Dailey's Dissertation Defense Powerpoint Presentation 2013

7/28/2019 Dailey's Dissertation Defense Powerpoint Presentation 2013

http://slidepdf.com/reader/full/daileys-dissertation-defense-powerpoint-presentation-2013 1/60

A Doctoral Defense

James Andrew DaileySubmitted to the Graduate School

Prairie View A&M University

In partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Spring 2013

Delco 220

Major Subject: Educational Leadership

1



James Andrew Dailey

M.Ed. – Prairie View A&M University

(May 16, 2009)

Educational Administration

B.A. – University of Houston

(May 1997)

Major: History

B.A. –

University of Houston(August 1995)

Major: English

2



Doctoral Committee

Patricia Hoffman-Miller, Ph.D.(Dissertation Chair)

Judith Hansen, Ed.D.(Member)

William Kritsonis, Ph.D.(Member)

Solomon G. Osho, Ph.D.(Methodologist)

Pamela Freeman, Ph.D.(Member)

3



Abstract

4

WHITLOWE R. GREEN COLLEGE OF EDUCATION

Doctoral Defense

ABSTRACT

SCHOOL SPENDING AND STUDENT ACHIEVEMENT: A CAUSAL COMPARATIVE

STUDY EXPLORING THE IMPACT OF PER PUPIL SPENDING ON STUDENT

ACADEMIC ACHIEVEMENT IN SOUTHEASTERN SCHOOL DISTRICTS IN TEXAS.

(Spring 2013)

James Andrew Dailey, M.Ed. - Prairie View A&M University;

B.A. – University of Houston

Dissertation Chair: Patricia Hoffman-Miller, Ph.D.



Abstract (Continued)

The causal-comparative study explored the impact of per pupil spending on student

academic achievement in southeastern school districts in the State of Texas. The available

research is convoluted with regression models attempting to account for socio-economic issues

rather than focusing on whether a correlation exists between per pupil spending levels and

academic achievement levels. The researcher utilized a causal-comparative research method to

identify and analyze the cause and effect relationship between schools’ per pupil spending rates

and academic achievement rates of high school students in school districts in southeastern Texas.

The research and analysis completed for this causal-comparative study provides a basic

understanding of per pupil spending and academic achievement, and lays a foundation for

analyzing how to adequately fund the reforms of a post modernistic future in the state of Texas

and nation as a whole.5



Dissertation Defense Format

Part I: Introduction

Part II: Review Of The Literature

Part III: Methodology

Part IV: Analysis of Data

Part V: Summary, Recommendations,

and Conclusion.

6



Part I

Introduction

7



Statement of the Problem

Despite decades of litigation and passage of educational reforms, there still

exists disparities in per pupil spending in educational districts across America, and

often these disparities exist between schools within the same district. According to

Rosa (2010): The basic premise of litigation was that discrepancies in access to resources gave

some students a higher quality of public education than others, violating

individual’s right to equal protection under the law. That is, the more a district

spent per pupil, the better education it could deliver than a district that spent less

per pupil. (p. 19). 8



Statement of the Problem

The educational reforms of the past scar the landscape of the present, and

cloud the road of the future, because many of these reforms lacked equitable funding

mechanisms to insure their success. Students today are faced with a multitude of

technological advances, and a globally connected economy. A basic understanding of

per pupil spending and academic achievement is needed to lay the foundation for

analyzing how to adequately fund the reforms of a post modernistic future in the state

of Texas and nation as a whole.

9



The Purpose of the Study

The purpose of this causal-comparative study was to determine if there is a

difference in student academic achievement between high schools with low per pupil

spending levels and high schools with high per pupil spending levels as reported in the

Texas Education Agency PEIMS and AEIS report of 2010-2011. This study focused on

four areas of TAKS testing outcomes as reported in the Texas Education Agency AEIS

report of 2010-2011: 10th grade Reading/ELA, 10th grade Mathematics, 11th grade

Reading/ELA, and 11th grade Mathematics.

10



The Research Questions

1. Is there a difference in student achievement between low per pupil spending and high per

pupil spending high schools, as reported on the Public Education Information Management

System (PEIMS) and the Academic Excellence Indicator System (AEIS) for Texas

Assessment of Knowledge and Skills (TAKS) in Reading/ELA for 10 th grade? 2. Is there a difference in student achievement between low per pupil spending and high per


System (PEIMS) and Academic Excellence Indicator System (AEIS) for Texas Assessment of

Knowledge and Skills (TAKS) in Mathematics for 10 th grade?

11



The Research Questions




Knowledge and Skills (TAKS) in Reading/ELA for 11th grade?




Knowledge and Skills (TAKS) in Mathematics for 11th grade?

12



Research Hypotheses

H1 - As measured by the PEIMS and AEIS reports, 10th grade students from high per

pupil spending schools will outperform 10th grade students from low per pupil

spending schools on the Reading/ELA TAKS assessments. H1: x1 ≠ x2

H2 - As measured by the PEIMS and AEIS reports, 10th grade students from high per


spending schools on the Mathematics TAKS assessments. H2: x1 ≠ x2

H3 - As measured by the PEIMS and AEIS reports, 11 th grade students from high per


spending schools on the Reading/ELA TAKS assessments. H3: x1 ≠ x2

H4 - As measured by the PEIMS and AEIS reports, 11 th grade students from high per


spending schools on the Mathematics TAKS assessments. H4: x1 ≠ x2

13



The Null Hypotheses

H01 – There is no statistically significant difference in student achievement between

low per pupil spending and high per pupil spending high schools, as reported

on the PEIMS and AEIS for TAKS in Reading/ELA for 10th grade. H01: µ1 – µ2 = 0


low per pupil spending and high per pupil spending high schools, as reported on

the PEIMS and AEIS for TAKS in Mathematics for 10th grade. H02: µ1 – µ2 = 0


low per pupil spending and high per pupil spending high schools, as reported on

the PEIMS and AEIS for TAKS in Reading/ELA for 11 th grade. H03: µ1 – µ2 = 0


low per pupil spending and high per pupil spending high schools, as reported on the PEIMS

and AEIS for TAKS in Mathematics for 11 th grade. H04: µ1 – µ2 = 0

14



Significance of the Study

• This researcher believes that a preliminary study exploring whether or not student

achievement is affected by the level of per pupil spending is a precursor to

determine how per pupil spending occurs at given schools and districts.

•

This study is significant for educational reform and equal educational opportunitieswith equitable funding, because it lays a basic foundation for understanding per

pupil spending and academic achievement.

• This study attempted to determine whether students in schools with the highest

level of per pupil funding outperform academically students in schools with the

lowest per pupil level of funding.

15



Limitations of the Study

1. There are encompassing socio-economic factors, not analyzed in this investigation,

which could affect testing results. 2. It is possible for specific school expenditures to vary greatly between high schools in

this study. 3. It is possible that geographical surroundings of the included high schools could affect

testing results. 4. Private, and Parochial high school per pupil spending rates were not included in the

2010-2011 AEIS reports, and therefore they are excluded from this study.

16



Delimitations of the Study

1. This was a causal-comparative quantitative study.

2. This study only focused on per pupil spending of the top ten high schools, and the bottom ten

high schools as reported on the AEIS report of 2010-2011for school districts within Harris

County. 3. Student achievement outcomes of the TAKS assessments were delimited to Reading/ELA and

Mathematics scores for only 10th and 11th grade.

4. Student achievement outcomes of the TAKS assessments were delimited to Reading/ELA and

Mathematics scores for 2010-2011. 5. The findings of this causal-comparative quantitative study are limited to Texas.

17



Operational Definitions

Achievement Gap: The difference between how well low-income and minority children perform on

standardized tests as compared with their peers. For many years, low-income and minority children have

been falling behind their white peers in terms of academic achievement (U.S. Department of Education,

2012). Per Pupil Spending: Per pupil spending is calculated by dividing the current expense of education by

average daily attendance (ADA) , which is defined as the total days of student attendance divided by the

total days of instruction.

Texas Assessment of Knowledge and Skills (TAKS): The Texas Assessment of Knowledge and Skills

(TAKS) assessments are criterion-referenced achievement tests designed to measure the extent to which a

student has learned and is able to apply the defined knowledge and skills at each tested grade level. The

TAKS program was launched in 2003 and was scheduled to be replaced by the STAAR (State of Texas

Assessment of Academic Readiness) program beginning in 2011 (Texas Education Agency, 2012).

18



Part II

Review of the Literature Review

19



Highlights of Major Themes • Separate But Equal • Legal Jurisprudence and Texas • State Per Pupil Funding • Accountability Standards • Privatization of Educational Reform • Federal Funding and Impact on

Student Achievement • African-Americans and Litigation

•School Finance and Texas’ MinorityPopulation

• Accountability and Equitable Funding

• Global Financial Crisis

• Per Pupil Spending and School

Expenditures

• Technology

• Teacher Efficacy and School Funding

• Educational Facilities and

Postmodernism

20



Part III

Methodology

21



Research Method

The researcher utilized a causal-comparative research method to identify and

analyze the cause and effect relationship between school per pupil spending

rates and academic achievement rates of high school students in select districts

in southeastern Texas.

According to Schenker and Rumrill (2004) causal-comparative designs often

involve derived groups to analyze differences between the derived groups on

dependent variables.

22



Research Design

The researcher utilized a causal-comparative design.

This type of design allowed the researcher to choose the independent variables for

which to analyze their effects on dependent variables (Fraenkel and Wallen, 2009).

The rationale for the researcher selecting a causal-comparative design was to determinehow per pupil spending rates impact student academic achievement levels.

In this causal-comparative study the independent variable was the per pupil rates for

each high school

The dependent variables were the tenth and eleventh grade TAKS scores for

Reading/ELA and Mathematics as reported on the AEIS reports.

23



Target Population

The public high schools chosen for the quantitative research study are located

in southeastern Texas. All private and parochial schools are excluded from this study.

The high schools serve students in grades 9-12. Tenth and eleventh grade test takers

are the target population for this quantitative causal-comparative research study. The

research included all tenth and eleventh grade student academic performance on the

summative state assessment during the 2010-2011 school year.

24



Sampling of Participants

The research utilized purposive sampling. According to Singh (2007) when a

specific sample needs to be targeted for research the selection of the targets becomes

purposive in nature. The researcher purposively selected the top ten per pupil spending

high schools, and the bottom ten per pupil spending high schools located within the

target area. This causal-comparative research study utilized 2010-2011 school year

data from the Public Education Information Management System (PEIMS) and

Academic Excellence Indicator System (AEIS) reports.

25



Description of the Instrumentation

Creswell (2009) argues that the validity of the instruments to collect data is

paramount when conducting quantitative research. The instrumentation for this

quantitative causal-comparative research study was the Texas Assessment of

Knowledge and Skills 2010-2011test, which is validated by the Texas Education

Agency (2012). The per pupil spending rates of each high school selected were

recorded from the Public Education Information Management System (PEIMS) report.

26



Statistical Technique(s)

• An independent T- Test was utilized in this causal-comparative research study.

• According to Singh (2007) an independent T-Test is an appropriate test to conduct

causal-comparative research.

• The results of the T-Test were utilized to determine whether there is a statistically

significant difference in the comparisons , and therefore, if the null hypothesis are

rejected (Creswell, 2009).

• Statistical Package for the Social Sciences (SPSS) Version 20 software was utilized

by the researcher to analyze the collected data of the causal-comparative researchstudy.

27



Summary of Research Procedures

A quantitative causal-comparative research study was conducted by the researcher

to identify and analyze the cause and effect relationship between a school’s per

pupil spending rates and academic achievement in high schools in school districts

located in southeastern Texas. The researcher purposively selected the top ten per pupil spending high schools

located within Harris County Texas, and the bottom ten per pupil spending high

schools located within Harris County Texas.

The research utilized purposive sampling. The instrumentation for this quantitative causal-comparative research study was the

Texas Assessment of Knowledge and Skills 2010-2011 test, which has been

validated by the Texas Education Agency (2012). An independent T- Test was utilized in this quantitative causal-comparative research

study.

28



Part IV

Analysis of Data

29



Findings

The purpose of this causal-comparative study was to determine if there is a

difference in student academic achievement between high schools with low per

pupil spending levels and high schools with high per pupil spending levels as

reported in the Texas Education Agency PEIMS and AEIS report of 2010-2011.

This study focused on four areas of TAKS testing outcomes as reported in the Texas

Education Agency AEIS report of 2010-2011: 10th

grade Reading/ELA, 10th

gradeMathematics, 11th grade Reading/ELA, and 11th grade Mathematics. The

instrumentation for this quantitative causal-comparative research study was the

Texas Assessment of Knowledge and Skills 2010-2011 test, which has been

validated by the Texas Education Agency (2012).

The researcher purposively selected the top ten per pupil spending high schools

located within Harris County Texas, and the bottom ten per pupil spending high

schools located within Harris County Texas.

30



Findings

The participants for this study were the top ten per pupil spending high

schools located within Harris County Texas, and the bottom ten per pupil spending high

schools located within Harris County Texas which consist of these school districts:

31

• Aldine Independent School District

• Alief Independent School District

• Channelview Independent School District

• Clear Creek Independent School District

• Crosby Independent School District

• Cy-Fair Independent School District

•

Dayton Independent School District• Deer Park Independent School District

• Galena Park Independent School District

• Goose Creek Independent School District

• Houston Independent School District

• Huffman Independent School District

• Humble Independent School District

• Katy Independent School District

• Klein Independent School District

• La Porte Independent School District

• New Caney Independent School District

• North Forest Independent School District

• Pasadena Independent School District

•

Pearland Independent School District• Sheldon Independent School District

• Spring Independent School District

• Spring Branch School District

• Stafford Municipal School District

• Tomball Independent School District

• Waller Independent School District



Top Ten Selected Schools

32

Table 1.0

2010-2011 school Per Pupil Spending rates report from the Public Education Information

Management System (PEIMS) of the Top Ten Selected Schools.

Campus Per Pupil Spending Rates

1. Campus J 14,784

2. Campus K 13,290

3. Campus Q 11,869

4. Campus A 11,375

5. Campus S1 11,336

6. Campus S2 9,380

7. Campus F 9,362

8. Campus S3 9,356

9. Campus N 9,355

10. Campus C 9,315

* Campuses in alphabetical order using the first letter.



Bottom Ten Selected Schools

33

Table 2.0

2010-2011 school Per Pupil Spending rates report from the Public Education Information

Management System (PEIMS) of the Bottom Ten Selected Schools.

Campus Per Pupil Spending Rates

1. Campus C1 5,142

2. Campus C2 5,171

3. Campus E 5,247

4. Campus T1 5,290

5. Campus T2 5,346

6. Campus C3 5,358

7. Campus S1 5,398

8. Campus S2 5,427

9. Campus V 5,436

10. Campus K 5,436

* Campuses in alphabetical order using the first letter.



Frequency Table Top Ten

34

Table 7.0

Frequency Table and Central Tendencies identifying the Mode, Mean, Median and Standard

Deviation of the Per Pupil Spending Rates, 10th

Grade Mathematics Scores, 10th

Grade English

Scores, 11th

Grade Mathematics Scores, and 11th

Grade English Scores using the 2010-2011

school Per Pupil Spending rates report from the Public Education Information Management

System (PEIMS), and 2010-2011 AEIS report of the Mathematics and English scores on the

Texas Assessment of Knowledge and Skills (TAKS) Exam of the Top Ten Selected Schools.

Spending 10th

Math 10th

ELA 11th

Math 11th

ELA

N Valid 10 10 10 10 10

Mean 10942.20 65.50 85.30 85.20 90.10

Std. Error of Mean 615.480 4.220 2.176 2.323 1.531

Median 10358.00 71.50 86.50 85.00 91.00

Mode 9315a

73 78 84 92

St. Deviation 1946.320 13.344 6.881 7.345 4.841

Range 5469 40 20 22 17

Minimum 9315 42 75 73 82

Maximum 14784 82 95 95 99

Sum 109422 655 853 852 901

______________________________________________________________________________

a. Multiple modes exist. The smallest value is shown.



Frequency Table Bottom Ten

35

Table 8.0

Frequency Table and Central Tendencies identifying the Mode, Mean, Median and Standard

Deviation of the Per Pupil Spending Rates, 10th

Grade Mathematics Scores, 10th

Grade English

Scores, 11th

Grade Mathematics Scores, and 11th

Grade English Scores using the 2010-2011

school Per Pupil Spending rates report from the Public Education Information Management

System (PEIMS), and 2010-2011 AEIS report of the Mathematics and English scores on the

Texas Assessment of Knowledge and Skills (TAKS) Exam of the Bottom Ten Selected Schools.

Spending 10th

Math 10th

ELA 11th

Math 11th

ELA

N Valid 10 10 10 10 10

Mean 5325.10 81.50 94.30 92.60 96.70

Std. Error of Mean 34.387 4.148 1.422 1.833 .817

Median 5352.00 81.00 95.00 92.50 97.50

Mode 5436 99

99 98a

99

St. Deviation 108.740 13.117 4.498 5.797 2.584

Range 294 35 11 16 6

Minimum 5142 64 88 83 93

Maximum 5436 99 99 99 99

Sum 53251 815 943 926 967

______________________________________________________________________________

a. Multiple modes exist. The smallest value is shown.



1st Null Hypothesis

36

1. H01 – There is no statistically significant difference in student

achievement between low per pupil spending and high per pupil spending high schools, as reported on the PEIMS and

AEIS for TAKS in Reading/ELA for 10th grade.



1st Null Hypothesis Findings

37

Table 9.0

Independent T-Test using the using the 2010-2011 school Per Pupil Spending rates report from

the Public Education Information Management System (PEIMS), and 2010-2011 AEIS report of

the 10th

Grade English scores on the Texas Assessment of Knowledge and Skills (TAKS) Exam

of the Top Ten and Bottom Ten Selected Schools.

F Sig. t df Sig. Mean Std. Error

(2-Tailed) Difference Difference

EnglishEqual variances

assumed 2.044 .170 -3.462 18 .003 -9.000 2.600

Equal variances

Not assumed -3.462 15.505 .003 -9.000 2.600

______________________________________________________________________________

* The level of significance p ≤ 0.05

The null hypothesis is rejected because the significance level is less than .05. The data

revealed that there is a significant difference between student achievement between low per pupil

spending and high per pupil spending high schools, as reported on the PEIMS and AEIS for

TAKS in Reading/ELA for 10th grade.



2nd Null Hypothesis

38



AEIS for TAKS in Mathematics for 10th grade.



2nd Null Hypothesis Findings

39

Table 10.0

Independent T-Test using the using the 2010-2011 school Per Pupil Spending rates report fromthe Public Education Information Management System (PEIMS), and 2010-2011 AEIS report of the 10th Grade Mathematics scores on the Texas Assessment of Knowledge and Skills (TAKS)

Exam of the Top Ten and Bottom Ten Selected Schools.



Math

Equal variances

assumed .000 1.000 -2.704 18 .015 -16.000 5.917

Equal variances Not assumed -2.704 17.995 .015 -16.000 5.917

______________________________________________________________________________





TAKS in Mathematics for 10th grade.



3rd Null Hypothesis

40



AEIS for TAKS in Reading/ELA for 11th grade.



3rd Null Hypothesis Findings

41

Table 11.0



the 11th

Grade English scores on the Texas Assessment of Knowledge and Skills (TAKS) Exam

of the Top Ten and Bottom Ten Selected Schools.



English

Equal variances

assumed 2.172 .158 -3.803 18 .001 -6.600 1.735

Equal variances

Not assumed -3.803 13.744 .002 -6.600 1.735

______________________________________________________________________________





TAKS in Reading/ELA for 11th grade.



4th Null Hypothesis

42



AEIS for TAKS in Mathematics for 11th grade.



4th Null Hypothesis Findings

43

Table 12.0



the 11th

Grade Mathematics scores on the Texas Assessment of Knowledge and Skills (TAKS)

Exam of the Top Ten and Bottom Ten Selected Schools.



Math

Equal variances

assumed .286 .600 -2.501 18 .022 -7.400 2.959

Equal variances

Not assumed -2.501 17.077 .023 -7.400 2.959

______________________________________________________________________________





TAKS in Mathematics for 11th grade.



Part V

Summary, Recommendations,

& Conclusion

44



Summary

45

This causal-comparative research study explored the impact of per pupil

spending on student academic achievement in southeastern school districts in Texas.

The researcher purposively selected the top ten per pupil spending high schools located

within Harris County Texas, and the bottom ten per pupil spending high schools located

within Harris County Texas. The purpose of this causal-comparative study was to

determine if there was a difference in student academic achievement between high

schools with low per pupil spending levels and high schools with high per pupil

spending levels as reported in the Texas Education Agency PEIMS and AEIS report of

2010-2011.



Summary Continued

46

• The average student population of the Top Ten per pupil spending schools was

1,037, while the average student population of the Bottom Ten per pupil spending

schools was 2,716.

•

The average At-Risk student population of the Top Ten per pupil spending schoolswas 66.42%, while the average At-Risk student population of the Bottom Ten per

pupil spending schools was 40.77%.

• The average years of experience of the teachers at the Top Ten per pupil spending

schools was 10.45 years, while the average years of experience of the teachers at theBottom Ten per pupil spending schools was 10.94 years.

• The average African-American student population of the Top Ten per pupil spending

schools was 39.91%, while the average African-American student population of the

Bottom Ten per pupil spending schools was 19.51%.



Summary Continued

47

• The average Hispanic student population of the Top Ten per pupil spending schools

was 48.61%, while the average Hispanic student population of the Bottom Ten per

pupil spending schools was 34.73%.

• The average White student population of the Top Ten per pupil spending schools

was 8.27%, while the average White student population of the Bottom Ten per pupil

spending schools was 34.11%.

• The average Asian student population of the Top Ten per pupil spending schools was

2.19%, while the average Asian student population of the Bottom Ten per pupil

spending schools was 9.31%.



Summary Continued

48

The research and analysis completed for this study explored whether or not

student achievement is affected by the level of per pupil spending to serve as a

precursor to determine how per pupil spending occurs at given schools and districts.

The study found that there is a statistically significant difference in student academic

achievement between high schools with low per pupil spending levels and high schools

with high per pupil spending levels as reported in the Texas Education Agency PEIMS

and AEIS report of 2010-2011.

R d i F F h



Recommendations For Further

Study

49

In accordance with the results of this causal-comparative study, the researcher

recommends the following for further study:

1. A study could be performed to explore the impact of student At-Risk populations on

whether there is a difference in student achievement between low per pupil

spending and high per pupil spending high schools, as reported on the Public

Education Information Management System (PEIMS) and Academic Excellence

Indicator System (AEIS) for Texas Assessment of Knowledge and Skills (TAKS).

2. A study could be performed to explore the impact of over-all student population on





R d i F F h




Study

50

3. A study could be performed to explore the impact of the allocation of school funds

on whether there is a difference in student achievement between low per pupil




4. A study could be performed to explore the impact of student gender populations on





R d ti F F th




Study

51

5. A study could be performed to explore the impact of Federal allocation of

educational funds on whether there is a difference in student achievement between

low per pupil spending and high per pupil spending high schools, as reported on the

Public Education Information Management System (PEIMS) and Academic

Excellence Indicator System (AEIS) for Texas Assessment of Knowledge and Skills

(TAKS).

6. A study could be performed to explore the impact of State allocation of educational

funds on whether there is a difference in student achievement between low per pupil spending and high per pupil spending high schools, as reported on the Public





Conclusion

52

In conclusion, according to the data, the Bottom Ten per pupil spending

schools located within Harris County Texas outperformed the Top Ten per pupil

spending schools located within Harris County Texas on the 2010-2011 TAKS: 10th

grade English Exam, 10th grade Math Exam, 11th grade English Exam, and the 11th

grade Math Exam.

With billions of dollars cut from current educational funding budgets in the

state of Texas, school districts are resorting to litigation to alleviate their financial crises,

and these difficult times in education in Texas are exasperated by the new state

accountability measures of the STAAR exams. The heart of all current educational

equity litigation in Texas today is that there are not enough funds to adequately provide

equity in education to be able to meet current accountability measures.



Conclusion

53

Texas Honorable District Judge John Dietz, on February 4, 2013, ruled that

the state's school finance system is unconstitutional, and suggested a figure of $2,000

needs to be added to the per pupil spending levels in the state of Texas (Peyton, 2013).

It is the funding schemes and formulas utilized at the local, state, and federal

levels which contribute to a widening of the achievement gap. Miles and Roza (2006)

argue that even though urban school districts across the United States of America are

aware that staff-based allocation of resources over student-centric formulas are creating

per pupil spending inequities, they continue to utilize such an antiquated inequitable

funding system.

The research and analysis completed for this causal-comparative study

provides a basic understanding of per pupil spending and academic achievement, and

lays a foundation for analyzing how to adequately fund the reforms of a post

modernistic future in the state of Texas and nation as a whole.



Thank You

54



References

Ajilore, O. (2006). Econometric Issues in Education Finance. Review Of RegionalStudies, 36(2), 192-204.

Archibald, S. (2006). Narrowing in on Educational Resources That Do Affect

Student Achievement. Peabody Journal Of Education (0161956X), 81(4),

23-42. doi:10.1207/s15327930pje8104_2

Battaglino, T., Haldeman, M., & Laurans, E. 2012-01-10 The Costs of Online Learning. Creating

Sound Policy for Digital Learning: A Working Paper Series from the Thomas B.

Fordham Institute. Thomas B. Fordham Institute. 1701 K Street NW Suite 1000,

Washington, DC.

Bernstein, S. (2004). Teaching and Learning in Texas: Accountability Testing, Language, Race,

and Place. Journal Of Basic Writing, 23(1), 4-24.

Boswell, C. (2010). State Education Finance and Governance Profile: Texas.

Peabody Journal Of Education (0161956X), 85(1), 101-103.

doi:10.1080/01619560903524001

Carr, M. J., Gray, N. L., & Holley, M. J. (2007). Shortchanging Disadvantaged Students:

An Analysis of Intra-District Spending Patterns in Ohio. Journal Of Educational

Research & Policy Studies, 7(1), 36-53.

Causey-Bush, T. (2005). Keep Your Eye on Texas and California: A Look at Testing, SchoolReform, No Child Left Behind, and Implications for Students of Color. Journal Of Negro

Education, 74(4), 332-343.

Creswell, J. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods

Approaches. Sage Publications: Los Angeles, London, New Delhi, Singapore.

55



References

Dills, A. K. (2004). Do Parents Value Changes in Test Scores? High Stakes Testing in Texas.Contributions To Economic Analysis & Policy, 3(1), 1-32.

Downes, T. A., & Shah, M. P. (2006). The Effect of School Finance Reforms on

the Level and Growth of Per-Pupil Expenditures. Peabody Journal Of

Education (0161956X), 81(3), 1-38. doi:10.1207/s15327930pje8103_1

Earthman, G. (2009). Planning Educational Facilities: What Educators Need To Know.

Maryland: Rowman & Littlefield Education.

Eaton, S., & Rivkin, S. (2010). Is Desegregation Dead? Parsing the Relationship between

Achievement and Demographics. Education Next, 10(4), 50-59.

Emery, K. (2007). Corporate Control of Public School Goals: High-Stakes Testing in Its

Historical Perspective. Teacher Education Quarterly, 34(2), 25-44.

Everhart, R. (2006). Why Are Schools Always Begging for Money? Phi Delta Kappan, 88(1),

70-75.

Financial Allocation Study for Texas (2010). Texas Comptroller of Public Accounts.

Fraenkel, J. & Wallen, N. (2009). How to Design and Evaluate Research in Education.

The Seventh Edition. McGraw-Hill. New York, NY.

Glen, W. J. (2006). Separate But Not Yet Equal: The Relation Between School Finance

Adequacy Litigation and African American Student Achievement. Peabody Journal Of Education (0161956X), 81(3), 63-93.doi:10.1207/s15327930pje8103_3

Greene, J. P., & Trivitt, J. R. (2008). Can Judges Improve Academic Achievement?. Peabody

Journal Of Education (0161956X), 83(2), 224-237. doi:10.1080/01619560801997010

Grubb, Norton W. (2009). The Money Myth: School Resources, Outcomes, and Equity. Russell

Sage Foundation: New York, New York.

Hart, P. (2004). Hood Riddance. Texas Monthly, 32(3), 58-88.

56



References

Hedman, C. (2011). SCHOOL LAW--THE FIFTH CIRCUIT PROLONGS EDUCATIONALINEQUALITIES IN UNITED STATES V. TEXAS. SMU Law Review, 64(2), 779-785.

Hines, M., Conner, J., Campano, G., Damico, J., Enoch, M., & Nam, D. (2007). National

Mandates and Statewide Enactments: Inquiry in/to Large-Scale Reform. English

Teaching: Practice And Critique, 6(3), 76-91.

Hoff, D. J. (2005). Texas School Finance Ruling Draws National Attention. Education

Week, 25(14), 25-27.

Hursh, D. (2005). The growth of high – stakes testing in the USA: accountability, markets and the

decline in educational equality. British Educational Research Journal, 31(5), 605-622.doi:10.1080/01411920500240767

Imazeki, J., & Reschovsky, A. (2005). Assessing the Use of Econometric Analysis in Estimating

the Costs of Meeting State Education Accountability Standards: Lessons from

Texas. Peabody Journal Of Education, 80(3), 96-125.

Ishaq, K., & Kritsonis, W. (2009). Applying Postmodernism: Solutions to all our Educational

Problems. ERIC Online Submission.

Janssen, J. J. (2000). Public School Finance, School Choice, and Equal Educational Opportunity

in Texas: The Enduring Importance of Background Conditions. Review Of Litigation,19(1), 1.

Jimenez-Castellanos, O. (2010). Relationship Between Educational Resources and School

Achievement: A Mixed Method Intra-District Analysis. Urban Review, 42(4), 351-371.

doi:10.1007/s11256-010-0166-6

Johnson, P. & Kritsonis, W.A. (2010) Greener Schools, Greater Learning, and the LEED Value.

Doctoral Forum: National Journal for Publishing and Mentoring Doctoral Student Research, 7 (1).

57



References

Lee, J. (2010). Dual Standards of School Performance and Funding? Empirical Searches of School Funding Adequacy in Kentucky and Maine. Education Economics, 18(2), 207-

228.

Lips, D., Watkins, S. J., Fleming, J., & Heritage, F. (2008). Does Spending More on

Education Improve Academic Achievement? Backgrounder. No. 2179. Heritage

Foundation.

Liu, G. (2008). Improving Title I Funding Equity Across States, Districts, and

Schools. Iowa Law Review, 93(3), 973-1013.

Mcfadden, L. (2009). Miami's "Zone" Teaches Lessons About Low-Performing Schools.(Cover story). Phi Delta Kappan, 90(8), 557-562.

Miles, K., & Roza, M. (2006). Understanding Student-Weighted Allocation as a

Means to Greater School Resource Equity. Peabody Journal Of Education

(0161956X), 81(3), 39-62. doi:10.1207/s15327930pje8103_2

Moore, S. D., Kochan, F. K., Kraska, M., & Reames, E. H. (2011). Professional Development

and Student Achievement in High Poverty Schools: Making the

Connection. International Studies In Educational Administration (Commonwealth

Council For Educational Administration & Management (CCEAM)), 39(2), 65-79.Odden, A., & Picus, L. O. (2011). Improving teaching and learning when budgets are tight. Phi

Delta Kappan, 93(1), 42-48.

Picus, L. O., Marion, S. F., Calvo, N., & Glenn, W. J. (2005). Understanding the Relationship

Between Student Achievement and the Quality of Educational Facilities: Evidence From

Wyoming. Peabody Journal Of Education (0161956X), 80(3), 71-95.

58



References

Preuss, G. (2009). The "Father" of Texas Education: A.M. Aikin and theModernization of Texas Public Schools. East Texas Historical Journal,

48(2), 17-25.

Ram, R. (2004). School expenditures and student achievement: evidence for the

United States. Education Economics, 12(2), 169-176.

Richwine, J., & Heritage, F. (2011). The Myth of Racial Disparities in Public School Funding.

Backgrounder. No. 2548. Heritage Foundation.

Roza, M. (2010). Educational Economics: Where Do School Funds Go? Washington, DC: The

Urban Institute Press.

Russo, C. J. (2009). ASBO at 100: A Supreme Court Retrospective on Equal Educational

Opportunities. School Business Affairs, 75(1), 36-39.

Ryan, K. & Cooper, J. (1995). Those Who Can, Teach. Boston: Houghton Mifflin.

Salinas, C. S., & Reidel, M. (2007). The Cultural Politics of the Texas Educational Reform

Agenda: Examining Who Gets What, When, and How. Anthropology & Education

Quarterly, 38(1), 42-56.

Schachter, R. (2010). The Price of a Good Education. District Administration,

46(9), 39-54.

Schenker, J. D., & Rumrill, J. D. (2004). Causal-comparative research designs. Journal Of

Vocational Rehabilitation, 21(3), 117-121.

Sharp, W. L. (1993). School Spending: Is There a Relationship between Spending and

Student Achievement? A Correlation Study of Illinois Schools.

Shelly, B. (2011). Money, Mandates, and Local Control in American Public Education. The

University of Michigan Press: Ann Arbor.

Singh, K. (2007). Quantitative Social Research Methods. Sage Publications: Los Angeles,

London, New Delhi, Singapore.

59



References

Smith, M. (2011, December 2). Robin Hood an Accepted Reality for Texas Schools. The Texas

Tribune, p. A1.

Smith, C. (2012, April 5). State comptroller says health care bill will reduce education spending.

The Bryan College Station Eagle, p. A1.

Stutz, T. (2012). School finance trial to start in late October. Dallas News April 16, 2012.

Texas Education Agency (2012). An Overview of the History of Public Education in

Texas.http://www.tea.state.tx.us/index4.aspx?id=148

Uline, C., & Tschannen-Moran, M. (2008). The Walls Speak: The Interplay of Quality Facilities,School Climate, and Student Achievement. Journal Of Educational Administration,

46(1), 55-73.

Uline, C. L., Wolsey, T., Tschannen-Moran, M., & Lin, C. (2010). Improving the Physical and

Social Environment of School: A Question of Equity. Journal Of School Leadership,

20(5), 597-632.

United States Census Bureau (2011). Public Education Finances: 2009. U.S. Department of

Commerce, May 2011.

U.S. Department of Education (2012).

Walsh, J., Kemerer, F., & Maniotis, L. (2005) the Educator's Guide to Texas School

Law. University of Texas Press, Austin.

Whitney, A. (2012, April 3). Districts Avoid School Finance Lawsuits, Cite Finances. The Texas

Tribune, p. A1.

Dailey's Dissertation Defense Powerpoint Presentation 2013

Documents

Transcript of Dailey's Dissertation Defense Powerpoint Presentation 2013