Randy L. Hoover, Ph.D.
Department of Teacher Education
Beeghly College of Education
Youngstown State University
Youngstown, Ohio
February 27, 2000

Section One
An Overview of
Forces and Factors Affecting Ohio Proficiency Test Performance:
A Study of 593 Ohio School Districts
Randy L. Hoover, Ph.D.
Beeghly College of Education
Youngstown State University
Youngstown, Ohio
February 27, 2000
The following pages contain information, data, analysis, and summary findings regarding a major study of Ohio school district performance on the 1997 Ohio Proficiency
Tests (OPT). The data are for 593 of the 611 Ohio School districts. Data for 18 districts were excluded due to either missing test scores or because of their extremely small size such as North Bass Island. A complete list of the districts used in the study and the basic data for those districts may be found in the appendix to this study.
This study examines the 593 Ohio districts on all sections of the 1997 fourth-grade, sixth-grade, ninth-grade, and twelfth-grade tests. Thus, as the outcome measure of district performance, the study uses 16 sets of scores for each Ohio School district. All data used in this study are taken directly from the online Ohio Department of Education’s
Educational Management Information System (EMIS) 1 of the State of Ohio and have not been derived from any secondary source. The variables examined against the 1997 district test data are also from the 1997 EMIS collection.
2 The data from 1997 were selected for analysis because they are the most recent online data 3 available from the Ohio Department of Education and the State of Ohio. and they are the most complete data available that is easily accessed by the public.
The data were analyzed using linear regression and Pearson’s correlation (Pearson’s r) procedures. A simplified explanation of the analysis is contained in the next section.
However, it is important to point out that the statistical analyses used are very simple an very straightforward in terms of the range of potentially very complex statistical
1 http://www.ode.ohio.gov/www/ims/
2 One set of data was drawn from the 1993 EMIS collection. Economic disadvantagement categorical data from 1993 were selected because later data are incorrect for several school districts.
3 http://www.ode.ohio.gov/www/ims/extract_emis_profile.html and http://www.ode.ohio.gov/www/ims/extract_vitals_data.html
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procedures. The statistical operations used in the study are quite typical of those used across many fields and disciplines including medicine, marketing, political science, and economics.
While certain results may call for additional and more sophisticated analysis, the results contained herein speak for themselves and for the power of basic statistical analysis.
Further, given the power of the primary results of the procedures and the statistical significance of those results, no additional more complex procedures were deemed necessary to achieve the basic ends of the study.
As with any research of education and social phenomena, there is always room for interpretation and reflective judgment. While this certainly applies to this particular study, the basic finding regarding district-level Ohio Proficiency test performance is remarkably clear: Performance on the Ohio Proficiency Test is most significantly related to the socialeconomic living conditions and experiences of the pupils to the extent that the tests are found to have no academic nor accountability validity whatsoever.
It is extremely important to know that findings do not single out students and districts in which levels of disadvantagement are high as being the only sector where the test is invalid. The findings clearly indicate that the range of performance across all social economic levels lacks validity in terms of assessing academic performance. Rejection of the findings regarding OPT validity (accepting the State of Ohio’s interpretation of OPT results) means that we accept the position that wealth defines academic intelligence, that the wealthier the students the more intelligent than less wealthy students. This position is absurd even at a common sense level; money does not define academic intelligence or learning capabilities.
Part of the problem in understanding OPT for what it is (or is not) rests in understanding that there are many different variables that affect how, what and whether a child learns in school. Explicit in the OPT program and State of Ohio policies on school district accountability is the assumption that these high stakes tests accurately assess student academic achievement and that all students are the same in terms of how, what, and whether they learn. The findings of this study contradict this assumption.
Implicit in the claims and slogans of the those who are using the OPT and Ohio
School Report Cards (OSRC) to assess public education in Ohio is the idea that district OPT performance is determined by one variable-- the teacher. Interestingly, the OPT proponents are often using the test more of an indicator of school district and teacher performance than of student performance as witnessed by the force of the Ohio School
Report Cards. The results of this study show that neither student academic learning, school district effectiveness, nor teacher effectiveness are validly measured by these tests.
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Indeed, the findings indicate that OPT results and OSRC ratings are, in most cases, extremely misleading at best.
Contained within the subsequent sections of this study are the primary and secondary findings of the study. Each section covers a particular variable or related set of variables and uses graphs and narrative to attempt to explain the meaning and the significance of the findings being discussed. Though the primary research interest motivating this study is OPT district-level performance, this study would be incomplete without some analysis and discussion of the Ohio School Report Cards since OSRC is driven primarily by OPT district-level performance. Therefore, there is a section dealing with the validity problems of OSRC as related to the primary findings of the study of OPT district performance.
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Section Two
Frequently Asked Questions and Explanation of Terms
The following are very brief summaries of key elements of the study. Each item presented below is explained in greater depth within the text of the study itself.
• What did the study involve?
Briefly stated, this research study involved the examination of 593 Ohio school districts across 40 variables using 16 sets of OPT scores for each school district. All data were collected from EMIS online data banks and the data were analyzed using statistical methods such as regression analysis and correlation analysis. Both school and non-school variables were used.
• What is the purpose of this study?
The purpose of the study was to attempt to identify both school and non-school variables most significantly associated with district test performance in order to illuminate the degree to which OPT is a valid and reasonable mechanism for assessing school performance in terms of academic achievement and educator accountability. Similarly, an attempt was made to isolate and examine any variables found to be likely significant in contributing to actual district performance.
• What is the difference between a school variable and a non-school variable?
School variables are those forces and factors that schools can control and adjust such as class size, per pupil expenditure, and teacher salary among many others. Non-school variables are forces and factors over which schools have no control such as mean family income, property values, and poverty levels among many others.
• What are the primary findings?
The study found that OPT district test performance is most strongly connected to the living conditions and the lived experiences of the students in terms of economic, social, and environmental factors. District test performance was found to correlate extremely high with advantagement-disadvantagement: The greater the wealth of the students of the school district, the better the district OPT performance. In this study, the term "Presage
Factor" is used to indicate the social, economic, and environmental variables of advantagement-disadvantagement.
The findings also show the Ohio School Report Card to be equally as invalid as OPT performance. This finding is not too surprising when we consider that OPT performance is the primary element that drives OSRC ratings. In other words, if OPT does not carry significant validity, then OSRC will not either because it is primarily a function of district
OPT performance.
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• What exactly is the Presage Factor?
The Presage Factor is a combination of the Ohio Department of Education’s online
EMIS variables that represent measures of advantagement-disadvantagement. It combines the following EMIS measures: percent ADC, percent enrolled in the subsidized school lunch program, percent economically disadvantaged, and mean family income. These variables are combined in a very straightforward manner using a simple calculus to derive a scaled measure of advantagement-disadvantagement. Section Three gives the precise formula for calculating the Presage Factor.
• What is meant by advantagement-disadvantagement?
Advantagement-disadvantagement is intended to represent the continuum of social-economic forces and factors that are indicated by the Presage Factor. They are the forces and factors that shape the lived experience of all children. The knowledge, culture, values, attitudes, and meanings that children bring to school are a largely shaped by their lived experiences. This particular term is not used the same way as the terms “educationally disadvantaged” or “educationally advantaged.” These terms refer to how schooling itself, through its practices and processes, is structured to reward or punish students for the knowledge, values, and cultural meanings they bring to school.
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• What are linear regression and statistical correlation?
Linear regression is used to examine the relationship between two variables such as the Presage Factor and the percent passing the OPT. Basically it allows us to perceive how the change in one set of variables relates to corresponding change in the other set of variables. Statistical correlation then allows us to determine the strength of the relationship between the two sets of variables. The correlation used in this study is called
"Pearson's correlation" or "Pearson's r."
It is this correlation that tells how significant the association is between the sets of variables. Correlation analysis yields what is called the "correlation coefficient" or "r."
The range of "r" is from -1.0 to 1.0. The closer that "r" is to -1.0 or 1.0, the stronger the relationship between the two sets of variables being analyzed. For example, where r=1.0, the correlation is perfect... where r=0.0, there is no relationship whatsoever. In cases where "r" is negative, the correlation is said to be inverse, meaning that as the value of one variable increases, the value of the other decreases. (See the graph of percent passing and
4 See P. 222 in Kretovics, J., Farber, K., & Armaline, W. “Blowing the Top off Urban
Education: Educational Empowerment and Academic Achievement” in Journal of Curriculum and Supervision, Spring 1991, Vol. 6, No. 3, 222-232.
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percent ADC for an example of an inverse correlation.) In cases where "r" is positive, as the value of one variable increases so does the value of the other variable.
In social science research, a perfect correlation is rarely, if ever, found. Indeed, correlations approaching either r=-0.50 or r=0.50 are usually considered relatively significant. It is suggested that you consult a good statistics text for better understanding of the details and assumptions involved with regression analysis and correlation. It needs to be noted that the primary finding of this study regarding the relationship between advantagement-disadvantagement and OPT district performance is r=0.80, a significantly high correlation by any statistical standards.
• What are residuals?
A residual is the difference between what the linear regression predicts a given value will be and what the value actually is based upon the line generated by the mathematics of linear regression. It is essentially the mathematical distance of a data point above or below the regression line. In the case of this study, district residuals from the Presage
Score/Percent Passing regression are used to postulate actual performance. Doing this gives us some idea of performance controlling for the Presage Factor.
• What exactly is a z-Score and why use it?
A z-score (often called a "standard score") is a transformation of a raw score into standard deviation units. Using z-scores allows us to immediately know how far above or below the mean is any given score, thus allowing us to visualize how extreme the score is relative to all other scores. The mean of any z-score distribution is always zero. Using zscores does not alter the distribution of scores in any way and does not affect the analysis or the findings. Converting to z-scores is a linear transformation and does not change the results of the data analysis in any way other than to make the data more understandable.
The advantage of the z-score is found in allowing us to understand one score relative to other scores. For example, the Presage score as a raw score for Youngstown City School
District is -173.08, which does not tell us how extreme the disadvantagement is. The
Presage z-score for Youngstown is -3.82, which tells us that it is 3.82 standard deviations below the State average, thus allowing us to see that Youngstown's students are very deeply in social-economic disadvantagement.
• What exactly is standard deviation?
Most simply put, standard deviation describes how a set of scores is distributed around the mean of the set. For use in this study, basic knowledge of standard deviation is helpful in reading and understanding the z-scores. Z-scores tell us how many standard deviations above or below the mean a score is. Z-scores greater than 1.0 or lower than -1.0 suggest more significant scores beyond those within 1.0 and -1.0. In the case of reasonably
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normal distributions such as with the data in this study, approximately 68% of the scores will fall within the 1.0 and -1.0 range of the first standard deviation and 95% of the scores will fall within the limits of the second standard deviation. Scores in the third standard deviation may be thought of as being extreme. Thus, the example of Youngstown given above as having a Presage z-score of -3.82 tells us that it is a case of children living in extremely disadvantaged environments.
• How significant or powerful are the findings?
The correlation between the measure of advantagement-disadvantagement (Presage
Factor) and OPT performance are extremely high (r=0.80). Indeed, these findings about this relationship are about as high as are ever found in social science research. . . the findings are very significant both statistically, conceptually, and practically.
• Can OPT scores be raised through school interventions?
The question as to whether OPT scores can be raised can certainly be answered in the affirmative, though it is not considered within the study. However, any educational imperative to raise scores must not be based on an invalid test nor must it be directed toward any form of high stakes testing. Instead, it must be driven by the vision of empowerment, the idea that what students are taught in schools must be personally experienced by the students. Knowledge must be taught in such a manner that it is felt as relevant and usable in the mind of the learner. To empower learners requires constructing learning activities that become personally felt lived experiences for the students in the classrooms, not abstract rote exercises over facts and ideas that the students perceive as meaningless and irrelevant. The usability of academic knowledge must be taught by the teachers and must be experienced by the students if we are to empower learners and raise scores significantly.
• What do the findings tell us about the validity of the OPT as an assessment of
academic achievement?
The findings tell us that OPT performance is in no manner a valid measure of academic achievement: The OPT measures almost exclusively only the quality of life in which the students of the district live.
• What do these findings suggest about the validity of the Ohio School Report Card?
The findings tell us that the Ohio School Report Card, because it is almost entirely based upon OPT performance, is a totally invalid assessment of actual school district performance and should not be used. OSRC is extremely misleading, and the general public should be outraged about its use. Likewise, the State Legislature and Governor should be held accountable for misleading the citizens of Ohio and using state monies for such an invalid assessment of school district performance.
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• What do the findings tell us about accountability on the part of districts,
administrators, teachers, and Ohio's public school pupils?
Accountability is the least understood term in the American political lexicon. For true accountability to be invoked, we must understand that valid accountability is a function of the decision latitude and amount of performance control vested in those being held accountable. In other words, it is wrong to hold districts, administrators, teachers, or students accountable for a test that measures variables over which they have absolutely no control. This study finds beyond the shadow of any doubt that the OPT is not a measure of virtually anything related to in-school variables; it is a measure of non-school variables, forces, and factors. Therefore, to hold those associated with schools accountable for OPT performance is absurd and wrong. It is tantamount to holding the TV weather person accountable for today’s weather.
• From this study, is it possible to assess with some degree of validity the actual
levels of Ohio school district performance?
The answer here is both yes and no. It is "yes" in terms of knowing that the Presage
Factor is so very powerful that if we control for its effects, we begin to get a much clearer and certainly much more valid picture of how each district is actually performing. It is "no" in the sense that this performance even controlling for the Presage variables still is primarily based upon the OPT itself. To assess school district performance using the OPT would be foolish and wrong in that it is the public school student who suffers most from the test. In other words, why hurt and mislead the children and parents of Ohio to assess district performance using an invalid instrument.
• When will copies of the findings be made public?
The study itself was officially released to the public and media as of 12:01 AM,
February 27, 2000. On April 27, 2000 the findings will be presented at Ohio's Teaching
Learning Conference 2000 in Columbus. The presentation is scheduled for 8:45 am, tentatively in Room C214 in the Columbus Convention Center. Copies of the final study will be available at this presentation and is available online March 1, 2000 at http://cc.ysu.edu/~rlhoover/OPT
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Section Three
The Primary Findings:
Advantage-Disadvantage as Predictor of District Performance
The fundamental purpose of this study was to examine what forces and factors may be affecting district-level performance on the Ohio Proficiency Tests and to attempt to determine to what degree these variables shape district-level performance. To this end, two categories of variables were used: school variables and non-school variables. School variables are those forces and factors that schools can control and adjust such as class size, per pupil expenditure, and teacher salary among many others. Non-School variables are forces and factors over which schools have no control such as mean family income, property values, and poverty levels among many others.
As briefly discussed previously, the primary finding is that OPT performance is affected most significantly by non-school variables representing the lived experiences of the children attending the school district. The lived experiences of children come from and happen within the advantagement-disadvantagement of their environments. These experiences of real-life are non-school variables that clearly shape how, what, and whether a child learns.
The term “Presage Factor” was chosen to indicate the data used collectively as a measure of the non-school variables that serve as the indicator of the degree of advantagement-disadvantagement experienced in the lives of the district’s school children.
The term was chosen because the word “presage” means to predict, foresee, or foreshadow, which is what knowledge of basic living conditions within the district allows us to do with OPT performance when we can mathematically quantify elements of those basic living conditions.
The graph below shows the power of the Presage Factor as a measure of advantagement-disadvantagement in predicting district OPT performance. The "Y" axis represents the mean percent of a district's students passing across the four sections of the
4th, 6th, 9th, and 12th grade 1997 Ohio Proficiency Tests:
• %Passing = [(%4Math + %4Reading + %4Writing + %4Citizenship + %6Math + %6Reading +
%6Writing + %6Citizenship + %9Math + %9Reading + %9Writing + %9Citizenship +
%12Math + %12Reading + %12Writing + %12Citizenship)/16].
The "X" axis represents the Presage Factor expressed in raw scores. The presage score is a measure of the degree of social-economic disadvantagement-advantagement
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derived from EMIS data 5 that combines the percent of the student population of the school district for Aid to Dependent Children, percent enrolled in the Free or Reduced Lunch
Program, percent listed by the State of Ohio in Economic Disadvantagement, and Mean Family
Income.
6 The formula or algorithm for the Presage Factor is:
• Presage Score = (%Free/ReducedLunch + %ADC + %EcoDis) - (MeanFamInc/1000)(-1).
From the data analysis represented in the graph below, we find that performance across the 593 Ohio districts included in this study is associated with non-school environmental conditions of advantagement-disadvantagement to the extent of r = 0.80. This is an extremely high correlation and clearly brings the validity of OPT into serious question.
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Interpretation of the correlation coefficient of r=0.80 tells us that, conservatively, the non-school related effects of advantagement-disadvantagement defined by the Presage
5 http://www.ode.ohio.gov/www/ims/extract_emis_profile.html and http://www.ode.ohio.gov/www/ims/extract_vitals_data.html
6 Specific definitions and the manner in which they are calculated from district “Student
Aggregation Records” may be found in the online EMIS Manuals.

Factor determine 7 64% of OPT performance. It is important to note that this 64% determination is restricted to the effects of the Presage Factor and, by definition, does not include other possible advantagement-disadvantagement effects outside the realm of those included in the Presage Factor.
Indeed, the idea that advantagement-disadvantagement limited to the scope of the
Presage Factor determines 64% of OPT performance is a conservative interpretation of the overall power of social-economic living conditions because it may well be excluding other significant non-school forces and factors. There is a real possibility that there are still social-economic effects beyond the range of those comprising the Presage Factor, though extremely powerful in its own predictive power. For more possible insights to additional non-school variable effects beyond those within the scope of the Presage Factor, see the sections on “Actual District Performance: Controlling for the Presage Factor” and “Percent
African-American and Percent White as Variables Across Presage Score, Percent Passing, and
Actual Performance.”
Because of the discovery that OPT performance is overwhelmingly determined by the social-economic living conditions that the students of the district experience growing up, the inescapable conclusion is that OPT is not a valid measure of either school or teacher effectiveness and should not be used for accountability assessment. The OPT is invalid because the results of this study show that it does not measure what it claims to measure:
Student performance on the OPT is, at best, academically meaningless. It is highly biased against economically disadvantaged students and highly biased in favor of economically advantaged students.
Using z-Scores for Graphs:
A z-score (often called a "standard score") is a transformation of a raw score into standard deviation units. Using z-scores allows us to immediately know how far above or below the mean is any given score, thus allowing us to visualize how extreme the score is relative to all other scores. The mean of any z-score distribution is always zero. Using zscores does not alter the distribution of scores in any way and does not affect the analysis or the findings. Converting to z-scores is a linear transformation and does not change the results of the data analysis in any way other than to make the data more understandable.
The advantage of the z-score is found in allowing us to understand one score relative to other scores. For example the Presage score as a raw score for Youngstown City School
7 This conclusion is drawn from the coefficient of determination (r2) derived by squaring the correlation coefficient derived from the Pearson Correlation procedure (r=0.80):
0.802=0.64.
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District is -173.08, which does not tell us how extreme the disadvantagement is. The Presage z-score for Youngstown is -3.82, which tells us that it is 3.82 standard deviations below the
State average, thus allowing us to see that Youngstown's students are very deeply in socialeconomic disadvantagement. Likewise, the presage score for Indian Hill Exempted School
District is 164.76, a figure that alone tells us little about the meaning of the score. However, the z-score for Indian Hill is 4.37, which tells us that it is a very advantaged district.
Most simply put, standard deviation describes how a set of scores is distributed around the mean (average) of the set. For use in this study, basic knowledge of standard deviation is helpful in reading and understanding the z-scores. Z-scores tell us how many standard deviations above or below the mean a score is. Z-scores above the mean are positive numbers and those below are negative numbers.
Z-scores greater than 1.0 or lower than -1.0 tell us that these scores are significantly more extreme than those within 1.0 and -1.0. In the case of reasonably normal distributions such as with the data in this study, approximately 68% of the scores will fall within the 1.0 and -1.0 range of the first standard deviation, and 95% of the scores will fall within the limits of the second standard deviation. Scores in the second standard deviation are more extreme than those in the first standard deviation, and those in the third standard deviation may be thought of as being very extreme. Thus, the example of Youngstown given above as having a Presage z-score of -3.82 tells us that it is a case of children living in extremely disadvantaged environments relative to what is typical within the State of Ohio.
The following graph is a z-score version of the previous graph showing the relationship between percent passing and the presage score. Both percent passing and the presage scores 8 have been transformed into z-scores. You will note that the graph is virtually identical to the previous one and has exactly the same correlation coefficient
(r=0.80). However, because we now have z-scores to view, we can easily see the categories near, above, or below the mean for each district.
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8Presage z-score: [(%Free/ReducedLunch + %ADC + %EcoDis) - (MeanFamInc/1000)(-1) ] - 15.36 /41.26
∑ V
123
- (V
4
/1000)(-1)
]
- µ
______________________ = PF
S
Where V1 = %Free/ReducedLunch; V2 = %ADC; V3 = %Economic Disadvantaged; V4 = Mean
Family Income, S = standard deviation and
µ = population mean.

In addition, categories of advantagement-disadvantagement have been added to the graph using the z-score divisions of standard deviation. The center column “Middle Class” is divided down the middle by the mean (average) for the state. Using the z-score divisions for standard deviations above and below the mean, we can then classify levels of advantagementdisadvantagement based upon those mathematical divisions, thus making it more clear as to just how the different districts can be seen to compare with each other.
9 Youngstown City and Indian hill districts that were used previously as examples of z-scores are both circled on the graph, showing the z-score significance visually.
Though categorical descriptors have not been added to the x-axis, we can still see how far above or below the state mean the various districts fall. If we were to create a grid by marking off the z-score standard deviations for the percent passing 1997, we would see
9 These classifications by standard deviation represent descriptions relative to the forces and factors included within the Presage Factor and do not necessarily represent any particular agreed-upon cut-off points outside the purposes of this study.
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that districts cluster in very similar ways where passing and presage scores have similarly high or low z-scores. This grouping is simply another way of seeing how districts with higher levels of advantagement cluster with higher levels of percent passing as low advantaged districts cluster with low percent passing. Once again, note how Youngstown City and Indian
Hill are respectively low-low and high-high within the clusterings that are shaped by the data as arrayed by z-score graphing.
Data Supporting the Presage Factor Significance:
What is somewhat unusual is that the variables combined through the calculus of the presage formula yield a more powerful predictive correlation than do any one of the individual variables used in the formulation. However fortuitous, it is important and illuminating to understand the significant degree to which district test performance is predicted by the individual variables of Free/Reduced Lunch enrollment, ADC, Economic Disadvantagement, and
Mean Family Income. The following four graphs visually represent these component variables used in the presage formula. I believe they help us understand the gravity of using tests such as OPT where the bias is so clearly shown.
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The graph of percent enrolled in the free/subsidized lunch program shows an inverse correlation of r=0.73, which should be considered an extremely significant correlation. It is an inverse correlation simply because as the percent enrolled in the program increases,

district performance drops. The primary evidence the finding provides is to validate the association of test performance with a specific measure of advantagementdisadvantagement.
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The State of Ohio’s own measure of economic disadvantagement also shows significant correlation with district OPT performance. As with the previous graph, the correlation is inverse, telling us that as the percent of economic disadvantagement goes up, district test performance goes down.

The graph of mean income provides us with both a significant correlation and a telling view of the mean income data itself. The correlation between the mean income of a district and OPT performance is r=0.58. Though lower than the correlations seen in the previous findings, r=0.58 is still a highly significant correlation coefficient. In terms of the coefficient of determination (r2), we find mean family income conservatively determining about 33% of district OPT performance.
However, the distribution is somewhat curvilinear. A curvilinear distribution is one in which the distribution points have a visible curvature of some sort. The curvilinearity is visible in the mean income graph as the array of points can be seen to bend to the right toward the quadrant formed by the above-average mean income and above-average district performance area of the graph.
Two findings can be drawn from the curvilinear spread. The first finding is the statistical reality that because the data array is clearly curvilinear, the correlation coefficient is underestimating the degree of association between the two variables. This means that the correlation coefficient of r=0.58 is most likely considerably lower than the actual degree of correlation. In other words, though r=0.58 is a relatively high correlation, it
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belies the reality of there being actually a higher correlation than seen due to the curvilinearity.
The second finding is serendipitous to the study but both relevant and interesting taken within the context of OPT and the effects of non-school variables on district performance. In examining the curved nature of the data, we can see implicit evidence of how mean income changes dramatically as we move from the upper middle class to the upper classes.
Because income distribution is the primary determiner of relative advantagementdisadvantagement disparity, the decision was made to examine how the continuum of advantagement-disadvantagement has been shaped by mean income changes over the past ten years and how it may have exacerbated the extremes of poverty and wealth affecting the lived experiences of Ohio’s children.
In other words, because we can think of the presage score as representing a point on the continuum of advantagement-disadvantagement and because the range (length) of that continuum represents the scope of disparity in living conditions, we can examine how that scope may have changed over the past several years. The relevance of this side-bar analysis to this study is to provide a context for better understanding who is intrinsically advantaged and who is intrinsically disadvantaged by OPT and how those may have changed as a function of the distribution of wealth over the past few years. The following graph shows how district mean family income has changed over the years 1987 through 1998.
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This graph shows how district mean family income changed from 1987 to 1998 in terms of the presage scores. The most striking finding is that income increased far greater for the wealthiest districts than for the less wealthy ones. Indeed, when the graph is examined closely, we see that increases in family income are relatively slight from the extremely disadvantaged upward through the middle class until we reach the upper end of the middle class and into the advantaged class, where it changed dramatically.
The most contrasting districts have been identified on the graph to better understand the extremes of the advantagement-disadvantagement continuum. As would be expected given the power of the Presage Factor, the mean percent passing for the 5 districts with the greatest increase in mean family income is 91.4%; the mean percent passing for the six districts with the least change in mean family income is 52.9%.
What the comparison in the above paragraph tells us is that OPT is very tightly tied to an explicit association with wealth. The degree to which the association with wealth is a function of living conditions and the lived experiences of the district’s children is told in the elements that comprise the Presage Factor and their individual contributions shown in this section above. However, the question also arises as to the degree of local financial
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contribution to the local districts funds given the wealth available to commit funds. Findings regarding funding variables will be examined briefly in Section Five, after examination of district performance controlling for the effects of the Presage Factor in Section Four.
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Section Four
Actual District Performance:
Controlling for the Presage Factor
An interesting way to examine district performance is to look at is controlling for the effects of the non-school forces and factors that comprise the Presage Factor. The concept of actual district performance reflects the idea that once we are able to establish the effects of the Presage Factor on district performance, we then are able to compare the predicted rate of passing with the actual rate of passing given the presage score for the district. In this sense, we are controlling for the effects of advantagementdisadvantagement for each of the 593 Ohio school districts and seeing OPT performance through a very different lens than does the State of Ohio.
In other words, since we know the power of the Presage Factor’s effect (r=0.80) and that most conservatively it accounts for 64% of the test performance, we can then examine district performance controlling for the Presage Factor’s effects by comparing the predicted passing rate to the actual passing rate. We then compare those performances.
The following is a graphing of what I term “actual” performance because it shows how districts are performing with the social-economic factors contained the Presage factor removed.
10 Essentially, it is a graph that indicates how far districts are above or below the regression line shown in the primary graph of “Advantagement-Disadvantagement as a
Predictor of District Performance.” (See Section Three.) The distance above or below the regression line of the aforementioned graph is termed a “residual” and represents the difference between where we would expect a district to fall based upon the predictive power of the Presage Factor and where the district actually falls.
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10 A list of the highest performing Ohio districts may be found in Appendix B. Only the top
204 districts are given because I do not wish to have these data used inappropriately against any Ohio school district.

• The upper left quadrant represents districts that are performing average or above average and have average or below average levels of advantagement.
• The upper right quadrant represents districts performing average or above average and have average or above average advantagement.
• The lower left quadrant represents districts that are performing average or below average and have average or below average advantagement.
• The lower right quadrant represents districts performing average or below average and have average or above average advantagement.
• The greater the distance above or below the x-axis (the horizontal red line), the more the district is performing respectively beyond or below what would be expected given the presage score of the particular district.
• Districts falling between +1 and -1 on the x-axis are all within one standard deviation of the mean and may be considered as having average performance that is about where we would expect them to perform.
• Any district above the +1 mark above the x-axis is performing significantly better than average and better than would be expected. Likewise, any district below the -1 mark below the x-axis is performing significantly lower than average and lower than would be expected.
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This graph shows that when the district OPT performance residuals (actual performance) from the primary graph are themselves compared to Presage levels, there is no correlation whatsoever. This is one of those rare cases when a low or zero-order correlation is good. What is shown is that actual district performance (performance determined by controlling for Presage Factor effects) is, indeed, free of any and all Presage
Factor effects as we would expect. This graph offers us a view from which we can examine district performance without having to be misled by Presage Factor effects. However, several caveats must be made to avoid misunderstanding actual performance.
• The term “actual” is restricted to describing performance controlling only for the
Presage Factor. There should be no claim made that this performance has any substantial validity beyond simply correcting for the bias of social-economic advantagement-disadvantagement as defined by the Presage Factor. However, we can and do argue that it does give a much more accurate view of district performance than does the format used by the State of Ohio.
• Though we are now able to view district performance free of Presage Factor effects yet through the lens of OPT, this study does not in any way wish to imply that OPT should ever be used as any measure of district, teacher, or pupil accountability whatsoever.
Indeed, this study does not address the severe psychological effects of OPT on Ohio’s public school children. Likewise, this study does not address the pedagogical effects of
OPT in terms of detrimental effects on empowering curriculum and instruction in Ohio’s schools. Carefully conducted studies of the psychological effects and the pedagogical effects of OPT are vital before any consideration is given to using any form of high stakes testing to draw conclusions about school district performance.
Some of the performance differentials between the OPT as reported by the State of Ohio and scores adjusted for the bias of the Presage Factor are striking. For example,
Youngstown City Schools ranks 581 out of 593 districts in percent passing the 1997 OPT.
However, when correcting for the bias of advantagement-disadvantagement, Youngstown’s rank is 58 out of 593. Youngstown City School District is in the top 10% in the rankings of actual performance. Indian Hill Exempted School District is 16th in percent passing 1997
OPT, but falls to 581 when correcting for advantagement-disadvantagement.
In the case of Youngstown City, we have a district that is steeped in disadvantagement as defined by the Presage Factor, and in the case of Indian Hill we have a district steep in advantagement. We know through the primary finding that the presage effects predict a low rate of passing for Youngstown City and a high rate of passing for
Indian Hill. However, by calculating the difference between the predicted rate of passing and the actual rate of passing, we can see the degree to which they are performing above or
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below expectations established by the power of the non-school variables of the Presage
Factor. Thus, we now have a new way to assess district performance, one that compares districts without the bias inherent in the non-school variables contained in the Presage
Factor.
Some districts show little or no change when correcting for Presage Factor OPT bias. For example, South Range Local School District is 15th in percent passing the 1997
OPT, yet is 3rd in actual performance when correcting for advantagementdisadvantagement. In other words, South Range’s performance is high in both systems.
The importance of what this aspect of the analysis shows us is that viewing or ranking districts without considering the social-economic bias of the OPT results in many districts being extremely over-rated or extremely under-rated. In this manner, the stakeholders of the state in general and the stakeholders within the local districts in particular are often being given monumentally misleading assessment information.
Unfortunately, this misleading information is used to drive public praise or public criticism of Ohio’s local schools. Indeed, many Ohioans are keenly interested in their public schools but are relying on invalid information to make informed decisions directly affecting the lives of both adults and children.
The problem resulting from failure to understand or correct for OPT bias is compounded through the format of the Ohio School Report Card. Because so many of the performance standards comprising the OSRC are directly dependent upon the percentages of students passing the various tests, the fundamental and significant bias of the tests carries over directly into the OSRC ratings. Section 9 of the study deals briefly with the validity of the Ohio School Report Card as affected by the findings about OPT performance.
Understanding the Next Sections:
We have now completed the basis for understanding the presentation and findings regarding the additional EMIS variables used in this study of forces and factors affecting
OPT district performance. In the following sections, these additional variables are presented using three perspectives on the data:
• The variable in relation to the Presage Factor.
• The variable in relation to percent passing the 1997 tests.
• The variable in relation to actual performance as defined in this section.
Using these three perspectives gives us a triangulation that illuminates the role of the particular variable beyond simply its association with passing rates that are biased by the loading of the non-school effects of advantagement-disadvantagement. Thus the interpretation of each of the following variables is intended to provide deeper insight into the significance and meaning of the variable as it affects or is related to the context of
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OPT performance. The following variables are examined and interpreted in the subsequent sections of the study:
• Section 5: Federal, State, and Local Funding
• Section 6: Teachers
• Section 7: African-American and White
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Section Five
Federal, State, and Local Funding Variables
This section addresses the role of federal, state and local funding percentages in terms of the Presage Factor, OPT performance, and actual OPT performance as defined in
Section 4. Funding has historically been a source of contention across the arguments of stakeholders regarding school and district effectiveness. The major reason the arguments have continued is that there has not been a valid outcome measure of effectiveness against which to base the arguments. With the institution of OPT, critics of schooling in Ohio have used percent passing as the outcome measure to support their claims. In doing so, these critics have assumed that OPT is a valid measure of academic achievement and a valid measure of professional accountability.
It is now important to realize that the assumptions about funding using OPT as the bottom-line are invalid because of the bias found in this study. However, knowing the bias and being able to triangulate funding effects using presage scores, percent passing, and actual district performance does tell us what we claims cannot be made and does illuminate possible effects of funding beyond current public discussions.
• Federal Revenue Effects
The first three graphs in the set below examine the percent federal funding a district receives and its association with district performance, Likewise, the subsequent sets of graphs examine state and local funding in the same manner.
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This first graph above tells us clearly that federal funding is inversely correlated with advantagement-disadvantagement (r=-0.80). This correlation obviates the reality that disadvantaged districts are more eligible for federal funding than are more advantaged districts because of federal funding criteria.
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0.65), which tell us the greater the amount of federal funding, the lower the district OPT
Percent passing OPT and federal revenue show a significant negative correlation (r=performance. Taken at face value outside the context of the study’s findings, it would seem that federal funding has, at best, no effect on district performance and at worst a negative effect. However, this would be a superficial and terribly inaccurate interpretation given what we know about district OPT performance in relation to the elements of the Presage
Factor.
Since we already know the bias of OPT against disadvantaged districts and because we know that disadvantaged districts receive far more federal revenue than advantaged districts, the results tell us nothing about the real effects of federal revenues. Thus, the primary finding here is that comparing federal revenue and district test performance tells us nothing about the effectiveness of the federal funding. Indeed, it is entirely possible, and I believe probable, that federal funding is tremendously beneficial to disadvantaged school districts.

Comparing actual performance as defined in Section 4 to federal revenue gives a correlation that is not statistically significant. However, the fact that there is no significant correlation considered along with the fact that the more advantaged districts do not receive any substantial federal funding suggests something potentially significant.
Remembering that actual performance controls for the bias of disadvantagement and that federal funding is a function of disadvantagement, a low correlation would be expected.
However, given that approximately 50% of the districts falling within the range of disadvantagement are performing at or above what would be expected of them, it is entirely possible that this performance may be helped by the federal funding in those districts. We can make no claim about federal funding being ineffective from the findings and must reserve judgment for further study to determine potential positive effects in terms of actual test performance. In other words, without federal revenue, it is entirely possible that actual district performance would be far below what is seen for disadvantaged districts.
• State Funding Effects
The next set of graphs does with state revenues what was done with the federal revenues discussed above. Likewise, the findings are somewhat similar to the findings for the effects of federal revenue.
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Percent state revenue and presage scores have an inverse correlation (r=-0.51).
Though less than the correlation found for federal revenue, the correlation is more than likely due to the same phenomenon. This is so because, despite the controversy regarding the inequity of Ohio’s funding formula, the state does fund in a compensatory manner relative to the advantagement-disadvantagement of the local districts. In general, under
Ohio’s current funding formula economically disadvantaged districts do receive greater state subsidies than do economically advantaged districts.
Because state funding is not as compensatory as federal funding, more advantaged districts acquire more total funding when consideration is given to the additive factor of local funding. Therefore, the first graph suggests that, like federal funding, state aid may be viewed to a large degree as a surrogate measure of the presage variables. Therefore, on this basis, the inverse correlation is what we would expect to find.
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Percent passing and its association with the percent of state funding, again, is what we would expect given the bias of OPT in favor of more advantaged school districts.
Likewise, the correlation coefficient is lower than the federal one because Ohio’s school funding formula is less compensatory than the federal formulas for all programs combined.
The finding must not be construed that state funding does not contribute to district effectiveness.
The correlation coefficient for actual performance (r=-0.10) and percent state revenue is not statistically significant. Just as with the interpretation given previously for the correlation of percent federal revenue, the effect of state funding on actual
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performance may be suggesting that it does have an effect that would more than likely be conspicuous by its absence but not provable by its presence because of OPT’s lack of validity and the compensatory relationship of state funding to factors associated with the elements of the Presage Factor.
In other words, without the existing levels of state funding, it is entirely possible that actual district performance would be far below what is seen for disadvantaged districts and possibly lower even for advantaged ones.
• Local Funding Effects
The set of graphs representing local revenue contributions stands in striking comparison to the two previous sets of graphs in their positive correlations. However, the same principles of interpretation apply to these findings as do the previous findings for federal and state revenues. It must be remembered that in these three sets of graphs dealing with federal, state, and local funding, we are dealing with percent of total funding, so the data and graphs have the common denominator of being complements of each other in terms of the total federal, state, and local funds equaling 100%.
31 would expect. Because district local revenue is a function of advantagement-
The correlation between presage scores and local revenue shows essentially what we disadvantagement with the percent of local funding increasing proportionally with the wealth of the district, this graph simply exposes the degree of that relationship. The data also does confirm the importance of understanding the power of local economics as related to school funding and advantagement-disadvantagement. Likewise, the finding also may point to possible questions of inequity in Ohio’s school funding program.

The correlation coefficient for percent local revenue and percent passing is, again, showing local revenue in its association with advantagement-disadvantagement given what know about the correlation of percent passing to presage effects and about local revenue being a function of advantagement-disadvantagement, as discussed previously. Taken only on its face, there appears to be the basis for the argument that increasing percent of local funding causes higher rates of passage. This claim of causality is not supportable given what we already know about the associations of higher levels of local funding being significantly correlated with higher levels of advantagement.
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Though there is a slight positive correlation between local revenue and actual district performance, it is not statistically significant. However, this finding lends support to the idea that actual performance as defined by controlling for presage effects is stable across the variables of federal, state, and local funding contributions. This stability across variables reinforces the proposition that the Presage Factor taps into a robust measure of predictive validity for district performance and also supports further the idea that actual performance is likely a function of school variables as opposed to non-school variables.
Additional Local Funding Variables
• Residential Valuation
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The graph of residential valuation as a percentage of total valuation shows a moderate correlation with presage scores. However, a visual examination of the data plots shows a rather non-linear pattern that is also not curvilinear in shape. The upper right quadrant representing both higher presage scores and a higher percent of residential valuation does have a visual linear shape. The finding here suggests that from what might be called the “middle class” and upward to the more advantaged districts have greater yield from residential valuations than do most districts. The spread in the lower end of the presage scores, although not analyzed separate from the other data, appears to be rather random with the lower left quadrant being those districts with low property values.

The correlation and shape of the data plots for percent passing and residential valuation are consistent with the correlation and shape of the graphing of first graph of this set. This graph simply lends support to the notion of the positive correlation between district advantagement and test scores.
The actual performance and residential valuation graph findings support the idea that actual scores created by controlling for the presage effects stands up to testing against residential valuation. In other words, it helps support the hypothetical validity of actual performance.
The Related Variable of Per Pupil Expenditure
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Though certainly not in the category of funding, per pupil expenditure in some ways can be seen to mirror funding and any discussion of revenue begs the question of spending.
Therefore the variable of per pupil expenditure has been included in the funding section to provide additional insight with regard to how funds are expended in terms of Ohio’s pupils.
• Per Pupil Expenditure
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The correlation between presage score and per pupil expenditure is slight, though positive. Because the distribution of data is skewed away from linearity, interpretation is difficult at best. However, considering the slight correlation and the distributions in the upper-right and upper-left quadrants, the array does show a very uneven spread of per pupil spending that may indicate inequities in Ohio school district funding.

the effects of per pupil spending except, again, in the upper right area of the data plots.
This graph clearly shows the general finding that there is little overall difference in
This area clearly has higher per pupil spending, somewhat higher percent passing, and it is congruent with the outliers seen in the data plot in the first graph of this set. The finding lends some support to the idea that there are some very wealthy districts in terms of advantagement and that these districts are showing more clearly in this particular analysis. supports the power of the Presage Factor as a relatively valid and stable indicator of OPT
Per pupil expenditure when viewed controlling for advantagement-disadvantagement bias. This is so because the correlation coefficient (r=0.04) is extremely low thus indicating
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a non-significant correlation even lower than the correlations of the first two graphs in this set.
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Section Six
Teacher Data
The following data analyze several variables directly related and indirectly related to the teachers in Ohio’s districts relative to the variables of Presage Factor, percent passing, and actual performance. It should never go unnoticed that classroom teachers bear the brunt of the accountability effects of using OPT as an assessment mechanism for teacher effectiveness via the district ratings of OPT. Likewise, stakeholders, the media. and educational administrators who accept OPT and OSRC at face value make classroom teachers the target of their focus when angry or frustrated about low district scores.
In my many and frequent discussions with classroom teachers, including those from districts where passing levels are above average, speak volumes to the problems OPT and
OSRC have created for classroom teachers. Almost without exception, they articulate how
OPT-driven management has taken from them the last vestiges of reflective, professional decision making about what is best for the children in their classrooms.
• Teacher Salary
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This graph shows the correlation between teacher salaries and the presage scores for the districts. In terms of the correlation coefficient (r=0.35), there is a moderately high degree of association between advantagement-disadvantagement and teacher salaries.
Simply put, we clearly see that teacher salaries increase as a function of the wealth of the districts. In terms of school variables, as opposed to non-school variables, this finding is significant in terms of understanding additional apparent inequities across Ohio’s school

districts. The finding here also underscores the problem of the spectrum of advantagement-disadvantagement when we consider the strong tendency for the more disadvantaged districts to have the most underpaid teaching staffs.
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The analysis of percent passing and teacher salary yields a moderately high correlation. This finding supports the notion from the graph of presage scores and teacher salary that advantaged districts tend to pay their teachers more than less advantaged districts. Likewise, the finding here supports the notion of OPT advantagementdisadvantagement bias because of the association of higher salary with higher percent passing. However, because percent passing is a function of OPT bias as established in the primary findings of this study, the claim that performance is a function of salary may be misleading.

In examining actual performance we see a slight correlation between teacher salary and performance. This finding suggests that to some degree, teacher salary is a positive school variable. We must remember that actual performance is derived from controlling only for the effects of Presage Factor. The finding is not absolute because we cannot declare actual performance to be a robust measure of real academic performance beyond its presage control. However, relative to the other variables run against actual performance, teacher salary has the highest correlation with the exception of extra academic performance which will be presented later in this section.
• Degree Status
The following sets of graphs examine district teacher degree status, the percent having no degree, bachelor’s degree, and master’s degree or higher.
Non-Degree:
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The analysis of the association of presage scores with the percent of teachers without a degree shows us that the correlation is not significant. However, the association of non-degree teachers tends to increase with greater disadvantagement.
The graph of the analysis of percent passing with non-degree teachers yields what we would expect given the finding of the Presage Factor. Since percent passing is so closely defined by presage effects, this graph supports the findings in the first graph of this set.
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Controlling for presage effects, this analysis of percent non-degree teachers across actual performance yields a non-significant correlation because r=0.02 is extremely low.
What minuscule correlation there is relates positively to increased actual performance.
However, no claim to any statistical significance can be made.
Bachelor’s Degrees:
The following graphs and analyses are best understood when examined in conjunction with the master’s degree graphs and findings because percent of teachers with master’s degrees and percent of teachers with bachelor’s degrees are fundamentally complimentary numbers, excluding the small percent of non-degree teachers. In other words, for any given district, the number of non-degree, bachelor’s degrees, and master’s degrees held by the teachers equals 100%.
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The graph of presage score and teachers with bachelor degrees shows a slight inverse correlation. This finding tells us that the percent of bachelor degrees decreases somewhat as advantagement increases. This finding in and of itself may be seen as somewhat puzzling until we examine the finding regarding the percent of teachers with master’s degrees or higher. (See the third set of graphs in this section.) Taken with the findings of the analysis of master’s degrees and presage scores, the conclusion is that as
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advantagement increases so does the percent of teachers with master’s degrees or higher.
11
44 clearly show the artifacts of OPT bias along the advantagement-disadvantagement
The findings of the analysis of percent passing and teachers with bachelor’s degrees continuum. The correlation (r-0.23) is moderately significant and does show the tendency of wealthier districts to have greater numbers of teachers with degrees beyond the bachelor’s level when we consider this finding along with the finding regarding master’s degrees as discussed above.
11 Master’s degrees or higher refers to teachers who have acquired their masters and those who have additional college credits beyond the masters or who have a doctorate.

Again, taking the finding of actual performance and teachers with bachelor’s degrees along with its complement of teachers with master’s degrees or beyond seen below, we find that actual performance does correlate inversely, though only slightly. (Refer to the discussion following the presentation of the graph showing actual performance and teachers with master’s degrees or higher for more interpretation of this analysis.)
Master’s Degrees:
The following graphs and analyses are best understood when examined in conjunction with the bachelor’s degree graphs and findings because percent of teachers with master’s degrees and percent of teachers with bachelor’s degrees are fundamentally complimentary numbers excluding the small percent of non-degree teachers. In other words, for any given district, the number of non-degree, bachelor’s degrees, and master’s degrees held by the teachers equals 100%.
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As we would expect, the percent of teachers with master’s degrees and beyond increases as a function of increase in advantagement. This finding is understandable in light of the extra expenditure required for paying salaries of teacher’s with graduate degrees.

The finding of a moderate correlation between percent passing and percent of teachers with master’s degrees is not unexpected given the very high correlation (r=0.80) between percent passing and presage scores. In other words, districts with greater advantagement are more likely to have more teachers with advanced college degrees than those with less advantagement.
The comparison of actual performance to the percent of teachers with advanced degrees shows a slight positive correlation. The analysis here tells us that within the previously discussed limits of actual performance in controlling for the presage effects, teacher’s having advanced degrees does contribute somewhat to actual performance.
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• Teacher Experience
The next section deals with the analysis of teacher experience across the variables of presage score, percent passing, and actual performance.
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The correlation between presage score and years of teacher experience is nonsignificant, though it shows a very slight inverse correlation that says there is a very slight tendency for more advantaged districts to have teachers with slightly less experience than less advantaged. However, the association is too low to support any claim other than there is no significant difference in the average years experience across Ohio’s school districts in terms of advantagement-disadvantagement.

The finding from the analysis of percent passing and teacher experience shows an almost perfectly random correlation. In other words, there is no difference whatsoever in terms of teaching experience and percent passing OPT.
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The analysis of actual performance and teaching experience shows a slight positive correlation. Though the correlation is slight, it nonetheless appears to be a possible contributor to academic performance when we control for the effects of advantagementdisadvantagement.
Related Variables:
Two variables related to teachers and teaching have been included in this section for possible illumination of the study’s findings. They are class size and extra-academic opportunities.
• Class Size
The EMIS provides two similar sources of information regarding teacher/student ratios, class size and teachers per 1000 students. Both variables yield almost exactly the same findings. Since class size is a more familiar concept than teachers per 1000 students, I chose to use it.

Class size is found to be inversely correlated to presage scores and is only slightly significant. This finding simply tells us that class size tends to be slightly lower the more advantaged the district is in terms of presage scores. size reiterate the relations between presage effects and percent passing.
Though slightly lower in terms of its statistical correlation, percent passing and class
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Analyzing class size and actual performance yields a non-significant correlation that approaches randomness in the relations between the two variables.
• Extra Academic Opportunities
The state defines extra academic opportunities as extracurricular activities that are academic in nature, such as debate team, French club, math club and similar activities open to student involvement outside the regular academic classes. Recreational and sports activities are not considered as extra academic opportunities.
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Analysis of the variables of extra academic opportunity and presage scores yields a moderate correlation. This means that extra academic opportunities increase as the advantagement as measured by the presage score increases.
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The correlation found with extra academic opportunities and percent passing is moderately high and tells us that the districts with greater numbers of such opportunities tend to perform better on OPT. However, it is important to remember that the bias of OPT toward more advantaged districts. Because of this, conclusions regarding the actual effects are somewhat unclear, but the suggestion that extra academic opportunities contribute to improving district test performance is evident.

The examination of extra academic opportunity and actual district performance shows a moderate correlation. This finding lends strength to the power of such opportunities in affecting actual test performance within the parameters of the Presage
Factor. As well, this finding suggests that the idea discussed in the previous graph that extra academic performance positively affects percent passing may have greater credibility.
• Teacher Data Comments:
Examination of the analyses and findings regarding the variables within this section on teachers as they may interrelate, indicates that most of the results are verifications of what we might expect given the power of the non-school forces and factors associated with district levels of advantagement-disadvantagement as described by the Presage Factor.
However, if we can accept that actual performance is indeed a usable measure of what may be happening academically in Ohio’s schools, several findings in this section suggest themselves as variables contributing to that performance.
Teacher salary, having a master’s degree or higher, years of teaching experience, and extra academic opportunities stand out as variables contributing to some degree to actual district performance. To examine further the efficacy of these variables, the four were converted to z-scores, added, and averaged to create a single measure. For lack of a better term, the combination into a single variable is called the “Teacher-Curriculum” variable (TC) to represent the domains of schooling from which the variables arise. The following three graphs examine the TC variable to better understand the potential of the four elements operating together.
52 associated with the advantagement defined by the presage scores.
The correlation with presage scores is moderate and suggests that the variable is

The teaching-curriculum variable attains an moderately high correlation when associated with percent passing. The degree of association is higher than with the presage scores as seen in the preceding graph, thus suggesting it is more closely associated with percent passing than with presage scores themselves.
Whereas three of the four variables that comprise the TC variable have less than r=0.15 correlation and the fourth variable of extra academic opportunity has a correlation coefficient of r=0.24, the combination of all four exceeds the average of the four coefficients. Again, it is important to remember that actual performance is a measure of
OPT performance controlling for presage effects that represent the overwhelming bias of
OPT. Another way to view this is to think of actual performance scores as possibly valid representations of academic performance. If this assumption is true, then the formulation of the TC variable begins to reach into the myriad of complex possibilities that shape authentic academic performance.
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The point here is not to posit a new way to assess district performance, but to demonstrate how complex the processes of teaching, schooling, and learning are in the real world of education. More specifically, the findings of this study to this point do not only tell us that OPT is a highly invalid assessment mechanism, but the findings also expose the tremendous complexity and difficulty of authentically and validly assessing academic performance on any level.
Even if the calculus used to formulate actual performance results in a valid assessment of district performance to a greater degree than does OPT, the problem still exists that it is based upon a high stakes test. The pressure to pass the test, the time spent practicing to take the test, and the denial of the credential of a high school diploma for those innocent children who often narrowly fail to make the cutoff score is all born by the children and parents of Ohio’s public school population. Thus, there is no suggestion whatsoever that the derived actual scores should in any way be used to hold anyone accountable because of the damage such testing does to the children and to the curriculum they should have the opportunity to experience.
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Section Seven
Percent African-American and Percent White as Variables
Across Presage Score, Percent Passing, and Actual Performance
The following are three sets of graphed data addressing how OPT may be seen to play out across African-American and White school district populations. The first set of graphs below compares the association between the Presage variable and the percent
African-American and the percent White of the district student population. The second set of graphs represents the percent passing per district as a function of percent African-
American and percent White student populations. The third set gives a comparison of performance controlling for the Presage variable. It is vital that these three sets of findings be viewed together for optimal understanding of the general comparative effects of OPT on these two groups.
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The first graph in the set tells us that there is a moderate negative correlation between advantagement and the percent African-American student population. The second one shows that there is a moderate positive correlation between advantagement and the percent White student population of the district. The graphs are essentially inverses of each other as would be expected because as percent White goes up, the percent of African-
American must go down and vice versa.
12
Most simply stated, these graphs tell us that the greater the White population of the school district, the greater the level of advantagement; the greater the African-
American district population, the greater the level of disadvantagement. Taken together, the findings support the argument that the effects of social-economic advantagementdisadvantagement are seen to a moderate degree in the racial composition of Ohio's school districts. The next set of graphs represents the findings of how the two populations are associated with OPT performance.
12 Races other than African-American and White have been omitted from analysis simply because their distribution across Ohio school districts is too few to yield any meaningful insights.
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This set of graphs shows us that there is a moderately significant differential between African-American and White performance on the OPT. The first graph in the set tells us that the greater the percent African-Americans in the district, the more likely fewer students will be achieving passing OPT scores. The second one shows the opposite for
White students. Again, the graphs are basically mirror images of each other, as noted previously.
Two points for objective interpretation are very important here. The first point is that the findings definitely reveal OPT bias against students in predominantly African-
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American school districts. It is also logically true that the findings may be interpreted as definitely revealing OPT bias in favor of predominantly White school districts. However, the second important point is that while the effects are real in terms of bias, it cannot and must not be concluded from this data array that the OPT bias is caused directly by racial differences between the two groups.
While the findings do show the racial bias to be real, attributing the bias to a specific source requires more critical examination of the data. This is so because the study’s primary and most powerful finding is that social-economic advantagementdisadvantagement is the most significant predictor of performance. In other words, the research question arises of whether the demonstrated OPT bias shown here is a function of social-economics or race, or both.
Indeed, it is somewhat interesting that the correlation coefficients for the first two sets of graphs are quite similar (r=-0.34, r=0.030 and r=-0.35, r=0.31). Considering the fact that we know from the primary findings of this study that the Presage factor is unusually powerful as a variable (r=0.80) of advantagement-disadvantagement for predicting
OPT performance, to determine first-order racial/cultural OPT effects, we need to examine actual OPT performance controlling for the Presage factor. In other words, the racial differential shown in this second set of graphs must be examined further before suggesting that it is caused by racial/cultural differences and not by the social-economic factors of the
Presage variable.
The graphs below show actual performance (performance controlling for the Presage variable) for White and for African-American populations and yield statistically nonsignificant effects in and of themselves. These non-significant effects are, however, very significant in understanding and knowing that when the factors of advantagementdisadvantagement as defined by the Presage Factor are removed, we find that race is not the primary factor affecting academic achievement in terms of district level OPT performance. However, given the correlations (r=-0.15 for African-Americans and r=0.11 for
Whites), we do see effects that could very well represent racial/cultural bias inherent in the tests.
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Though the correlations are low, the question does arise as to the source of why there would be any difference between African-Americans and Whites across OPT performance when controlling for the Presage Factor. Though nothing definitive about the primary source of the differential is immediately apparent, I would suggest two possibilities be given consideration.
The first possibility is that there are other social-economic effects showing up that are not within the scope of the Presage Factor that are experienced differentially by the two groups such as is seen in the group correlations in the first set of graphs. The second
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possibility and certainly the more serious of the two is that OPT contains significant racial/cultural bias.
My best professional judgment tells me that it is quite likely that the findings shown in the last set of graphs are related to racial/cultural OPT bias. I base this speculation on my experience and my intuition combined with the language/reading dependency of the test.
13 Minimally, these data call for a complete and thorough examination of OPT for racial/cultural bias if the State of Ohio wishes to make any claims of test validity.
Summary Reflections:
• Together, all three sets of comparisons re-confirm that the social-economicenvironmental factors that shape the conditions of advantagement-disadvantagement are the clear bias of OPT regardless of race. In other words, disadvantaged children are likely to perform more poorly on the test than advantaged children regardless of whether they are African-American or White.
• The first two sets of graphs do tell us that African-American children are more likely to suffer from the conditions of disadvantagement than are White children and that because of this powerful effect, far more African-American children are victims of OPT bias than are White children.
• The last set of graphs suggests that there is most likely some racial/cultural bias in the test. Though the effect of this bias on district level performance is significantly less than the effect of the Presage factor, it does make a difference especially in districts with high African-American student populations because passing rates load so powerfully on the Ohio School Report Card ratings.
• If indeed, the third set of graphed data is showing the artifacts of inherent racial/cultural bias, then the effects are particularly significant for individual African-
American students in Ohio's schools. My own professional judgment tells me that this scenario is probable given the legacy of racial/cultural bias in standardized testing. At the very least, these data indicate a moral responsibility and a legal obligation on the part of the State of Ohio to suspend testing until further study of the racial/cultural bias is openly and honestly conducted.
• It is vital to recognize that the data represented in all three sets of graphs removes from discourse and discussion the question of African-American students ability to learn as well as other racial groups. There is simply no evidence whatsoever to support any arguments regarding inferior academic performance. Therefore, for anyone to make the
13 Though not included here in graphic form, the correlation between reading performance and performance on the writing, citizenship, and math sections of the fourth-grade OPT is r=0.99.
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claim that African-American children do not have the same native ability as Whites in terms of academic achievement is as absurd as it is ignorant and racist.
• Given the previous point, it would be remiss to fail to point out that OPT district performance as reported by the state (See, “Percent Passing and Percent African-
American Students” in the second set of graphs in this section) makes it appear that
African-American students are academically inferior to White students. The findings of this study do not support this implicit claim made by the State of Ohio through the OPT and OSRC. Indeed, the data indicate the claim is totally false and dangerously misleading in its racial significance.
• Again, the findings reported in this section of the study indicate a moral responsibility and a legal obligation on the part of the State of Ohio to suspend proficiency testing until the possible racial/cultural bias is thoroughly examined and the misleading racial overtones found in the results are corrected.
61

Section Eight
Advantagement-Disadvantagement as a Predictor of
Ohio School Report Card Ratings
The Ohio School Report Card, 2000, correlates with the Presage Factor (r=0.78) almost as significantly as the 1997 OPT district performance does (r=0.80). Practically speaking, they are virtually the same. What this means is that OSRC carries with it the same advantagement-disadvantagement bias.
What these findings tell us is that to a very significant degree (conservatively,
60.1% based upon r=0.78), the OSRC reports social-economic living conditions of the district and not the academic growth of the pupils nor the effectiveness of educators in the
district. OSRC is open to the old computer adage of “garbage in, garbage out.” The fundamental unit of assessment that drives the OSRC ratings is the percent of the district’s pupils passing the OPT; if the unit of assessment is flawed, so are the cumulative results reported in the OSRC. Again, as with OPT itself, validity in the statistical/mathematical sense of tests and measurements is the flaw of the OSRC.
By the time OSRC ratings reach the public, they are impersonal representations of
Ohio’s school children framed invisibly by their very real lives on the spectrum of advantagement-disadvantagement. Yet, OSRC is used to reward or punish the very people who have to deal with the day-to-day reality of those children’s lives, educators being held accountable for that over which they have virtually no control or decision latitude whatsoever.
Given that the OSRC is aimed directly at assessing the district’s educators in general and its teachers in particular, the findings of this study point to the following advisories:
• Teachers and educators in districts rated low on the OSRC may, indeed, be performing extremely well such as is known from this study to be the case with Youngstown City
Schools as noted in the previous section on actual district performance. In other words, beware that there may be no validity whatsoever to the rating given by the OSRC that places our most disadvantaged districts in the “academic emergency” category.
• Teachers and educators in districts rated high on OSRC may, indeed, be performing nowhere near their potential. From the results of this study, this is shown to be the case with many OSRC top-ranked districts. In other words, just as noted above regarding underestimating low-ranking districts, the same caveat applies to the highest ranked districts.
62

• Teachers and educators in districts given OSRC ratings anywhere in-between the extremes of the “academic watch” and “effective” categories likewise cannot be said to be valid with any certainty at all. These OSRC mid-range school districts represent the vast majority of Ohio’s schools. Some of these districts are performing as claimed by
OSRC ratings. However, an equal number are either performing far below what might be expected and others far above.
There is an imminent reality implicit in this study that cuts directly to the assumptions and interpretation of OSRC as a measure of district educator effectiveness.
That reality is a judgment that I make with reflective confidence based upon these findings and upon my professional experience as a classroom teacher and a university teacher educator. My judgment is that if we were to ever switch the staff of a district rated high by the OSRC with that of a district rated low, in five-year’s time there would likely be no change whatsoever in the OPT ratings of either school district. Indeed, if any change were to be observed, it would most likely be that the scores of the low OSRC-rated district would drop slightly due to the highly rated educators having to deal with problems and issues in the lives of the children of the district that they have never experienced anywhere before in their professional experience.
63
Section 9

A Brief Closing Statement
The primary purpose of this study was to examine forces and factors that affect
Ohio Proficiency Test performance. The primary finding is that OPT is extremely biased across the elements defined within the parameters of the Presage Factor. The significance of the primary finding is that OPT is not a valid measure of either academic performance or school accountability at any level including the Ohio School Report Card ratings.
However, nothing within the study’s findings or inferences should be viewed as blaming or making excuses for students not learning, educators not educating, or districts not performing. On the contrary, the findings and inferences lead us away from excuse making into the realm of validly assessing accountability of actual academic and school performance. There is vast difference between an excuse and an explanation of OPT performance.
This study of OPT performance explains why scores are invalid regardless of social economic level. It is no more an excuse for poor performance than it is for high performance. Rather the findings show that regardless of social economic status, the results are not valid; OPT performance of advantaged districts is just as invalid as the performance of less advantaged districts. Indeed, when we control for social economic factors, the findings show that actual academic performance is evenly distributed across all levels of advantagement-disadvantagement. Children from disadvantaged environment are shown to be equally successful as those from advantaged environments.
Also, it was not the intent of the study to beg the question of educational accountability or academic standards. On the contrary, accountability and standards are both requisite to establishing a quality system of public schooling. However, it is incumbent upon stakeholders in general and state education policy makers in particular to establish assessments and standards that meet the well established standards for test validity and appropriateness. The simple irony implicit in the findings and inferences of the study is that the citizens of Ohio have a right to hold public schools accountable just as they have the right to hold accountable those who shape public school policy.
“The problem with truth is its verification, the problem with fiction is its veracity.”
64

65
School District County
Ada
Adams /Ohio
Adena
Akron
Alexander
Allen East
Alliance
Amanda/Clearcrk
Hardin
Adams
Ross
Summit
Athens
Allen
Stark
Fairfield
Amherst
Anna
Ansonia
Ant. Wayne
Lorain
Shelby
Darke
Lucas
Antwerp
Arcadia
Paulding
Hancock
ArcanumButler Darke
Archbold Fulton
Arlington
Ashland
Ashtabula
Athens
Aurora
Austintown
Avon Lake
Avon
Hancock
Ashland
Ashtabula
Athens
Portage
Mahoning
Lorain
Lorain
Ayersville Defiance
Barberton Summit
Barnesville
Batavia
Belmont
Clermont
Bath
Bay Village
Allen
Cuyahoga
Beachwood Cuyahoga
Beaver Columbiana
Beavercreek Greene
Bedford Cuyahoga
Bellaire Belmont
Bellefontaine Logan
Bellevue
Belpre
Huron
Washington
Benjamin Logan Logan
% Passing
1997
81.25
60.25
68.75
66
76.19
88.75
85.94
68.12
75.44
70.5
59.75
74.62
90.25
73.06
81.94
82.31
79.81
62.19
67.62
61
73.94
69.12
68.25
81.56
79.88
71.75
82.81
73.88
74.31
77.88
76.5
71.44
54.5
63.81
54.62
63.38
70.44
60.69
64.62
Appendix A
Basic District Data
Passing
Rank
80
534
382
449
179
16
35
404
197
332
538
217
11
253
73
70
102
516
410
527
237
371
401
78
100
298
66
239
226
142
172
309
579
486
577
500
333
529
471
Presage
Score
% ADC
1997
22.87
-14.101
-88.081
-42.031
49.805
-22.049
40.117
23.05
18.688
-88.061
-62.113
-6.041
-7.695
50.564
74.717
-45.336
-7.214
-83.075
3.54
22.47
-22.697 10.99
-104.984 35.16
-47.22
6.73
-83.178
-27.199
16.49
2.32
24.15
7.99
21.467
11.603
-11.063
28.04
-3.555
11.802
3.608
24.249
2.57
1.56
4.48
2.7
3.39
3.29
2.07
2.28
42.061
-22.731
-75.846
-32.536
-11.776
-22.516
4.19
1.39
12.05
27.77
13.37
4.98
13.95
3.74
2.12
25.74
15.51
13.15
5.58
0.85
0.96
9.76
2.31
6.37
26.1
14.93
1.3
7.34
1.8
1.4
% Lunch
1997
5.43
47.14
26.57
24.57
17.77
4.93
3.73
34.25
7.09
23.57
44.57
26.75
2.79
22.21
6.31
5.09
3.26
23.26
40.22
24.47
19.82
17.02
11.08
7.32
7.26
16.57
7.22
14.17
7.74
14.15
8.52
14.46
46.79
21.22
53.82
23.96
10.52
49.1
18.72
MeanInc
1997
35938
25619
22167
32879
31255
57644
80007
27774
32270
31239
26389
30949
58795
29901
48227
39240
37657
31723
26187
42760
29205
30132
31728
35049
29886
22985
26813
29296
26130
30270
26572
31911
47111
29379
23044
31304
30024
30354
31110
0
9.7
9.7
40.8
42.2
1.2
15.6
1.3
11.9
0
0
15.4
43.8
31.3
4.9
22.4
0.6
29.1
0.4
16.8
30.9
26
17
21.9
12.1
36.5
32.4
6.3
11.3
16.2
4.8
15.2
7.3
% Disad
1993 14
19.1
36.8
17.3
45.3
32.9
10.7
14 Data from 1993 were used due to conspicuous errors in the EMIS values for this category for later years.

Benton Carroll
Berea
Berkshire
Berlin-Milan
Ottawa
Cuyahoga
Geauga
Erie
Berne Union Fairfield
Bethel Miami
Bethel-Tate Clermont
Bexley Franklin
Big Walnut
Black River
Delaware
Medina
Blanchester Clinton
Bloom Carroll Fairfield
Bloom-Vernon Scioto
Bloomfld-Mespo Trumbull
Bluffton
Boardman
Allen
Mahoning
Botkins Shelby
Bowling Green Wood
Bradford Miami
Brecks-Brdview Hts Cuyahoga
Bridgeport
Bright
Bristol
Belmont
Highland
Trumbull
Brookfield
Brooklyn
Brookville
Brown
Trumbull
Cuyahoga
65.94
71.69
Montgomery 72.12
Carroll 69.38
Brunswick
Bryan
Medina
Williams
Buckeye Central Crawford
Buckeye Medina
78.81
76
71.31
73.56
76.88
75.25
72.31
87.5
55.94
58.88
75.06
73.44
71.69
67.56
74.69
54.88
73.56
79.75
83.06
82.38
75.94
74.75
84.81
65.25
75.81
74.25
86.06
Buckeye
Buckeye
Ashtabula
Jefferson
Buckeye Valley Delaware
Bucyrus Crawford
Caldwell Noble
Cambridge Guernsey
Campbell Mahoning
Canal Winchester Franklin
Canfield
Canton
Canton
Cardinal
Mahoning
Stark
Stark
Geauga
Cardington-Lincoln Morrow
Carey Wyandot
Carlisle
Carrollton
Cedar Cliff
Warren
Carroll
Greene
87.88
55.06
69.62
77.12
60.5
77.5
67.5
72.19
80.94
71
62.25
68.62
66.44
64.19
63.75
63.62
76.25
11.924
-11.604
-26.967
44.795
-78.153
-46.275
-1.89
-18.145
-1.423
7.129
-26.577
11.587
2.443
3.534
1.709
-8.704
5.378
15.445
5.427
-16.904
31.708
-16.545
15.977
16.697
-8.042
-26.234
28.57
-97.942
-64.799
17.892
10.522
-31.849
-66.765
11.716
-44.108
7.6
20.54
3.42
12.97
-50.957
-73.994
11.57
20.72
-115.399 32.33
28.937 0.94
43.777
-125.05
-16.384
-22.901
-28.991
-5.363
-1.982
-50.405
4.417
1
33.36
7.75
5.83
12.3
4.92
5.92
9.58
5.24
11.87
6.41
3.21
9.19
2.73
4.37
5.3
5.25
0.43
6.01
5.8
1.49
26.65
10.03
5.21
4.35
3.53
8.48
1.91
32.16
8.98
0.95
4.67
3.22
4.52
1.37
2.83
6.63
1.92
10.69
1.12
450
300
282
360
127
181
310
244
167
202
277
20
566
545
206
246
300
414
215
575
244
106
62
69
184
214
44
463
188
230
31
19
573
352
158
531
149
418
280
86
320
515
387
436
480
491
495
176
30504
31296
25633
52505
23087
25305
29600
28405
28167
32649
29553
35387
34613
28714
36219
40017
31408
27776
38210
23268
24341
32272
37572
30446
32358
32605
34067
27406
41108
29305
25217
52077
23540
28526
27509
25829
25377
31218
26535
29667
27031
25385
37016
24972
24213
25716
25841
37787
33.58
20.18
11.11
19.44
14.17
12.7
19.88
14.86
8.05
15.09
19
5.22
38.39
27.15
26.28
9.27
23.32
21.93
6.83
41.25
50.56
7.23
12.38
15.03
11.16
7.99
13.61
14.48
3.18
18.16
5.02
2.3
58.13
20.56
20.28
21.62
12.42
15.18
34.56
10.21
24.08
42.11
9.68
37.41
28.8
36.29
53.41
7.91
1.1
3
11.2
27.5
6.9
15.1
0
14.4
10.1
21.8
27.8
1
36.2
34.4
0
9.7
12.6
23.6
0.9
47.8
29.6
6.2
10
20.9
11.3
7.8
12.2
23.2
4.3
17
3.1
5
57.1
16.6
24.3
20.9
13.4
12.1
32.8
9.8
27.2
29.5
12.2
18.7
34.8
42.7
55.5
0
66

Celina Mercer
Centerburg Knox
Centerville
Central
Montgomery
Defiance
77.38
71.62
85.44
66.5
Chagrin Falls
Champion
Cuyahoga
Trumbull
Chardon Geauga
Chesapeake Union Lawrence
92.5
77.81
83.38
69.88
Chillicothe
Cincinnati
Circleville
Clay
Ross
Hamilton
Pickaway
Scioto
Claymont Tuscarawas
Clear Fork Valley Richland
Clearview Lorain
Clermont-Northeast Clermont
Clev. Hts/Univ Hts Cuyahoga
Clinton-Massie
Cloverleaf
Clinton
Medina
Clyde-Green Springs Sandusky
Coldwater Mercer
Colonel Crawford Crawford
64.06
56.5
69.88
64.81
64.81
68.69
65.62
63.81
67.06
36.25
72.06
75.75
72.81
82.75
68.81
Columbia
Columbiana
Lorain
Columbiana
Columbus Franklin
Columbus Grove Putnam
Conneaut Area Ashtabula
Conotton Valley Harrison
Continental Putnam
Copley-Fairlawn Summit
Cory-Rawson Hancock
Coshocton Coshocton
Coventry
Covington
Summit
Miami
Crestline
Crestview
Crestview
Crestview
Crawford
Van Wert
Columbiana
Richland
Crestwood
Crooksville
Portage
Perry
Cuyahoga Falls Summit
Cuyahoga Heights Cuyahoga
Dalton
Danbury
Wayne
Ottawa
Danville Knox
Dawson-Bryant Lawrence
Deer Park Community Hamilton
78.25
58.19
71.31
81.12
79.88
71.62
66.19
66.25
73.75
69.94
65.75
70.62
75.94
61.06
82.19
73.25
72.19
73.06
77.69
49.88
79.38
62.75
69.44
72.81
84.31
0.968
-56.561
-23.995
0.895
-60.078
8.944
-28.886
-23.673
-2.007
-72.576
-3.739
22.302
20.196
-5.275
-21.472
-75.991
-3.644
-7.51
4.652
49.503
1.113
89.245
9.07
20.019
-74.224
-44.543
-78.782
-40.146
-63.278
-57.447
2.453
-101.815
-13.8
-88.963 15.42
-131.861 65.66
1.622
3.142
5.25
3
-4.327
11.759
-2.56
6.27
1.3
2.96
12.136
-22.559
5.16
6.89
-134.864 40.36
-9.741 7.1
-67.284
-37.766
-16.119
32.003
15.88
7.83
8.78
2.42
19.09
44.52
14.54
21.07
14.68
2.57
22.13
7.49
4.79
1.98
2.05
2.74
0.31
3.96
1.71
26.16
4.81
17.1
5.6
0.78
2.53
2.67
7.4
23.25
5.47
1.17
16.29
8.21
3.98
15.75
2.03
7.06
5.19
253
145
585
115
510
358
262
48
425
593
286
191
262
67
379
483
562
344
467
467
383
458
486
152
304
39
435
4
144
59
344
137
551
310
82
100
304
444
441
240
342
454
327
184
526
71
252
280
44447
23049
30312
34282
26593
29229
30080
34366
29751
27606
31049
24826
23924
27921
47633
30337
33618
29924
27372
21773
29763
26415
32140
27020
31162
56853
30063
90095
34420
40819
27146
31243
22304
32831
37182
32116
27985
23288
23789
30126
32598
27529
32805
29115
25432
29444
27584
27767
14.97
21.42
62.51
14.59
41.63
28.66
16.36
8.11
91.39
8.55
12.04
14.54
24.65
9.27
14.18
33.59
67.88
24.03
34.48
28.94
12.34
61.2
17.05
17.64
9.43
3.1
14.21
0.44
9.89
8.19
40.31
13.54
39.18
15.07
7.8
9.39
14.09
18.96
43.63
14.5
13.76
33.6
25.49
15.04
36.86
8.07
22.21
20.65
2.1
24
59.6
19.1
34.6
25.2
18.9
5.1
26.6
80.7
11.4
13.6
0
6.9
15.5
22.2
0
31.5
35.1
35.6
12.4
44.9
21.4
12.1
15.1
2.2
12
0.1
11.5
10.9
34.9
14.9
38.6
15.9
6.3
0
16.5
18.4
32.9
13.8
16.7
34.2
23.1
9.2
32.9
10.4
27.2
25.6
67

Defiance
Delaware
Delphos
Dover
Defiance
Delaware
77.06
69.75
Allen 80.56
Tuscarawas 75.44
Dublin Franklin
East Cleveland Cuyahoga
East Clinton Clinton
East Guernsey Guernsey
84.12
42.81
66.31
74
East Holmes Holmes
East Knox Knox
East Muskingum Muskingum
East Palestine Columbiana
Eastern
Eastern
Eastern
Eastwood
Meigs
Brown
Pike
Wood
83
75.75
74.44
72.81
64.56
63.75
41.12
79.44
Eaton
Edgerton
Preble
Williams
Edgewood Butler
Edison Jefferson
Edon-Northwest Williams
Elgin
Elida
Marion
Allen
Elmwood
Elyria
Euclid
Evergreen
Wood
Lorain
Cuyahoga
Fulton
Fairbanks
Fairborn
Union
Greene
Fairfield Butler
Fairfield Union Fairfield
72.75
64.81
58.88
75.81
72.69
69.25
72
71.19
68.94
74.94
76.75
71.25
68.5
70.31
74.19
Fairland
Fairlawn
Lawrence
Shelby
Fairless Stark
Fairport Harbor Lake
Fairview Park Cuyahoga
Fayetteville-Perry Brown
Federal Hocking Athens
Feli-Franklin Clermont
Field
Findlay
Finneytown
Firelands
Portage
Hancock
Hamilton
Lorain
77.44
72.81
79.44
67.5
Forest Hills
Fort Frye
Fort Loramie Shelby
Fort Recovery Mercer
Fostoria
Hamilton 87.5
Washington 65.94
Seneca
84.81
81.75
63.81
71.88
63.12
70.56
63.94
76.94
74.31
55.31
65.31
267
467
545
188
272
366
288
316
376
207
168
314
390
337
231
63
191
223
262
473
491
592
113
160
349
88
197
51
591
438
235
151
262
113
418
20
450
44
75
486
294
505
330
485
165
226
570
462
4.98
18.24
15.76
3.76
2.39
11.9
3.91
7.37
6.77
4.05
4.08
15.68
1.83
5.27
7.61
0.33
5.75
4.99
9.93
22
9.02
25.83
3.23
9.68
7.91
4.46
4.5
0.65
51.84
9.18
13.95
6.56
6.72
4.37
3.14
1.73
11.71
1.91
0.72
17.21
16.18
2.34
7.41
9.76
3.43
5.43
22.56
19.38
-17.804
-5.702
2.34
-45.71
-11.758
-14.047
-11.983
-24.627
-22.981
-46.349
-11.371
21.313
-22.186
15.229
-17.469
-10.881
-4.357
-23.398
10.578
53.898
-99.101
-24.107
-57.31
-3.086
-17.138
-6.391
-56.417
-66.277
-50.49
-74.773
0.564
-39.385
10.77
-23.482
16.519
28.541
-13.916
-78.834
-63.733
-12.092
-11.212
14.464
16.8
46.583
-36.949
16.499
-0.1
-61.549
32046
30778
32300
27800
28522
28413
31677
27023
29999
29221
32209
36653
30504
36659
30111
25664
29902
30799
25003
25803
25170
25227
32684
33229
33423
28322
32008
59378
22389
26203
23480
32608
35698
39654
33480
55483
25951
33439
29200
27991
29235
28290
27878
27279
40381
30334
25286
26557
23.57
32.44
37.21
19.12
7.65
24.49
9.02
18.81
18.28
14.83
12.28
32.43
17.65
21.19
18.25
14.92
19.89
16.2
34.69
34.78
39.84
25.67
13.09
15.53
16.07
26.16
13.23
2.13
66.15
17.23
31.14
16.64
19.79
12.52
10.44
3.07
29.39
6.03
12.18
36.73
20.84
7.68
21.15
0
4.21
21.42
43.66
35.11
23.1
2.3
22.6
20.7
5.3
16.3
8.5
21.4
24.8
17.6
13.6
25.4
20.8
16
17.8
13.5
21.4
16
36.8
35.3
26.8
48.5
15.8
18.9
13.8
21.1
3.7
2.7
3.5
23.9
35.7
21.5
20.4
8.3
3.1
4.1
21.8
9
16.4
35.6
31.6
7.5
22.8
1
4.2
17.4
37.9
35.8
68

Franklin
Franklin
Warren
Muskingum
Franklin-Monroe Darke
Fredericktown Knox
68.12
65.88
77
72.12
Fremont
Frontier
Sandusky 74.31
Washington 65.69
Gahanna-Jefferson Franklin
Galion Crawford
78.44
65.56
Gallia County
Gallipolis
Gallia
Gallia
55.56
57.62
Garaway Tuscarawas 80.06
Garfield Heights Cuyahoga 63.06
Geneva Area Ashtabula
Genoa Area Ottawa
Georgetown
Gibsonburg
Brown
Sandusky
67.62
68.69
69.81
76.62
Girard Trumbull
Gorham Fayette Fulton
Goshen
Graham
Clermont
Champaign
Grand Valley Ashtabula
Grandview Heights Franklin
Granville Licking
Green
Green
Wayne
Summit
Green Scioto
Greeneview Greene
Greenfield
Greenville
Highland
Darke
Groveport Madison Franklin
Hamilton Butler
84.12
80.56
55.62
67.19
63
70.25
67.56
61.81
75.25
69.94
67.25
71.88
63.81
87.12
91.31
Hamilton Franklin
Hardin Northern Hardin
Hardin-Houston Shelby
Harrison Hills Harrison
Heath
Hicksville
Highland
Highland
Licking
Defiance
Medina
Morrow
Hilliard
Hillsboro
Hillale
Holgate
Franklin
Highland
Ashland
Henry
79.31
63.44
78.81
74.69
Hopewell-Loudon Seneca
Howland Trumbull
68.69
79.81
Hubbard Trumbull 74.62
Huber Heights Montgomery 71.31
Hudson Summit 89.31
63.69
67.88
69.5
65.5
70.44
75.5
84.69
70.62
51
88
567
423
508
338
414
520
202
342
422
294
486
23
7
568
555
99
507
410
383
347
171
404
452
163
282
226
455
136
459
118
499
127
215
383
102
217
310
12
493
408
355
461
333
195
46
327
3.9
5.4
26.65
6.2
13.14
5.18
8.65
21
17.49
5.69
5.37
4.81
6.98
1.42
0.85
25.62
24.38
3.01
11.38
12.79
4.28
8.43
15.41
9.13
11.73
0.5
4.15
13.41
15.69
5.71
12.43
1.93
11.03
2.6
3.84
3.75
6.63
9.14
5.66
0.69
9.69
6.39
2.76
17.79
4.45
7.33
1.25
9.87
-44.254
-2.038
-22.567
-0.199
-34.702
16.091
56.106
6.877
9.625
-84.431
-4.427
-55.288
-21.188
-12.921
-69.752
-28.951
-48.16
5.129
-5.556
-37.249
-70.369
25.908
-36.599
-88.548
-53.36
-17.032
-37.982
-58.14
-0.804
-27.523
-28.736
-29.016
-14.766
3.985
-67.994
8.535
8.291
34.257
-35.943
25.758
-39.608
4.741
15.235
9.569
25.098
-36.534
1.863
72.609
28436
26122
29253
30611
27288
36241
58956
30157
39835
26299
32153
23582
28592
30559
28768
26662
29410
24278
27608
27690
33686
27137
28754
29339
24420
32699
28674
28601
24171
46538
26641
42668
27562
28951
30215
29019
45018
29146
34993
77279
27194
27554
28985
23436
29395
30651
46317
27877
17.78
11.21
44.08
13.58
24.83
18.1
18.13
39.42
26.7
21.57
21.85
16
26.81
9.83
0
44.09
28.29
18.7
26.71
36.04
10.51
23.23
22.98
22.66
30.15
13.47
13.88
31.54
40.15
6.22
22.71
6.68
23.34
12.71
11.04
15.7
11.89
28.04
19.17
1.28
25.12
17.53
10.34
37.24
13.21
14.23
6.01
24.65
1.6
13.6
40
16.8
40.9
26.5
16.7
38.1
28.5
0.9
24.6
10
28.2
8.9
2
45.5
30.1
19.6
27.5
37
19.7
23
19.1
26.5
30.7
13.6
16.2
20.9
38.7
8.7
28.1
8.3
32.8
8.9
0.1
0
1.4
28.5
8.3
2.7
21.4
18.4
11.9
36.4
3.2
0.8
4.8
29.3
69

Huntington
Huron
Ross
Erie
Independence Cuyahoga
Indian Creek Jefferson
Indian Hill
Indian Lake
Indian Valley
Ironton
Hamilton
Logan
55.19
77.5
85.56
73.44
92.38
64.06
Tuscarawas 72.75
Lawrence 68.56
Jackson Center Shelby
Jackson Jackson
Jackson Stark
Jackson-Milton Mahoning
65.69
60.31
86.75
68.81
James A Garfield Portage
Jefferson Area Ashtabula
74.94
72
Jefferson Madison 64.12
Jefferson Township Montgomery 56.75
Jennings Putnam
Johnstown-Monroe Licking
Jonathan Alder Madison
Joseph Badger Trumbull
Kalida
Kenston
Kent
Putnam
Geauga
Portage
Kenton
Kettering
Kings
Kirtland
LaBrae
Lake
Lake
Lakeview
Hardin
Montgomery
Warren
Lake
Trumbull
Stark
Wood
Trumbull
64.19
77.06
80.62
83.88
74.81
82.69
75.94
79.69
79.81
70.12
75.88
74.38
86.94
86.38
77.69
Lakewood Cuyahoga
Lakewood Licking
Lakota
Lakota
Butler
Sandusky
Lancaster
Lebanon
Fairfield
Warren
Ledgemont Geauga
Leetonia Columbiana
Leipsic
Lexington
Putnam
Richland
Liberty Center Henry
Liberty Trumbull
Liberty U/Thurston Fairfield
Liberty-Benton Hancock
Licking Heights Licking
Licking Valley Licking
Lima Allen
71.31
79.25
75.38
78.5
74.94
77.38
70.62
62.62
56.06
71
60.25
83.81
63.62
63.81
70.69
64.31
68.38
480
160
87
53
212
68
184
108
102
341
187
224
24
28
145
455
532
27
379
207
288
482
559
571
149
38
246
6
483
267
389
310
120
199
133
207
152
327
512
564
320
534
55
495
486
325
477
394
31004
32890
31760
29942
45025
58670
31189
27071
43942
47914
59657
28046
38366
32321
38742
29202
26240
46079
31875
29105
27845
30065
29155
25126
40895
43210
30683
171026
29489
23978
26793
26023
38965
32593
49836
30829
38165
33948
29631
24653
33514
28835
50592
27750
29057
36544
32493
24888
24.424
7.04
-5.1
-33.178
30.715
44.23
-27.921
-46.889
12.412
32.734
51.107
-56.774
21.626
-1.019
15.662
-64.434
20.625
36.38
-29.907
164.756
-29.361
-48.812
-64.147
4.792
-49.52
38.149
-26.295
-17.575
-42.045
-14.175
-86.895
-25.416
-31.105
45.192
-32.52
-36.463
15.554
-3.187
-43.142
-44.287
20.345
1.583
7.456
11.69
2.14
3.59
10.51
-13.771
23.495
9.928
-18.369
5.22
1.54
7.67
5.43
-120.187 33.26
13.29
10.62
1.28
9.05
11.73
5.57
3.59
9.59
11.12
4.77
2.16
2.19
13.13
2.11
5.33
3.88
0.21
3.75
4.4
7.27
0.13
2.19
13.87
6.1
16.11
2.23
12.24
8.07
9.63
5.08
29.33
17.37
3.84
0.94
16.35
1.07
12.5
8.03
30.04
30.94
13.06
7.32
6.06
40.99
7.33
14.31
9.2
6.37
10.5
14.86
25.75
4.48
6.15
22.84
15.81
31.75
5.1
19.73
21.21
26.46
22.26
64.92
36.09
12.13
3.99
24.74
2.7
21.45
31.26
31.7
28.42
7.88
12.92
18.77
15.78
7.33
13.15
16.77
58.08
23.84
25.72
3.62
23.82
28.39
15.42
14.09
25.54
31.9
13.7
5.7
0.3
30.7
7.3
13.7
10
0
11.6
17.6
30.1
9.7
6.1
22.4
2.5
27.9
0.6
26.2
17.4
33.8
16.9
21.8
36.1
4.3
1.9
19.5
2.5
24.9
33.5
29.2
30.2
8.6
14.5
13.1
23.6
5.8
3.2
25.8
53.5
21.8
23.6
0.5
27.4
25.4
0
18
32.9
70

Lincolnview
Lisbon
Little Miami
Lockland
Van Wert
Columbiana
Warren
Hamilton
Logan Elm Pickaway
Logan-Hocking Hocking
London
Lorain
Madison
Lorain
Lordstown Trumbull
Loudonville/Perrysvill Ashland
Louisville
Loveland
Stark
Hamilton
80.56
72.56
79.38
76.69
Lowellville
Lucas
Mahoning
Richland
74.06
69.31
Lynchburg-Clay Highland
Mad River Montgomery
66.94
64.56
66.88
76
78.56
66.25
64.25
66.56
69.25
49.62
Mad River-Green Clark
Madeira Hamilton
Madison
Madison
Lake
Butler
Madison Richland
Madison-Plains Madison
Manchester Summit
Mansfield Richland
Maple Heights Cuyahoga
Mapleton Ashland
Maplewood Trumbull
50.75
59.75
70.88
77.56
Margaretta Erie
Mariemont Hamilton
Marietta
Marion
Washington
Marion
71.81
91.31
64.62
58.19
77.94
92.81
71.62
69.12
57.19
68.69
79.75
Marion
Marlington
Mercer
Stark
Martins Ferry Belmont
Marysville Union
Mason
Massillon
Mathews
Maumee
Warren
Stark
Trumbull
Lucas
Mayfield
Maysville
McComb
McDonald
Cuyahoga
Muskingum
Hancock
Trumbull
Mechanicsburg Champaign
Medina Medina
Meigs
Mentor
Miami East
Meigs
Lake
Miami
88
62.81
70.25
84.38
64.25
80.19
56.75
79.38
75.31
84.31
72.94
69.81
76.44
85.62
60.31
74.81
83.31
23.628
-23.042
-58.961
3.611
37.91
-65.244
-11.433
14.694
22.914
-39.328
-19.304
-1.118
-17.196
25.123
-99.834
22.651
24.15
-1.815
-38.524
7.975
-78.483
1.3
12.72
3.98
27.96
1.666
-49.843
7.79
12.72
-26.167 9.94
-115.845 33.18
-10.216
-27.452
-13.844
33.401
-38.831
9.343
-36.817
-41.587
3.24
5.54
5.23
6.26
14.78
1.84
7.86
6.54
11.672
41.873
5.285
-1.14
-44.071
-10.689
12.422
-131.437 31.04
-41.737 14.99
-22.748
-22.887
4.72
7.94
-0.043
39.371
1.081
-63.862
3
2.72
13.46
18.16
4.56
1.51
6.34
7.55
7.18
5.58
3.4
4.1
10.79
5.34
7.4
7.27
3.77
30.47
2.71
1.14
0.34
6.97
26.61
5.14
0.98
17.96
6.61
3.72
583
538
323
147
296
7
471
551
141
3
304
371
557
383
106
88
273
115
169
234
364
428
473
431
181
132
441
478
433
366
586
18
509
338
47
478
97
559
115
201
48
259
347
173
37
532
212
60
35252
50513
31525
34320
27679
30161
34122
27383
28623
27352
30593
30337
54061
31241
26068
37814
26658
31526
51721
26709
31523
26053
28823
28945
25896
36035
26477
30096
25637
31533
26985
47834
23422
28566
29812
29654
45573
24166
39251
33490
30458
33538
24669
34741
42300
26986
32057
38114
61.78
31.67
20.88
21.74
17.48
2.97
0
36.77
8.92
4.83
17.7
12.81
27.87
15.47
9.1
13.59
18.37
18.54
7.16
34.06
11.54
21.61
34.67
16.36
30.5
11.48
44.5
16.64
28.96
23.46
59.95
9.12
25.96
21.93
10.53
14.68
7.68
50.23
9.79
8.2
3.19
21.31
30.92
11.59
2.81
39.27
16.78
9.2
66
23.7
24.5
23.8
9.9
9
16.7
35
10.1
2.3
2.2
15.1
36.7
19.8
9.2
31.2
30.2
21.6
4.9
16.7
8.8
33.4
29.2
13.1
21.2
12.6
32.5
4
33.8
24.3
49.7
11.7
26
20.6
13
24.9
9
43.3
4.1
0
3.3
28.3
26.1
14.4
0.6
35
20.1
10.5
71

Miami Trace Fayette 58.88
Miamisburg Montgomery 79.81
Middletown Monroe Butler
Midview Lorain
57.12
71.75
Milford Clermont
Millcreek-West Unity Williams
Miller -New Cleveland Putnam
Milton-Union Miami
79.69
72.31
86.88
80.31
Minerva
Minster
Stark
Auglaize
Mississinawa Valley Darke
Mogadore Summit
Mohawk
Monroeville
Montpelier
Morgan
Wyandot
Huron
Williams
Morgan
72
86.88
68.12
78.5
75.38
70.38
69.38
61.19
Mount Gilead Morrow
Mount Healthy Hamilton
Mount Vernon Knox
Napoleon Area Henry
Nelsonville-York Athens
New Boston* Scioto
New Bremen Auglaize
New Lebanon Montgomery 69.25
New Lexington Perry 61
New London Huron
New Miami Butler
58.19
57.31
New Philadelphia Tuscarawas 73.06
New Richmond Clermont 69.75
New Riegel
Newark
Seneca
Licking
81
62.69
68.94
60
68.38
79
66.38
68.25
86
Newbury Geauga 78.5
Newcomerstown Tuscarawas 70.56
Newton Falls
Newton
Trumbull
Miami
60.62
72.06
Niles
Noble
Trumbull
Noble
Nordonia Hills Summit
North Baltimore Wood
69.75
68.81
79.25
64.56
North Canton Stark
North Central Williams
North Central Wayne
North College Hill Hamilton
North Fork Licking
North Olmsted Cuyahoga
North Ridgeville Lorain
North Royalton Cuyahoga
North Union Union
86.38
71.12
69.88
63.31
69.44
80.12
74.31
81.25
67.94
9.172
-73.689
-28.109
17.024
-50.057
-53.706
16.561
-29.337
20.852
-8.365
-5.334
-48.162
0.228
8.693
4.878
30.514
-12.534
-28.27
14.228
-53.25
-2.213
16.001
-13.021
14.674
9.011
-30.84
30.903
-38.625
13.479
-4.427
0.606
-23.034
-59.753
-17.839
-65.933
-25.972
-6.59
8.96
20.68
9.84
6.1
-98.859 29.27
-149.988 50.38
21.194 0.97
-12.285
-87.981
-27.689
-70.323
-10.085
-42.676
22.766
-51.156
6.82
19.72
8.95
21.83
8.5
12.22
1.37
15.4
7.98
0.71
15.37
2.34
2.8
3.96
6.74
19.53
9.15
6.75
17.73
5.37
4.25
3.72
0
5.48
2.27
2.59
3.67
14.49
5.46
3.69
4.18
1.78
4.54
8.83
14.06
12.99
1.8
19.69
10.68
3.87
8.27
366
527
551
556
253
349
85
511
376
537
394
125
437
401
32
288
25
404
133
199
335
360
525
545
102
558
298
108
277
25
94
28
317
344
502
358
98
226
80
407
133
330
530
286
349
379
120
473
28371
28527
28768
32140
21881
19772
41234
29415
23709
26851
25227
28985
32714
27176
28164
27670
41693
23565
31409
28703
29336
26156
23747
26640
35208
30310
33777
39301
26209
27764
32051
42212
26675
29256
27238
28198
35983
34428
40724
29996
42352
23181
29501
29954
28173
23874
37641
27233
18.28
53.77
22.59
41.12
13.47
31.97
2.44
32.62
18.75
41.88
22.5
14.43
45.27
67.88
7.47
26.03
4.48
31.52
8.29
14.03
24.27
20.55
37.17
20.56
13.63
34.33
15.32
9.45
18.01
6.49
14.96
6.89
11.55
15.02
37.11
18.91
16
12.57
6.93
16.39
13.75
39.81
22.52
11.13
29.54
28
8.21
22.7
16.6
38.2
23
32.6
17.1
31.2
0.6
31.3
18.5
31.9
22.4
18.2
46.2
51.5
11.6
24.5
5.6
15.3
7.3
16.3
0.5
21.9
26.8
25.2
0.6
31.5
15.3
9.6
17.5
6.6
2.6
12.2
20.9
15.9
23.8
3.6
7.6
12.8
1.5
21.6
10.6
43
22.1
0
29
38.9
9
25.6
72

Northeastern Defiance
Northeastern Clark
75.5
69.5
Northern Perry 63.38
Northmont Montgomery 80.25
Northmor Morrow
Northridge Licking
68.31
77.38
Northridge Montgomery 65.56
Northwest Stark 78.81
Northwest Hamilton
Northwest Scioto
Northwestern Wayne
Northwestern Clark
Northwood Wood
Norton Summit
Norwalk Huron
Norwood Hamilton
69.38
46.5
75.25
71.69
61.56
74.62
67.5
63.69
Oak Hills Hamilton 79.31
Oakwood Montgomery 94.69
Oberlin
Old Fort
Lorain
Seneca
68.5
77.88
Olentangy Delaware
Olmsted Falls Cuyahoga
Ontario Richland
85
81.69
79.62
Orange
Oregon
Orrville
Osnaburg
Cuyahoga
Lucas
Wayne
Stark
Otsego
Ottawa Hills
Wood
Lucas
Ottawa-Glandorf Putnam
Ottoville Putnam
90.56
72.88
71.69
62.19
73.75
94.38
83.69
83.56
Painesville Lake
Painesville Township Lake
Paint Valley Ross
Pandora-Gilboa Putnam
Parkway
Parma
Mercer
Cuyahoga
Patrick Henry Henry
Paulding Paulding
Perkins
Perry
Perry
Perry
Erie
Lake
Stark
Allen
Perrysburg
Pettisville
Wood
Fulton
Pickerington Fairfield
Pike-Delta-York Fulton
Piqua Miami
77.56
86.12
81.44
69.31
89.25
81.12
85.94
73.06
59.44
54.81
74
67.56
82.94
68.38
72.75
77.06
67.62
-75.082
23.146
-40.348
6.729
-6.927
-11.528
6.3
3.648
13.847
11.255
-14.717
-58.049
43.349
16.623
42.147
-18.79
-11.716
36.693
75.876
-33.039
1.241
64.369
19.272
20.663
136.447
-16.741
-9.829
-21.407
2.594
122.811
3.853
27.277
28.136
17.74
-35.602
22.137
0.19
2.85
11.03
2.32
-28.311
0.334
9.25
3.93
-106.724 27.85
-0.31 4.17
-10.66
-99.834
-26.776
8.158
-6.193
-6.133
-22.015
-65.46
6.54
26.67
5.01
4.17
4.69
4.1
10.9
21.67
0.47
7.53
7.97
7.19
4.24
0.11
4.21
0
2.09
0.15
14.96
11.02
1.26
2.83
2.02
2.5
1.81
5.24
15.25
1.78
3.09
0.42
4.01
12.57
23.86
3.62
15.93
0.89
3.3
6.05
4.28
6.96
10
260
300
516
240
2
56
57
118
1
390
142
42
77
111
360
589
202
300
521
217
418
493
195
355
500
96
399
152
459
127
147
30
79
364
13
82
35
253
541
576
235
414
65
394
267
160
410
39283
76906
35121
28541
76189
36562
39143
141567
33069
30311
27063
35414
122921
31263
29937
35380
23616
28464
33788
30567
32587
30505
25120
36296
35450
27778
37687
28249
32414
26316
33240
38507
34355
30563
25591
54129
32243
45397
29860
30174
28368
38516
26472
28669
26693
31122
28240
28878
3.95
21.68
23.67
23.18
11.78
0
8.8
2.16
0
0.38
28
15.58
5.46
7.36
7.06
19.6
44.18
25.53
10.36
15.77
14.12
23.82
34.21
7.97
7.16
26.15
10.03
22.21
10.85
58.29
10.28
9.96
10.49
17.54
33.79
4.6
12.53
1.83
21.14
28.82
43.29
11.75
26.79
8.25
16.72
18.5
11.86
16.77
0.7
20.6
8.5
18.1
16.8
0
14.4
0.5
0.5
0.5
25.2
0.7
5.1
7.1
9.4
19.9
52.6
24.7
11.1
16.3
20.5
17.8
34.7
0
7.7
26.2
3.2
25.1
17.3
46.9
19.1
12.2
10.8
22.5
34.6
4.4
0
1
23.5
0.5
36.3
0
24.1
12.8
13.6
18.1
5.8
1.5
73

Plain
Plain
Pleasant
Plymouth
Franklin
Stark
Marion
Richland
Poland
Port Clinton
Mahoning
Ottawa
Portsmouth Scioto
Preble Shawnee Preble
Princeton
Put-in-Bay
Hamilton
Ottawa
Pymatuning Valley Ashtabula
Ravenna Portage
Reading Community Hamilton
Revere Summit
Reynoldsburg Franklin
Richmond Heights Cuyahoga
Ridgedale
Ridgemont
Ridgewood
Ripley-Union-Lewis
Rittman
River Valley
River View
Marion
Hardin
Coshocton
Brown
Wayne
Marion
Coshocton
Riverdale
Riverside
Hardin
Logan
Rock Hill Lawrence
Rocky River Cuyahoga
Rolling Hills
Rootstown
Ross
Rossford
Guernsey
Portage
Butler
Wood
Russia
Salem
Shelby
Columbiana
Sandusky Erie
Sandy Valley Stark
Scioto Valley
Scioto Valley
Ross
Pike
Sebring Mahoning
Seneca East Seneca
Shadyside Belmont
Shaker Heights Cuyahoga
Shawnee Allen
Sheffield-Shef. Lake Lorain
Shelby
Sidney
Richland
Shelby
Solon Cuyahoga
South Central Huron
South Euclid-Lyndhst Cuyahoga
63.62
64.38
58.44
85.38
61.31
66.56
76.31
78.12
66.25
64.69
69.62
55
61.44
76.44
65.88
72.31
74.56
62.06
70.25
79.56
89.19
78.19
80.5
85.06
75.75
69
66.31
87.25
73.06
56.12
65.69
71.06
80.56
79.69
68.31
72.5
64.94
91.12
68.38
78.19
86
74.94
55.19
71.94
63.12
48.12
74.94
79.19
93.754
-20.975
6.164
-55.454
0.35
9.97
5.92
11.92
32.852
-19.344
1.17
8.89
-118.054 43.42
-5.984 6.28
-3.288
35.92
-61.945
-55.122
14.459
68.039
18.539
17.82
12.69
1.23
12.75
16.1
9.44
1.14
5.83
3.01
-10.254
-20.635
-37.229
-71.194
-32.087
6.735
-27.448
-1.719
-18.511
3.53
7.24
-105.384 32.17
43.342 1.57
-65.763
9.075
4.488
-1.937
13.75
3.79
4.61
7.46
3.36
5.27
8.8
15.59
6.66
3.77
5.8
24.931
-29.102
-88.239
-39.411
0
10.62
24.86
9.34
-32.164 20.26
-102.035 28.24
-40.766
5.935
12.95
4.22
-13.625
67.78
26.162
-4.114
-21.137
-18.846
51.848
-23.569
23.728
13.38
8.1
2.09
6.45
7.19
9.3
0.58
7.39
2.22
495
476
549
40
524
433
175
140
441
470
352
574
523
173
452
277
221
518
338
112
14
138
93
41
191
374
438
22
253
563
455
319
88
108
399
274
465
9
394
138
32
207
571
291
505
588
207
124
29496
26705
25341
24006
27973
32335
27152
27241
27849
23156
53882
23537
32145
35398
38093
41682
37150
24015
28388
29399
75639
33959
38000
98194
37465
36824
25986
43872
32036
28286
29456
26695
82920
47202
33926
29233
33074
57468
26391
39628
39361
29778
27641
26629
26946
24665
24074
28955
16.23
19.52
43.77
3.37
34.25
8.78
12
17.67
19.09
19.67
23.07
42.41
24.5
12.23
26.1
32.28
0
30.91
34.01
0
2.66
8.59
11.47
4.09
25.47
11.34
34.12
5.55
21.79
58.02
20.76
12.24
5.14
9.35
16.49
22.88
22.02
2.24
24.17
8.68
6.83
24.66
50.52
26.5
31.75
44.16
30.39
13.4
9.2
19.6
52.6
5.6
41.3
10.5
14.3
14.9
17.3
22.4
30.7
37.2
28.9
9.6
22.7
0
0
42.3
33.4
5.5
3.8
1
5.7
0
23
13.4
35.4
4.3
20.7
44.9
8.4
14.7
1.9
9.6
15.1
20.3
20.6
2.8
18.4
5
7.6
23.6
40.5
30.2
7.1
54.3
21.5
5.4
74

South Point Lawrence
South Range Mahoning
South-Western Franklin
Southeast Wayne
Southeast Portage
Southeastern Clark
Southern
Southern
Meigs
Columbiana
Southern Perry
Southington Trumbull
Southwest Licking Licking
Southwest Hamilton
Spencerville
Springboro Comm
Springfield
Springfield
Allen
Warren
Clark
Mahoning
Springfield
Springfield
Lucas
Summit
St Bernard-Elmwood Hamilton
St Clairsville-Richland Belmont
St Henry Mercer
St Marys Auglaize
Steubenville Jefferson
Stow-Munroe Falls Summit 79.25
Strasburg-Franklin Tuscarawas 68.44
Streetsboro Portage
Strongsville Cuyahoga
71.94
83.12
Struthers Mahoning
Stryker Williams
Sugarcreek Greene
Swanton Fulton
66.19
72.12
86
72.88
69.62
67.56
71.81
75.81
82.19
75.12
73.94
54.62
67
69
70.38
77.12
83.88
46.38
76.25
66.75
88.88
58.06
78.62
73.31
73.31
60.06
59.62
Switzerland of Ohio Monroe
Sycamore Community Hamilton
Sylvania Lucas
Symmes Valley Lawrence
Talawanda Butler
Tallmadge Summit
Teays Valley
Tecumseh
Pickaway
Clark
Three Rivers Hamilton
Tiffin Seneca
Tipp
Toledo
Miami
Lucas
Toronto Jefferson
Tri-County North Preble
Tri-Valley
Tri-Village
Triad
Muskingum
Darke
Champaign
76.69
67.19
81.06
50.94
69.19
71.25
71
66.94
61.88
67.31
84.88
78.69
63.31
72.75
77.25
66.19
68.19
-19.845
-40.695
-40.674
-10.553
24.374
-9.069
-99.26
25.878
-8.279
-2.293
36.341
-74.289
-7.063
47.439
-5.11
-66.716
9.083
-32.252
-28.578
26.34
5.58
12.02
2.9
-9.247
-2.746
6.61
7.29
-110.657 27.24
-64.892 14.24
-109.684 24.52
-14.305 6.38
3.591
-3.896
5.73
8.94
2.964
45.084
-58.672
-15.463
4.61
0.73
27.82
9.46
2.63
5.59
7.23
1.24
21.25
3.15
0.99
5.67
12.49
12.69
19.4
9.58
0.77
3.3
42.06
-78.845
49.787
18.53
2.47
35.025 3.8
-101.297 27.86
-0.172
11.766
-16.209
-26.563
5.5
5.58
8.04
10
-9.05
-21.891
9.39
7.44
21.823 2.56
-116.926 42.51
-46.303
6.86
-10.894
-10.072
2.035
21.14
6.07
6.11
3.75
4.03
120
393
291
61
444
282
32
260
352
414
296
188
71
205
237
577
427
374
335
158
53
590
176
432
15
554
131
249
249
536
540
169
423
84
582
369
314
320
428
519
421
43
130
502
267
155
444
403
42305
29785
25566
31987
31474
29941
28780
41378
28181
30467
46471
23591
28057
51529
32800
22356
31845
34741
32204
28694
54174
27168
27617
27164
36793
30928
25382
30823
28994
26123
25508
38050
27539
40293
28814
27987
33080
28686
26078
30945
24925
63277
55525
23543
34198
36526
30731
30177
6.77
11.47
18.83
4.59
45.13
13.37
0
16.54
27.06
31.69
11.94
17.56
3.43
16.51
42.58
54.32
13.47
12.22
12.76
21.12
3.26
45.32
31.12
34.04
20.03
27.46
25.76
16.06
14.75
52.54
37.96
19.11
21.79
5.61
56.03
27.65
18.35
15.67
21.5
12.18
42.84
5.42
7.2
45.08
14.87
9.58
22.5
27.34
6.1
19.4
6.7
4.3
31.5
18.6
3.1
15.7
22.6
26.1
34.9
15.4
2.9
19.2
43.4
53.2
26.3
13.2
14.4
0
5.1
12.7
2.5
33.5
2.1
23.7
25.3
17.4
9.7
57
38.2
18.6
20.2
10.3
47.2
25.5
1.8
17.8
10.9
12.7
42.4
5.6
9.5
51.9
14
9.6
16.4
19.4
75

Trimble
Triway
Athens
Wayne
50.62
74.62
Trotwood-Madison Montgomery 53.75
Troy Miami 71.56
Tuscarawas Valley Tuscarawas 79.25
Tuslaw Stark 74.19
Twin Valley Preble
Twinsburg Summit
73.31
75.56
Union-Scioto Ross
United Columbiana
Upper Arlington Franklin
Upper Sandusky Wyandot
66.31
74.19
88.19
72.5
Upper Scioto Valley Hardin
Valley Scioto
Valley View
Van Buren
Montgomery
Hancock
63.5
59.25
67.81
76
Van Wert Van Wert 72.81
Vandalia-Butler Montgomery 76.06
Vanlue
Vermilion
Hancock
Erie
65
66.94
Versailles Darke
Vinton County Vinton
Wadsworth Medina
81.88
61.56
80.31
Walnut Township Fairfield
Wapakoneta Auglaize
Warren
Warren
Trumbull
Washington
Warrensville Heights Cuyahoga
Washington Ct. Hse Fayette
Washington Lucas
Washington-Nile Scioto
67.06
74.5
48.38
68.5
58.62
67.62
71.94
55.56
Waterloo
Wauseon
Waverly
Wayne
Portage
Fulton
Pike
Warren
Wayne Trace Paulding
Waynesfield-Goshen Auglaize
Weathersfield Trumbull
Wellington Lorain
Wellsville Columbiana
West Branch Mahoning
68.88
77.19
West Carrollton Montgomery 69.38
West Clermont Clermont 72.44
West Geauga Geauga
West Holmes Holmes
West Liberty-Salem Champaign
West Muskingum Muskingum
Western Brown Brown
83.5
71.12
73
70.75
62.38
71.56
79
64.88
74.38
69.12
76.94
72.12
68.62
-7.806
-11.633
-66.574
17.506
1.254
-1.005
-37.382
10.584
-61.513
-18.7
-3.338
-5.614
48.284
-6.578
8.904
-9.839
-46.37
-87.968
3.863
-89.288
-5.229
-14.793
2.44
-3.541
0.35
-11.256
-28.752
66.766
-7.251
-32.352
-85.353
17.78
32.356
-19.068
21.161
3.272
-0.869
16.944
-72.752
7.804
-26.416
-7.129
7.45
4.69
-117.695 41.68
-15.566 7.91
-18.359
-40.794
-28.511
-96.653
26.87
14.55
12.05
30.7
5.1
2.88
3.87
4.68
0.54
19.07
3.47
11.3
6.77
0.25
3.13
7.66
27.66
2.8
1.13
31.81
4.12
24.2
7.22
3.46
5.71
6.5
7.46
23.51
5.83
6.63
6.72
1.02
3.96
1.94
8.84
9.64
6.15
3.75
19.25
3.13
7.02
3.51
12.59
4.15
425
222
587
390
548
410
291
568
262
180
464
428
74
521
94
438
231
17
274
498
544
409
181
584
217
580
307
120
231
249
194
378
156
360
276
58
317
258
324
514
307
125
466
224
371
165
282
387
27972
40161
29622
36321
30774
24938
36324
31914
29301
28235
29704
27061
26046
31239
25277
33404
28088
70136
27829
25938
26777
33990
38196
20752
30073
29822
36371
30697
30330
30619
39950
23407
30060
32082
32606
54984
25542
31334
34901
25740
30214
30277
28466
39106
28384
30135
28968
32604
23.98
16.14
50.85
20.06
17.45
26.09
21.4
46.53
21.44
7.72
13.78
14.51
4.99
39.32
12.45
17.86
21.67
0.92
15.45
22.33
41.27
6.31
4.71
41.01
13.39
49.91
15.98
17.43
20.78
23.76
15.14
23.91
21.53
14.19
14.1
3.88
25.76
8.89
16.7
26.27
17.87
16.16
36.39
9.37
20.11
11.63
29.26
17.17
26.9
15.6
53.4
17.3
1.1
26.2
26.3
44.7
20.5
8.4
8.7
18
8.3
39.3
12.6
15.5
28.4
2.2
16.5
28.3
43.2
7.1
0
35.9
8.7
45
18.4
24.6
1.4
3.9
17
37.5
21.4
14.6
17.4
1.8
2.4
11.6
19.2
36.2
14
22
39.4
9.1
0
16
24.5
0.7
76

Western Pike
Western Reserve Mahoning
Western Reserve Huron
Westfall Pickaway
Westlake Cuyahoga
Wheelersburg Scioto
Whitehall
Wickliffe
Franklin
Lake
Willard Huron
Williamsburg Clermont
Willoughby-Eastlake Lake
Wilmington Clinton
66.12
69.19
77.19
68.38
Windham Portage
Winton Woods Hamilton
59.31
69.5
Wolf Creek Washington 72.75
Woodmore Sandusky 80.56
56
73.75
66.06
58.25
83
73.62
59.44
70.69
Woodridge Summit
Wooster Wayne
Worthington Franklin
Wynford Crawford
Wyoming Hamilton
Xenia
Yellow Springs
Greene
Greene
Youngstown Mahoning
Zane Trace Ross
Zanesville Muskingum
76.25
77
81.75
73.38
92.5
62.44
84.31
51.19
56.62
63.19
176
163
75
248
4
513
48
581
561
504
447
369
156
394
543
355
267
88
565
240
448
550
63
243
541
325
-143.851 36.03
18.97 2.46
-12.582
-24.925
4.75
8.59
55.119
-36.014
-50.88
-8.523
2.28
17.21
15.03
7.18
-43.459
3.08
-12.787
-16.602
-89.289
-6.502
-28.37
-4.004
14.06
10.17
4.86
10.57
24.31
8.84
1.53
9.76
-12.978
-20.972
46.895
-13.869
64.175
-33.16
13.849
-173.083 58.63
-3.785 7.37
-86.593 26.91
16.26
9.04
0.87
6.03
2.7
15.3
8.84
43442
37828
52935
28311
73965
31090
38979
22417
31075
25977
27841
32210
32883
31128
24341
35478
25890
32946
21519
34140
29468
30265
58699
29716
26410
29367
22.16
24.56
4.27
17.15
4.59
26.65
7.99
71.67
15.99
50.26
29.34
17.66
18.61
20.36
43.12
18.84
28.03
13.39
60.64
12.71
18.5
21.3
0
22.12
32.56
16.91
18
25.2
0.9
19
2.5
22.3
8.3
65.2
11.5
35.4
27.9
1.3
22.2
16.8
46.2
14.3
24.7
13.8
68.7
0
18.8
25.3
1.3
26.4
29.7
13.8
77

School
District
Appendix B
Actual District Performance
(Peformance Controlling for Presage Scores)
County Rank Performance z-Score
Presage z-Score
New Boston 15
Steubenville
South Range
Madeira
Bloomfield-Mespo
LaBrae
Delphos
McDonald
Mariemont
Grandview Heights
Miller New Clev.
Northridge
Scioto
Jefferson
Mahoning
Hamilton
Trumbull
Trumbull
Allen
Trumbull
Hamilton
Franklin
Putnam
1
2
3
4
5
6
7
8
9
10
11
Montgomery 12
Perry
East Guernsey
Lake
Guernsey
13
14
Perry
Mayfield
Stark
Cuyahoga
Benton Carroll Salem Ottawa
Berlin-Milan Erie
15
16
17
18
Campbell
Nelsonville-York
Southeast
East Holmes
Clearview
Newcomerstown
Bexley
Ottawa-Glandorf
Mahoning
Athens
Wayne
Holmes
Lorain
Tuscarawas
Franklin
Putnam
23
24
25
26
19
20
21
22
Garaway
Girard
Green
Chesapeake Union
Tuscarawas
Trumbull
Wayne
Lawrence
North Canton
East Palestine
Stark
Columbiana
Lisbon Columbiana
Cleveland Hts-Univ Hts Cuyahoga
Kent Portage
Lordstown
New Bremen
Sebring
Solon
Trumbull
Auglaize
Mahoning
Cuyahoga
31
32
33
34
35
27
28
29
30
36
37
38
39
3.74
1.61
1.61
1.61
1.6
1.59
1.56
1.56
1.55
1.54
1.52
1.77
1.76
1.72
1.72
1.7
1.7
1.69
1.66
1.49
1.48
1.43
1.42
1.86
1.85
1.83
1.82
1.8
1.8
1.8
1.78
3.14
2.42
2.07
1.96
1.93
1.91
1.89
1.88
15 EMIS data may be incorrect for this district, therefore the ranking may be invalid.
-3.26
0.47
-0.04
-0.7
-1.67
-1.43
0.88
-1
-0.56
-1.78
-0.3
0.5
-2.42
-2.02
0.15
0.3
-2.1
-1.41
0.76
0.12
0.89
-0.62
1.63
0.76
0.73
0.38
-1.03
-1.02
0.65
0.93
0.16
-2.03
0.59
1.39
-1.2
-1
-0.19
0.35
1.33
Presage
Raw
Score
-149.99
5.43
-115.4
-98.86
-9.25
-3.09
-101.82
-73.69
15.98
3.85
-17.03
-44.25
-84.43
-74.22
20.85
-56.42
-38.52
-88.96
-27.92
-10.22
21.19
-40.77
51.85
16.09
14.67
0.33
-58.05
-57.31
11.26
22.92
-8.7
-99.26
9.08
41.87
-64.8
-56.77
-23.4
-1.12
39.37
78

Athens
Fort Loramie
Yellow Springs
Tuscarawas Valley
Louisville
Fort Recovery
Perrysburg
Pandora-Gilboa
Russia
Gibsonburg
Columbiana
Maplewood
Aurora
Granville
Kalida
Minster
Poland
Oakwood
Youngstown
Wyoming
Indian Valley
Woodmore
Boardman
Switzerland
Western
Cardinal
Wauseon
ColumbusGrove
Carrollton
Hubbard
Symmes Valley
Bellaire
Lowellville
Fremont
Maumee
Wooster
Coldwater
Crestview
West Branch
Marion
Canfield
Napoleon Area
Joseph Badger
Cedar Cliff
Martins Ferry
Jackson
Lockland
Salem
Belmont
Mahoning
Sandusky
Lucas
Wayne
Mercer
Van Wert
Mahoning
Mercer
Mahoning
Henry
Trumbull
Greene
Belmont
Stark
Hamilton
Columbiana
Athens
Shelby
Greene
Tuscarawas
Stark
Mercer
Wood
Putnam
Shelby
Sandusky
Columbiana
Trumbull
Portage
Licking
Putnam
Auglaize
Mahoning 56
Montgomery 57
Mahoning
Hamilton
58
59
Tuscarawas
Sandusky
Mahoning
60
61
62
Monroe
Pike
Geauga
Fulton
Putnam
Carroll
Trumbull
Lawrence
67
68
69
70
63
64
65
66
52
53
54
55
48
49
50
51
44
45
46
47
40
41
42
43
83
84
85
86
87
79
80
81
82
75
76
77
78
71
72
73
74
1.26
1.26
1.26
1.25
1.25
1.24
1.24
1.24
1.32
1.32
1.31
1.29
1.29
1.28
1.28
1.36
1.35
1.35
1.35
1.34
1.34
1.34
1.32
1.42
1.41
1.41
1.39
1.38
1.38
1.37
1.37
1.1
1.09
1.09
1.08
1.08
1.07
1.06
1.05
1.04
1.22
1.22
1.2
1.19
1.17
1.17
1.16
1.14
32.85
75.88
-173.08
64.18
-48.81
-4
10.52
-78.85
-143.85
-22.9
-11.63
-9.74
-50.4
-36.53
-101.3
24.93
-28.74
-22.56
-22.89
49.81
56.11
30.72
30.9
-42.03
16.5
13.85
-14.79
-13.84
-0.1
43.35
6.73
23.63
43.78
-6.59
-33.18
4.42
-58.96
38.15
-78.48
-29.1
-75.85
-38.83
-37.25
14.69
-20.97
11.76
-28.89
-18.7
-1.54
-3.11
-0.18
0.09
0.14
-0.85
-0.51
-2.08
1.17
2.21
-3.82
1.93
-0.81
0.28
0.63
0.98
-0.32
-0.17
-0.18
1.58
1.73
1.12
1.12
-0.65
0.77
0.71
0.01
0.04
0.37
1.42
0.54
0.94
1.43
0.21
-0.43
0.48
-1.06
1.3
-1.53
-0.33
-1.47
-0.57
-0.53
0.73
-0.14
0.66
-0.33
-0.08
79

Northwestern
Wheelersburg
Bay Village
Ravenna
Wayne
Scioto
Cuyahoga
Portage
Chardon
Ironton
Geauga
Lawrence
Wellsville Columbiana
Brecksville-Broadview Cuyahoga
Barnesville
Dawson-Bryant
Belmont
Lawrence
Field Portage
Willoughby-Eastlake Lake
Perry
Southern
Plain
Jefferson Area
Allen
Columbiana
Stark
Ashtabula
Eastwood
Forest Hills
Independence
Edison
Struthers
United
Leipsic
Wood
Hamilton
Cuyahoga
Jefferson
Mahoning
Columbiana
Putnam
Mason
Versailles
Defiance
Northwest
Warren
Darke
Defiance
Scioto
Wadsworth
Springfield
Medina
Lucas
St Bernard-Elmwood Hamilton
Lake Stark
Green
Sandy Valley
Ottoville
Celina
Copley-Fairlawn
Highland
Milton-Union
Fairland
Kenston
East Knox
Indian Creek
Weathersfield
North Olmsted
Pickerington
Crestwood
Niles
Woodridge
Scioto
Stark
Putnam
Mercer
Summit
Morrow
Miami
Lawrence
Geauga
Knox
Jefferson
Trumbull
Cuyahoga
Fairfield
Portage
Trumbull
Summit
111
112
113
114
115
116
117
118
104
105
106
107
108
109
110
96
97
98
99
100
101
102
103
92
93
94
95
88
89
90
91
127
128
129
130
131
132
133
134
135
119
120
121
122
123
124
125
126
0.86
0.86
0.86
0.85
0.85
0.85
0.85
0.85
0.93
0.93
0.91
0.91
0.9
0.9
0.87
0.98
0.97
0.96
0.96
0.95
0.94
0.94
0.94
1.04
1.04
1.03
1.02
1.02
1.02
0.98
0.98
0.81
0.81
0.8
0.79
0.79
0.79
0.79
0.78
0.77
0.84
0.83
0.82
0.82
0.81
0.81
0.81
0.81
0.56
46.58
36.38
-45.71
-74.29
-28.75
-44.29
37.91
16.94
-10.88
-10.66
7.8
-19.84
-40.67
21.63
-62.11
-75.99
-12.09
-12.79
-14.72
-64.89
93.75
-42.04
8.16
-36.01
50.56
-55.12
20.02
-64.15
-61.51
44.79
44.23
-17.14
-29.91
-37.38
8.69
42.15
-2.01
-50.06
-12.98
9.62
-39.41
27.28
-7.51
32
34.26
9.01
-39.38
1.29
0.78
0.11
0.11
0.56
-0.11
-0.61
0.9
0.39
1.5
1.25
-0.74
-1.43
-0.32
-0.7
-1.13
-1.47
0.08
0.06
0.02
-1.2
2.64
-0.65
0.57
-0.5
1.6
-0.96
0.86
-1.18
-1.12
1.46
1.44
-0.04
-0.35
-0.53
0.58
1.39
0.32
-0.84
0.06
0.61
-0.58
1.03
0.19
1.15
1.2
0.59
-0.58
80

Carey
Rossford
Zanesville
Olmsted Falls
South Point
Crestview
Three Rivers
Avon
Wyandot
Wood
Muskingum
Cuyahoga
Lawrence
Richland
Hamilton
Lorain
Noble
Pettisville
Seneca East
Frontier
Noble
Fulton
Seneca
Washington
Hillsdale
Ayersville
Ashland
Defiance
James A Garfield Portage
St Henry Consolidated Mercer
Anthony Wayne
Rocky River
Anna
Amherst
Geneva Area
Sugarcreek
Evergreen
Lucas
Cuyahoga
Shelby
Lorain
Ashtabula
Greene
Fulton
Wolf Creek
Old Fort
Washington
Seneca
St Clairsville-Richland Belmont
Manchester Summit
Bath
Minerva
Richmond Heights
Harrison Hills
Allen
Stark
Cuyahoga
Harrison
Northridge Licking
Loudonville-Perrysville Ashland
Miamisburg
Toronto
Montgomery
Jefferson
167
168
169
170
Revere
Liberty Un Thurston
Bowling Green
Tipp City
Summit
Fairfield
Wood
Miami
171
172
173
174
Arcanum Butler
Cuyahoga Heights
New Richmond
Liberty
Darke
Cuyahoga
Clermont
Trumbull
Bethel-Tate
Little Miami
Clermont
Warren
Waynesfield-Goshen Auglaize
Reading Community Hamilton
New Riegel Seneca
175
176
177
178
179
180
181
182
183
159
160
161
162
163
164
165
166
152
153
154
155
156
157
158
144
145
146
147
148
149
150
151
136
137
138
139
140
141
142
143
0.63
0.62
0.61
0.61
0.58
0.58
0.56
0.56
0.66
0.65
0.65
0.64
0.64
0.64
0.63
0.71
0.71
0.71
0.68
0.68
0.68
0.67
0.67
0.76
0.76
0.75
0.75
0.74
0.74
0.73
0.72
0.54
0.53
0.53
0.53
0.52
0.52
0.52
0.5
0.5
0.56
0.56
0.56
0.55
0.55
0.55
0.55
0.55
28.04
43.34
11.6
21.47
-58.14
47.44
-11.37
-28.37
1.24
-10.55
12.42
-7.7
-30.84
17.82
-67.99
-53.71
16.62
5.93
-70.37
4.74
18.69
-17.58
24.37
-5.36
-1.94
-86.59
19.27
-66.72
-23.67
-9.05
23.05
-106.72
-27.45
14.23
-46.3
68.04
-13.77
-11.6
21.82
3.61
22.3
-42.68
7.46
-16.55
7.97
-1
14.46
22.77
-0.32
0.4
0.12
0.67
0.19
-0.38
0.8
-1.28
1.05
1.42
0.65
0.89
-1.04
1.52
0.1
-0.93
0.78
0.52
-1.33
0.49
0.83
-0.05
0.96
0.24
0.33
-1.73
0.84
-1.24
-0.2
0.15
0.93
0.46
0.91
-0.66
0.55
-0.03
0.57
0.35
0.72
0.92
-2.21
-0.29
0.72
-0.75
2.02
0.04
0.09
0.9
81

Lima
Elmwood
Bradford
Marlington
Finneytown
Austintown
Lakeview
Washington
Newbury
Canton
Chagrin Falls
Circleville
Highland
Milford
Rock Hill
Brunswick
Centerville
Mathews
Jonathan Alder
Portsmouth
Strongsville
Allen
Wood
Miami
Stark
Hamilton
Mahoning
Trumbull
Lucas
Geauga
Stark
Cuyahoga
Pickaway
Medina
Clermont
Lawrence
Medina
Montgomery 200
Trumbull 201
Madison
Scioto
Cuyahoga
202
203
204
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
0.48
0.48
0.47
0.47
0.47
0.47
0.47
0.47
0.5
0.5
0.49
0.49
0.49
0.48
0.48
0.48
0.45
0.45
0.45
0.45
0.45
0.59
-2.66
2.54
-0.6
-0.5
0.76
-2.18
0.65
-2.54
-0.22
-0.28
-0.19
0.72
-0.16
0.75
-0.32
1.57
0.1
0.25
-2.49
1.25
-120.19
-24.63
-26.97
-23.04
14.46
-22.05
15.66
-28.51
9.17
-125.05
89.25
-40.15
-35.94
16
-105.38
11.59
49.5
-11.43
-5.1
-118.05
36.34
82