIPPs annual report card that they release.

"This post is a follow up to my previous post in which I discussed the misguided arguments for maintaining a system of annual standardize testing of all students. In my post, I skipped over one argument that seems to be pretty common among the beltway pundits. I skipped this argument largely because the point is moot if we plan on using testing data appropriately to begin with. My point in the previous post was about tests, testing data and how to use it appropriately. But just as the beltway pundit crowd so dreadfully misunderstands tests and testing data, they also dreadfully misunderstanding demography and geography and the intersection of the two. A related example of the complete lack of demographic "data sense" in the current policy discourse is addressed in my recent post on "suburban poverty.""

"In the education community, many proclaim themselves to be "completely data-driven." Data Driven Decision Making (DDDM) has been a buzz phrase for a while now, and continues to be a badge many wear with pride. And yet, every time I hear it, I cringe. Let me explain."

Theories are like maps, the authors argue. They are "useful in helping us get somewhere." "Rather than attempt to imitate the hard sciences, social scientists would be ... doing what they do best: thinking deeply about what prompts human beings to behave the way they do." This includes gathering data-anecdotes, myths, and other such "soft" insights along with the so-called "hard" ones. It means including direct data, not just indirect test data which we hope "correlates" with reality." It means acknowledging tradeoffs: Do life, liberty, and happiness sometimes clash? Of course, this kind of "soft science" leads to less certainty. But less certainty where certainty doesn't exist is a good thing. One reason we need to stick with even flawed forms of democracy is that there isn't any flawless form! Every form of voting, for example, rests on a bias about whom and what is more important. Anyone studying the gerrymandering of districts in New York state notes that the latest plan makes it likely that a majority of voters will be "out-voted" by a minority when it comes to our state's legislative bodies. Our Constitution rests on similar "gerrymandering"-some voters count more than others.

There's a time and place for "show me the data"! "What's the 'evidence'?" is one of our five hallowed habits of mind at Mission Hill and Central Park East. But "evidence" comes in many forms, and the trade-offs involved are part of the data, too-if we pay equal attention to them. Maybe "At what price?" should be the 6th "habit of mind." As I've said before, we're entering an era reminiscent of bad science fiction where everyone is wondering "Who's following me? Who's collecting the data on me? Is there no place to hide?"

SchoolBook has published the teacher data reports using a new tool that was created by interactive journalists at The New York Times and WNYC. The goal of the tool: to make the data easier to understand and put the rankings into context.

"I make numbers talk," Richard Bowman likes to say when describing his new profession. But he isn't in finance or economics, he's in education policy, and he hopes to use his analytic expertise to help reform the country's public school systems with the help of a program at Harvard's Graduate School of Education (HGSE). Since 2008, the Strategic Data Project (SDP), under Harvard's Center for Education Policy Research, has placed fellows like Bowman in state education agencies, school districts, and charter school management organizations where they are helping policymakers to decode an avalanche of educational data. Their mission is to transform the use of data in education to improve student achievement.

The Daily News reported yesterday that fully 1/3 of the Teacher Data Reports - 4000 reports - are unreliable. And just to add a little context: They all have multiple years of data, which are supposed to be more reliable. Hundreds and hundreds have margins of error of less than 10 percentage points - five on either side - giving the public, parents, and teachers assurances that these reports are quite correct. In fact, the DOE was so confident in its findings that 46 of these reports had no margins of error at all! That's 4000 reports. And that means since teachers are ranked against each other, that all the reports are unreliable. Or, let's just get right to it: "unreliable" is a euphemism for wrong.

I was admittedly in a bit of a rush the other day to pull together some figures on CT charter schools based largely on data I had previously compiled, some of which only included Achievement First charter schools.  Here, I include all charter schools in Hartford, New Haven and Bridgeport, and address only the % Free Lunch numbers using the most recent available data from the NCES Common Core of Data, which are from 2009-10.  A few quick points are in order.

This week, the Wall St Journal reported that the NY Board of Regents approved the state's sharing of student and teacher information with a new national data base, to be funded by the Gates  Foundation, and designed by News Corp's Wireless Generation. All this confidential student and teacher data will be held by a private limited corporation, called the Shared Learning Collaborative LLC, with even less accountability,  which in July was awarded $76.5 million   by the Gates Foundation, to be spent over 7 months.  According to an earlier NYT story,  $44 million of this funding will go straight into the pockets of Wireless Generation, owned by Murdoch's News Corp and run by Joel Klein.

The New York Times, yesterday, released the value-added data on 18,000 New York City teachers collected between 2007 and 2010.  Though teachers are irate and various newspapers, The New York Post, in particular, are gleeful, I have mixed feelings. For sure the 'reformers' have won a battle and have unfairly humiliated thousands of teachers who got inaccurate poor ratings.  But I am optimistic that this will be be looked at as one of the turning points in this fight.  Up until now, independent researchers like me were unable to support all our claims about how crude a tool value-added metrics still are, though they have been around for nearly 20 years.  But with the release of the data, I have been able to test many of my suspicions about value-added.  Now I have definitive and indisputable proof which I plan to write about for at least my next five blog posts.

How, for example, if I was the principal of a given, average sized school in NYC, might I use the VA data on my teachers to council them? to suggest personnel changes? assignment changes, or so on? Would these data, as they are, provide me any useful information about my staff and how to better my school? For this exercise, I've decided to look at the year to year ratings of teachers in a relatively average school. Now, why would I bother looking at the year to year ratings when we know that the multi-year averages are supposed to more accurate - more representative of the teacher's over time contributions? Well, you'll see in the graphs below that those multi-year averages also may not be that useful.

In 2009, the state law granting the Mayor control of the New York City public school system was renewed. That renewal included a requirement that the New York City Independent Budget Office "enhance official and public understanding" of educational matters of the school system. The law also requires the Chancellor of the school system to provide IBO with the data that we deem necessary to conduct our analyses. That data began to flow to IBO at the beginning of the 2010-2011 school year. This report is our first annual summary of that data. Over the course of the last year, we have issued a number of detailed analyses of specific topics, and we will continue to produce those types of reports. This current report is designed as a descriptive overview of the school system rather than as an in-depth look at particular issues. It is organized into three main sections. The first presents demographic information on the students who attend New York City's public schools. The next section describes the resources-budgets, school staff, and buildings-that the school system utilizes. The final section describes the measurable outcomes of the school system's efforts for particular subgroups of students.

Data are important. However, data should be used to inform education, rather than to drive it. Right now, data have taken on a life of their own to the detriment of everything else. As Albert Einstein wrote: "Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted."

"The question that comes to mind is: "If these officials really cared about data, wouldn't they make sure that the data they are using to drive their decisions is accurate?" And this then leads me to a whole series of similar questions about the mighty agents of reform that are disrupting and transforming our schools from coast to coast and beyond. To be clear, the proponents of reform I am describing include the Gates Foundation, the Federal Department of Education, and their allies and grant recipients around the nation."

"In this brief, I present preliminary findings that are part of a larger, national analysis of newly released federal data, a primary objective of which is to evaluate the extent to which those data yield findings consistent with findings arrived at using state level data sources. In this brief, I specifically explore variations in student characteristics and resources across schools in Newark, NJ."

Education Week reports on the growth of websites that attempt to provide parents with help in choosing schools, including rating schools according to testing results. The most prominent of these sites is GreatSchools.org. Its test-based school ratings could not be more simplistic - they are essentially just percentile rankings of schools' proficiency rates as compared to all other schools in their states (the site also provides warnings about the data, along with a bunch of non-testing information). This is the kind of indicator that I have criticized when reviewing states' school/district "grading systems." And it is indeed a poor measure, albeit one that is widely available and easy to understand. But it's worth quickly discussing the fact that such criticism is conditional on how the ratings are employed - there is a difference between the use of testing data to rate schools for parents versus for high-stakes accountability purposes. In other words, the utility and proper interpretation of data vary by context, and there's no one "correct way" to rate schools. The optimal design might differ depending on the purpose for which the ratings will be used. In fact, the reasons why a measure is problematic in one context might very well be a source of strength in another.

In a recent story, the New York Daily News uses the recently-released teacher data reports (TDRs) to "prove" that the city's charter school teachers are better than their counterparts in regular public schools. The headline announces boldly: New York City charter schools have a higher percentage of better teachers than public schools (it has since been changed to: "Charters outshine public schools"). Taking things even further, within the article itself, the reporters note, "The newly released records indicate charters have higher performing teachers than regular public schools." So, not only are they equating words like "better" with value-added scores, but they're obviously comfortable drawing conclusions about these traits based on the TDR data. The article is a pretty remarkable display of both poor journalism and poor research. The reporters not only attempted to do something they couldn't do, but they did it badly to boot. It's unfortunate to have to waste one's time addressing this kind of thing, but, no matter your opinion on charter schools, it's a good example of how not to use the data that the Daily News and other newspapers released to the public.