optimizing the efficiency of tennessee prekindergarten ... 5...1eds, mba,doctoral student, college...

© 2016 Research Academy of Social Sciences

http://www.rassweb.com 51

Journal of Empirical Economics

Vol. 5, No. 1, 2016, 51-73

Optimizing the Efficiency of Tennessee Prekindergarten

Through Twelfth Grade Public Education: Urban-Centric Locales

J. Eric Ellerbrook1, Ben T. Phillips2, Andrew A. Tiger 3, Norma S. Gerrell4

Abstract

Previous studies that assess public school district efficiency have been restricted in practical research. Most

studies base efficiency on student achievement, which can be convoluted due to the demographics of a public

school district. This research utilized student achievement, student value-added gains, as well as graduation

rates of individual school districts. Specifically, this study sought to identify the most efficient public school

districts in Tennessee to advocate for a collaborative better business practice approach in public education.

After identifying the significantly correlated variables the researcher utilized those variables to perform a

data envelopment analysis to determine a relative efficiency rating of each school district in Tennessee.

Moreover, an efficiency reference set was utilized to determine the most efficient school districts regarding

urban-centric locales in the state of Tennessee.

Keywords: economics of education, input and output analysis, program evaluation, rural education, urban

education

1. Introduction

Corporations worldwide strive for a level of performance those results in efficiency. Business efficiency

relates to the use of all the industry inputs for an optimal output, which enables the company to contend with

other national or international competitors. Comparatively, the business of public education within the

United States is not only competing nationally to be more efficient with maximum output but more

importantly, the United States public education system is competing on a global scale. The business realm of

Tennessee public education will be studied throughout this research to determine the efficiency of school

systems to enable the collaboration among the responsible stakeholders for better business practices.

In the United States, there have been legislative laws passed by Congress to improve the efficiency of

the overall public education system. Thus, the No Child Left Behind (NCLB) Act of 2001 was enacted to

provide aid to disadvantaged students in public education. The NCLB requires states to test students in order

to receive federal school funding resulting from annual academic progress. Moreover, the NCLB considers

the student achievement between a rural school district with less than 100 students compared to an enormous

inner-city school with more than 2,500 students to be equal. Furthermore, equating the two conflicting

extremes of school districts as equivalent is rudimentary and indefensible. School districts with severe

discrepancy variables would not permit the data to be analogous, therefore leaving an ambiguous analysis.

(Crow 2010).

1EdS, MBA,Doctoral Student, College of Education, Union University. 2EdD,Chair, Department of Education Leadership, Professor of Education Leadership, & Director of Ed.S. and Ed.D.,

Jacksonassociate Dean of Education, College of Education & Human Studies, Union University. 3PhD,Acting Chair, director of Accreditation and Professor of Management,McAfee School of Business, Union

University. 4EdD, Interim Chair, Department of Educational Studies, University of Tennessee at Martin.

J. E. Ellerbrook et al.

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Statement of the Problem

In 2010, the Tennessee Department of Education was granted $501,000,000 by RTTT to increase the

public Prekindergarten through 12th grade (PK-12) educational system (State Collaborative on Reforming

Education 2010). Many researchers have studied the correlation between school district expenditures and

student achievement. Contrary to this, there is limited research on the correlation of school district

expenditures related to student growth scores, ACT achievement scores, and graduation rates. Moreover,

there is a lack of research to determine the efficiency of Tennessee school districts. Thus, a data envelopment

analysis (DEA) was conducted to determine school district efficiency for the state regarding all public P-12

and K-12 school districts. Only when such ratings are available can the problems experienced by all the

school districts be addressed to understand efficiency in education.

Purpose of the Study

The purpose of this study was to provide a better comprehension of educational efficiency for

Tennessee school districts. This specific study defines educational efficiency as school districts that utilize

their resource inputs to optimally grow the individual school districts’ outcomes (value-added gains, ACT

composite score, and graduation rates) to produce highly efficient public school districts. The relative

efficiency of each school district is scored on a scale from 0% - 100% (Standard & Poor’s 2007).

Additionally, an education DEA was conducted to determine the efficiency of Tennessee school districts to

determine the most efficient school districts in Tennessee in regards to the independent variables within this

study. Therefore, the purpose of the study was to identify the school district variables that positively or

negatively affect the dependent variables of the school district scores regarding student growth regarding the

Tennessee Value-Added Assessment System (TVAAS): Tennessee Comprehensive Achievement Program

(TCAP) tests, End of Course (EOC) tests, and ACT tests - composite scores. The last variable regarding the

dependent variables is the graduation rates related to the NCLB Act by utilizing a DEA.Therefore, this study

utilized a DEA as well as an efficiency reference set (ERS) to find the most efficient school districts in

Tennessee. Most studies define efficiency as, “obtaining the maximum possible performance for any given

expenditure of resources” (Hanushek 1993, p. 64).

Significance of the Study

This study aimed to compare Tennessee school district scores of the TCAP testing, EOC testing, and

ACT comprehensive testing related to student achievement scores/TVAAS growth to school districts. The

student achievement scores and TVAAS growth index scores are used to determine if there are statistical

differences between the variables of ethnicity, economically disadvantaged status, school expenditures

(instructional and non instructional), and teacher-student ratio utilizing an education production function

(EPF). Research has indicated that there are statistical differences in various student variables regarding

student achievement. Moreover, the significance of this study is to differentiate Tennessee public school

districts by utilizing a DEA.

Similarly, this study intended to find the discrepancies in the education financial variables within the

DEA that were related to TVAAS growth scores and ACT achievement scores, as well as graduation rates

for Tennessee school districts.Additionally, do Tennessee school districts have efficient budget categories

that are related to the independent variables that tend to enable favorable student growth scores and ACT

achievement scores, as well as graduation rates?

1. To what extent can the independent variables predict the dependent variables regarding the academic

average for the years of 2011-2012, 2012-2013, and 2013-2014?

a. TCAP Math: Grades 3-8 (TVAAS scores)?

b. TCAP English Language Arts:Grades 3-8 (TVAAS scores)?

c. TCAP Science: Grades 3-8 (TVAAS scores)?

d. TCAP Social Studies: Grades 3-8 (TVAAS scores)?

e. EOC Math: Algebra I and Algebra II (TVAAS scores)?


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f. EOC Science: Biology I and Chemistry (TVAAS scores)?

g. EOC English: English I, English II, and English III (TVAAS scores)?

h. EOC U.S. History (TVAAS scores)?

i. ACT composite (TVAAS composite achievement scores)?

j. Graduation rates (No Child Left Behind)?

2. What are the school district efficiency ratings per urban-centric locale in the state of Tennessee

regarding the academic average for the years of 2011-2012, 2012-2013, and 2013-2014?

2. Review of Literature

The purpose of this literature review was to synthesize, analyze, and evaluate research relating to the

impact of educational efficiency regarding the utilization of a DEA for the Tennessee public school districts

regarding urban-centric locales. Researchers have looked for the correlation between inputs to education and

the outputs of student achievement for several decades to better understand an efficient and adequate

education. The confounding answer to this relationship has become the "Holy Grail" of school financing

research (Knoeppel, Verstegen, and Rinehart 2007). Most researchers who attempted to interpret the

relationship between the inputs and outputs of education primarily utilized the EPF and more recently have

used the DEA method. There is very limited research or background on the DEA. Therefore, this literature

review will delve into the beginnings of the educational multiple inputs and multiple outputs relationship to

better understand the use of efficiency in Tennessee public school districts related to efficiency.

Within the literature review the following topics are discussed: (a) education production function

research utilizing original studies and (b) data envelopment analysis research utilizing original studies.

Education Production Function Research Utilizing Original Studies

The primary source to which most researchers in the education field refer, and which utilizes the EPF

that specifically related to student achievement and school expenditures, was performed by Coleman et

al.(1966). This educational cornerstone study has been more famously known as the “Coleman Report.”The

Equality of Educational Opportunity Study was commissioned by the United States Department of Health,

Education, and Welfare. Furthermore, the purpose of this study was to assess the availability of equal

educational opportunities to children of different race, color, religion, and national origin. The independent

variables in this study that included students and teachers as well as school administrators (Coleman et al.

1966).

The Coleman Report was commonly presented as documentation that school funding has little effect on

student achievement. However, the report did find that the students’ background and socioeconomic status

were much more important in determining educational outcomes than increases in school funding.

Additionally, teachers have a significant impact on student outcomes according to the Coleman Report.

Furthermore, the report supplied evidence that changing conditions in multiple schools could lead to various

outcomes for different groups of students. In the final analysis, Coleman et al. (1966) tested the per pupil

expenditure variable in the analysis but found it to have ambiguous results. Moreover, several legislators

throughout the country as well as public school detractors bought into the Coleman Report, which gave them

an assertion that school inputs didn't matter and that money did not make a difference for educational

problems (Coleman et al. 1966).

School district and student inputs were comprised of the school environment (i.e., student-teacher ratio

[class size], physical facilities, per pupil expenditure, school policies, socioeconomic ratio, and race ratio),

teacher quality (i.e., experience, ability, and education levels of teachers), and student inputs (i.e., family

size, amount of education of parent(s), race, and socioeconomic class). Additionally, the researcher implied

that the educational system accomplishes two primary

Hanushek’s (1986) research surveyed 147 previous studies related to education production functions.

Most of the research incorporated per pupil expenditures, teacher education, student/teacher ratio, and


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teacher experience; additionally the surveyed studies included family characteristics as the prime factors of

student achievement. Hanushek’s research concluded that there is no significant correlation between school

expenditures and school achievement; contrary to this, the results indicated that family characteristics do

have significant correlation related to student achievement (Hanushek 1986).

The study related to the current study of EPFs is Hanushek’s (1989) research, which was conducted to

better understand the impact of expenditures correlated to school performance within public schools.

Hanushek’s research utilized the aggregate measures of per pupil spending in school systems and student

achievement, anticipating a positive relationship between the two measures (Hanushek’s 1989). Hanushek

studied over 100 EPFs that utilized many different independent variables to study the nature of school

expenditures. The dependent variable was the measurement of student achievement according to a specified

standardized test score. The independent variables that were used in most of the studies included the

following: facility expenditures, per pupil expenditure, administrative inputs, teacher experience, teacher-

pupil ratio, and teacher education. Hanushek’s results showed only a 7% positive relationship between the

input and the output variables, while the teacher experience input variable had a more statistically significant

relationship at 29% (Hanushek 1989).

At the conclusion of the research, Hanushek (1989) noted that there was no strong evidence between

that of the teacher-student ratios, teacher education, or teacher experience that correlated to any positive

outcomes for student achievement. Hanushek’s findings suggested that school decision-making must move

away from traditional “input directed” policies to ones that provide performance-enhanced incentives for

educators that would move in the opposite direction of most school systems’ organizational structures.

Hanushek found that as much as 80% of regression models did not indicate a statistically significant

relationship between expenditures and student performance utilizing the EPF. Subsequently, this study

established that it would be difficult and controversial for researchers to correlate funds spent on education

and specific student achievement out comes.In conclusion, Hanushek noted that there were not enough

sufficient studies that were significantly correlated to either expenditures per pupil or characteristics of

facilities in regards to student achievement outcomes (Hanushek 1989).

However, several studies have investigated the meta-analyses relationship of the EPFs that examine the

educational inputs and student achievement of school districts. For instance, a study that refuted Hanushek’s

(1989) work was Hedges, Laine, and Greenwald’s (1994), who researched the correlation between education

budget allocations and school performance. A study that refuted Hanushek’s (1989) was conducted by

Hedges et al. (1994), which researched the relationship between education budget allocations and school

performance. Hedges et al. (1994) utilized Hanushek’s (1989) data from his study, however he arrived at a

completely different conclusion. The education in the meta-analysis technique utilized in Hedges et al.’s

research consisted of the input variables for per pupil expenditures, teacher experience, teacher education,

teacher salary, teacher-pupil ratio, administrative inputs, and facilities (Hedges et al. 1994).

The first results of the reanalysis using Hanushek’s (1989) EPF studies data indicated a positive

relationship between inputs and outcomes excluding the variable of facilities (Hanushek 1989).The second

outcome from the Hedges et al. (1994) study, referred to as the effect size analysis, resulted in a significant

positive effect for per pupil expenditure and teacher experience. Furthermore, Hedges et al. found an

overwhelming number of significant positive relationships between school expenditures and student

outcomes, concluding that money does matter for student performance. Consequently, the findings of Hedges

et al. led to the final conclusion in this study that school expenditures do matter in some special situations,

but that “throwing money at schools” is not an effective approach to improving student achievement (Hedges

et al. 1994).

Data Envelopment Analysis Research Utilizing Original Studies

More recently, researchers have been utilizing the nonparametric DEA method to better understand the

efficiency of public school districts. The DEA has very limited research related to education efficiency,

which is contrary to the education production function (Cooper et al. 2004). Bessent and Bessent (1980) were


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some of the first researchers to introduce the DEA to the educational field. The researchers began research by

explaining in detail the DEA model, since it was moderately new at the time. Afterwards, the study began

specifying that an urban/city school district was chosen that had an enrollment of 60,000 students. The 55

elementary schools were used as the decision-making units (DMU). The dependent variable for the research

was conveyed as the median student achievement (reading and mathematics). The independent variables for

the study included were home conditions (percent of Anglo Americans, percent of students not from low-

income families, percent in average daily attendance, and total enrollment), school conditions (number of

professional staff per 100 pupils and total per pupil expenditure for instruction), school organizational

climate questionnaire results (teacher job satisfaction, social interaction between teachers, motivation of the

principal, principal’s friendliness, and cooperativeness with the teacher), and classroom instructional

processes which includes total individualize instruction index and the results taken from an inventory

(Bessent and Bessent 1980).

The results of the Beseent and Bessent(1980) DEA study showed that there were 31 schools that were

efficient among the 55. The researchers’ study displayed individual schools that were inefficient relative to

the input factors and student achievement. The researchers noted that it was beyond the scope of the study to

determine administrative reallocation of finances to achieve a better efficiency. However, they noted it was

vital to identify inefficient units that contribute to efficiency status related to the inputs and outputs for the

educational administrative decision-making process (Bessent and Bessent 1980).

Melvin and Sharma (2007) researched three types of school districts in the state of Illinois to study their

technical efficiency: unit school districts (USD), which include elementary and secondary schools; high

school districts (HSD); and elementary school districts (ESD), which include only elementary schools.

Furthermore, the DEA independent variables to this study include per student operating expense, teacher-to-

student ratio, and assessed property value per student. Also noted was that the production function would

change over time, so time trend was included as a input. Additionally, more independent variables included

percentage of low-income students, involvement of parents, percentage of male teachers, percentage of

teaches who held a master’s degree, size of the school district, and ratio of student to administrator (Melvin

and Sharma 2007).

The results of the Melvin and Sharma (2007) associated with technical efficiency in Illinois school

districts rendered the USD of an average of 90%, the ESD had an average of 85%, and the HSD had an

average of 82%. Furthermore, results were given for each individual school to show relevance related to

efficiency. A positive correlation associated with inefficiency of a public school district is the enrollment

size; additionally the percentage of free and reduced lunch students are positively related to inefficiency.

Furthermore, a positive correlation exists with efficiency between school districts that have a larger

proportion of certified teachers with advanced degrees. Moreover, school districts that have a lower

percentage of students per administrator increases technical efficiency (Melvin and Sharma 2007).

The landmark study for this research was conducted by the Standard and Poor’s School Evaluation

Services (2007). The subject of the study was to research the efficiency of 257 public school districts in the

state of Kansas for the 2004-2005 and 2005-2006 academic years. The study was not conducted to answer

how much money a school district should spend, related to adequacy. Rather, the study was conducted to

determine how efficiently a school district was spending money that was appropriated to the district.

Educational efficiency was determined by the student performance on the Kanas State Assessments of

reading, and mathematics related to the money spent by the school districts. Constraint variables within the

study included the percentage of students related to economically disadvantaged backgrounds, disabilities,

and limited English proficiency. The primary input variable for the research was per pupil expenditures for

each school district. The study utilized a nonparametric DEA model that ranged from 0% - 100% to discover

the list of most cost efficient school districts in Kansas by benchmarking. In conclusion, the study was able

to separate the efficiency scores of the Kansas public school districts by enrollment, region, and locale. The

report was conducted to allow for the school districts in Kansas to use a better business practice type

approach to have schools become more efficient (Standard and Poor’s 2007).


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This specific research is different from the previous studies that utilized the DEA due to the correlated

independent variables to the public school district outcomes. Previous studies used predetermined variables

that are assumed to contribute to education efficiency. Additionally, this groundbreaking research uses an

ERS to determine the recommended model school districts to emulate.

3. Method

Purpose of the Study

Based on previous studies, most states have based educational efficiency for school districts with a

correlation to student achievement, a criterion-referenced-based test that is related to state standards

(Coleman et al., 1966; Hanushek, 1986, 1989; Hedges et al., 1994; Knoeppel et al., 2007).However, few, if

any, studies have examined the efficiency of school districts in the state of Tennessee specifically related to

value-added scores. The bulk of public school efficiency research has been associated with student

achievement. This particular study was focused on student value-added gains related to value-added models

(VAM) and school district efficiency for a business efficiency model.More specifically, do Tennessee school

districts have efficient budget or demographic categories that are related to the independent variables that

tend to enable favorable, TVAAS student growth, ACT composite achievement scores, and/or graduation

rates associated with NCLB.

Research Questions



a. TCAP Math: Grades 3-8 (TVAAS scores)?

b. TCAP English Language Arts: Grades 3-8 (TVAAS scores)?

c. TCAP Science: Grades 3-8 (TVAAS scores)?

d. TCAP Social Studies: Grades 3-8 (TVAAS scores)?

e. EOC Math: Algebra I and Algebra II (TVAAS scores)?

f. EOC Science: Biology I and Chemistry (TVAAS scores)?

g. EOC English: English I, English II, and English III (TVAAS scores)?

h. EOC U.S. History (TVAAS scores)?

i. ACT composite (TVAAS composite achievement scores)?

j. Graduation rates (No Child Left Behind)?

2. What are the efficiency ratings for all Tennessee school districts regarding the academic average for

the years of 2011-2012, 2012-2013, and 2013-2014?

Variables

The data were collected from the Tennessee Department of Education Website associated with the

yearly Report Cardthe U.S. Census Bureau, and the National Center for Educational Statistics. The school

district academic years researched within the study include 2011-2012, 2012-2013, and 2013-2014.

o Independent Variables: School District Finances

The independent variables, referred to as the school district financial variables for public school districts

in the state of Tennessee by 3-year averages, were the following: average salary for a classroom teacher,

percentage of teachers with advanced degrees, student average daily attendance (ADA)/teacher ratio, per

pupil expenditures per ADA, total instructional expenditures, total non instructional expenditures, and total

current expenditures (Tennessee Department of Education 2012b; 2013b; 2014b).


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o Independent Variables: School District Fiscal Indicators

The independent variables, referred to as the school district fiscal indicator variables for public school

districts in the state of Tennessee by 3-year averages are: median value of owner-occupied housing units, per

capita personal income, and percentage of persons 25 or older that were high school graduates or higher

(United States Census Bureau 2015).

o Independent Variables: School District Demographics

The independent variables, referred to as the school district demographic variables for public school

districts in the state of Tennessee by 3-year averages, were the following: percentage of free and reduced

lunch students, minority percentage, percentage of student with disabilities, and percent of English

learners(Tennessee Department of Education 2012a; 2013a; 2014a).

o Dependent Variables

The dependent variables studied within the research consist of Tennessee school districts’ student

growth scores, college readiness scores, and graduation rates. The student growth scores are broken into two

categorical tests that students take in Tennessee. The first category of testing is the TCAP, which was given

to Grades 3, 4, 5, 6, 7, and 8 in the subjects of math, English language arts ELA, science, and social studies.

The second testing category that wasgiven to students in Tennessee was the EOC tests. These sets of tests are

given to ninth-, 10th-, and 11 in the subjects of math (Algebra I and Algebra II), English (English I, English

II, and English III), science (Biology I), and history (U.S. History). The more academically advanced

students taking the Algebra I EOC in eighth grade was the only exception of not taking the EOC in the high

school grades (Tennessee Department of Education 2012a; 2013a; 2014a).

Another dependent variable used in this research was the ACT composite, which has been associated

with college readiness scores. Furthermore, every Tennessee school district requires 11th graders to take the

ACT exam (Tennessee Department of Education 2014a).The last dependent variable in relation to this study

was school district graduation rates in Tennessee. The graduation rate for school districts in Tennessee is

associated with the NCLB Act of 2001 (Balfanz,Bridgeland, Bruce, and Fox 2012). EOC Chemistry I was

not a dependent variable used in this research because the test was included in the battery of EOC testing for

the academic year of 2013-2014. Therefore, there was not any data available for a 3-year average pertaining

to EOC Chemistry I, according to the Tennessee state report card (Tennessee Department of Education

2014a).

o Independent Variables Removed Due To Mulitcollinearity

The independent variables that were removed from the study due to mulicollinearity, based on the

variation inflation factor (VIF) being greater than five consisted of several variables related to public school

districts in the state of Tennessee by 3-year averages. The removed variables from the research were: total

instructional expense, total non instructional expense, total current expenditures, classroom teacher salary

multiplied by student teacher ratio, median value of owner-occupied housing units/per average daily

attendance ratio, per capita personal income/per average daily attendance ratio, percentage funded by the

local government, percentage funded by the state government, and percentage funded by the federal

government.

Participants

The Tennessee Department of Education has a total of 117 PK-12/K-12 public school districts, which

were included within this research. Every PK-12/K-12 public school district within the Tennessee

Department of Education was utilized to determine the efficiency rating urban-centric locales, from the

average academic years of 2011-2012, 2012-2013, and 2013-2014.

An additional method utilized within the study was to determine efficiency by the Tennessee public


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school district locale data, which was collected from the National Center for Education (NCES). In 2006, the

NCES revised the school district locales to the new locale system named the urban-centric locale, which

allowed for more correctness in labeling an area. The locale system was revised due to changes related to the

population within the U.S. and the description of important geographical views. The locale codes were

determined by a database referred to as the Topographically Integrated and Geographically Encoded

Referencing (TIGER) system. The Tennessee Department of Education urban-centric locale code categories

are as follows (NCES, 2014b). For the purpose of this study, the Tennessee school districts were separated

per locale, which was determined by the urban-centric locale system referred to in Table 1 and Table 2.

Table 1: Tennessee Urban-Centric Locale Codes

Urban-Centric Locale Urban-Centric Code

City 11, 12, 13

Suburb 21, 22, 23

Town 31, 32, 33

Rural 41, 42, 43

Note. Adapted from Common Core of Data (CCD), by National Center for Education

Statistics, 2014a. Retrieved from http://nces.ed.gov/ccd/rural_locales.asp

Table 2: Locale Code Requirements

Urban-Centric Code Locale Requirement

11 City, large: Territory inside an urbanized area and inside a

principal city with population of 250,000 or more.

12 City, midsize: Territory inside an urbanized area and inside

a principal city with population less than 250,000 and

greater than or equal to 100,000.

13 City, small: Territory inside an urbanized area and inside a

principal city with population less than 100,000.

21 Suburb, large: Territory outside a principal city and inside

an urbanized area with population of 250,000 or more.

22 Suburb, midsize: Territory outside a principal city and

inside an urbanized area with population less than 250,000

and greater than or equal to 100,000.

23 Suburb, small: Territory outside a principal city and inside

an urbanized area with population less than 100,000.

31 Town, fringe: Territory inside an urban cluster that is less

than or equal to 10 miles from an urbanized area.

32 Town, distant: Territory inside an urban cluster that is more

than 10 miles and less than or equal to 35 miles from an

urbanized area.

33 Town, remote: Territory inside an urban cluster that is more

than 35 miles of an urbanized area.

http://nces.ed.gov/ccd/rural_locales.asp


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Table 2 (continued)

Urban-Centric Code Locale Requirement

41 Rural, fringe: Census-defined rural territory that is less than

or equal to 5 miles from an urbanized area, as well as rural

territory that is less than or equal to 2.5 miles from an

urban cluster.

42 Rural, distant: Census-defined rural territory that is more

than 5 miles but less than or equal to 25 miles from an

urbanized area, as well as rural territory that is more than

2.5 miles but less than or equal to 10 miles from an urban

cluster.

43 Rural, remote: Census-defined rural territory that is more

than 25 miles from an urbanized area and is also more than

10 miles from an urban cluster.

Note. Adapted from Common Core of Data (CCD), by National Center for Education Statistics, 2014a.

Retrieved from http://nces.ed.gov/ccd/rural_locales.asp

The population of the research consisted of PK-12 school districts as well as K-12 school districts.

School districts that did not meet PK-12/K-12 requirements were not included within the data of the research.

The CSD had a total of nine participants. The SSD had a total of 13 participants. The TSD had a total of 21

participants. The RSD had a total of 74 participants. This research directive of locale efficiency has a total of

117 PK-12/K-12 school districts in the state of Tennessee that were included within the research.

Statistical Methods

The instruments utilized in this research included the Statistical Package for the Social Sciences (SPSS)

and a DEA.

Research Question 1#

Do any of the school district financials, fiscal indicators, or demographic variables have a significant

impact regarding the academic average for the years of 2011-2012, 2012-2013, and 2013-2014?

The SPSS Version 22 was utilized to conduct a multiple regression analysis on the school district

independent variables (school district financial variables, school district fiscal indicators, and school district

demographics) to find a relationship between the independent variables related to the school district

dependent variables: TCAP Math, TCAP English Language Arts, TCAP Science, TCAP Social Studies,

EOC Math, EOC Science, EOC English, EOC History(growth value-added scores related to TVAAS); the

ACT composite scores(achievement scores); and school district graduation rates over the 3-year average for

the academic years of 2011-2012, 2012-2013, and 2013-2014. Furthermore, the correlations detected were a

significance level among the independent variables related to school district dependent variables. Moreover,

the model summary was analyzed to interpret the R, R2, adjusted R2, and the significant F change.

Additionally, the coefficients were analyzed for the collinearity statistics of tolerance and the variance

inflation factor among the independent variables against the dependent variables. Lastly, a prediction

equation was determined related to the dependent variables.


What are the efficiency ratings per urban-centric locales Tennessee school districts regarding the

academic average for the years of 2011- 2012, 2012-2013, and2013-2014?

http://nces.ed.gov/ccd/rural_locales.asp


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A DEA was administered by utilizing the significant correlations of the series of multiple regression

analyses that was performed in Questions 1a through1j related to the Tennessee school districts’ financial,

fiscal indicator, and demographic variables regarding the academic average for the years of 2011-2012,

2012-2013, and 2013-2014. The independent variables were compared against school district TVAAS

student growth scores/graduation rates and rendered an efficiency rating regarding four different urban-

centric locale categories for school districts (CSD, SSD, TSD, and RSD). Every school district rendered a

DMU that was compared for the efficiency rating within each of the urban-centric locales.

The DEA efficiency refers to a relationship among inputs and outputs.Therefore, efficiency of each

public school district was defined by the traditional relationship of outputs to inputs.The relationship between

the inputs and outputs rendered a number between 0 and 1, however it was expressed as a percentage

between 0 and 100%. The public school district was inefficient when the efficiency percentage was less than

100% (Cooper et al., 2004). Within the public educational sector, if a school district’s efficiency was 100% it

was considered efficient. The efficiency 100% did not mean that the public school district had the best

educational outputs or the lowermost levels of educational inputs. Additionally, the efficiency of 100% did

not mean that the public school district was achieving proficient or advanced in the state standardized testing

related to the outputs. However, the efficiency of 100% meant that there were no other public school districts

within the urban-centric locales of the Tennessee public school district reference set accomplishing similar or

superior outputs with fewer amount of inputs (Haveman, 2004).

Procedures

o Procedures for Research Question 1#

The first step the researcher utilized was to access the Tennessee Department of Education’s report card

data for school districts to obtain the pertinent variables for each of the 117 PK-12/K-12 school districts in

Tennesseeas well as the obtaining the demographic data from the United States Census Bureau. The data was

collected for the academic years of 2011-2012, 2012-2013, and 2013-2014 from the Tennessee Department

of Education Web site, and the data were averaged. Once the data were collected, the researcher began to

prepare for the statistical analysis of a multiple regression analysis to find relationships of significance of the

independent variables related to the dependent variables.

o Procedures for Research Question 2#

The first step the researcher utilized was to access the Tennessee Department of Education’s report card

data for school districts to obtain the pertinent variables for each of the 117 PK-12/K-12 school districts in

Tennessee as well as the obtaining the demographic data from the United States Census Bureau. The data

were collected for the academic years of 2011-2012, 2012-2013, and 2013-2014 from the Tennessee

Department of Education Web site, and the data were averaged. Once the data were collected, the researcher

performed a series of multiple regression analyses for Questions 1a through 1j related to the Tennessee

school districts’ financial, fiscal indicator, and demographic variables regarding the academic average for the

years of 2011-2012, 2012-2013, and 2013-2014. Next, a DEA was administered by utilizing the significant

correlations from the regression analysis to benchmark efficiency ratings for the 117 PK-12/K-12 Tennessee

school districts regarding urban-centric locales. Subsequently, an ERS was utilized to discover the most

referenced public school districts. The specific independent variable of student/teacher ratio was the only

variable that was inputted into the DEA inversely. The student/teacher ratio indicated that the higher the ratio

–the more students per teacher. This higher student/teacher ratio may be financially beneficial for a public

school district but Krueger’s (1999) Project STAR research valued the lower ratio more for academic

purposes. The Project STARanalysis concluded that students in small classes were more inclined to exhibit

improved achievement than students in larger classes (Krueger, 1999). Therefore, the researcher calculated

an inverted quotient of the student/teacher ratio variable for a more efficient output related to the public

school districts in the DEA.


61

4. Findings

Analyses of statistical data collected during this study are presented in this chapter. The data used in the

analyses were collected from the Tennessee State Department of Education, U. S. Census Bureau, and the

National Center for Education Statistics. These data were collected on the following variables: average salary

for a classroom teacher, percentage of teachers with advanced degrees, student ADA/teacher ratio, total

instruction and non instructional expenses ratio, per pupil expenditures per ADA, median value of owner-

occupied housing units, per capita personal income, percentage of persons 25 or older that were high school

graduate or higher, percentage of persons 25 or older that were bachelor’s degree graduates or higher,

percentage of free and reduced lunch students, minority percentage, percentage of students with disabilities,

percent of English learners, the TVAAS growth scores for the TCAP Math test, TCAP English test, TCAP

Science test, TCAP Social Studies test, EOC Math test, EOC English test, EOC Science test, EOC History

test, ACT composite achievement test scores, and graduation rates related to NCLB.

Statistical Results

Data were collected and all statistical procedures including descriptive statistics, correlations, multiple

regression, and logistic regression were administered using SPSS for Windows, Version 22. Additionally, a

DEA was utilized to evaluate the efficiency of Tennessee school districts. Data from all 117 PK-12/K-12

school districts in Tennessee were obtained for this study.




A stepwise multiple regression method was utilized to discover strong correlations between the

independent variables and dependent variables regarding the 117 Tennessee public school districts associated

with the academic average for the years of 2011-2012, 2012-2013, and 2013-2014. TCAP Math (1a) had

two of the correlations that were significant at the = .10 level. The variables of student ADA/teacher ratio

as well as the percentage of free and reduced lunch were significantly related to the TCAP math test

regarding the TVAAS gain scores due to the analysis of variance (ANOVA) table computing the regression

level at p = .000. The model summary calculated the R = .390, R2 = .152, adjusted R2= .137, and significant F

change = .006. Moreover, the analyzed Tolerance and VIF statistics, respectively, for the student

ADA/teacher ratio and the percentage of free and reduced lunch as well as for the percentage of free and

reduced lunch (.928, 1.077) fell within the adequate ranges for the lack of multicollinearity. Additionally, the

variables can be used to build a regression equation: TCAPMath = 10.447 – .382(student average daily

attendance/teacher ratio) – .034(percentage of free and reduced lunch).

TCAP ELA (1b) had three of the correlations that were significant at the = .10 level. The variables of

free and reduced lunch percentage, student ADA/teacher ratio, and the percentage of students with

disabilities were significantly related to the TCAP English Language Arts test regarding the TVAAS gain

scores due to the ANOVA table computing the regression level at p = .008. The model summary calculated

the R = .316, R2 = .100, adjusted R2 = .076, and significant F change = .034. Moreover, the analyzed

Tolerance and VIF statistics, respectively, for free and reduced lunch percentage (.917, 1.091), student

ADA/teacher ratio (.750, 1.334), and the percentage of students with disabilities (.768, 1.302) fell within the

adequate ranges for the lack of multicollinearity. Additionally, the variables can be used to build a regression

equation: TCAPELA = 4.617 – .017(free and reduced lunch percentage) – .166(student average daily

attendance/teacher ratio) – .073(percent of students with disabilities).

TCAP Science (1c) had two of the correlations that were significant at the = .10 level. The variables

of minority percent of students and the percentage of free and reduced lunch students were significantly

related to the TCAP Science test regarding the TVAAS gain scores due to the ANOVA table computing the

regression level at p = .000. The model summary calculated the R = .434, R2 = .188, adjusted R2= .174, and

significant F change = .001. Moreover, the analyzed Tolerance and VIF statistics, respectively, for minority


62

percent of students (.975, 1.026), and the percentage of free and reduced lunch students (.975, 1.026) fell

within the adequate ranges for the lack of multicollinearity. Additionally, the variables can be used to build a

regression equation:TCAPScience = 2.145 + .033(minority percent of students) – .036(percentage of free and

reduced lunch).

TCAP Social Studies (1d) had two of the correlations that were significant at the = .10 level. The

variables of minority percent of students and median value of owner-occupied housing units were

significantly related to the TCAP Social Studies test regarding the TVAAS gain scores due to the ANOVA

table computing the regression level at p = .000. The model summary calculated the R = .457, R2 = .209,

adjusted R2 = .195, and significant F change = .003. Moreover, the analyzed Tolerance and VIF statistics,

respectively, for minority percent of students (.982, 1.019), and the median value of owner-occupied housing

units (.982, 1.019) fell within the adequate ranges for the lack of multicollinearity. Additionally, the variables

can be used to build a regression equation:TCAPSocial Studies = –.415 + .028(minority percent of students)

+ .000009797(median value of owner-occupied housing units).

EOC Math (1e) had two of the correlations that were significant at the = .10 level. The variables of

average teacher salary and minority percentage were significantly related to the EOC Math test regarding the

TVAAS gain scores due to the ANOVA table computing the regression level at p = .006. The model

summary calculated the R = .296, R2 = .086, adjusted R2 = .070, and significant F change = .098. Moreover,

the analyzed Tolerance and VIF statistics, respectively, for the average teacher salary (.952, 1.050) as well as

for the minority percentage (.952, 1.050) fell within the adequate ranges for the lack of multicollinearity.

Additionally, the variables can be used to build a regression equation:EOC Math = –26.424 + .001(average

teacher salary) + .087(minority percentage).

EOC Science (1f) had no correlations were significant at the = .10 level. Therefore, no prediction

equation could be calculated.

EOC English (1g) had two correlations that were significant at the = .10 level. The variables of the

percentage of persons 25 or older that were high school graduates or higher and student ADA/teacher

ratiowere significantly related to the EOC English test regarding the TVAAS gain scores due to the ANOVA


adjusted R2= .082, and significant F change = .084. Moreover, the analyzed Tolerance and VIF statistics,

respectively, the percentage of persons 25 or older that were high school graduates or higher (.940,

1.064),and student ADA/teacher ratio(.940, 1.064) fell within the adequate ranges for the lack of

multicollinearity. Additionally, the variables can be used to build a regression equation:EOCEnglish = –

9.112 + .162(percentage of persons 25 or older that were high school graduates or higher) – .275(student

average daily attendance/teacher ratio).

EOC U.S. History (1h) had three correlations that were significant at the = .10 level. The variables of

the percentage of persons 25 or older that were bachelor’s degree graduates or higher, minority percentage,

and student ADA/teacher ratio were significantly related to the EOC U.S. History test regarding the TVAAS

gain scores due to the ANOVA table computing the regression level at p = .000. The model summary

calculated the R = .409, R2 = .168, adjusted R2= .146, and significant F change = .059. Moreover, the

analyzed Tolerance and VIF statistics, respectivelythe percentage of persons 25 or older that were bachelor’s

degree graduates or higher (.889, 1.125), minority percentage (.918, 1.089), and student ADA/teacher

ratio(.967, 1.034), fell within the adequate ranges for the lack of multicollinearity. Additionally, the variables

can be used to build a regression equation:EOCHistory = 3.807 + .223(percentage of persons 25 or older that

were bachelor’s degree graduates or higher) – .061(minority percentage) – .465(student average daily

attendance/teacher ratio).

ACT composite (1i) had five correlations that were significant at the = .10 level. The variables of the

percentage of free and reduced lunch, average teacher salary, minority percentage, percentage of persons 25

or older that were bachelor’s degree graduates or higher, and per capita personal income were significantly

related to the ACT composite test regarding the TVAAS composite achievement scores due to the ANOVA



63

adjusted R2 = .776, and significant F change = .005. Moreover, the analyzed Tolerance and VIF statistics,

respectively, the percentage of free and reduced lunch (.376, 2.656), average teacher salary (.566, 1.768),

minority percentage (.701, 1.426), percentage of persons 25 or older that were bachelor’s degree graduates or

higher (.221, 4.528), and per capita personal income (.249, 4.0421) fell within the adequate ranges for the

lack of multicollinearity. Additionally, the variables can be used to build a regression equation:ACT

composite = 16.117 – .054(percentage of free and reduced lunch) + .000(average teacher salary) –

.010(minority percentage) + .058(percentage of persons 25 or older that were bachelor’s degree graduates or

higher) – .000083185(per capita personal income).

Graduation rates (1j) had three of the correlations that were significant at the = .10 level.The variables

of percentage of free and reduced lunch, per capita personal income, and the student ADA/teacher ratio were

significantly related to the graduation rate associated with the NCLB due to the ANOVA table computing the

regression level at p = .000. The model summary calculated the R = .545, R2 = .297, adjusted R2= .278, and

significant F change = .096. Moreover, the analyzed Tolerance and VIF statistics, respectively, for

percentage of free and reduced lunch (.490, 2.042), per capita personal income (.501, 1.997), and the student

ADA/teacher ratio (.926, 1.080) fell within the adequate ranges for the lack of multi-collinearity.

Additionally, the variables can be used to build a regression equation:Graduation rates = 121.970 –

.313(percentage of free and reduced lunch) + .001(per capita personal income) – .410(student average daily

attendance/teacher ratio).


2. What are the efficiency ratings per urban-centric locales for Tennessee PK-12/K-12 public school

districts regarding the academic average years for the of 2011-2012, 2012-2013, and 2013-2014?

Following the execution of the series of multiple regression analyses to determine the significant

correlations related to the dependent variables regarding the per urban-centric locale in Tennessee PK/K-12

public school districts, the significant correlations were then utilized as the inputs of data envelopment

analyses. The significant correlations of the multiple regression analyses, which were utilized as the inputs of

the DEA, consisted of the following: student (ADA)/teacher ratio, free and reduced lunch percentage,

percentage of students with disabilities, minority percentage, median value of owner of occupied-housing

units, average teacher salary, percentage of persons 25 or older that were high school graduates or higher,

percentage of persons 25 or older that were bachelor’s degree graduates or higher, and the per capita personal

income. The inputs of the data envelopment analyses were calculated against the outputs (dependent

variables in the multiple regression analyses) to get an efficiency score percentage that was then computed

into a ranking/benchmarking analyses. Table 3 has the urban-centric locale status for the study, whereas

Table 4 has the breakdown of the urban-centric locale status. Table 5 has the benchmarking analyses

pertaining to locales of the CSDin Tennessee PK/K-12 public school districts according to the efficiency

score percent.

Table 3: Urban-Centric Locale Status

Urban-Centric Number of Urban-Centric Average

Locale Districts Locale Codes Relative Efficiency

City 9 11, 12, & 13 98.93%

Suburb 12 21, 22, & 23 98.91%

Town 21 31, 32, & 33 97.82%

Rural 75 41, 42, & 43 96.99%


64

Table 4: Breakdown of Urban-Centric Locale Status

Urban-Centric Number of Urban-Centric Average

Locale Districts Locale Codes Relative Efficiency

City 1 11 (Large) 98.79%

City 1 12 (Midsize) 97.87%

City 7 13 (Small) 99.11%

Suburb 6 21 (Large) 97.91%

Suburb 3 22 (Midsize) 99.81%

Suburb 3 23 (Small) 100%

Town 4 31 (Fringe) 99.12%

Town 11 32 (Distant) 96.89%

Town 6 33 (Remote) 98.83%

Rural 26 41 (Fringe) 96.98%

Rural 37 42 (Distant) 96.89%

Rural 12 43 (Remote) 97.29%

Table 5: Locales: City School Districts (CSD)

Rank School District Locale Code Relative Efficiency

T1 Bristol City 13 100%

T1 Hamblen County 13 100%

T1a Jackson-Madison

Consolidated 13 100%

T1 Johnson City 13 100%

T1 Kingsport City 13 100%

T1 Montgomery County 13 100%

7 Davidson County 11 98.79%

8 Hamilton County 12 97.87%

9 Cleveland City 13 93.74%

Note. The Memphis City School District (Not Listed) merged with the Shelby County School District in the 2013-

2014 academic year. Therefore, the pre- and post merger records of the Memphis City School District and Shelby

County School District will not be used due to confounding data. Adapted from Common Core Data (CCD): Local

Education Agency (School District) Locale Code Files,by National Center for Education Statistics, 2014b. Retrieved

from http://nces.ed.gov/ccd/ccd Locale Code District.asp. T = A tie in rank between public school districts with the

numerical rank following the T.

a Top 10% frontier school districts, which are referenced the most within the efficiency reference set and are

recommended as being model school districts.

The CSD Locales, referred to in Table 5, consisted of nine school districts. According to the analysis,

six school districts were relatively efficient, while three were relatively inefficient. More importantly, the

DEA focused the manager's attention on a subgroup of the school districts referred to as the second stage or

ERS. This ERS included the group of service units within the CSD to reference the most efficient school

districts, which produce better outcomes with fewer correlated factors to assist the least efficient public

school districts.Analyzing the reference sets, the four frontier school districts that were referenced the most

in the CSD Locale ERS were Bristol City (3), Jackson-Madison Consolidated (4), Johnson City (2), and

Kingsport City (1). Therefore, school districts that are under 100% are recommended to consult with the

topmost four frontier school districts regarding CSD Locale to improve efficiency for school districts in the

state of Tennessee.

Thus, Tennessee public education requires improvement to develop more efficient school districts

regarding significant correlations related to TVAAS value-added scores, EOC value-added scores, ACT

http://nces.ed.gov/ccd/ccd%20Locale%20Code%20District.asp


65

composite achievement scores, and graduation rates. As a better business practice approach amongst

Tennessee public education regarding PK-12/K-12 school districts, the four most referenced frontier school

districts in the ERS frontier school districts are designated to be model districts for the state of Tennessee

according to the data for CSD Locale. The three most inefficient public school districts in CSD Locale for

the DEA relative efficiency scores were Davidson County (98.79%), Hamilton County (97.87%), and

Cleveland City (93.74%).Table 6has the benchmarking analyses pertaining to locales of the suburb school

districts (SSD)in Tennessee PK/K-12 public school districts according to the efficiency score percent.

Table 6: Locales: Suburb School Districts (SSD)


T1a Alcoa City 21 100%

T1 Bradley County 23 100%

T1 Carter County 22 100%

T1 Elizabethton City 22 100%

T1 Hawkins County 23 100%

T1 Lenoir City 21 100%

T1 Loudon County 21 100%

T1 Sullivan County 23 100%

9 Knox County 21 99.60%

10 Rutherford County 22 99.43%

11 Maryville City 21 96.01%

12 Sumner County 21 91.48%

Note.The Shelby County School District (Not Listed) merged with Memphis City School District (Not Listed) in

the academic-year of 2013-2014. Therefore, the pre- and post merger records of the Memphis City School District and

Shelby County School District will not be used due to confounding data. Adapted from Common Core Data (CCD):

Local Education Agency (School District) Locale Code Files,by National Center for Education Statistics,

2014b.Retrieved from http://nces.ed.gov/ccd/ccdLocaleCode District.asp. T = A tie in rank between public school

districts with the numerical rank following the T.

a Top 10% frontier school districts, which are referenced the most within the efficiency reference set and are


The SSD Locales, referred to in Table 6, consisted of 12 school districts. According to the analysis,

eight school districts were relatively efficient, while four were relatively inefficient. More importantly, the

DEA focused the manager's attention on a subgroup of the school districts referred to as the second stage or

ERS. This ERS included the group of service units within the SSD to reference the most efficient school

districts, which produce better outcomes with fewer correlated factors to assist the least efficient public

school districts.Analyzing the reference sets, the five frontier school districts that were referenced the most in

the SSD Locale ERS were Alcoa City (4), Bradley County (3), Carter County (4), Lenoir City (1), and

Sullivan County (1). Therefore, school districts that are under 100% are recommended to consult with the

topmost five frontier school districts regarding SSD Localeto improve efficiency for school districts in the

state of Tennessee.




Tennessee public education regarding PK-12/K-12 school districts, the five most referenced frontier school


according to the data for SSD Locale. The four most inefficient public school districts in SSD Locale for the

DEA relative efficiency scores were Knox County (99.60%), Rutherford County (99.43%), Maryville City

(96.01%), and Sumner County (91.48%).Table 7has the benchmarking analyses pertaining to locales of the

http://nces.ed.gov/ccd/ccdLocaleCode%20District.asp


66

town school districts (TSD)in Tennessee PK/K-12 public school districts according to the efficiency score

percent.

Table 7: Locales: Town School Districts (TSD)


T1 Dickson County 32 100%

T1 Dyersburg City 33 100%

T1 Humboldt City 31 100%

T1 Johnson County 32 100%

T1 Lauderdale County 32 100%

T1 Lewis County 33 100%

T1 Maury County 32 100%

T1 Oak Ridge City 31 100%

T1 Sevier County 31 100%

T1 Tullahoma City 32 100%

T1 Union City 33 100%

T1a White County 33 100%

16 Richard City 32 96.44%

17 Oneida City 33 96.40%

18 McKenzie Special 32 94.53%

19 Trenton City 32 94.07%

20 Roane County 32 93.47%

21 Haywood County 32 93.35%

Table 7 (continued)


22 Greeneville City 32 92.93%

Note.Adapted from Common Core Data (CCD): Local Education Agency (School District) Locale Code Files,by

National Center for Education Statistics, 2014b. Retrieved from http://nces.ed.gov/ccd/ccdLocaleCode District.asp. T

=A tie in rank between public school districts with the numerical rank following the T. a Top 10% frontier school districts, which are referenced the most within the efficiency reference set and are


The TSD Locales, referred to in Table 7, consisted of 22 school districts. According to the analysis, 12

school districts were relatively efficient, while 10 were relatively inefficient. More importantly, the DEA

focused the manager's attention on a subgroup of the school districts referred to as the second stage or ERS.

This ERS included the group of service units within the TSD to reference the most efficient school districts,

which produce better outcomes with fewer correlated factors to assist the least efficient public school

districts..Analyzing the reference sets, the eight frontier school districts that were referenced the most in the

TSD Locale ERS were these: Dickson County (5), Johnson County (1), Lewis County (1), Maury County

(3), Sevier County (7), Tullahoma City (6), Union City (2), and White County (10). Therefore, school

districts that are under 100% are recommended to consult with the topmost eight frontier school districts

regarding TSD Locale to improve efficiency for school districts in the state of Tennessee.




Tennessee public education regarding PK-12/K-12 school districts, the eight most referenced frontier school


according to the data for TSD Locale. The 10 most inefficient public school districts in TSD Locale for the



67

DEA relative efficiency scores were these: Hardeman County (97.87%), Putnam County (96.58%), Unicoi

City (96.49%), Richard City (96.44%), Oneida City (96.40%), McKenzie Special (94.53%), Trenton City

(94.07%), Roane County (93.47%), Haywood County (93.35%), and Greeneville City (92.93%).Table 8 has

the benchmarking analyses pertaining to locales of the rural school districts (RSD)in Tennessee PK/K-12

public school districts according to the efficiency score percent.

Table 8: Locales: Rural School Districts (RSD)


T1 Bledsoe County 42 100%

T1 Blount County 41 100%

T1 Chester County 41 100%

T1 Claiborne County 41 100%

T1 Coffee County 41 100%

T1 Crockett County 42 100%

T1 Cumberland County 41 100%

T1 Decatur County 43 100%

T1 DeKalb County 41 100%

T1 Franklin County 42 100%

T1 Grainger County 42 100%

T1 Grundy County 43 100%

T1 Hancock County 42 100%

T1 Hardin County 41 100%

T1 Henry County 42 100%

T1a Hollow Rock–Bruceton Special 42 100%

T1 Houston County 42 100%

T1 Huntingdon Special 42 100%

T1 Jefferson County 41 100%

T1 Lincoln County 42 100%

Table 8 (continued)


T1 Marshall County 42 100%

T1 McMinn County 41 100%

T1 Moore County 42 100%

T1 Morgan County 41 100%

T1 Pickett County 43 100%

T1 Scott County 42 100%

T1a Sequatchie County 42 100%

T1 South Carroll Special 43 100%

T1 Stewart County 42 100%

T1 Trousdale County 42 100%

T1 Union County 42 100%

T1 Warren County 41 100%

T1a Washington County 41 100%

T1 West Carroll 42 100%

T1 Williamson County 41 100%

T1 Wilson County 41 100%

37 Perry County 43 99.58%

38 Wayne County 43 99.23%

39 Lawrence County 42 99.03%

40 Hickman County 43 98.98%


68

41 Bedford County 42 98.82%

Table 8 (continued)


42 Meigs County 42 97.65%

43 Lake County 43 97.38%

44 Giles County 41 97.02%

45 Smith County 42 96.76%

46 Dyer County 41 96.69%

47 Macon County 41 96.39%

48 Cheatham County 42 96.01%

49 Monroe County 42 95.87%

50 Greene County 42 95.65%

51 Overton County 41 95.53%

52 Jackson County 43 95.45%

53 Fayette County 42 95.31%

54 Henderson County 42 94.84%

55 Gibson Special 41 94.41%

56 Robertson County 41 94.13%

57 Van Buren County 43 94.04%

58 Campbell County 41 93.60%

59 Cocke County 42 93.59%

60 Rhea County 41 93.25%

61 Clay County 43 92.87%

62 Polk County 42 92.75%

Table 8 (continued)


63 Milan City Special 41 92.22%

64 Bradford Special 42 92.09%

65 Tipton County 41 92.07%

66 Humphreys County 42 91.56%

67 McNairy County 42 91.47%

68 Benton County 41 91.39%

69 Weakley County 42 91.27%

70 Cannon County 42 90.61%

71 Fentress County 43 89.96%

72 Marion County 42 89.02%

73 Obion County 42 85.72%

74 Anderson County 41 84.80% Note: Adapted from Common Core Data (CCD): Local Education Agency (School District) Locale Code Files,by

National Center for Education Statistics,2014b.Retrieved from http://nces.ed.gov/ccd/ccdLocaleCode District.asp. T =A

tie in rank between public school districts with the numerical rank following the T. a Top 10% frontier school districts, which are referenced the most within the efficiency reference set and are


The RSD Locales, referred to in Table 8, consisted of 74 school districts. According to the analysis, 36

school districts were relatively efficient, while 38 were relatively inefficient. More importantly, the DEA

focused the manager's attention on a subgroup of the school districts referred to as the second stage or ERS.

This ERS included the group of service units within the RSD to reference the most efficient school districts,

which produce better outcomes with fewer correlated factors to assist the least efficient public school



69

districts.Analyzing the reference sets, the 10 frontier school districts that were referenced the most in the

RSD Locale ERS were the following: Blount County (8), Hancock County (21), Hardin County (6), Hollow

Rock-Bruceton Special (32), Houston County (19), Marshall County (8), Moore County (7), Scott County

(12), Sequatchie County (38), and Washington County (25). Therefore, school districts that are under 100%

are recommended to consult with the topmost 10 frontier school districts regarding ADA enrollment to

improve efficiency for school districts in the state of Tennessee.




Tennessee public education regarding PK-12/K-12 school districts, the 10 most referenced frontier school


according to the data for RSD Locale. The 10 most inefficient public school districts in RSD Locale for the

DEA relative efficiency scores were the following: Tipton County (92.07%), Humphreys County (91.56%),

McNairy County (91.47%), Benton County (91.39%), Weakley County (91.27%), Cannon County (90.61%),

Fentress County (89.96%), Marion County (89.02%), Obion County (85.72%), and Anderson County

(84.80%).

5. Conclusions and Discussions

The purpose of this study was to determine the following: (a) the financial factors and/or control factors

that have a substantial influence on student gains associated with the TCAP, EOC testing, ACT achievement

composite scores, and graduation rates for Tennessee PK-12/K-12 school districts and (b) efficiency ratings

regarding the Tennessee public school districts statewide. In order to fulfill these purposes, a series of

multiple regression analyses was performed with the SPSS, Version 22.0, using data collected on all 117 PK-

12/K-12 school districts in Tennessee by the State Department of Education. Additionally, a series of DEA

was performed to benchmark urban-centric locales for the 117 PK-12/K-12 school districts in Tennessee

determining the rankings statewide.

Summary of Findings


To what extent can the independent variables predict the dependent variables regarding the academic

average for the years of 2011-2012, 2012-2013, and 2013-2014 related to the dependent variables?The

statistical analysis for the data showed that two independent variables (student ADA/teacher ratio as well as

the percentage of free and reduced lunch percentage) were significantly correlated with TVAAS value-added

scores related to TCAP Math; three independent variables (free and reduced lunch percentage, student

ADA/teacher ratio, and the percentage of students with disabilities) were significantly correlated with

TVAAS value-added scores related to TCAP English Language Arts; two independent variables (minority

percent of students and the percentage of free and reduced lunch) were significantly correlated with TVAAS

value-added scores related to TCAP Science; two independent variables (minority percent of students and

median value of owner-occupied housing units) were significantly correlated with TVAAS value-added

scores related to TCAP Social Studies; two independent variables (identifying average teacher salary and

minority percentage) were significantly correlated with TVAAS value-added scores related to EOC Math;

there were no significant correlations related to the EOC Science; two independent variables (percentage of

persons 25 or older that were high school graduates or higher and student ADA/teacher ratio) were

significantly correlated with TVAAS value-added scores related to EOC English; three independent variables

(percentage of persons 25 or older that were bachelor’s degree graduates or higher, minority percentage, and

student ADA/teacher ratio) were significantly correlated with TVAAS value-added scores related to EOC

U.S History; five independent variables (percentage of free and reduced lunch, average teacher salary,

minority percentage, percentage of persons 25 or older that were bachelor’s degree graduates or higher, and

per capita personal income)were significantly correlated to the ACT composite; three independent variables


70

(percentage of free and reduced lunch, per capita personal income, and the student ADA/teacher ratio) were

significantly correlated with graduation.

Subsequently, the aggregate correlated factors related to the output variables (TCAP, EOC testing, ACT

achievement composite scores, and graduation rates) of this research regarding the public school district

efficiency in Tennessee were the following: student ADA/teacher ratio, percentage of free and reduced

lunch percentage, percentage of students with disabilities, minority percent of students, median value of

owner-occupied housing units, average teacher salary, the percentage of persons 25 or older that were high

school graduates or higher, the percentage of persons 25 or older that were bachelor’s degree graduates or

higher, and per capita personal income. The significant factors were valuable data information regarding

public school district value-added scores in Tennessee.These consequentlycan assist in finding efficiency

scores operating a DEA to optimize education performance by utilizing a better business practice approach of

collaboration.


What are the school district efficiency ratings per urban-centric locale in the state of Tennessee

regarding the academic average for the years of 2011-2012, 2012-2013, and 2013-2014? The relative

efficiency ratings for the urban-centric locales regarding Tennessee school districts associated with the

independent variables within this study correlated to thedependent variables (TCAP value-added growth

scores, EOC value-added growth scores, ACT composite achievement scores, and NCLB graduation rates)

were calculated utilizing a DEA. The city school district (CSD) locale had the highest relative efficiency

among the urban-centric locale school districts, with a 98.93%. The urban-centric locale of suburban and a

code of 22 or small averaged a 100% relative efficiency score. Additionally, the second highest average

relative efficiency score was by the urban-centric locale of town and a code of 31 or fringe, which averaged

99.12%.

Overall, the top 10% prominent frontier school districts that were referenced the most in the urban-

centric locale ERS were the following:CSD: Jackson-Madison Consolidated; SSD: Alcoa City; TSD: White

County; and RSD: Hollow Rock-Bruceton Special, Sequatchie County, and Washington County. Therefore,

school districts that are under 100% are recommended to consult with the most referenced frontier school

districts within their own urban centric localeto improve efficiency. As a better business practice approach

amongst Tennessee public education regarding PK-12/K-12 school districts, the most referenced top 10%

frontier school districts in the ERS frontier school districts are designated to be model districts for the state of

Tennessee according to the data.

Conclusions

This paper examined the relative efficiency of public school systems in Tennessee related to the

dependent variables (TCAP value-added growth scores, EOC value-added growth scores, ACT achievement

scores, and NCLB graduation rates).The bulk of public school efficiency research is associated with student

achievement. Utilizing past research,the researcher concluded that school district achievement of the students

is almost a foregone conclusion that was determined since demographics and background of the enrolled

individual school district students are directly correlated. Therefore, this particular study utilized student

value-added gain scores,which are related to VAMs, to measure the efficiency of a public school district for a

better business efficiency model.Utilizing the relative efficiency data as a better business practice approach

amongst Tennessee public education regarding the top 10% frontier school districts that are referenced the

most as the designated model districts for PK-12/K-12 education is extremely valuable to optimize the

efficiency of public school districts in Tennessee.


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Limitations of the Study

More information related to the school district independent variables that were utilized within the

multiple regression analyses should be researched more in depth. Within this study there were limited

correlations regarding TCAP value-added growth scores and EOC value-added growth scores.Additionally,

the Memphis City School District as well as the Shelby County School District were excluded from this

research due to missing data related to the merging of the two school districts. Excluding the two districts put

limitations on the research due to the largest school district in the state of Tennessee not being included in the

study. Valuable information related to the multiple regression analysis and data envelopment analysis put

limitations on the study.

Educational Implications

The purpose of this research was for the Tennessee PK-12 public school districts to collaborate with the

most efficient school districts for a better business practice approach. Additionally, this study did not

attempt to answer the issue of how much money the individual school districts should spend on education.

This study enables Tennessee school districts to be made more aware of data-driven information related to

better business practices among education partners to benefit school district efficiency as a whole for better

instructional leadership as well as education reform. Further research must be conducted to better understand

the impacting factors regarding the value added scores of the Tennessee TVAAS test scores related to the

TCAP and the EOC. In turn, future research will enable efficiency to be more defined for the Tennessee

Department of Education related to individual school districts.

Recommendations

There are several areas of exploration beyond those addressed in this study. The researcher recommends

a closer examination of significant correlations related to the value-added gain scores related to TCAP, EOC

testing, ACT composite scores, and graduation rates. Secondly, the researcher recommends further research

be conducted by interviewing the most top 10% referenced frontier school districts within the ERS as well as

inefficient public school districts to better understand how to scale up education in Tennessee. Thirdly,

research school districts categorically by statewide, by Tennessee’s Core of Regional Excellence (CORE)

territories, as well as by school districts average daily attendance enrollment. Fourthly, research school

districts within the same county to explore the possibility of consolidating. Moreover, several school districts

in Tennessee are located within the same county; therefore city taxpayers are contributing to both, city and

county, school districts to perform at efficient or highly efficient levels. Fifthly, a similar research is to be

performed to evaluate states that utilize value-added models to investigate school district efficiency

nationwide. Lastly, the Tennessee Department of Education should commission an annual public school

district efficiency report. The state commissioned report is proposed to be a resource for less efficient school

districts to collaborate with more efficient or frontier efficient school districts that will aid in the efforts to

become more efficient in education. Moreover, the recommendation from this research is to name the state

commissioned program “Building Efficient Schools in Tennessee” (BEST). The Tennessee BEST program

should be data driven regarding specific correlated inputs and outputs related to school district efficiency.

The Tennessee Department of Education is recommended to strive for an optimal collaborative effort

between school districts associated with better business practices, which will increase the efficiency of

Tennessee public education.

Furthermore, this public education efficiency study could be utilized on the U.S. national level,

depending upon the individual states that use similar VAMs to measure standardized testing. Moreover,

public education could utilize the BEST program on a national level as an educational tool by aiding

education state departments increasing efficiency within the individual school districts. For the United States

Department of Education to utilize this study properly all individual state education departments must use a

uniform VAM to compare the efficiency scores. In addition, all the education departments nationwide would


72

need similar educational standards for the value-added scores to be compared on standardized tests, thus

contributing to an efficiency score that correlates nationwide. Increasing educational efficiency has the

possibility of raising the worldwide Program for International Student Assessment ranking of the United

States, thereby increasing the gross domestic product. Subsequently, this research has the potential to change

educational policy regarding school district organization evaluations.

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