Education Graphics:  Patterns in Data

This website contains substantive information on Texas school test score performance and expenditures, relationships between donors and recipient members of the State Policy Network, and details of charter school funding in Texas.  But the main focus is on the use of several methods of interactive computer graphics to investigate and present that information. 



The above graph is an example of how the techniques used here can provide new and interesting views of raw data.  In this graph each of the 3,453 faintly visible gray points represents an elementary or a middle school among 90 of the largest school districts in Texas.  Those schools included in six school districts are highlighted in distinct colors.  The highlighted schools demonstrate that within the overall negative relationship between academic performance (vertical axis) and percentages of students who are economically disadvantaged, there are very distinct patterns within individual districts. This data is made available within the interactive graphics presentation included in the Big90 Graphs option to the left.

The software not only highlights individual or sets of schools, but also displays numerical information corresponding to those which were selected.

A Java language version of this software is described, and will soon be made available for downloading. This version, to be run on a desktop or portable computer, not within a web browser, has more features and capabilities than the web version available here.

The ten options available on the select list to the left are briefly described:

Big90 Graphs. When first selected a brief description of the data being used is presented.  More importantly, a link to the interactive graphics page is presented.  Clicking on this link will present a page with two graphs and a data area beneath them.  Beneath this link, there are three labels Show steps. Clicking on these results in drop-down step-by-step instructions that will permit interacting with the graphs presented. Actually, if the label [AF] is selected (i.e. clicked on using the left-mouse button)  an automatic display of the schools among the 90 included school districts will be initiated, the districts being randomly selected in groups of five. The process can be interrupted by clicking on [Clear].  Clicking on [AS] gives a more slowly paced automatic display.

Beneath the row of select buttons is the link [Examples]. This includes step-by-step instructions for two more elaborate illustrations.

Clicking on the link [Instructions] displays a detailed, multi-page set of instructions for using the software.

The Javascript code used for this page can be packaged with other data sets. Also, even for the data included in this presentation, provision is made for a user to create different graphs, using the data included.  The parameters which designate any such newly created graph can be saved to local storage, retrieved, copied and pasted to an email, and sent to a colleague to be viewed by him or her on their own computer when connected to this web page.

Charters v ISDs. In this section you can run a program that displays the degree of success of over 8,000 Texas schools in achieving “Meets Grade Level” criteria for their students.  The MGL success data are for school year 2021-2022.  The principal focus of the displayed data is upon academic success relative to the proportion of economically disadvantaged students in each school.  The individual school data can be displayed for all the schools in each county or for all the schools in a district, and the results can be distinguished  between those for charter schools and for schools included in regular school districts. All of the features described above for the program which displays data for the largest 90 school districts in Texas are also available here.

ScatterBrain™. This selection presents a description of an earlier version of the Java-based ScatterBrain program.  Recently several significant new features have been added and the revised program will be made available for downloading. This page will soon be updated to permit downloading the current version of ScatterBrain.

It is pointed out that ScatterBrain does not involve any coding on the part of the user. It does require that the tab-delimited data set be structured in a very simple, but specific form, and that each graph to be displayed be defined—variables to be plotted selected, axes’ scales determined and labels entered.  Data to be displayed in the data table must also be selected and formats prescribed. From one to four interlinked graphs can be displayed simultaneously. The descriptions for an individual presentation, for all graphs and the data table, can then be saved in a setup file for future use.

ScatterBrain™ Videos. This page provides access to several videos made with ScatterBrain, using a variety of data obtained from the Texas Education Agency’s website.  A set of data that includes information for most school districts in the U.S is also used in one video, obtained from the Stanford Center for Education Policy Analysis. These were my first attempts at creating videos, so they are not very polished, but they are intended to demonstrate how the program can be used in meaningful ways.

Charter Papers. Sometime in 2016 I decided to attempt to resuscitate a Texas school finance simulation program that was developed nearly 25 years previously. Doing this required incorporating charter schools into the original program, as charter schools did not exist in Texas when the program was originally written. In carrying out this exercise it soon became apparent that the frequent claim by charter school proponents that charter schools received $1,000 less per student from the state than was received by regular school districts from both state and local funds was false. The series of papers and exercises contained in this section resulted from an attempt to support this realization.

These papers formed part of the underpinnings of a paper by David S. Knight and myself—"Do charter schools receive their fair share of funding? school finance equity for charter and traditional public schools”-- that can be accessed at the following link:


SPN/ALEC. The State Policy Network (SPN) and The American Legislative Exchange Council (ALEC), have been working for decades to redesign and reorient the U.S. economic and political systems.  In short, their goal has been to reverse progressive gains made during the New Deal era—lower taxes, less government regulation of the economy, and less autonomy for local governments.  The movement for so-called choice in public education, while often viewed as a separate movement to reform U.S. education, has in fact been one of the major goals of the State Policy Network and its affiliates. The relationship between the two movements can be demonstrated by observing the overlap in their funding sources.  The discussion papers and exhibits in this section amplify this assertion, which is illustrated in the following diagram.


Common Contributors to Pro-Choice Network and to State Policy Network Gave

$1,102 million to PCN and $472 million to SPN (plus Associate Members)


TCCRI (Texas Conservative Coalition Research Institute. The very membership makeup of TCCRI’s board of directors seems designed to arouse interest and curiosity. The board of directors of TCCRI includes Lt. Gov. Dan Patrick, Railroad Commissioner Christi Craddick, Texas Comptroller Glen Hegar, 9 state senators and 10 state representatives. In addition, 13 members of TCCRI’s board are registered lobbyists, including such powerhouses as Mike Toomey, Lara Keel, and Bill Oswald.  Mr. Oswald represents the interests of Koch Industries in Texas, and is the registered lobbyist for 9 Koch Industries’ Texas subsidiaries. Tax returns show that the Charles Koch Foundation contributed $1.8 million to TPPF in 2018 and another $1.5 million in 2019. In turn, records at the Texas Ethics Commission indicate that the members of TPPF’s board of directors made campaign contributions to board members of TCCRI totaling $485,705 during the years 2019-2021. Approximately two-thirds of that amount went to Lt. Gov. Patrick. See the additional descriptions and summaries below for additional facts about TCCRI.


Civitas Institute.  During the past year a number of articles were written about the stealth installation at UT Austin of a right-wing institute “dedicated to the study and teaching of individual liberty, limited government, private enterprise and free markets.” [Texas Tribune, Kate McGee, 8-26-2021] Originally dubbed the Liberty Institute, but finally named the Civitas Institute, Lt. Gov. Dan Patrick raised the possibility that the goals of the new institute might be more pointedly political. “I will not stand by and let looney Marxist UT professors poison the minds of young students with Critical Race Theory.” Patrick wrote on the social media platform Twitter. “We banned it in publicly funded K-12 and we will ban it in publicly funded higher ed.  That’s why we created the Liberty Institute at UT.” [reported in the Texas Tribune, McGee 2-16-2022]  Two days later the Texas Tribune also reported “Lt. Gov. Dan Patrick said Friday that he will push to end professor tenure for all new hires at Texas Public universities and colleges in an effort to combat faculty members who he says ‘indoctrinate’ students with teachings about critical race theory.”[McGee 2-18-2022] See the additional description at Civitas Institute linked to in the side bar.

Texas Income Tax. Reasons for taking a new look at the prospects and need for a Texas Income Tax.


Negative Impact of High Poverty Levels on the Academic Performance of All Texas Students (EDvNED). This version of ScatterBrain (Javascript) has been modified to display a data set that shows student achievement in 1,019 elementary schools and middle schools in 16 of the larger school districts in Texas. The emphasis is on demonstrating the impact of poverty upon academic performance.  The sad fact is that 60 percent of Texas’ public-school students are classified as being economically disadvantaged.  Twenty years ago this number was 45 percent.  This one-third increase in the number of Texas school children who are eligible for the federal Free or Reduced-Price Lunch Program has made it extra difficult to improve their educational performance.  Furthermore, the very high proportions of economically disadvantaged students in many schools greatly multiply the difficulties of achieving satisfactory academic progress. In addition, the concentration of large percentages of economically disadvantaged children in a given school also reduces the progress of students in that school who are not designated as economically disadvantaged.  The Texas school funding system’s additional grant provided to school districts for each economically disadvantaged student fails to meet the greatly increased cost of educating such students in schools with very high concentrations, nor does it acknowledge the collateral costs imposed on the non-economically disadvantaged students who attend the same schools.  


Negative Impact of High Poverty Levels on the Academic Performance of All Texas Students (EDvNED All Districts)Version II

Version II is similar to the version described in the previous section, but Version II contains data for 775 regular Texas school districts, containing 4,827 elementary and middle schools enrolling some 2.9 million students.  Test score results are still for school year 2021-2022, using the percentages of students achieving the Making Grade Level standard for all subjects and for all grades in the schools.

Again, the exhibits are designed to emphasize the differences in test results for economically disadvantaged students and non-economically disadvantaged students in each school. The displays can be thought-provoking.

One change made here is the added [Single] button. When this button is clicked on and changed to red, the two graphs are superimposed.  The reason for this feature is to be able to perform direct comparisons between economically disadvantaged students and those not disadvantaged in the same schools.  Note that Meets Grade Level (MGL) for these types of students are shown separately.  The LH graph plots ED students on the vertical axis, while the RH graphs plots non-ed students on the vertical axis. If an individual district is selected, using the [Hlt/Sel] button and the district select list, and the [Single] button is selected (turned red) ED and non-ED students in the same schools are plotted directly above/below one another.  If the [G0/1] button is selected (red) and the [Multi] function turned off (not red) then the mouse can be used to identify the upper (aqua) point. MGL for both ED and non-ED students in the identified school will be written to the data window. If it is desired to identify a pair based on the lower (orange) point the [G0/1] button should be white.

In addition, because of complications arising with the two graphs being superimposed, the [Mult] function, identifying multiple or groups of schools, only operated in the RH graphs when the two graphs are separate ([Single] turned off or white).

Negative impacts of poverty—ED 0423 and ED 1723.

The next two options on the left use ED data from two different years simultaneously to be able to observe the degree to which ED percentages change during the intervals for all schools and for individual schools for which data were available for both years.  Only non-charter elementary and middle schools are included in these data sets. For the ED0423 data set there are 4,281 elementary and middle schools enrolling 2.3 million students; for ED1723 5,257 such schools with 3 million students.

When either of these data sets are displayed the RH graph will included a 45-degree line; schools plotted above this line will have experienced an increase in the percentages of students qualifying for the FRPL program, those beneath that line a decrease in the percentages of such students.

Note that if it is desired to select the schools in an individual district, first click on [Hlt/Sel], turning it on (red), select the desired district from the district select list. After the schools are selected, turn off the [Hlt/Sel] button.  Use [Multi] (red) to aggregate data for multiple schools, using the mouse feature, or turn [Mult] off (white) to identify individual schools.  Individual schools can also be selected from the school select list.  When an individual district is selected, the schools in that district are written to the school select list.

Note that regression lines can be controlled with the [Regr] button feature, which pops up a small menu permitting the selection of regression line options for global (all schools in data set), local (just those schools currently selected).

Leaving the global regressions line permits observing where individual schools are relative to poverty levels for all schools in the state.  The local regression lines illustrate the level and slope of the characteristics of the schools in an individual district, relative to all schools.



Larry Toenjes

Clear Lake Shores, TX