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Personnel resource allocation strategies in a time of fiscal crisis: case study of elementary schools in a California school district

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Personnel resource allocation strategies in a time of fiscal crisis: case study of elementary schools in a California school district

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Content Running Head: PERSONNEL RESOURCE ALLOCATION 1
PERSONNEL RESOURCE ALLOCATION STRATEGIES IN A TIME OF FISCAL CRISIS:
CASE STUDY OF ELEMENTARY SCHOOLS IN A CALIFORNIA DISTRICT

by

Melissa Marie Sais

A Dissertation Presented to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDCUATION
UNIVERSITY OF SOUTHERN CALIFORNA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF EDUCATION

May 2013

Copyright 2013 Melissa Marie Sais
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Dedication
This dissertation is dedicated to my parents, Michael and Kathy Sais for encouraging me
not only to purse a doctorate degree, but also for always believing that I had the ability to
successfully complete the program. Thank you to my sisters, brother, extended family and
friends for understanding the time commitment required of this pursuit and the sacrifices that I
often had to make. I could not have done this without all of your loving words of encouragement
and support.
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Acknowledgements

I would like to express my gratitude to Dr. Lawrence Picus for agreeing to be my
dissertation chair. His countless hours of time spent reading my work and providing timely and
insightful feedback contributed to the positive experience of this process.
I would also like to thank the members of the Orange County Cohort for not only offering
support through the program but also more importantly being incredible friends. Thank you
Sarah Ragusa, Amber Lane, Steve Behar, Larry Hausner, Tamra Rowcliffe, Jonathan Swanson,
and Sandra Garica for all the great memories. Writing my dissertation would not have been the
same without your continuous friendship and encouragement.

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Table of Contents
Dedication 2

Acknowledgements 3

List of Tables 6

List of Figures 7

Abstract 8

Chapter 1: Overview of the Study 9

Background 9

Statement of the Problem 14

Purpose of Study 15

Research Questions 16

Importance of Study 16

Methodology 17

Limitations 17

Delimitations 18

Assumptions 18

Definition of Terms 18

Chapter 2: Literature Review 23

School Improvement Strategies 23

Allocation and Use of Human Resources 33

Limited Resources/Fiscal Constraints 45

Gap Analysis 56

Summary 66

Chapter 3: Methodology 68

Overview of the Methodology 69

Research Questions 69

Sample and Population 70

Instrumentation 74

Data Collection 75

Data Analysis 78

Summary 80

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Chapter 4: Findings 81

Overview of the District 82

Introduction of the Findings 85

Data for Research Question One 86

Data for Research Question Two 98

Data for Research Question Three 111

Data for Research Question Four 115

Chapter 5: Conclusions 121

Overview of the Study 121

Purpose of the Study 121

Importance of Study 122

Methodology 122

Summary of the Findings 124

Findings for Research Question One 124

Findings for Research Question Two 125

Findings for Research Question Three 126

Finding for Research Question Four 126

Limitations 130

Implications for Practice 131

Recommendations for Future Research 133

Conclusion 134

References 137

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List of Tables
Table 1:Commonly Identified Strategies for Increasing Student Achievement 32

Table 2: School Expenditure Structure and Resource Indicators of the
Evidence-Based Model 42

Table 3: Four Levels of Evaluation 66

Table 4: Characteristics of Each Elementary School 72

Table 5: 2011-2012 Demographics and Achievement of Each Elementary School in Study 84

Table 6: The Four Elements of Instructional Rounds 89

Table 7: District’s Allocation of Core Teachers and Specialist Teachers by School
Allocated by Grade Level 100

Table 8: District’s Allocation of Certificated Staff 104

Table 9: District’s Allocation of Classified Staff 107

Table 10: District’s Allocation of Office Staff 110

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List of Figures
Figure 1: Share of Total Public School Revenues 34

Figure 2: The Evidence-Based Model (Odden & Picus, 2008). 45

Figure 3: California’s K-12 Revenues are Allocated through a Complex System 47

Figure 4: School District Funding by Type, 2009-2011 48

Figure 5: Allocation of State Fiscal Stabilization Fund (SFSF) Dollars 53

Figure 6: Steps of Instructional Supervision. 88

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Abstract
The purpose of this study was to analyze human resource allocation data for all
elementary schools in large urban school district to determine whether resources were allocated
in ways in that research suggests can lead to improved student achievement. Data from all 46
elementary schools that participated in the study were compared to the allocation
recommendations of the Evidence-Based Model to identify allocation gaps. The following four
research questions guided this study: (1) What research based human resource allocation
strategies improve student achievement? (2) How are human resources allocated across Garden
Grove Unified School District’s elementary schools? (3) Is there a gap between current human
resource allocation practices and what the research suggests is most effective? (4) How can
human resources be strategically re-allocated to align with strategies that improve student
achievement? The findings suggest that the 46 elementary schools in Southern California do not
have the financial resources available to allocate personnel at the levels recommended by the
Evidence-Based Model, therefore educational leaders must be strategic in allocating their limited
human resources toward areas that research asserts will directly contribute to increased student
achievement. To address this notion recommendations were made to reallocate resources toward
increasing the amount of instructional coaches, reassigning credentialed teachers to support “at
risk” students, and allocating additional time during the school day for teacher collaboration.
Results from this study will support educational leaders in making more informed decisions
pertaining to human resource allocation and reallocation to support increased student
achievement.
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Chapter 1: Overview of the Study
Background
Performance-Based Accountability. In the spring of 1999, California lawmakers passed
the Public Schools Accountability Act (PSAA). This legislative act sought to improve the
academic performance of California’s students by publicly rating and comparing schools based
on student test scores and delivered sanctions to schools whose students did not make
satisfactory progress. PSSA utilized a performance indicator known as the Academic
Performance Index (API) to rate schools along a scale ranging from 200 to 1,000. In addition,
two decile rankings were created, a similar schools and an absolute statewide rank. California’s
PSAA focused on the academic performance of all students by ranking similar schools through a
decile distribution, indentifying yearly growth targets based on API, and calculating an
Academic Performance Index (API) score for each school based on the distribution of scores on
the state’s annual standardized tests (Edsource, 2005).
Three years later in December 2001, federal lawmakers reauthorized the government’s
Elementary and Secondary Act (ESEA) by passing the No Child Left Behind Act (NCLB), an
effort to motivate all states to strengthen their assessment and accountability system. NCLB
places greater accountability on school districts and their subsequent schools because both
groups are expected to make Adequate Yearly Progress (AYP). This increased emphasis on
accountability represents an important change from past federal educational initiatives, which
focused primarily on the provision of services. Supporters of NCLB argued that previous
educational reforms were unsuccessful in large measure because they ignored student outcomes.
This law introduced separate consequences for schools and school districts if test scores for
specific groups of their students (based on ethnicity, family income, English fluency, or
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disability) were below expectations. In the process it increased the pressure on districts to
improve schools.
Dual Accountability Systems in California. PSAA and NCLB both emphasize regular
assessments and the public release of school performance data on those standardized assessments.
Both utilize California’s rigorous content standards as the basis for performance measurements.
Unfortunately, the two accountability systems are fundamentally different in their definition of
school success. California’s system uses a growth model for accountability, rewarding schools
that display meaningful progress regardless of their starting point. The federal system provides
little credit for growth. Instead it uses a “status model”, AYP, which focuses on whether a
specified percentage of students have attained proficiency in math and English language arts in a
given year. The percentage required to meet the target rises each year and will culminate in
2013-2014, when all students are expected to reach proficiency in both subject areas. Due to the
two systems utilizing different criteria, a school can perform well on one measure and not on the
other. Many schools “in need of improvement” under the federal accountability model are
considered successful according to the state’s accountability system (Edsource, 2005).
Since 2003, California schools have been required to meet growth targets under the
state’s accountability system and ensure that every numerically significant student subgroup
meets the participation and proficiency status targets required under NCLB. Odden (2003)
believes both standards-based education reforms seek to educate more students to high levels of
achievement, a goal that has both equity and excellence built into it. The equity part of the goal
requires dramatically diminishing the achievement gap between low income and minority
student and all other students. The excellence part of the goal requires at least a doubling of the
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performance of our education system over the next decade. This educational reform strategy has
already begun to change education finance.
California’s School Finance System. California’s schools have sustained significant
funding cuts since 2007, yet substantial evidence indicates that Californians do not want to
realize financial reductions to their schools. Despite an extraordinarily difficult economy in the
fall of 2008, the vast majority of state residents who were asked to raise their own taxes in
support of local schools agreed to do so (Edsource, 2009). Unfortunately, California law
severely limits local school districts’ revenue-raising authority compared to most other states and
compared to what was possible in California prior to 1972.
Beginning in 1968, a California court case, Serrano v. Priest (1976), was one of the first
lawsuits to challenge the U. S. tradition of using property taxes as the primary source of revenue
for public schools. Anticipating an outcome in the case that would demand that funding be
equalized among districts, state assemblymen passed Senate Bill (SB) 90 in 1972, creating the
“revenue limit” system that placed a ceiling on the amount of general purpose money each
district could raise. Many believe these changes eroded the connection between schools and
their communities and help explain why California today funds its schools well below the
national average.
The state largely controls the revenues that school districts receive. Funding for school
operations comes from several sources, only one of which is under the control of California’s
local school district. According to Edsource (2009), school district funding allocations are as
follows:
• 59%-state general fund, which is generated mainly by income, sales, corporate, and
capital gains taxes.
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• 23%-property taxes, which are collected by counties. The state determines how to
allocate them among school districts and other local governments. The tax rate is set in
the state constitution at one percent as established by Proposition 13.
• 10%-federal government, which generally provides only categorical funding (money
earmarked for specific purposes, such as compensatory education for low-income
students). The state distributes most of this funding. The recent stimulus package has
temporarily increased the federal share.
• 1% to 2%-state lottery.
• 7% to 8%-local miscellaneous sources, such as donations to local schools, interest
income, and parcel taxes. Local school districts and their communities largely control
these revenue sources. The amounts vary dramatically form one district to another.
California school finance researchers are addressing several issues: whether spending levels
are adequate to meet the needs of all children; how educational resources are allocated and used;
and how funding levels are linked to student outcomes (Picus, 2000). When compared to other
U.S. States, California’s per pupil funding is ranked 29
th
(California School Finance, 2008). The
question California policymakers need to ask is “Are we appropriating sufficient funds to our
schools to ensure students meet the proficiency benchmarks?” Given the limited resources
available to California schools, both district and school administrators must closely examine how
to effectively allocate resources in a manner that positively impacts student achievement.
Educational Adequacy. Long focused on fiscal equity, school finance is now shifting
toward fiscal adequacy. This shift represents a fundamental change: it means that school finance
today encompasses not only fiscal inputs but also their connection to educational programs,
teacher compensation, and student achievement (Ladd & Hansen, 1999). Despite the shift to
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adequacy, those who determine school finance policy must be conscious of fiscal disparities
caused by the unequal distribution of the local property tax base. Though eliminating these
inequities was the focus for the last half of the 20
th
century and some progress was made,
unequal access to local education revenues is still a problem in most states (Biddle & Berliner,
2002).
Under standards-based education reform, the hallmark test of school finance policy is
whether it provides sufficient or adequate revenues per pupil for districts and schools to institute
educational strategies that are successful in educating students to high standards of performance
(Edsource, 2003). To determine adequacy revenue levels one must first identify the cost of
effective programs and strategies then translate those costs into appropriate school finance
structures. Next one must ensure that the resources are utilized in districts and schools as
intended to produce desired results. Unlike the past, this new focus on school finance results in
states no longer allowing districts to select their own spending levels. Additionally, under the
adequacy framework, all districts and schools must spend at least at an adequate level.
Four models of educational adequacy. Determining an adequate school finance system
requires the state to identify both an adequate expenditure level for the typical student in the
typical district and sufficient adjustments for various student needs. It also requires districts and
schools to manage these resources so that students achieve performance standards required by
the state. Four methods have been utilized to determine an adequate foundation expenditure
level: the successful district approach, the cost function approach, the professional judgment
approach, and the evidence-based approach (Odden, 2003).
Each of the four educational adequacy models will be described. The successful district
approach seeks to identify districts that have been successful in teaching students to meet
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proficiency standards. The cost function approach employs regression analysis with expenditure
per pupil as the dependent variable and student and district characteristics, as well as
performance levels desired, as the independent variables. The professional judgment approach
asks a group of educational experts to identify effective educational strategies and a total
expenditure per pupil price is devised based on the total identified strategies. The fourth
approach, evidence-based approach, identifies a set of approaches that are required to deliver a
high-quality, comprehensive, school-wide instructional program. It then determines an adequate
expenditure level by assigning a price to each ingredient and aggregating to a total cost.
Evidence-Based Model. California schools are currently functioning during a time when
accountability measures are high and budgets are dwindling. Since the Evidence-Based Model
more directly identifies educational strategies that produce desired results and guides schools in
the most effective use of its limited financial resources, it was the model that served as the tool in
this case study. The Evidence-Based Model was developed by Dr. Allen Odden from the
University of Wisconsin-Madison and Dr. Lawrence O. Picus from the University of Southern
California in an effort to re-conceptualize school finance adequacy. Odden and Picus (2008)
focus their model on spending within the instructional function, enhancing spending in core
subjects and spending more directly on strategies that have evidence of being successful. The
Evidence-Based Model includes research-based practices addressing instructional leadership,
school and educational improvement, staffing, teacher professional development, and curriculum.
Statement of the Problem
During an era in which performance-based accountability is at the forefront of public
education, especially for California public schools that face a dual and often contradictory
accountability system, it is imperative that human resources be strategically allocated to ensure
PERSONNEL RESOURCE ALLOCATION 15

increased student achievement. A confounding element to the already challenging time of
increased performance accountability for California public schools is that exorbitant budget cuts
have been instituted statewide. Currently educational leaders are faced with the daunting task of
determining the optimal allocation of limited human resources that most effectively promote
attainment of the rigorous performance targets set forth by both the state and federal government.
A study of elementary schools in a California school district utilizing the Evidence-Based
Model by Odden and Picus (2008) was conducted. Both at the district and school site level, it is
necessary for educational leaders to attain the knowledge of how to effectively and efficiently
allocate their limited human resources toward the goal of increased academic achievement. The
evidence-based approach to adequacy provides a formula for implementing research-based
school reform practices at the school site level and therefore served as the model in this study.
Purpose of Study
The purpose of this study is to examine the human resource allocation of the 47
elementary schools in a large urban district located in Southern California that has defied the
typical large urban school district stereotype of being “under performing” and has been
recognized for its academic achievement. Data for both the actual and optimal allocation of
human resources of each elementary school was collected and analyzed to determine how
resources are focused to increase student achievement. Then a gap analysis was conducted
comparing the actual and optimal allocations data to the recommended human resource
allocation of the Evidence-Based Model by Odden and Picus (2008). The results from this study
provide educational leaders with an understanding of school level resource use and which
resource allocation patterns directly lead to improved student outcomes.
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Research Questions
The research questions for this study are:
• What research based human resource allocation strategies improve student achievement?
• How are human resources allocated across Garden Grove Unified School District’s elementary
schools?
• Is there a gap between current human resource allocation practices and what the research
suggests is most effective?
• How can human resources be strategically re-allocated to align with strategies that improve
student achievement?
Importance of Study
Consistently diminishing levels of financial resources and increasing levels of student
performance accountability characterize the current state of California public schools. Measures
have been instituted by both state and federal government reforms. Now more than ever, those
in charge of making school-based financial decisions must be informed on how to effectively
allocate limited resources to optimize student achievement. Analyzing the gap between current
use of elementary school-level human resources and the recommendations presented in the
Evidence-Based Model by Odden and Picus (2008) and any implications on student achievement
will provide useful insight for educational leaders.
The results from this study provide a framework for site administrators to use when faced
with making difficult decisions of how to effectively allocate limited human resources to support,
maintain, and increase student achievement.
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Methodology
A mixed methods approach was utilized to conduct the study and analysis of the 47
elementary schools in an urban California school district. Qualitative in nature, interviews with
various district office personnel were conducted to determine instructional components, resource
utilization, and the district’s current focus and overall vision. Quantitative data derived from the
district office was analyzed to determine site-based allocations of personnel, professional
development, and any other human resource expenses associated with student achievement. This
quantitative data was input into a database and compared against the district’s ideal allocations
and to the human resource allocation that the Evidence-Based Model generated for each school.
Each school was analyzed and used to make comparisons of the similarities and differences in
human resource use across all schools in the study. More importantly, the data was utilized to
identify how the district’s resource use was aligned to the recommended allocations in the
Evidence-Based Model.
Limitations
Data collection for this study was limited to elementary schools and to one select school
district. The information gathered from the interviews was derived from the perceptions of the
select district office personnel who were willing to participate in the process and might not have
constituted a representative sample of all other district office administrators. Due to the
sampling, the findings may not be generalized to other schools and different student populations,
especially those with different student demographics. Finally, California schools are
experiencing an economic crisis and contend with limiting funding policies, therefore current
school funding formulas do not allow for full implementation of the Evidence-Based Model even
if it provided the ideal allocation of human resources.
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Delimitations
This study did not examine elementary schools’ resource allocation over an extended
period of time. The study focused on human resource allocation during a specified period of
time, the 2011-2012 school year. A purposeful sample of only elementary schools from one
district does not allow for results to be generalized to other California schools or school districts,
especially those that serve students at the intermediate and high school levels.
Assumptions
All school documents and existing data collected and utilized from each school and the
district were assumed to be complete, accurate, and reflective of human resource allocation to
support the current instructional practices at each school site. It was assumed that all data
collected via district office personnel and principal interviews was honest and candid.
Definition of Terms
1. Academic Performance Index (API) - A numeric index that is annually assigned to each
California School as per the 1999’s Public School’s Accountability Act (PSAA). The
range is from a low of 200 to a high of 1000. Each annual API cycle includes a “Base
API” and a “Growth API”. The Base API, released in March, is calculated from statewide
test results of continuing and new assessments from the prior school year and serves as
the baseline for comparisons with the Growth API. The Growth API, released in August,
is calculated in exactly the same way and with the same indicators as the Base API but
utilizes test results from the following school year. The Growth API establishes whether
schools met their API targets (California Department of Education, 2007).
2. Adequate Yearly Progress (AYP) – AYP is an accountability measure of the 2001
federal law No Child Left Behind (NCLB). Progress is based on whether the school or
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district met its Annual Measurable Objectives and demonstrated 95% participation on
standardized tests, achieved its target on the Academic Performance Index and, for high
schools, met target graduation rates. The eventual goal is for 100% of the students to be
proficient in English Language Arts and Mathematics by 2014 (Ed-Data, 2008).
3. Content Standards- Standards adopted by the state that describe what concepts students
must master in each grade level in language arts, mathematics, science, social studies,
physical education, and visual/performing arts.
4. Cost Function Approach- Econometric approach to identifying school costs associated
with achieving required and/or desired student achievement outcomes.
5. Educational Adequacy- School finance concept that describes the provision of resources
to provide an adequate education so students achieve proficiency levels in core academic
content areas.
6. English Learners- Most current designation for students not yet sufficiently proficient in
the English Language to access and learn from the regular instructional programs offered
in general education. English language proficiency is assessed annually through the
California English Language Development Test (CELDT).
7. Evidence-Based Model- A resource allocation model that provides recommendations for
allocating school resources to improve student achievement. Its school instructional
improvement design is grounded in scientifically based research and widely documented
effective best practices.
8. Expert Judgment Approach- Use of a panel of educators, specialists, and administrators
to determine the necessary resources to achieve required and/or desired student
achievement outcomes.
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9. Full Time Equivalent- Abbreviated “FTE”, it indicates one person for each full time
position.
10. Funding Formulas- State specific formulas used to fund educational systems.
11. Growth Target- Derived from the California’s PSAA. California sets Academic
Performance Index growth targets for each school as a whole and for each numerically
significant subgroup in the school. The annual growth target for a school is 5% of the
difference between a school’s Base API and the statewide performance target of 800.
Any school with an API of 800 or more must maintain an API of at least 800 or more in
order to meet its growth target.
12. Instructional Leadership- A style of leadership that is considered an important
component for school improvement. It encompasses a leader who engages in the act of
continuously improving a school’s capacity to teach children by modeling and being an
instructional facilitator to the school’s staff and is grounded in a solid foundation of
knowing what constitutes effective instruction and how to maximize student learning.
13. No Child Left Behind (NCLB) - The 2001 reauthorization of the federal Elementary and
Secondary Education Act (ESEA) that places comprehensive accountability requirements
on all states, with increasing sanctions for schools and districts that do not make adequate
yearly progress toward proficiency in English/language arts and mathematics or that fail
to test 95% of all students and all significant subgroups. In California, those sanctions
currently apply only to schools and districts that accept Title I funding (Ed-Data, 2008).
14. Per Pupil Funding- A specific monetary allotment that is given to school systems for
each currently enrolled student. Per pupil funding is determined through state funding
formulas and is weighted depending on student’s demographics.
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15. Professional Judgment Approach- A resource allocation model that utilizes a panel of
educators, specialists, and administrators to determine the resources a school needs to
achieve required and/or desired student achievement outcomes.
16. Proposition 13- Passed in California in 1978, this initiative limited the property tax rate
to 1% of assessed value and capped increases at 2% or the percentage growth in the
state’s Consumer Price Index, whichever is less (Edsource, 2009).
17. Public Schools Accountability Act (PSAA) - In 1999, the California legislature passed the
Public Schools Accountability Act (PSAA). This legislation was intended to ensure the
creation and implementation of a system to monitor achievement in all schools and
provide incentives or sanctions to schools depending on whether they met their API goals.
18. Research Based Instructional Practices- Educational practices that have a research base
that attests to their effectiveness in improving student achievement.
19. Resource Allocation- Resource allocation is used to describe the operational activities
required to run schools and encompasses all inputs, defined by dollars spent the resources
these dollars buy, and the way these resources are used by educational institutions (Nakib,
1995).
20. Resources- Indicates the products and resources (monetary, personnel, time, materials, or
facilities) required and expended to educate students in a school system.
21. Similar Schools Rankings- California schools are ranked in ten categories of equal size,
called deciles, from 1 (lowest) to 10 (highest). These ranks are provided in the Base API
reports. This rank compares a school’s API to 100 other schools that are similar and with
similar challenges and opportunities (California Department of Education, 2007).
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22. Statewide School Rank- This rank compares a school’s API to the APIs of all other
schools statewide of the same type (elementary, middle, or high school).
23. Successful District Approach- A resource allocation approach that identifies programs,
strategies, and resources used in “successful schools” with the intention of transferring
them to other schools to achieve required and/or student achievement outcomes.
24. Socio-Economically Disadvantaged: Students who participate in the free/reduced price
lunch program.
25. Teacher Professional Development- Training teachers receive, of any length, that
provides them with additional strategies and knowledge to support the teaching of
students.

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Chapter 2: Literature Review
This chapter, the literature review has four sections that provide a theoretical base and
framework for this study. Funneling from a broad theoretical perspective of commonly
identified research-based effective school improvement strategies to a specified framework to
optimize resource allocation, each section moves the reader from general recommendations for
school success to ultimately employing a gap analysis to identify the deficit between
recommended resource allocations provided by the Evidence-Based Model (Odden & Picus,
2008) and actual school district resource allocations. The literature is divided into four sections:
1) school improvement strategies, 2) allocation and use of human resources, 3) limited
resources/fiscal constraints, and 4) gap analysis.
School Improvement Strategies
During an era of increased accountability imposed by both federal and state legislation,
effective school reform has been the subject of many studies. School leaders search for research-
based strategies to improve student achievement. A multitude of studies have been conducted to
determine which strategies are the best for promoting student achievement. The following six
important studies and meta-analyses on the topic will be examined:
1. Odden’s (2009), Ten Strategies for Doubling Student Performance, which
provided a meta-analysis and synthesis of researchers’ findings who examined
schools that utilized specific strategies to double student performance.
2. Duke’s (2006) meta-analysis of 5 studies conducted between 1995 and 2004 of
low performing schools that were able to significantly raise student
achievement. The purpose of the study was to inform a training program for
school turnaround.
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3. Research conducted by Reeves (2003) identified schools in Milwaukee and
Wisconsin that had over 90% eligibility for free or reduced lunch, 90%
minority ethnic demographics, and 90% of students meeting district or state
academic targets. Common characteristics of these schools were examined.
4. A study by Togneri and Anderson (2003) identified districts in California,
Texas, Maryland, Minnesota and Rhode Island that had poverty rates of at least
25%, a three-year trajectory of improved scores in math and reading, and
showed evidence of closing the achievement gap.
5. Fermanich, Mangan, Odden, Gross, and Rudo’s (2006) conduced a successful
district analysis in Washington by linking resource use in schools to
instructional improvement strategies.
6. Darling-Hammond’s (2002) study examined small, new-model schools that had
been opened in New York over time to address high dropout rates and low
attendance and academic success rates.
Strategies for Doubling Student Performance. Odden’s Ten Strategies for Doubling
Student Performance (2009) provided both a meta-analysis and a synthesis of thirteen
researchers’ findings of schools that were able to utilize specific strategies to double student
performance as measured by state tests. The book used “doubling” performance in a generic
fashion to indicate large, absolute gains in student achievement. For example, in very low
performing schools, a 5% to 10% or 10% to 20% were technically doubling performance, but all
of the cases and studies in the book reference schools that started at a much higher level and then
doubled performance. For districts starting at higher levels of student performance, the
definition of doubling would include a district or school increasing the percentage scoring at or
PERSONNEL RESOURCE ALLOCATION 25

above proficiency from 65% to 95%; even though not literally doubling, such an increase
represents large, absolute gains. The following are 10 strategies Odden (2009) found to double
student performance:
1. Understanding the performance problem and challenge – engaging in a variety
of activities to understand the performance problems and to fully understand
the distance between the current and desired outcomes;
2. Set ambitious goals – set “very” high and ambitious performance goals
regardless of student demographics or current performance levels;
3. Change the curriculum program and create a new instructional vision – focus
on the curriculum and instructional programs, which are the core educational
issues over which educators have control;
4. Formative assessments and data-based decision making – implement formative
assessments to provide teachers with data on what skills and standards students
have mastered and what areas must be retaught;
5. Ongoing, intensive professional development – implement a widespread,
systemic, and ongoing professional development program that includes training
on how to analyze assessments, implement new curriculum and programs, and
incorporate effective research-based instructional strategies;
6. Using time efficiently and effectively – this includes restructuring the school
day to better utilize time. Additional time should be allocated to reading and
mathematics, and interruptions during those times should not be allowed;
7. Extending learning time for struggling students – provide multiple “extra help”
strategies for students who are struggling to achieve proficiency. Extended
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learning time should take place during the instructional day, outside of the
instructional day, and outside of the regular school year;
8. Collaborative, professional culture – work in a collaborative and professional
learning community to create a common, school-wide, professional approach to
effective instructional practice;
9. Widespread and distributed instructional leadership – the principal, teachers,
and district office staff provide strong instructional leadership; and
10. Professional and best practices – create highly professional organizations that
actively consult the research about how to improve schools, including how to
provide the most effective reading, mathematics, science, and professional
development programs.
Odden’s (2009) ten strategies for doubling student performance consistently recur in the
succeeding studies and will therefore serve as a framework for comparison of each of the
following study’s identified characteristics.
School turnaround characteristics. With the purpose of informing a training program
for the Virginia School Turnaround Specialist Program, Duke (2006) conducted a meta-analysis
of five studies of low performing schools that were able to significantly increase student
achievement. The following characteristics were commonly identified across three or more of
the studies: 1) assistance: Students experiencing problems with learning required content
received prompt assistance; 2) collaboration: Teachers were expected to work together at
various levels to plan, monitor student progress, and provide assistance to struggling students: 3)
data-driven decision making: Data on student achievement were used on a regular basis to make
decisions regarding resource allocation, student needs, teacher effectiveness, and other matters;
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4) leadership: The actions of principals and teacher leaders set the tone for the school
improvement process; 5) organizational structure: Aspects of school organization-including
roles, teams, and planning processes-were adjusted to support efforts to raise student
achievement; 6) staff development: Teachers received training on a continuous basis in order to
support and sustain school improvement efforts, 7) alignment: Tests were aligned with
curriculum content, and curriculum content was aligned with instruction, 8) assessment:
Students were assessed on a regular basis to determine their progress in learning required content,
9) high expectations: Teachers insisted that students were capable of doing high-quality
academic work, 10) parent involvement: School personnel reached out to parents to keep them
apprised of their children’s progress and to enlist them in supporting school improvement efforts,
and 11) scheduling: Adjustments were made in the daily schedule in order to increase time for
academic work, especially in the key areas of reading and mathematics.
All eleven of Duke’s (2006) identified characteristics with the exception of parent
involvement were strikingly similar to Odden’s (2009) ten identified strategies of high
performing schools. Duke’s (2006) observation of the importance of parental involvement is one
that should not be overlooked when consulting research in determining the common
characteristics of high performing schools.
Common leadership behaviors of “90/90/90” Schools. Reeves (2003) provided a
review of research in high poverty schools that have also demonstrated high academic
performance. The term “90/90/90” was originally coined by Reeves in 1995 based on
observations in Milwaukee, Wisconsin, where schools had been identified with the following
characteristics: 90% or more of the students were eligible for free and reduced lunch, 90% or
more of the students were members of ethnic minority groups, and 90% or more of the students
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met the district or state academic standards in reading or another area (Reeves, 2000). Research
on 90/90/90 Schools included both site visits and analyses of accountability data with the
intention of identifying the extent to which there was a common set of behaviors exhibited by the
leaders and teachers in schools with high achievement, high minority enrollment, and high
poverty levels.
As a result, five characteristics were identified as being common to all 90/90/90 Schools.
Even though the last two characteristics were more specific than those of Odden’s (2009)
strategies, each of the five characteristics were aligned with the themes revealed in Odden’s
(2009) study. These characteristics were: 1) A focus on academic achievement, 2) clear
curriculum choices, 3) frequent assessment of student progress and multiple opportunities for
improvement, 4) an emphasis on nonfiction writing, and 5) collaborative scoring of student work.
Islands of excellence-schools defying odds. This study, conducted by Togneri and
Anderson (2003), was based on an examination of five improving high-poverty school districts.
All of the participating districts had experienced a rise in poverty over a decade, had experienced
significant changes in ethnic and racial makeup, and had seen an increase in the percentage of
English as a Second Language students. All of the districts had also demonstrated improvement
in academic achievement as measured by test scores across grades, subjects, and racial/ethnic
makeup. These districts were deemed “islands of excellence”. Islands of excellence are schools
and districts exhibiting gains in student achievement in isolation-apart from neighboring schools
and districts. This study’s purpose was to illuminate how those gains were being made so that
such strategies could be utilized system-wide by more schools and districts, thus expanding the
pockets of success.
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They researched districts in California, Texas, Maryland, Minnesota, and Rhode Island
that had poverty rates of at least 25%, a three-year trajectory of improved scores in math and
reading, and marked evidence of closing the achievement gap. It was found that districts
implemented a strikingly similar set of strategies to improve instruction. Seven factors emerged
as essential to improvement: 1) districts had the courage to acknowledge poor performance and
the will to seek solutions, 2) districts put in place a system wide approach to improving
instruction-one that articulated curricular content and provided instructional supports, 3) districts
instilled visions that focused on student learning and guided instruction improvement, 4) districts
made decisions based on data, not instinct, 5) districts adopted new approaches to professional
development that involved a coherent and district-organized set of strategies to improve
instruction, 6) district redefined leadership roles, and 7) districts committed to sustaining reform
over the long haul.
Six of these seven factors were synonymous with Odden’s (2009) ten improvement
strategies. Togneri and Anderson’s (2003) research revealed that districts committed to
sustaining reform over the long haul where Odden (2009) identified understanding the
performance problem and challenge but did not highlight the importance of leaders committing
to long-term reform.
Successful systemic reform. Fermanich, Mangan, Odden, Gross, and Rudo (2006)
conducted a successful district analysis in Washington. The Successful District approach to
school finance establishes a set of school performance criteria and then uses the expenditure
level of school districts meeting those criteria as an estimate of the funding levels needed for all
districts to meet those criteria. First a state level analysis was conducted, and then 31 successful
schools in nine districts were selected for further analysis. The study focused on resource use in
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schools that was linked to instructional improvement strategies. Several themes emerged that
were synonymous with those of Odden’s (2009) ten improvement strategies to form core
elements of successful systematic reform. These elements were: 1) focus on educating all
students, 2) use data to drive decisions, 3) adopt a rigorous curriculum and align to state
standards, 4) support instructional improvement with effective professional development, 5)
restructure the learning environment, and 6) provide struggling students with extended learning
opportunities.
New-model school principles. Focused on the organizational arrangement of new-
model schools, Darling-Hammond (2002), studied small, new-model schools that had been
opened in New York over time to address high dropout rates and low attendance and academic
success rates. In the early 1980’s, a group of restructured schools holding similar values joined
together to create the Center for Collaborative Education (CCE), a network that sponsors
cooperative work on school renewal, professional development, and community education.
Darling-Hammond (2002) noted that the CCE schools, both elementary and secondary, have
achieved success by utilizing similar organizational features and educational commitments.
Each school arrived at different manifestations of twelve principles they all commonly shared.
These principles emphasized the following characteristics: 1) school purposes (“helping young
people learn to use their minds well”), 2) high and universal academic standards, 3)
interdisciplinary, multicultural curriculum focused on powerful ideas, 4) small size and
personalization, 5) commitment to a goal of student-as-worker and student-as-citizen, 6)
performance-based assessment aimed at clearly stated competencies, 7) respectful tone and
values that emphasize unanimous expectation and decency, 8) family involvement, 9) shared
decision making, 10) commitment to diversity among students and staff, 11) selection of the
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school by student choice, and 12) administrative and budget targets featuring a reduced student
load and shared planning time for teachers and a budget comparable to that of other school.
Similar to Duke (2006), Darling-Hammond (2002) emphasized the importance of family
involvement as a key element in school improvement. Although her findings were similar to the
other researcher’s findings, she elaborated even further than both Duke (2006) and Odden (2009)
on her improvement strategies and included principles that focused on a commitment to honoring
diversity and to the success of students as citizens.
Recurring themes in literature. An examination of these five studies rendered several
recurring themes that support increased student achievement. A synthesis of the literature
reviewed consistently revealed the following four strategies for increasing student achievement:
leadership, assessment and data-based decision making, collaboration, and professional
development. Table 2.1 illustrates these four commonly identified strategies for increasing
student achievement and identifies researchers that purport various aspects of the strategies.
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Table 1
Commonly Identified Strategies for Increasing Student Achievement
Leadership • Sharing a vision, setting goals, and maintaining focus (Odden,
2009; Duke, 2006; Togneri & Anderson, 2003; Reeves, 2003;
Hallinger & Heck, 2002)
• Redefining leadership roles, shared decision-making (Odden,
2009; Togneri & Anderson, 2003; Darling-Hammond, 2002)
• Restructuring the school day (Odden, 2009; Duke, 2006)
Assessment/Data-
Driven Decision Making
• Importance of frequent assessments (Duke, 2006; Reeves,
2003)
• Alternative forms of assessment (Darling-Hammond, 2002)
• Data-based decision making (Odden, 2009; Duke, 2006;
Togneri & Anderson, 2003; Fermanich et al., 2006)
Collaboration • Realizing value in the process (Odden, 2009; Duke, 2006;
DuFour et al., 2006)
• Sharing best practices (Odden, 2009; DuFour et al., 2004)
• Analyzing student work (Reeves, 2003; DuFour et al., 2006)
• Structure of teams-subject areas or interdisciplinary (Darling-
Hammond, 2002)
Professional
Development
• Ongoing and intensive (Odden, 2009; Fermanich et al. 2006;
Duke, 2006; Birman et al., 2000)
• Adoption on innovative approaches (Togneri & Anderson,
2003; Corcoran, 1995)
• Resource allocation (Archibald & Gallagher, 2002; Desimone
et al., 2002; Fermanich, 2002; Miles, Odden, Fermanich, &
Archibald, 2004; Odden, Archiblad, Fermanich, Gallagher,
2002)

The reviewed literature supports in highlighting which strategies were consistently most
effective for improving student achievement. As schools seek methods to increase performance,
the commonly identified characteristics from the reviewed literature should be aggressively
explored and considered for school-wide adoption. Especially since schools are currently faced
with both limited resources and increased accountability, it is crucial to employ strategies and
practices that are going to increase student achievement and abandon those that are less effective.
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Allocation and Use of Human Resources
When schools are aware of which practices are most effective, they can begin to allocate
their resources in directions that support those practices. During this time of fiscal constraints,
allocating the very limited resources toward the most effective practices can be a daunting task.
This section will provide a brief discussion of how educational spending has fluctuated over time
and how educational adequacy has played a role in those fluctuations. To conclude this section,
the Evidence-Based Model, the adequacy tool that will provide the conceptual framework for
this study, will be explored in detail.
Changes in level of resources over time. To the disbelief of many educators as well as
the majority of the public and even policymakers, real dollars per pupil for public schools rose
consistently during the twentieth century and continue to rise in the twenty-first century (Odden
& Picus, 2008). According to Hanushek and Rivkin (1997), real dollars per pupil (i.e., resources
after inflation) increased by approximately 3.5% annually, a rise from $2 billion to over $187
billion from 1890 to 1990. This spending has been propelled not only by the expansion of
schooling in the population but also by increases in real per-pupil resources. Although resource
increases slowed down substantially during the first half of the 1990s, they began rising in the
beginning of the twenty-first century at about the same pace as prior to 1990. This century-long
expansion of elementary and secondary school spending may, however, now be threatened as
student enrollment grows and policymakers and the public increasingly consider resource
constraints (Hanushek & Rivkin, 1997).
The federal, state, and local government provides the three primary sources of education
revenue. The percentage share contributed by these three entities has fluctuated over time.
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Figure 1 identifies the share of total public school revenues contributed by federal, state, and
local sources since 1960.

Figure 1. Share of Total Public School Revenues
These numbers seem to reflect three trends at various time periods: the first, from 1960
to 1980; the second from 1980 to1990; and the third from 1990 to present. From 1960 to 1980
the trend was toward a rising federal and state share of education funding and a declining local
share. The federal share peaked at 9.8% in 1980; the state share peaked at just less than 50% in
the mid-1980s (Odden & Picus, 2008; Monk, Roellke, & Brent, 1996; and Nakib, 1995).
Historically over the last 50 years, there have been nationally typical expenditure
distribution patterns. Odden and Picus (2008) found that slightly above 60% of district funds
were spent on instruction, approximately 5% for instructional support, about 5% for student
support, nearly 5% for site administration, around 10% for operations and maintenance, and
slightly less than 10% for transportation, food, and other services. Since per pupil funding has
steadily increased over time and accountability demands have become more rigorous, on would
expect that spending patterns across various categories would have changed in some way.
Changes is resource use over time. Numerous studies over the last three decades have
focused on educational resources, but the focus has almost solely been on equity issues, usually
PERSONNEL RESOURCE ALLOCATION 35

the effect of court decision on how money is generated and distributed across districts in a state.
Studies regarding how resources are actually used within school districts and especially within
school are far more limited. During a time in which demands for higher standards of student
achievement have increased dramatically and budget constraints are significant, how the
education dollar is spent should be the primary focus.
Hannaway, McKay, and Nakib (2002) conducted a study from 1992-1997 on the general
patterns in resource levels and resource use for districts nationally. Districts in the U.S. have
steadily increased spending each year since 1991-1992, resulting in an increase of approximately
7% by 1997. Although districts felt the demands of federal and state performance accountability
reform, there were limited changes proportionately to how the additional funds were spent.
Proportionately more was spent on instruction, instructional support services, and school
administration but only slightly. The total instructional expenditure per pupil ranged from
$3,300 in 1991-1992 to $3,622 in 1996-1997 (Hannaway, McKay, & Nakib, 2002). It was
somewhat surprising that investment in instruction, especially instructional support services, was
not heavier due to the demands of current education reform.
Although there have been limited changes proportionately in the percentages spent in the
various categories in education over time, the additional 7% increase in funds has been allocated
across districts and schools. The four subcategories that have realized an increase in funding are
teacher salaries, special education, instructional aides and support staff, and elective teachers.
All of these subcategories fall under the general category of instruction, which accounts for
approximately 60% of the total education expenditures. Each of the four subcategories of
instruction that have received increased funding will be explained in greater detail in the
following section.
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Teacher salaries. Even though the relative earning of teachers compared to other
professions has decreased over time, the cost of teacher salaries in actual dollars continues to
increase thus accounting for the expenditure of additional funds (Hanushek & Rivkin, 1997;
Odden & Picus, 2008). Between 1970 and 1980 the price of instructional staff increased by
almost $40 per day, imposing tremendous cost pressures on schools. Even substantial teacher
salary increases did not keep teacher salaries comparable with wages in other occupations and
became problematic for women at the end of the twentieth century (Odden & Picus, 2008).
Hanushek and Rivkin (1997) believe this supply and demand movement offers an explanation
for the instructional staff price increase from $34 per day in 1890 to more than $183 per day in
1990, an increase that accounts for more than 40% of the increase in total expenditure on
instructional staff over the century.
Special education. The growth in students with identified disabilities coupled with the
legal requirements for providing educational services for them has realized increased
expenditures in the special education sector (Hanushek & Rivkin, 1997; Odden & Picus, 2008;
Monk, Roellke, & Brent, 1996; Hannaway McKay & Nakib, 2002; Lankford & Wyckoff, 1995).
An increase from 4.0 million in 1980 to 4.7 million in 1990 of students classified with
disabilities accounts for an increase from 9.7% to 11.6% during this period.
Two factors are believed to have contributed to the increased identification of special
education students. First, the enactment of the Education for All Handicapped Act in 1975,
which forced schools to expand staff and programs in order to comply with the federal law and
subsequent laws and regulations. Second, the existence of partial categorical funding from
outside and of intensive instruction for individual students created incentives for schools to
expand the population of identified special education students and incentives for parents to
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actively seek special education admission in the program. Even though there is a wide variation
in the cost of educating a child with special needs, the average cost is 2.3 times what it costs to
educate a regular education student (Hanushek & Rivkin, 1997). Since more teaching staff was
used to expand services for special needs students, during the 1980’s special education services
accounted for a particularly large portion of increased education expenditures.
Instructional aides and support staff. Between 1960 and 1990 one of the fastest
growing categories as a percentage of instructional expenditures is “other instructional”
expenditures. Other instructional expenditures include the fees associated with learning
materials and school-level support staff. During this period, Title I was passed, special education
was mandated via federal legislation, and court ordered desegregation activity was at an all-time
high. The increase in the number of instructional aides from 57,000 in 1970 to 326,000 in 1980
is attributed to the expansion of these programs during the 1970s (National Center for Education
Statistics, 1995). Odden and Picus (2008) highlight the fact that instructional aides grew from a
virtually zero base in 1960 to a substantial level by the close of the last century. The marked
increase of instructional aides and support staff is another subcategory that consumed a portion
of the increased expenditures in the category of instruction.
Elective teachers. Beginning in the mid-1960s, school systems began to provide teachers
with “planning and preparation” time during the regular school day by hiring art, music, physical
education, library, and other teachers for noncore subjects. All of these new specialist teachers
were, and continue to be, tracked and expensed as a portion of the instructional function,
primarily because they provide instruction to students. Odden and Picus (2008) document that
non-core teachers provide preparation time to elementary schools and expanded offerings to
secondary school students, where they comprise over 50% of the teaching staff. They assert that
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the largest portion of additional per pupil dollars have been allocated to these elective or
specialist teachers. A study of New York schools conducted by Monk, Roellke and Brent (1996)
found that more funds were spent on the combination of special education and vocational
education than was spent on core academics. These vocational teachers account for another
category that consumes a portion of the increased per pupil funding.
Although there has been an increase in funds available to schools, only a small portion of
the additional dollars have been allocated to core subjects of mathematics, English language arts,
social studies, and science causing core content teachers to become a declining portion of the
school staff (Odden & Picus, 2008). This fact leads one to assume that student performance has
not been a main factor considered in the allocation of human resources.
Adequacy. The standards-based reform movement has strongly influenced judicial
concepts of adequacy, which have tended to interpret constitutional requirements for a "thorough
and efficient" or a "sound basic" education in terms of a "high minimum" level related to the
state learning standards. Rebell (2007) asserts the combination of explicit, higher expectations
for student achievement set forth in state learning standards and mandates to provide all students
the resources to meet these standards in the state court education adequacy decisions has been
the driving force behind the accelerating utilization of adequacy cost studies.
There is not and probably never will be a single standard that applies across states as the
absolute cost of an adequate education. Even under the stringent federal scrutiny of the No Child
Left Behind Act of 2001, state legislatures retain significant latitude to define the average level
of desired student outcomes, within the boundaries of their own state’s constitutional
requirements regarding public schooling. In addition there is the confounding issue that readily
available standards regarding the additional costs associated with educating children with
PERSONNEL RESOURCE ALLOCATION 39

specific educational needs, under varied circumstances, to a specific set of educational outcomes
has been elusive (Baker, 2005).
Educational adequacy consists of two components: absolute standards of adequacy and
relative standards of adequacy. Absolute standards of adequacy concern the over-all level of
financial support for public schooling associated with the overall level of desired outcomes of
public schooling. Relative standards concern the differences in costs of achieving outcomes for
children with different educational needs or children learning in different educational contexts.
Therefore, educational adequacy may vary from school to school based on student population.
Adequacy in California is framed by both federal and state standard-based reform
designating that all students will achieve at a level of proficient or advanced in reading and
mathematics by 2014 as well as master all designated grade-level core content standards each
year. State adequacy studies seek to determine if a state’s school finance system provides
adequate revenues for the average school to teach the average student to state-determined
performance standards and whether adequate additional revenues are provided for the additional
support students with special needs required to achieve those same performance levels.
Adequacy cost study models. Determining adequate revenue levels entails identifying
the costs of implementing effective programs and strategies, then translating those costs into
appropriate school finance structures, and finally ensuring that the resources are utilized in
districts and schools to produced desired results (Odden, 2003). Therefore, the state must
establish an adequate foundational level for the typical student in the typical district.
Establishing an adequate foundation level for the typical student is achieved by utilizing one of
the four adequacy models. These models are: successful district approach, cost function
approach, professional judgment approach, and evidence-based approach.
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Successful district approach. The successful district approach identifies districts that
have been successful in teaching students to meet proficiency standards. It then designates the
adequacy level at the weighted average of the per pupil expenditures of those differences. A
strength of this model is it establishes a direct, quantifiable link between costs and desired
outcomes (Rebell, 2007). Limitations to this model are that the identified school districts are
often non-metropolitan, average size, and have relatively homogeneous demographics (Odden,
2003). Rebell (2007) adds the limitation that the successful district approach is dependent on the
availability of accurate data on a broad variety of input and output variables, data that may be
lacking or incomplete in many places.
Cost function approach. The cost function approach is a statistical model that employs
regression analysis. The per pupil expenditure is the dependent variable and student
characteristics, district characteristics, and desired performance levels are the independent
variables. Statistical calculations produce an adequate per pupil expenditure level for the
average district. Once the average expenditure level is determined, that figure is adjusted to
account for differences in pupil needs, educational pricing, and diseconomies of both large and
small districts. The expenditure level required generally rises or falls depending on the desired
performance level. According to Odden (2003), a downfall is the analysis conducted from this
approach usually produces an adjustment for districts of two to three times the average
expenditure level which, when combined with the complex regression analysis, makes its use
problematic in a real political context.
Even though both the successful district and cost function approaches link spending
levels to performance levels, which is aligned with standards-based reform, neither identifies
what educational strategies will produce the desired performance levels. Conversely, the
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professional judgment and Evidence-Based Model approaches strive to address the deficits of the
previous two models. Odden (2003) asserts that the professional judgment and Evidence-Based
Model not only identify adequate per pupil expenditure levels but also educational strategies that
can increase student achievement.
Professional judgment approach. The professional judgment approach solicits a group
of educational experts to identify effective educational strategies for elementary, middle, and
high schools. Then the experts specify the ingredients required for each strategy, attach a price
to them, and sum all costs to obtain a total per pupil expenditure. This strategy can make
adjustments to compensate both small and large schools, for a variety of students’ special needs,
and for geographic price variations so that the adequate expenditure level is sufficient for various
regions and types of schools in a state. According to Odden (2003), a major advantage of this
approach is that it identifies strategies required to produce the desired level of student
performance. A disadvantage is that outside of the expert educational judgments, the strategies
and ingredients have no evident link to actual performance levels and produces inconsistencies
within and across states depending upon how the process is conducted.
Evidence-Based Model. In contrast to the previous three adequacy models, the
Evidence-Based Model (EBM) developed by Odden and Picus (2008) focuses exclusively on
school improvement strategies that have been proven by research to be the most effective in
increasing student achievement. The EBM is based on a cost reporting expenditure structure
developed by Odden, Archibald, Fermanich, and Gross (2003) that allows for the examination of
school-level spending. This expenditure structure is displayed in Table 2. The six instructional
categories and three non-instructional categories arrange spending according to its educational
strategy or purpose, enabling a study of the school’s budget to reflect it educational programs
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and priorities at the same time. Items in this structure are drawn from research on high
performing schools and the structure reflects a commitment to the core subjects and to
supporting the learning of all students in those subjects, reflected by minimal time spent in low-
level and elective classes especially at the high school level.
Table 2
School Expenditure Structure and Resource Indicators of the Evidence-Based Model
School Resource Indicators
Student enrollment Length of class periods
Percent low-income Length of reading class
Percent special-education Length of mathematics class
Percent ESL/LEP Reading class size
Expenditures per pupil Mathematics class size
Professional development expenditures per teacher Regular class size
Special academic focus of school/unit Percent core
a
teachers
Length of instructional day
School Expenditure Structure: Instructional
1. Core academic teachers
Grade-level teachers
English/reading/language arts
History/social studies
Math
Science
Foreign language
2. Specialist and elective teachers/planning and preparation
Art, music, physical education, etc.
Academic focus with or without special funding •Vocational
Driver education
3. Extra help •Tutors
Extra-help labs
Resource rooms (Title I, special-education, or other part-day pull-out programs)
Inclusion teachers
English as a Second Language classes
Special-education self-contained classes for severely disabled students (including aides)
Extended-day
Summer school
District-initiated alternative program
4. Professional development
Teacher time—substitutes and stipends •Trainers and coaches
Administration
Materials, equipment, and facilities •Travel and transportation •Tuition and conference fees
PERSONNEL RESOURCE ALLOCATION 43

Table 2, continued
5. Other nonclassroom instructional staff
Coordinators and teachers on special assignment
Librarians
Instructional aides
6. Instructional materials and equipment
Supplies, materials, and equipment
Computers (hardware, software, peripherals)
School Expenditure Structure: Noninstructional
7. Student support services
Counselors
Nurses
Psychologists
Social workers
Extracurricular and athletics
8. Administration
Principal/assistant principal
Clerical staff and supplies
9. Operations and maintenance
Custodial
Utilities
Security
Food service
Note. Source: Revised version of framework in Odden, Archibald, Fermanich, and Gross, 2003.
a
Math,
English/language, arts, science, social studies, and world languages.
The Evidence-Based Model has been utilized in a total of seven states (KY, AZ, AR, WY,
WI, WA, and ND) to develop evidence-based estimates of educational costs. The estimation of
funding levels using the evidence-based approach varies from traditional school finance models in
two ways. First, the Evidence-Based Model builds funding estimates from the school level, not the
district level. Even though states may continue to distribute money to school districts, and districts
retain authority for how revenues will be used, the Evidence-Based Model provides a school-by-
school estimate of the resources that are generated at that level. Second, the Evidence-Based Model
assumes that all resources are available for the educational strategies it recommends. This also is a
substantial variation from traditional school funding systems that view new programs and/or
strategies from a marginal rather than comprehensive basis (Odden & Picus, 2008).
The evidence-based approach relies on the best available educational research to identify
strategies that when implemented at the school level will lead to dramatic gains in student
PERSONNEL RESOURCE ALLOCATION 44

achievement over a four-to six-year time frame (Odden & Picus, 2008). Figure 2 identifies the
components of the Evidence-Based Model. The EBM components include:
• Small classes in core subjects (math, science, language arts, social studies and
world languages)
• Specialist teachers to ensure a rich liberal arts program of art, music and physical
education, and to provide planning and collaboration time for core teachers
• Strategies to help struggling students and support them returning to the regular
classroom and district curriculum as quickly as possible
• Resources for professional development including instructional coaches, intensive
summer workshops for teachers and resources to hire outside experts and/or travel
to conferences
• Resources for special education, gifted and talented, career and technical
education, school site administration, central district administration, and for
maintenance and operations
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Figure 2. The Evidence-Based Model (Odden & Picus, 2008).
During a time in education where standards-based reform is prominent, schools must
effectively allocate resources toward programs and strategies that are going to directly contribute
to increased student achievement. The Evidence-Based Model will served as the theoretical
framework for the basis of this study because it recommends only research-based school
improvement strategies and links school spending explicitly to those strategies.
Limited Resources/Fiscal Constraints
At the beginning of 2011, Superintendent of Public Instruction Tom Torlakson declared
“a financial emergency” for California public schools. He noted that three years of budget cuts
in education had resulted in almost 2 million students, roughly 30% of pupils in California,
attending a school in a district facing serious financial jeopardy (Edsource, 2011). The state
currently has a multibillion-dollar budget deficit. Making the issue even more severe, this deficit
is structural and occurs annually. In order to balance the budget, California policymakers must
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raise more revenue and/or make additional funding cuts. If more drastic cuts are made to K-12
schools, they could impact some districts’ ability to function at a minimal level and would likely
eliminate any meaningful efforts at educational improvement throughout the state.
In addition to the financial crisis occurring in the California education system, districts
also face an increasingly rigorous student performance accountability measures imposed by
federal legislation under the NCLB Act. California schools are floundering to meet the NCLB’s
demands in part because they depend on Title I funding. Although these funds represent a small
percentage of the education budget, about 3%, it is still meaningful especially during this
challenging economic time. Some state officials object to the strong conditions on a relatively
modest sum of money, but no state has actually refused the funds and the strings attached to it
(Edsource, 2005). Due to diminishing funds and increasing student performance accountability
facing California schools, it is imperative that the allocation of limited resources be thoughtfully
executed in the most optimal way to ensure academic success for students.
California school finance. California’s current finance system is largely a product of
two events occurring in the 1970s that shifted the majority of school funding from the local to
the state level. In 1971, the California Supreme Court ruled in Serrano v. Priest that the state’s
school finance system was unconstitutional and ordered the state to equalize general purpose
funding across districts. Then, in 1978, Proposition 13 lowered the amount of local property tax
revenue available to cities, counties, and schools. Prior to these events, school districts set their
own property tax rates, and this local revenue constituted the majority of school district funds.
Since the events, the responsibility for financing California’s schools has shifted largely to the
state. In 1977, 33% of school revenues came from state sources; currently the state provides
approximately 60 % (Weston, 2010).
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Today, California spends more of its general fund budget on K-12 education than on any
other area of the state budget (California State Budget 2010–11 Summary Charts). The K-12
contribution, which consumes 40% of the total budget, is determined through a series of complex
formulas governed by Proposition 98, a voter-approved initiative that dictates the minimum
amount that the state must spend. Proposition 98 revenues include local property taxes and
money from the state’s general fund. Weston (2010) revealed that these sources account for
slightly over 70 % of all annual K-12 revenues (Figure 3). The majority of Proposition 98 funds
support the largest K-12 funding category, known as “revenue limits”, which pays for basic
school operations. Every school district has a base revenue limit, a dollar amount per pupil
funded through the district’s share of local property tax. If a district’s property taxes cannot
completely fund this base amount, the state pays the difference.

Figure 3. California’s K-12 Revenues are Allocated through a Complex System
All of the revenues allocated to K-12 education can be classified into two categories:
unrestricted general purpose funds that can be spent on any educational purpose, and restricted or
categorical funds that are earmarked for specific programs or purposes. Unrestricted funding
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includes revenue limits, a share of state lottery revenues, and most additional funds. Unrestricted
funding accounts for nearly 70 % of all California school district revenues (Figure 4). Restricted
or categorical funding includes state and federal categorical programs and constitutes about 30 %
of district revenues (Timar, 2006).

Figure 4. School District Funding by Type, 2009-2011
The three significant events that have reshaped the funding structure of California schools
over the past 30 years are: the Serrano v. Priest I and II court decisions in 1971 and 1976; the
passage of Proposition 13 in 1978; and the passage of Proposition 98 in 1988. The following
sections will detail each of the events and display how they have contributed to California
schools’ financial hardships during the current economic downturn.
Serrano v. Priest. The Serrano v. Priest court case was one of the first lawsuits to
challenge the U.S. tradition of using property taxes as the principal source of revenue for public
schools. Lawyers for the plaintiffs argued that the wealth-related revenue disparities among
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school districts violated the “equal protection” clause of the state constitution (Edsource, 2009).
In this case, wealth was a product of the assessed properties divided by the number of
schoolchildren in the district. The courts agreed with the plaintiffs primarily on equal protection
grounds and required the state to equalize the funding among school districts.
In 1971, the California Supreme Court ruled in Serrano v. Priest that education was a
“fundamental interest” of the state and sent the case back to lower courts to determine whether
the discrepancies described by the plaintiffs existed. Anticipating an outcome that would
demand that funding be equalized among districts, state legislators passed Senate Bill (SB) 90 in
1972, creating a revenue limit system that placed a threshold on the amount of general purpose
money each district could raise. State and federal categorical funding, which is allocated based
on specific student programs, was exempted from this equalization effort.
An additional case, referred to as Serrano v. Priest II, was settled in 1976 by the courts
ruling that the changes made in SB 90 were not adequate. In 1977, the state passed Assembly
Bill (AB) 65, which made further adjustments to the current system by utilizing a “power
equalization” plan that would redistribute state aid based on differences in district property tax
revenues per pupil. The favorable rulings in both Serrano v. Priest cases paved the way in the
monumental shift of funding control from local school districts to the state.
Although these rulings would appear to equalize funding across school districts, 40 % of
state funds provided to districts are in the form of categorical programs and those programs do
not qualify under the Serrano requirements intended to reduce spending differences among
school districts. In the majority of the cases, regulations accompany the categorical funds to
ensure that the money is spent on the target students or on a designated purpose that the state or
federal government intended (Edsource, 2009). Therefore, the target populations and specified
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purposes associated with categorical funds have reverted back to substantial inequalities in the
level of funds available to students across school districts.
Proposition 13. In 1978, Proposition 13 was passed and its provisions limited the
property tax rate to 1% of assessed value and capped increases in assessed value at 2% or the
percentage growth in the state’s Consumer Price Index, whichever is less. Proposition 13’s
provisions eliminated more than half of local property tax revenues and therefore invalidated
much of AB 65’s financial reform, including power equalization (Hill, 1999; Edsource, 2009).
Property taxes were distributed by a formula; therefore, state aid, including income taxes and
sales taxes, were necessary to fund schools to the revenue limit. The shift in funding resulted in
a fracture between schools, districts, and their local communities. No longer are funding
decisions able to be made solely by the school boards and districts since approximately 59% of
school funding is generated from the state general fund (Edsource, 2009).
The Serrano ruling combined with Proposition 13 suppressed school district revenue
growth and virtually eliminated local control over most school funding. Since these changes
have occurred, California’s investment in education, relative to the national average, has
declined. According to Edsource (2009) in 2005-2006, the per pupil expenditure was $614
below the national average, and more recent funding cuts are likely to increase the gap
dramatically. In addition, the fiscal stability of local school districts is impacted to the extent
that their revenues are part of the state’s often dysfunctional budget process.
Proposition 98. The final event that made a significant impact of school funding was the
passage of Proposition 98. This constitutional amendment, enacted in 1988, has continued to be
the current structure used to fund schools today. Proposition 98 guaranteed a minimum funding
level from state and property taxes for K-14 public schools. This foundation baseline creates a
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guaranteed minimum floor for per pupil funding. Per pupil funding is multiplied by the number
of students in attendance at school each day to determine average daily attendance (ADA). The
district’s per pupil revenue limit equals its total revenue limit income, which is the majority of
funds available for general purpose expenditures. General purpose expenditures include
employee salaries, supplies, materials, utilities, and maintenance. If the revenues fall below the
guaranteed minimum per pupil funding, the state is required to supplement the shortage.
While Proposition 98 determines how much funding the state must provide to K-14, the
governor and legislature continue to decide how those funds will be allocated to the school
districts within their state (Timar, 2006). As a result of the current fiscal crisis that California
faces, school districts continue to receive less than their designated amount of funds promised
from Proposition 98. Funding deadlines implemented by the state are routinely missed, the
minimum Cost of Living Adjustment (COLA) is unlikely, and budgets are being cut mid-year
causing school districts to scramble for compensation (Edsource, 2009).
Federal response to fiscal crisis. On February 17, 2009, President Barack Obama
signed into law the American Recovery and Reinvestment Act (AARA), commonly known as
the “stimulus”. Enactment in the midst of a deep recession brought on by global financial
collapse, ARRA provided more than $800 billion in federal spending and tax cuts intended to
stimulate the economy while laying the foundation for future long-term growth. A substantial
portion of that funding, more than $100 billion, was dedicated to education. Mead, Vaishnav,
Porter, and Rotherham (2010) assert that this unprecedented one-time spending boost is larger
than the entire budget of the U.S. Department of Education.
Also unprecedented were the ambitious education reform goals that accompanied
ARRA’s education spending. ARRA’s constituents sought not only to save teacher jobs and
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supplement state and local budgets but also to advance education reform in four key areas:
implementing college and career-ready standards and high-quality assessments, creating pre-K-
20 longitudinal data systems, increasing teacher effectiveness, and turning around chronically
low-performing schools. ARRA legislation introduced a new federal approach to education
reform – one that emphasizes the four reform priorities mentioned and utilizes incentives, rather
than mandates, to drive state and local reform. The result of this approach was legislation
incorporating competing goals of short-term stimulus and long-term reform (Mead, Vaishnav,
Porter, & Rotherham, 2010).
ARRA included more than $100 billion in funding for education. Some of that funding
was dedicated to college financial aid or to early childhood education programs, but the aid
package included more than $70 billion in funding for public elementary and secondary schools
and higher education. The largest portion of ARRA funding for education is the $48.6 billion
State Fiscal Stabilization Fund (SFSF), which was intended to supplement the deficits that the
economic crisis brought about in state funding for public education and other services.
Funds are distributed to states according to a formula based on population (Figure 5).
States must use 81.8% ($39.75 billion) of SFSF funds to support public elementary and
secondary schools and postsecondary institutions. States may use the remaining 18.2% ($8.85
billion) to maintain government services, such as education, public safety, or other services.
States may choose how to divide the $39.75 billion in Education Stabilization Funds between K-
12 postsecondary education (most states allocate the majority of the funds to K-12). Funds
allocated to elementary and secondary education are distributed directly to local educational
agencies according to specified formulas designated by ARRA. The ARRA legislation instituted
a required component pertaining to the SFSF stimulus. To receive funds, states must commit to
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take action to advance education reform in the four “assurance” areas spelled out in the law.
States do not control how this money is spent.

Figure 5. Allocation of State Fiscal Stabilization Fund (SFSF) Dollars. Note. Modified from
Mead, S., Vaishnav, A., Porter, W., and Rotherham, A. J. (2010)
ARRA funds were intended to counter the economic impact of state and local budget cuts
while accelerating state and local education reform efforts. These funds certainly were sufficient
in making up for K-12 funding shortfalls in the majority of states (Roza, 2010). Unfortunately in
many cases, the funds solely helped districts tread water, as several states reduced education
budgets similarly to the amounts they received in ARRA allocations. Many states factored its
SFSF funds into existing state grants, enabling it to supplant state dollars with stimulus funds. In
43% of those districts, SFSF funds made up for less than half of the decrease. Among the largest
urban districts, 75% reported that ARRA funds were insufficient to make up for state and local
funding cuts (Center of Education Policy, 2010).
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State-level response to fiscal crisis. As a result of the extreme reduction in resources for
California schools, districts, and County offices of education, the state has made several changes
to state education policy in an effort to ease the impact on districts and establish a balanced
budget each year. Each of these policy changes could potentially have large effects on district-
decision-making in the areas of staffing, instructional materials or programs, length of school
year, and non-instructional services.
Increased funding flexibility. The California legislature, under pressure from a deflated
economy that had reduced revenues, approved the creation of the new categorical flexibility
items in February 2009. The overarching purpose was that in exchange for large cuts (almost
20% from 2007-2008 levels), local districts would be given substantial flexibility pertaining to
categorical funds (Weston, 2011; Shambaugh, Kitmitto, Parrish, Arellanes & Nakashima, 2011).
Prior to 2009-2010, California had a large number of state categorical program funds (i.e. funds
earmarked for specific types of programs or expenditures, such as arts and music, education
technology, school libraries, or school safety). Beginning in the 2008-2009 school year, districts
received temporary flexibility, which is currently set to expire in 2012-2013, in 39 of these
categorical grant programs (California Education Code 42605). Some of the funds that had
historically been provided for a specific program could now be used for any education purpose
deemed appropriate by the county, district, or school.
Class size reduction flexibility. Beginning in 1996, with the belief that small class sizes
lead to more effective learning environments for students, California provided financial
incentives to districts for all classes that contained 20 or fewer students in kindergarten through
third grade. In the 2007-2008 school year, prior to the fiscal crisis, 98% of districts in California
participated in the class-size reduction program (California Department of Education, 2009). As
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a portion of the budget compromise, the state relaxed the requirements for receiving class-size
reduction funds beginning in the 2008-2009 school year and for the next consecutive four years,
so that districts can increase class size and not lose funding. Now if a class contains 25 or more
students, the district will only lose 30% of the funds. Therefore, a district can now receive 70%
of its class-size reduction money without reducing class size at all.
Instructional materials adoptions. Traditionally, school districts are required to
purchase new instructional materials approximately every three years for English language arts,
mathematics, science, social science, and bicultural subjects, and approximately every four years
for other subjects, to ensure that they provide the most current textbooks for students. In an
effort to reduce the financial impact that districts realize from this requirement, the state relaxed
the requirement and now districts do not need to purchase new materials through the 2013-2014
school year (California E.C. 60200.7).
Reduction of required school days. Prior to the fiscal crisis, California required students
to attend school for 180 days per year (California E.C. 46200). In 2008, state policymakers have
reduced this requirement, allowing school districts to offer five fewer days annually. Typically,
the way a school district shortens its school year is by giving teachers unpaid furlough days.
With an anticipated $2 billion shortfall becoming a real possibility, any reduction in the school
calendar would have to be negotiated with teachers’ unions (Edsource, 2011).
Funding of maintenance projects. California provides districts and county offices of
education dollar-for-dollar matching funds for both “routine restricted maintenance” and
“deferred maintenance programs” for improvements to their existing school buildings such as
roofing, plumbing, heating/air conditioning, and electrical systems. Traditionally, a district or
county was required to set aside 1% to 3% of its general fund for routine maintenance and set
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aside 0.5% of its general fund in a deferred maintenance account to receive a dollar-for-dollar
match of state funds (Shambaugh, Kitmitto, Parrish, Arellanes & Nakashima, 2011). In 2009-
2010, the state removed this requirement, no longer requiring the district or county to set aside
these amounts or match the state’s contribution.
Financial reserves requirements. Lastly, prior to this fiscal crisis, the state required
districts to maintain a certain portion of their funds in a reserve account, ranging between 1%
and 5% depending on the size of the district (California E.C. 33128.3). In 2009-2010, the state
reduced this amount by two thirds. For example, if a district was previously required to maintain
3% of its total revenue in reserves, it is now only required to maintain a reserve of 1%.
Therefore, allowing districts to utilize more of its funding during the challenging economic time.
School districts in California reported that while these adjustments did not make the
budgetary crisis easy to endure, the flexibility did provide some relief during this difficult time.
In an era of fiscal constraints, education policymakers must invest any available funds as wisely
as possible. Reflecting upon the education spending patterns established over the past several
decades, it appears unlikely that, even after economic recovery, California schools will have the
resources available to students in other states. Therefore, it is imperative to be able to identify,
learn from, and share cost-effective strategies in districts to ensure our schools will be as well-
equipped as possible to provide students with resources that directly support increased student
achievement.
Gap Analysis
In order to determine the difference or “gap” between actual human resource allocations
of a school district, the optimal allocations of that same school district, and the recommended
human resource allocation as per the Evidence-Based Model, a gap analysis can be conducted.
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Gap analysis diagnoses the human causes behind performance gaps. Conducting a gap analysis
not only reveals the size of the gap but also can help in determining the potential causes of the
gap. Once this information is unveiled, a school district can make the necessary changes that
will move them in the direction toward closing the identified gap. The gap between desired and
actual performance must be assessed and closed if organizational goals are to be achieved (Clark
& Estes, 2002). In terms of this study, once the gap in human resource allocation was revealed,
recommendations for how to close the gap were proposed.
The Gap Analysis model is a dynamic process that examines the key components that
lead to improved work processes. Clark and Estes (2002) identify the gap analysis model as a
six-step process that leads to improved work performance and goal achievement. The steps are
as follows:
1) Identify key business goals;
2) Identify individual performance goals;
3) Determine performance gaps;
4) Analyze gaps to determine causes;
5) Identify knowledge/skill, motivation, and/or organizational solutions and implement;
6) Evaluate results, tune system and revise goals.
In essence, Clark and Estes (2002) assert that business goals are not realized due to gaps
in individual performance. Performance gaps are attributed to three primary causes: lack of
knowledge and/or skills, lack of motivation, or lack of efficient and/or effective organizational
work processes and material resources. In order to select the correct solutions to close these
performance gaps, it is crucial that organizations analyze and determine the correct root cause(s).
Once solutions have been identified, it is imperative that an organization implement and evaluate
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these solutions to determine whether or not the performance gap has been closed. In the
following section, each of the six steps in the gap analysis model will be described in detail and
an explanation of how each of these individual steps contribute in the overall gap analysis
process will be discussed.
Identify key business goals. The ultimate objective for performance improvement is
that it must support the larger goals of the organization. Therefore, the first step in the gap
analysis process is to be aware of the business goals you will support if you solve the
performance problems that you are facing. Each member of each work team should have a clear
and specific description of their own performance goals, how to know when they are or are not
achieving those goals, and the business reason for each goal. Clark and Estes (2002) assert that
organizational goals must be flexible to reflect changing business conditions and specific enough
to meet the need for day-to-day guidance. As it pertains to education, a school goal might be one
that seeks improved academic performance for all students.
Identify individual performance goals. The performance goals that managers assign
must directly support evolving organizational goals. Without clear and specific performance
goals, people tend to focus on tasks that help advance their careers instead of helping the
organization achieve its goals (Clark & Estes, 2002). As to whether people should contribute to
creating their own work goals, some believe that unless people are involved in setting their own
goals, they will not be motivated to achieve them. This is a misconception. Locke and Latham
(1990) have conducted research on this topic for many years and provide compelling evidence
that people can easily accept and be motivated to put forth their best effort with assigned goals.
Also supporting this notion, DuFour, DuFour, Eaker, and Many (2006), assert that via the
implementation of Professional Learning Communities (PLCs), educators are guided by a clear
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and compelling school-wide vision and establish collective commitments to achieving
predetermined goals.
In addition to the qualities of a trusted leader who provides a challenging vision, high
expectations, and clear feedback, the structure of performance goals plays a major role on their
impact and acceptance. According to Clark and Estes (2002) the most effective performance
goals are C3 Goals. These goals maintain the following characteristics:
• Concrete (clear, easily understandable, and measurable);
• Challenging (attainable but difficult); and
• Current (short-term daily or weekly goals are more motivating than long-term monthly or
annual goals).
An employee’s work motivation depends, in large part, on the availability and quality of
performance goals. When setting goals, the key is to limit them to only those few that will result
in the maximum business impact. Without clear and measurable performance goals, it is not
possible to know whether a business is succeeding or failing.
Determine performance gaps. Clark and Estes (2002) suggest utilizing a six-step
process in determining a performance gap. The steps are as follows:
1. List the areas where you will set goals and describe the indicators that you will
use to determine the achievement of each goal.
2. Benchmark and quantify the industry leader’s achievement in each area.
3. Quantify your organization’s current achievement in each area.
4. Compute the gap by subtracting your achievement from the industry leader.
5. Determine the benefit of closing the gap.
6. Identify individual and team goals that will close the gap.
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In the education arena, the most commonly identified performance gap is in the area of
student achievement. At the beginning of each new school year, districts and schools identify
their performance gap by subtracting their students’ actual performance on the California
Standards Test (CST) from the expected student performance set forth in NCLB legislation.
After the performance gap has been identified, an analysis is conducted to determine the causes
of the gap.
Analyze gaps to determine causes. In order to close performance gaps and achieve
goals, the cause of the gap must be identified, therefore the type of performance improvement
program can be implemented. While the first three steps in the gap analysis model are absolutely
crucial, it is impossible for any real change to occur without taking that information to the next
level: diagnosing the human causes and identifying appropriate solutions. The more novel and
complex a goal, the more extensive the performance support required for people to achieve it
(Clark & Estes, 2002).
This analysis process must entail surveying people, examining records, and observing
work processes to determine if the performance gap is due to knowledge, motivation, and or
organizational barriers. Clark and Estes (2002) believe the analysis process must also capture
important documentation and other data about the goal and the links between relevant work
systems. Adequate analysis of the reasons why goals are not being met requires the review of
many kinds of work records and other performance data.
Three critical factors must be examined during the analysis process. The three factors
include:
• People’s knowledge and skills;
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• Their motivation to achieve the goal (particularly compared with other
performance goals they must also achieve); and
• Organizational barriers such as a lack of necessary equipment and missing or
inadequate work processes.
The purpose of both individual and team gap analysis is to identify whether all employees
have adequate knowledge, motivation, and organizational support to achieve designated
performance goals. Each of the three factors that can contribute to a performance gap will be
discussed in the next section.
Knowledge and skills. During gap analysis, it is imperative to determine whether people
know “how” to achieve their performance goals. Since people are often unaware of their own
lack of knowledge and skill deficits, others will be required to provide accurate feedback.
Indicators of knowledge problems consist of non-achievement of a current goal even if they
“really had to do it” or non-achievement of a similar goal in the recent past, poor communication,
and withholding important information.
Motivation. Motivational gaps are more complex to diagnose than knowledge and skills
problems because fewer performance specialists are familiar with them. In general, motivation
is the internal, psychological process that gets us going, keeps us moving, and helps us get jobs
done (Pintrich & Schunk, 1996). Motivation influences three very critical aspects of our job
performance-first, choosing to work toward a goal; second, persisting at the goal until it is
achieved; and third, how much mental effort is invested to complete the goal.
Organizational barriers. Organizational barriers consist of processes within an
organization that do not operate properly and/or inadequate resource levels, which prevent or
delay performance. Frequently, when knowledge and motivation can be ruled out, some form of
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organizational barrier is the source of the issue. Many organizational issues can be traced to
business or work processes that are not in alignment with the business strategy or organizational
structure. Additionally, whenever you alter the knowledge level or the motivation level of an
organization, the work processes must be adjusted accordingly.
Performance is largely governed by people’s beliefs about themselves and their
environment. In order to redirect performance to new goals or to improve performance, the
beliefs and perceptions of the people doing the work must be acquired. Information regarding
what employees believe is blocking them or their team from reaching goals and what kind of
support they believe they require is crucial to uncovering the causes of performance gaps.
Asking questions regarding the cause of performance gaps requires significant advance planning.
The goal is to classify the gaps described by participants into one of three categories-knowledge
and skills, motivation, and organizational barriers to achieving performance goals.
Identify knowledge/skill, motivation, and/or organizational barriers and implement
solutions. Once the cause or causes of the performance gap has been determined, appropriate
solutions must be developed and implemented to address the specific cause of the gap. For each
of the three causes of performance gaps-knowledge and skills, motivation, and organizational
barriers, there are recommended solutions to most effectively close the gap. The recommended
solutions for each cause of performance gaps will be detailed in the next section.
Knowledge and skills solutions. Increasing an employee’s knowledge and skills is
required for increased job performance under two conditions. First, they are required when
people do not know how to accomplish their performance goals, and second, when it is
anticipated that future challenges will require novel problem solving. The first condition
requires a need for the employee to be provided with information, job aids, or training.
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Continuing and advanced education is required for the second condition. Support providers must
ensure that there is no confusion regarding the characteristics of the four types of solutions-
information, job aids, training, and education to ensure that the appropriate solution is applied to
the knowledge and skills issue.
Motivation solutions. Spitzer (1995) reports that half of all American workers confess
that they only invest the minimum effort required to avoid being fired. An overwhelming 84%
admit that they could work much harder at their jobs. Perhaps the most important fact to be
aware of is that motivation at work is the result of an employee’s own experience and beliefs
about his/herself, his/her coworkers, and his/her prospects for being effective. Most researchers
agree that there are three types of motivational processes that are factors in a work environment.
These processes are active choice, persistence, and mental effort (Clark & Estes, 2002).
Motivation research tends to be fragmented, but there are four factors that influence motivation
and can be synthesized by a number of studies (Hardt & Rodin, 1999; Ford, 1992; Klein, 1999).
These four factors consistently asserted to increase motivation are:
1) Personal and team confidence;
2) Beliefs about organizational and environmental barriers to achieving goals;
3) The emotional climate people experience in their work environments,
4) The personal and team values for their performance goals.
The gap analysis process often recommends a mix of motivational programs. It is crucial
that this mix be designed, developed, and implemented appropriately in order to close the
performance gap. Waiting to design motivational programs for employees until these programs
can be integrated with knowledge/skills and organizational processes renders the most success.
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Organizational barriers solutions. The third and final cause of performance gaps is the
lack of efficient or effective organizational work processes and material resources. Even if
employees exhibit top motivation and exceptional knowledge and skills, missing or inadequate
processes and materials can prevent the achievement of performance goals. Whenever
employees express that formal or informal organizational policies, processes or resource levels
prevent the closing of a performance gap, then there is evidence of organizational barriers. Since
people would rather be thought to be critical than uniformed or unmotivated, one must be
cautious in seeking accurate organizational barrier feedback from employees.
The result of gap analysis is most often a mix of organizational processes and work
material programs. Dixon (1994) suggests successful organizational changes are directional-
those shifting their business processes to achieve improvement in efficiency, service, or quality.
He identified four factors that tended to predict success in all projects. In addition, two more
factors are included from the National Research Council’s findings. These six factors are:
1) Have a clear vision, goal, and way to measure progress.
2) Align the structure and the processes of the organization with goals.
3) Communicate constantly and candidly to those involved about plans and progress.
4) Top management must be continually involved in the improvement process.
5) Provide adequate knowledge, skills, and motivational support for everyone.
6) Use extreme care when selecting specific change processes to achieve organizational
goals.
When closing a performance gap that is due to organizational barriers, organizational
processes and work material programs must be designed, developed, and implemented at the
appropriate time. Clark and Estes (2002) recommend waiting to design organizational change
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programs for employees until these programs can be fully integrated with knowledge and
motivational changes. Since fully integrated performance improvement programs are more
effective, significantly more efficient, and considerable easier to evaluate, it is recommended to
wait for implementation until after a complete analysis of all solutions to gaps has been
conducted.
Evaluate results, tune system and revise goals. Evaluation is crucial when attempting
to close a performance gap or improve performance. It is the only method to determine the
connections between performance gaps, improvement programs, and cost-effectiveness.
According to Clark and Estes (2002) evaluation is one of the most cost-effective activities in
performance improvement, because it is the one activity, if applied correctly, can ensure success.
All evaluation studies must satisfy two criteria: reliability and validity. Reliability
requires that all evaluation methods provide the same results each time they are measured. This
safeguards against measures that constantly change and produce different results each time they
are used due to the measuring instrument. Validity, requires that all evaluation measure exactly
and solely what it is intended to be measuring. If an instrument reported the same invalid result
each time it was used, it is still reliable, which is the reason for including both reliability and
validity for all evaluation activities.
Don Kirkpatrick (1998) described a four-level evaluation system that addresses most of
the questions anyone may want to ask regarding training systems (Table 3). Kirkpatrick’s
evaluation model has the benefit of nearly a half-century of use in many different organizational
settings and cultures. Although there have been many improvements suggested about “how” to
evaluate each level he recommends, it is still the preferred approach for most trainers.
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Table 3
Four Levels of Evaluation
Level Question
1: Reactions Are the participants motivated by the program?
Do they value it?
2: Impact During the Program Is the system effective while it is being
implemented?
3: Transfer Does the program continue to be effective after
it is implemented?
4: Bottom Line Has the transfer contributed to the achievement
of organizational goals?

Unfortunately, many organizations that perform evaluations make changes in response to
their findings but fail to be systematic about it. Kirkpatrick’s four levels of evaluation design
supports in determining which changes make a difference. Otherwise, modifications may be
without direction, or previous problems may be repeated with a revised but equally ineffective
program. Effective evaluation consists of fine-tuning performance improvement programs,
which includes revising goals to increase their impact and fuel productivity.
Summary
Education researchers who study the impact of various strategies employed by districts
and schools arrive at the same conclusion; in order for a district or school to meet the rigorous
standards set forth by both federal and state reform, implemented strategies must be directly
linked to student achievement. The literature that was reviewed focused on school improvement
strategies, allocation and use of human resources, limited resources and fiscal constraints, and
gap analysis. All of this literature focused on topics that influence whether or not a district or
school will be successful as determined by standards-based accountability measures.
The Evidence-Based Model by Odden and Picus (2008) and the gap analysis framework by
Clark and Estes (2002) were the primary structures utilized in this study. The Evidence-Based
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Model supported in determining how districts and schools allocate resources for school
improvement as compared to an optimal resource allocation model. Conducting a gap analysis
revealed where there are shortcomings in effective resource allocation and recommendations will
be provided on how district administrators can reallocate those resources to improve student
performance. The following chapter will outline the methodology used in this study.
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Chapter 3: Methodology
This chapter provides an overview of the methodology of this study, including the
research questions, design of the study, sample and population, instrumentation, data collection,
and data analysis processes. Accountability reform has catapulted student achievement
accountability measures to the forefront of educational decision-making, therefore human
resources allocation conducted with the intent of increasing student achievement has become
increasingly important. The combined impact of accountability reform enforced with sanctions
that directly impact districts/schools decision-making and the dwindling availability to hire and
retain staff due to decreasing budgets, make it more crucial than ever for districts/schools to
effectively allocate their limited human resources. The purpose of this study is to collect and
analyze human resource allocation data at the elementary school level to inform educational
leaders as they make decisions on how to allocate human resources in the most effective way to
in support, maintain, and increase student achievement.
Current human resource allocation patterns for each of the elementary schools included in
this study were reported. They were compared to both desired human resource allocations for
each school as well as to the Evidence-Based Model (EBM) staffing recommendations (Odden &
Picus, 2008). The EBM served as the framework to analyze current and desired resource
allocations for each school. Data collection for each school was input into a model that
estimated the overall gaps between actual, desired and EBM allocations. If gaps were revealed,
then they were analyzed to determine potential causes. The study concluded with
recommendations of how districts can more effectively allocate human resources to move toward
closing the gap between the actual and the recommended resource allocations of the EBM or
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alternatively how to reallocate resources they do have toward strategies that research suggests
are likely to be successful.
Overview of the Methodology
A qualitative method was utilized to conduct this study of the 47 elementary schools in
the one specified Southern California urban school district. For this mixed methods study, two
types of qualitative data were collected. The first type was numerical school based data that
included student information, current API scores, current staff allocations, and desired staff
allocations. The second type was data in the form of district office personnel interviews.
Interviews of various district office personnel including the district superintendent provided
insight into the formulas of how human resources were allocated across schools in the district
and how this allocation pattern supports the district’s vision. The qualitative data was
triangulated through document analysis of budgets, policies/procedures/algorithms for allocating
personnel to schools, utilization of the model to create a simulation for various school allocation
scenarios, and the information revealed from interviewing district office personnel and the
superintendent.
The described qualitative approach utilizing multiple methodologies was appropriate for
this study because school-based allocation analysis only yields the “how” of human resource
allocation. The interviews of various district level personnel and the superintendent informs the
“why” providing a rationale supportive in constructing a comprehensive understanding of human
resource allocation in a school district.
Research Questions
The research questions for this study were:
• What research based human resource allocation strategies improve student achievement?
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• How are human resources allocated across Garden Grove Unified School District’s elementary
schools?
• Is there a gap between current human resource allocation practices and what the research
suggests is most effective?
• How can human resources be strategically re-allocated to align with strategies that improve
student achievement?

Sample and Population
Using a purposive sampling method, this study focused on 47 elementary schools with
varying API scores, AYP percentages/Program Improvement (PI) statuses, and student
demographics that constitute all of the elementary schools in one urban school district in
Southern California. Notorious for being a high-achieving urban district that is consistently on
the forefront of faithfully implementing effective research-based instructional strategies with
limited human resources, this district was chosen because the researcher is interested in learning
more about human resource allocation patterns and the rationale for these allocations across the
elementary schools in the district. The chief researcher for this study is currently employed in the
district, and the design of the study will reflect methodologies that facilitate objectivity as
described in the Data Collection and Data Analysis sections of this chapter.
All elementary schools in the district were selected for the sample to ensure a variety of
school profiles based on academic achievement and student demographics while maintaining the
consistency of one school district. Patton (2002) describes this type of sampling as both
maximum variation and criterion purposive sampling. Maximum variation sampling occurs
when the researcher purposefully chooses a wide range of cases to acquire variation on
dimensions of interest, as in this case schools that have a variety of API scores and student
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characteristics. In criterion sampling the researcher includes all cases that meets some criterion,
for this sample, all elementary schools are within one school district. Providing information rich
cases, both sampling frames utilized in this study are purposive in nature.
This study chose to examine the funding levels comprised from both federal and state
funding for 47 elementary schools, which took consideration all federal and state funding, and
how these funds were allocated to support student achievement at those school sites from the
district perspective. The majority of schools in the sample received some level of federal Title I
funding to support their students but this funding level varied depending upon student population.
Schools qualify for Title 1 funding when there is 40% or more of students who receive free or
reduced lunch. This variation in federal funding support was utilized to make comparisons
between schools in terms of the effects of funding levels on human resource allocation and
student achievement. Additionally, this study included all elementary schools from a single
school district to derive a comprehensive understanding of the role a district assumes in the
allocation of human resources.
All schools in this sample with the exception of four are traditional public schools
severing student in grades K-6. Two of the schools serve GATE (Gifted and Talented
Education) students in grades 1-6 and the other two schools serve GATE students on the same
site but are separated into two campuses, one serving grades K-3 and the other serving grades 4-
6. For the purposes of this study, these two campuses will be reported as one K-6 campus to
provide consistency for comparison. Due to this modification, data will reported for 46 schools
instead of 47. The actual name will be used for the school district but numbers will be used in
place of school names to honor anonymity of the elementary schools participating in the study.
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Table 4 displays a list of each elementary school in Garden Grove Unified School District and
their characteristics by number.
Table 4
Characteristics of Each Elementary School
School # 2011
Growth-API
2011 PI
Status
AYP ELA
Schoolwide
2011
%Prof/Adv
AYP Math
Schoolwide
2011
%Prof/Adv
2011 % English
Learners
2011 % Free
or Reduced
Lunch
1 980 Not in PI 91.0 96.6 26.84% 35.29%
2 869 Year 3 67.9 80.3 50.73% 67.52%
3 939 Not in PI 84.8 89.8 3.12% 12.21%
4 780 Year 3 45.4 65.7 51.59% 77.30%
5 794 Year 4 47.9 72.2 68.72% 87.52%
6 829 Year 1 54.1 76.5 69.62% 81.25%
7 753 Not in PI 48.2 66.4 44.01% 70.07%
8 788 Not in PI 41.5 68.3 63.27% 80.34%
9 881 Not in PI 65.9 88.2 57.18% 71.05%
10 792 Year 1 52.7 69.6 50.09% 66.42%
11 782 Year 4 42.6 64.2 66.26% 85.49%
12 855 Not in PI 63.6 79.2 12.14% 28.38%
13 807 Year 3 47.4 71.8 54.73% 74.21%
14 883 Not in PI 63.9 85.0 59.70% 76.16%
15 834 Year 1 58.6 72.6 49.28% 64.41%
16 875 Not in PI 65.7 77.5 5.86% 20.92%
17 818 Year 3 54.3 75.6 44.46% 64.09%
18 843 Year 1 52.8 81.2 71.55% 92.31%
19 784 Year 3 44.3 69.5 72.97% 91.41%
20 858 Not in PI 66.2 80.1 57.62% 67.44%
21 802 Year 1 49.5 74.3 51.05% 75.52%
22 864 Not in PI 61.4 82.5 61.93% 73.77%
23 818 Year 1 49.6 78.6 64.11% 76.64%
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Table 4, continued
School #
2011
Growth-API
2011 PI
Status
AYP ELA
Schoolwide
2011
%Prof/Adv
AYP Math
Schoolwide
2011
%Prof/Adv
2011 % English
Learners
2011 % Free
or Reduced
Lunch
24 831 Year 4 65.6 79.5 38.32% 54.86%
25 841 Year 4 61.2 77.9 65.86% 72.32%
26 887 Not in PI 68.2 89.4 57.08% 72.15%
27 788 Year 2 46.3 69.3 66.73% 84.42%
28 830 Year 1 53.5 78.4 59.80% 73.06%
29 870 Not in PI 65.2 83.2 54.44% 66.60%
30 845 Year 1 56.7 76.8 47.14% 70.23%
31 942 Not in PI 84.1 89.9 11.69% 20.95%
32 866 Year 1 64.0 79.3 55.35% 75.71%
33 870 Not in PI 62.9 84.6 56.25% 66.61%
34 776 Year 2 39.1 63.0 59.78% 77.90%
35 833 Year 1 52.4 76.0 65.47% 82.51%
36 777 Year 4 40.0 68.6 83.93% 97.30%
37 805 Not in PI 47.7 71.4 70.52% 92.87%
38 776 Year 3 39.4 68.9 74.48% 95.43%
39 859 Not in PI 64.5 80.5 50.32% 71.77%
40 810 Year 1 50.8 69.8 40.91% 63.88%
41 908 Not in PI 72.1 90.2 56.07% 65.65%
42 766 Year 1 37.1 66.0 70.52% 86.94%
43 791 Year 2 42.4 68.7 66.50% 82.61%
44 768 Year 2 43.9 67.0 60.07% 79.12%
45 774 Year 3 37.7 69.1 69.39% 90.35%
46 822 Not in PI 55.1 75.2 54.73% 69.63%
Note. Source GGUSD-Evaluation and Research
Overview of the district. The Garden Grove Unified School District was established in
1965 and is the third largest among 28 public school districts in Orange County. Serving more
than 48,000 students, Garden Grove Unified School District, ranks 11
th
in size in the state of
California and 96
th
out of more that 17,000 districts in the nation. Garden Grove Unified School
District includes 47 elementary schools, 10 intermediate schools, 9 high schools and 2 special
education schools. All schools in the district focus on a core curriculum of basic skills
development in reading, writing, science, history-social science, science and mathematics, while
providing varied educational experiences through comprehensive music and visual arts
instruction, electives, athletics, and leadership development.
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In 2004, Garden Grove Unified School District was honored with the Broad Prize for
Excellence in Urban Education as the best urban school district in the United States. The Broad
Prize for Urban Education is the largest education award in the country given to urban school
districts that demonstrate the best overall performance and improvement in student achievement
while reducing achievement gaps among poor and minority students. Twice before, the district
had been a Broad Prize finalist. In 2010, The Garden Grove Unified School District realized
significant gains on the Academic Performance Index (API); with 86 percent of schools realizing
growth from the previous year and for the first time the district-wide average API surpassed the
state target of 800. The 2004 Broad Prize recipient status and the 2010 API surpassing the state
target may increase the likelihood of this study’s utility, in that policy makers as well as
educational leaders who are interested in the practices of a district that has earned such honors
will likely find the presented results and recommendations insightful.
Instrumentation
This study was one of seventeen similar studies conducted via the thematic dissertation
process at the University of Southern California Rossier School of Education. The 17
researchers participating in this thematic study received expert training provided during the
spring semester of 2012. Training included the introduction and demonstration of a model that
allowed the input and simulation of data into an online Excel spreadsheet, a discussion of who
from the district should be interviewed that will provide the necessary data to satisfy the model,
and the types of questions that need to be posed in the interview process. The data collection
instruments for this study were the model designed to allow the researcher to input and
manipulate numerical data and staffing rations for the chosen sample, various district and school
documents, and standardized open-ended interviews.
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Each researcher in the thematic dissertation group utilized the prebuilt school simulation
model designed to allow the researcher to enter and manipulate personnel allocations for each
school in the district of study. This model contains the following six tab sections; student input
data, staff input data, current allocation output, desired allocation output, Evidence-Based Model
output (Odden & Picus, 2008), and gap analysis (Clark & Estes, 2002). When the student input
data and staff input data was entered, the model determined the current allocation output and
compared these allocations to the recommended allocations in the Evidence-Based Model output
column and computed this difference in the gap analysis column creating a user-friendly
summary of the district’s data. Once the researcher completed the desired allocations output
column, the model computed the gap between both the desired allocations output and the
Evidence-Based Model output and the current resource allocation output and the desired
allocations output displaying these differences in the gap analysis column.
Data Collection
Each researcher was responsible for contacting the necessary school district personnel to
acquire the human resource allocation data required for each school to satisfy the simulation
model. In January of 2012, the researcher met with the Superintendent of Schools for the Garden
Grove Unified School District to request the district’s participation in this research study. After
agreeing to the request, the researcher provided an overview of the study and the necessary data
required for the model that was utilized. The superintendent was also informed that her
agreement to participate in the study would include being the subject of future interviews that
would require answering a series of questions that would direct the researcher to the appropriate
district office personnel. The answers to these questions may provide the researcher with
information regarding the decision making process of allocating resources to district schools thus
PERSONNEL RESOURCE ALLOCATION 76

informing the researcher of which district office personnel to interview. The superintendent
assured the researcher that she would support the study and act as the liaison between the district
and the researcher.
Interviews. To glean information of current spending patterns and how those relate to
the district’s spending goals, interviews were conducted with district office personnel beginning
with the superintendent. An interview protocol was utilized and questions were focused on
acquiring the district’s instructional priorities and the district’s current human resource allocation
ratios used to distribute personnel at the elementary school level. In addition to requesting
resource allocation data and formulas during the interview with the superintendent, the
researcher will request desired resource allocation data and the rationale for why the
superintendent would allocate human resources in this fashion if she had the ability to do so.
Based on the information provided from the interview with the superintendent, the
researcher requested interviews with business office personnel to acquire any additional human
resource allocation data required to fulfill the requirements of the simulation model that was not
revealed during the interview with the superintendent. These additional interviews were
conducted and utilized the same standardized open-ended interview protocol that was used with
the superintendent.
Once all interviews were conducted and the necessary numerical data had been acquired
during the process, interview data was summarized for further analysis and numerical data was
input into the prebuilt database created for this study.
Numerical data. The following information gathered during the interview process was
entered into the simulation instrument’s database:
• Enrollment data by grade
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• Student demographics data, including participation in special programs
• Class size
• Staff roles, including instructional aides, specialists teachers, instructional
coaches, tutors, LEP teachers, special education teachers, special education aides,
librarians, library technicians, pupil support staff, principals, assistant principals,
and secretaries
• Information about extended day programs
• Information about summer school programs
• Fiscal data regarding expenditures in the categories of technology, instructional
materials, assessment, student activities, professional development, substitutes,
and teacher compensation
Documents. Specific school and district documents were collected and analyzed to
determine whether current human resource allocations aligned with the asserted mission and
vision and to identify potential areas for reorganization of resources to increase student
achievement. At the district level, the 2011-2012 budget and mission/vision statements were
collected. The Single School Plans for Student Achievement, School Accountability Report
Card, mission and vision statements, daily schedules including the allocation of instructional
minutes, staff rosters, and budgets were collected at the school level.
These three data collection methods were selected because they were all directed at
answering the study’s research questions, specifically providing the researcher with information
about the sample district’s current resource allocation patterns and any gaps that exist between
those patterns, the district’s desired allocations, and the Evidence- Based Model’s recommended
allocations. Collection of the district’s numerical data, various documents, and interview field
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notes provided an objective representation of the current fiscal constraints that the sample district
is facing from both the state and federal government. Once collected, all three forms of data
were analyzed as detailed in the following section.
Data Analysis
Collected data was analyzed to generate information that supported in answering each of
this study’s four research questions. The three data analysis processes for this study were
determining gaps, assessing gaps, and simulating options. Once the necessary numerical data
has been collected it was entered into the model for analysis. The researcher utilized the school
simulation model to perform the two functions of determining gaps and simulating options.
Interview field notes supported the researcher in simulating options based on superintendent
feedback of desired resource reallocation. Clark and Estes (2002) gap analysis model served as
the framework for assessing the causes of the gaps and providing solutions to close any
identified gaps.
Determining gaps. Two different gaps were determined by using the school simulation
model. The identification of these gaps provided the researcher with an objective picture of how
the district’s current allocations compare to both the recommendations of the Evidence-Based
Model and to desired allocations. The first gap was the difference between the Evidence-Based
Model allocations and the actual district allocations. The researcher predicted that this would be
the larger of the two gaps as the Evidence-Based Model allocations are assuming a much higher
level of per pupil funding than is present in the state of California especially during this time of a
budgetary crisis. The second gap was the difference between the desired allocations and the
actual allocations. The researcher believed that this would be the smallest of the three gaps
because previous years’ allocations in the district of study have never been allocated similarly to
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the Evidence-Based Model, therefore the superintendent would likely feel that desired
allocations should be similar to what allocations were prior to the budgetary crisis that has
significantly limited funding. These allocations were far more modest than that of the Evidence-
Based Model.
Assessing gaps. When a gap between the actual allocations and the Evidence-Based
Model recommendations had been determined, the researcher assessed the causes of the gap and
provided recommendations for closing the gap. Utilizing Clark and Estes’ (2002) gap analysis
model, the researcher analyzed the gap to determine whether the cause was due to motivation,
knowledge/skills, and/or organizational barriers. Based on the cause or causes of the gap,
appropriate solutions for closing the gap were recommended. The researcher also conducted a
correlation analysis between the size of this gap and the school’s current API. In addition to
providing solutions for closing the gap by utilizing Clark and Estes’ (2002) gap analysis model,
the researcher made recommendations, based on the themes presented in the school improvement
strategies section of the literature review, of which research-based strategies can be implemented
utilizing the current available resources to increase student achievement.
Simulating options. The final function that the model provided was the ability for the
researcher to simulate options for reallocating resources to move toward closing the gap between
current allocations and Evidence-Based Model allocations. It also supported the researcher in
identifying research based resource allocation strategies that might further improve student
performance within the district’s fiscal constraints. The researcher simulated options by
redistributing the current personnel allocations and recommend where additional personnel
should be allocated to improve student achievement if funds for hiring personnel became
available.
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Summary
This chapter described the qualitative methodology was utilized to conduct this study and
support in answering the four research questions. Detailed information of the study’s school
district sample and its population as well as the specifics of the data collection process was
discussed. Lastly, the conceptual frameworks and the methodological tools for analysis were
described. The following chapter will report the findings and how they address each proposed
research question of this study.

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Chapter 4: Findings
The findings of the study are presented in this chapter. Findings were derived via the
study district’s human resource allocation data which were input into the school simulation
model and used to estimate gaps between: 1) the district’s current allocations and the
recommended allocations of the Evidence-Based Model; and 2) between the district’s current
allocations and the district’s desired allocations. The researcher evaluated these gaps utilizing
Clark and Estes (2002) gap analysis framework to determine potential causes of the gaps, and
then simulated options for human resource reallocations that are focused on improving student
achievement.
The first section of this chapter presents an overview of the district and the elementary
schools that participated in this study. The second section presents the findings as they relate to
each of the four following research questions:
• What research based human resource allocation strategies improve student achievement?
• How are human resources allocated across Garden Grove Unified School District’s
elementary schools?
• Is there a gap between current human resource allocation practices and what the research
suggests is most effective?
• How can human resources be strategically re-allocated to align with strategies that
improve student achievement?
The chapter concludes with a reflection of the Evidence-Based Model recommendations and
implications of the study.

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Overview of the District
This section provides an overview of the district and elementary schools that participated
in the study. Utilizing a purposive sampling method, Garden Grove Unified School District
(GGUSD), located in Orange County, California, was chosen as the study district. Serving over
48,000 students, Garden Grove Unified School District is comprised of 47 elementary schools,
10 intermediate schools, 9 high schools, and 2 special education schools and is considered a
large urban school district. Garden Grove Unified School District was awarded The Broad Prize
for Urban Education in 2004 and in 2010 surpassed the state Academic Performance Index (API)
target of 800 district-wide.
This study focused specifically on the 47 elementary schools in the district. All schools in
this sample with the exception of four are traditional elementary schools severing students in
grades K-6. Two of the schools serve Gifted and Talented Education (GATE) students in grades
1-6 and the other two schools serve GATE students on the same campus but are separated into
two separate schools on that campus, one serving grades K-3 and the other serving grades 4-6.
For the purposes of this study, these two schools will be reported as one K-6 campus to provide
consistency for comparison. Due to this modification, data will be reported for 46 schools
instead of 47. The actual name will be used for the school district but numbers will be utilized in
place of school names to honor anonymity of the schools participating in the study. Table 5
displays each elementary school by number that participated in the study along with their
demographics and academic achievement.
The demographic columns include percent of English Learners, percent of Special
Education Students, and percent of free or reduced lunch students. The percent of English
Learners includes all students that are not classified as English Only or reclassified as fluent
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English proficient (RFEP) based on the annual California English Language Development Test
(CELDT). The percent of Special Education students consists of all students who receive
Special Education services but are mainstreamed into a general education classroom for the
majority of the school day. The percent of free or reduced lunch students includes students who
qualify under federal regulations for free lunch or lunch at a reduced price. These students also
determine a school’s Title I status. Title I is a federal program that provides funding to schools
that serve low-income students. If a school has 40 percent or more of its student population
receiving free or reduced lunch, then it is considered a Title I school.
The academic columns include Academic Performance Index (API) and Program
Improvement (PI) status. The Academic Performance Index (API) is a measurement of academic
performance and progress of individual schools in California, United States. It is one of the main
components of the Public Schools Accountability Act passed by the California legislature in
1999. API scores ranges from a low of 200 to a high of 1000. All Title I funded schools that do
not achieve Adequate Yearly Progress (AYP) are identified for Program Improvement (PI) under
the Elementary and Secondary Education Act (ESEA). Schools that do not qualify Title I
school-wide status are not subject to the Program Improvement requirements. Adequate Yearly
Progress, or AYP, is a measurement defined by the United States federal No Child Left Behind
Act that allows the U.S. Department of Education to determine how every public school and
school district in the country is performing academically according to results on standardized
tests (California Department of Education, 2012).
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Table 5
2011-2012 Demographics and Achievement of Each Elementary School in Study
School
#
Enrollment
% English
Learners
%
Special Education
Students
% Free
or
Reduced
Lunch
Academic
Performance
Index
API
Program
Improvement PI
Status
1 406 26.84% 3.9% 35.29% 980 Not in PI
2 537 50.73% 16.8% 67.52% 869 Year 3
3 405 3.12% 4.0% 12.21% 939 Not Title I
4 582 51.59% 8.4% 77.30% 780 Year 3
5 619 68.72% 3.4% 87.52% 794 Year 4
6 687 69.62% 4.2% 81.25% 829 Year 1
7 298 44.01% 31.9% 70.07% 753 Not in PI
8 671 63.27% 2.8% 80.34% 788 Not in PI
9 385 57.18% 5.2% 71.05% 881 Not in PI
10 561 50.09% 17.8% 66.42% 792 Year 1
11 573 66.26% 3.7% 85.49% 782 Year 4
12 599 12.14% 14.0% 28.38% 855 Not Title 1
13 585 54.73% 3.4% 74.21% 807 Year 3
14 515 59.70% 1.9% 76.16% 883 Not in PI
15 658 49.28% 2.9% 64.41% 834 Year 1
16 256 5.86% 4.3% 20.92% 875 Not Title 1
17 610 44.46% 3.8% 64.09% 818 Year 3
18 680 71.55% 3.4% 92.31% 843 Year 1
19 625 72.97% 2.4% 91.41% 784 Year 3
20 404 57.62% 19.3% 67.44% 858 Not in PI
21 660 51.05% 2.3% 75.52% 802 Year 1
22 528 61.93% 2.5% 73.77% 864 Not in PI
23 557 64.11% 5.0% 76.64% 818 Year 1
24 378 38.32% 31.2% 54.86% 831 Year 4
25 517 65.86% 10.6% 72.32% 841 Year 4
26 469 57.08% 2.3% 72.15% 887 Not in PI
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Table 5, continued
School
#
Enrollment
% English
Learners
%
Special Education
Students
% Free
or
Reduced
Lunch
Academic
Performance
Index
API
Program
Improvement PI
Status
27 550 66.73% 5.3% 84.42% 788 Year 2
28 499 59.80% 4.0% 73.06% 830 Year 1
29 507 54.44% 2.6% 66.60% 870 Not in PI
30 478 47.14% 4.4% 70.23% 845 Year 1
31 719 11.69% 4.0% 20.95% 942 Not Title I
32 962 55.35% 1.8% 75.71% 866 Year 1
33 567 56.25% 2.6% 66.61% 870 Not in PI
34 534 59.78% 3.9% 77.90% 776 Year 2
35 709 65.47% 3.7% 82.51% 833 Year 1
36 785 83.93% 2.4% 97.30% 777 Year 4
37 415 70.52% 2.2% 92.87% 805 Not in PI
38 521 74.48% 5.4% 95.43% 776 Year 3
39 647 50.32% 3.2% 71.77% 859 Not in PI
40 432 40.91% 4.9% 63.88% 810 Year 1
41 670 56.07% 3.7% 65.65% 908 Not in PI
42 617 70.52% 2.9% 86.94% 766 Year 1
43 432 66.50% 3.7% 82.61% 791 Year 2
44 507 60.07% 3.2% 79.12% 768 Year 2
45 517 69.39% 7.0% 90.35% 774 Year 3
46 348 54.73% 4.9% 69.63% 822 Not in PI
Note. Source GGUSD-Evaluation and Research (2012)
Introduction of the Findings
This section will provide a discussion of the study findings as they relate to each of the
four research questions. The research questions will be addressed by utilizing information
generated during interviews with district office personnel and the data provided in the school
simulation model including the researcher’s proposed human resource re-allocations that are
focused on increased student achievement. Data provided in this school simulation model is a
result of reviewing district personnel allocation documents and conducting interviews with the
Superintendent, Assistant Superintendent of Elementary Education, Assistant Director of
Business Services, and the Director of K-12 Educational Services of Garden Grove Unified
School District in the fall of 2012.
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Data for Research Question One
Research question one: What research based human resource allocation strategies
improve student achievement?
A synthesis of the literature reviewed in Chapter 2 consistently revealed the following
four strategies for increasing student achievement: leadership, assessment and data-based
decision making, collaboration, and professional development. The following section will
provide the findings of how human resources are allocated from across the district of study to
target each of these four areas. Information was acquired via interviews conducted with the
Assistant Superintendent of Elementary Education and the Director of K-6 Instruction in
GGUSD.
Leadership. Research reveals a link between student achievement and specific
leadership qualities such as sharing a common vision, setting rigorous goals, and maintaining
focus (Duke, 2006; Togneri & Anderson, 2003; Reeves, 2003). During an era in which high
stakes accountability measures that are directly linked to student achievement are being imposed
by the state and federal government, it is imperative that districts allocate resources toward
ensuring that that there is widespread and distributed instructional leadership at both the district
and school site level. Human resource allocation strategies that foster a district’s ability to
execute these practices include ensuring that administrators are trained and held accountable for
the implementation of high quality instructional leadership practices, as well as hiring an
adequate amount of staff that assume leadership roles so that they may focus on the district’s
vision and goals and not be derailed by daily supervisory duties (Odden, 2009).
Leadership occurs when the actions of principals and teacher leaders set the tone for the
school improvement process (Duke, 2006). During the 2011-2012 school year the principals of
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all 46 elementary school that participated in this study participated in two specific year-long
leadership trainings that were developed to set the tone for school improvement. These two
trainings were titled Instructional Supervision and Instructional Rounds. Ten district-selected
principals participating in this study also received an additional third training titled Instructional
Leadership Team. Both Instructional Supervision and Instructional Leadership Team are two-
year long trainings in which principals received the first year of training during the 2011-2012
school year.
The first leadership training program, Instructional Supervision, was delivered in three
modules occurring over the course of the school year. The need for this leadership training was
identified by the Assistant Superintendent of Personnel services when she realized via concerns
brought forth by the teachers’ union that there were inconsistencies in formal teacher evaluations
completed by principals. Some principals were thorough and specific in their feedback while
others were perceived by teachers as being vague or providing unexpected feedback that was not
substantiated with factual information. After discussing this concern with the Assistant
Superintendent of Elementary Education and the Director of K-6 Instruction, the team realized
that principals require leadership training that clearly articulates the expectations of principals as
instructional leaders and provides strategies that will enable them to supervise all teachers
throughout the year regardless of whether or not the teacher is being formally evaluated.
In a collaborative effort, the district’s Assistant Superintendent of Elementary Education
and the Assistant Superintendent of Personnel Services developed a specific model of
Instructional Supervision that was the basis for the training. The processes and strategies of the
training were primarily derived from the following two texts: The Skillful Leader: Confronting
Mediocre Teaching by Platt, Fraser, Tripp and Ogden and Effective Supervision: Supporting the
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Art and Science of Teaching by Robert Marzano. Its purpose was to develop consistency and
transparency in expectations so that all stakeholders have a clear understanding and clear
expectations for instruction. The first year of the training, which occurred from August through
January of the 2011-2012 school year consisted of three modules focused on providing
individual feedback. Figure 6 describes the three steps of providing feedback to individuals
using the Instructional Supervision model. Each of the three modules focused on one step in the
process.

Figure 6. Steps of Instructional Supervision. Note. Source GGUSD-Office of Elementary
Education (2012)
Implementation of Instructional Supervision training at the school site level was expected
to occur immediately following each of the three training sessions. Full implementation of the
entire process was expected to occur after the final training session in January. Principals were
held accountable for utilizing the Instructional Supervision at their sites by a variety of sources.
These sources include; feedback from the teachers’ union representatives during contract
maintenance, table talks at principals meetings in which principals were required to provide
examples of ways they have implemented the process, goal setting meeting follow-ups with the
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Assistant Superintendent of Elementary Education, and reviews of written formal teacher
evaluations by the Assistant Superintendent of Personnel Services.
The second leadership training program provided to district principals was about
Instructional Rounds. This program was adopted from the book Instructional Rounds by City,
Elmore, Flarman, and Teitel. The Instructional Rounds model is intended to help education
leaders and practitioners develop a shared understanding of what high-quality instruction looks
like and what schools and districts need to do to support it. Inspired by the medical rounds
model used by physicians, whereby doctors observe each patient and then debrief as group to
determine next steps, a new form of professional learning known as the Instructional Rounds
Networks is used to improve teaching and learning. The instructional improvement process of
Instructional Rounds is anchored in the “Instructional Core”. The Instructional Core is
comprised of the teacher and the student in the presence of content. Through this process,
educators develop a shared practice of observing, discussing, and analyzing learning and
teaching. Table 6 details the four elements of Instructional Rounds.
Table 6
The Four Elements of Instructional Rounds
Note. Source GGUSD-Office of Elementary Education (2012)
Problem of Practice Observation of Practice Observation Debrief Next Level of Work
• Focus on the
instructional core
• Directly observable
• Actionable-within the
school/districts control
and can be improved in
real time
• Connected to a broader
strategy of
improvement
• High leverage
• Descriptive and
measurable, not
evaluative
• Specific
• Focused on the
instructional core
(student, teacher,
content)
• Related to the problem of
practice
• Observation teams have
conversations and
discuss the data:
• Describe what you
observed in the context
of the problem of
practice
• Analyze the description
of evidence (What
patterns and trends do
you see?)
• Predict what students are
learning
• Share theory of
action
• Brainstorm next
level of work for
this week/month/by
end of year
• Align suggestions
to the school’s
theory of action
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All 46 elementary school principals in the district were required to read the book
Instruction Rounds prior to the planning process of conducting an Instructional Rounds, which
occurred at a principals’ meeting. Each principal then practiced the process of conducting
Instructional Rounds on two separate occasions during the months of November and January at
two different elementary schools in the district. The expectation was that each principal
conducted some form of Instructional Rounds at their own school site during the 2011-2012
school year. As an accountability measure, principals were required to provide the date of the
site-based Instructional Rounds to the Office of Elementary Education.
A third leadership training that was instituted for ten pre-selected principals in the district
was titled Instructional Leadership Team (ILT). This was a mandatory two-year training for
principals and their designated leadership team who represent underperforming schools as per
federal and state mandates. All schools participating in the program were of Year 2 Program
Improvement status or greater. Bill Saunders and David Marcelletti, members of The Talking
Teaching Foundation, developed the ILT training. The district committed to a two-year contract
with the two creators in order to appropriately train and implement ILT in the designated schools.
The goal of the Instructional Leadership Team training was to improve student
achievement in English Language Arts through the development, delivery, analysis, and
refinement of direct instruction lessons by grade level teams. The purpose of the training was to
build a cadre of site leaders who can facilitate a cycle of plan, do, analyze, and reflect with their
grade level teams to implement the direct instruction lesson design in English Language Arts.
The cadre of leaders was also trained to use this same process to help lead discussions about the
implementation of the Common Core Standards.
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Principals attended monthly meetings to prepare for their leadership team meetings. Site
team meetings were held monthly and principals worked directly with their leadership teams to
provide support in: facilitating grade level meetings that plan lessons using the direct instruction
model; ensuring that team members complete the lessons; and analyzing the results of the lessons
using data. This process was cyclical in nature and principals met monthly with their leadership
team to support the effectiveness of the process.
Teacher leaders, referred to as Teachers On Special Assignment (TOSAs) in the district
also attended the monthly meetings with the principals of their designated sites. Their role was
to provide the opportunity for teachers to meet the expectations that have been established by the
principal. The TOSA was directed to provide observations to the principal not recommendations.
The principal established all leadership decisions but the TOSA followed through and monitored
the implementation of the leadership decisions instituted at the school site.
While the study district has implemented the majority of the important leadership
strategies that are linked to increased student achievement, they still exhibit growth opportunities
in the area of leadership. Even though the leadership trainings that were provided by the district
for principals are well-developed and focused on student achievement, consistent follow-up for
implementation is limited. To increase the principals’ level of implementation
at the school site
of the leadership trainings that they participated in, the researcher recommends that principals be
required to show evidence of implementation of the trainings on a more frequent and formalized
basis. Currently, the principal only receives one site visit from the Assistant Superintendent of
Elementary Education per year.
Assessment and data-based decision making. According to Duke (2006) schools that
utilized data on student achievement on a regular basis to make decisions regarding resource
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allocation, student needs, teacher effectiveness, and other matters were able to significantly
increase student achievement. Resource allocation strategies that support the implementation of
these practices include adopting common district-wide standards-based assessments in English
Language Arts and Math, investing in a user-friendly data information system, and ensuring that
student assessment data results drive classroom instruction. The district of study utilizes
common assessments in both English Language Arts and Math. Principals set annual school-
wide goals based on CST data results, principals and teachers access student assessment results
on a continual basis via the data system Data Director, and students are identified for
interventions based on assessment results.
The district has committed time and resources to adopting and creating district-wide
assessment at the elementary school level in English Language Arts and math. English
Language Arts is assessed on a quarterly basis by using the district-adopted Houghton Mifflin
summative assessment. The quarterly exams are not cumulative and are not consistently
standards-based. Although this is problematic, the district’s financial crisis has prohibited them
from adopting or developing assessments that are more closely aligned to state standards. Math
assessments are administered on a trimester basis. These assessments are district-created and are
closely aligned to state standards. Each assessment is cumulative and all three assessments are to
be administered prior to the CST.
In addition to the quarter and trimester exams, the district has adopted English Language
Arts and Math formative assessments that are to be administered on a monthly/unit basis. It is
against district policy for teachers to substitute any teacher-created assessments for district-
adopted assessments. Teachers may create quizzes to utilize as an additional formative
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assessment tool to determine student needs and support students in preparation for the district-
adopted assessments.
During the principals’ annual goal setting and evaluation meeting occurring in the fall
with the Assistant Superintendent of Elementary Education, there is a reflective discussion of the
previous year’s CST results. Based on the discussion, the principal sets student achievement
goals for the current year. Individual teacher data are also discussed and decisions made
regarding types of support that individual teachers require. This support can range from simply
monitoring student achievement data and classroom instruction over the course of the year to
requiring that a teacher participate in the formal evaluation process regardless of whether or not
it during his/her evaluation cycle. During the spring, the Assistant Superintendent of Elementary
Education visits the principals’ school site to discuss progress toward goals and to visit
classrooms.
Principals and teachers in the district of student consistently access data via the Data
Director data system. Both teachers and principals have been trained to use Data Director and
ongoing support is available from the district’s evaluation and research department. Every
administered exam is scored with a data scanner and results are automatically uploaded to Data
Director. Each school site has a data scanner in which principals, teachers, and/or the testing
clerk use to scan assessments. A variety of reports are generated in Data Director that are
accessed to analyze student results and to plan for instruction accordingly. Principals are
required to have data discussions either with individual teachers or grade-level teams to review
the results of quarterly and trimester district-adopted assessments. During data discussions,
teachers identify students that require extra support and develop a plan to meet their individual
needs. Extra support for these students occurs in the classroom via small group differentiated
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instruction provided by the teacher, outside of core subjects during the school day provided by a
Resource teacher, or outside of the school day as extended-day intervention.
Collaboration. One of Odden’s (2009) ten strategies for doubling student achievement
is to create a collaborative, professional culture in which teachers work in a collaborative and
professional learning community to create a common, school-wide, professional approach to
effective instructional practices. In terms of resource allocation, the greatest contribution a
district can make toward collaboration is to provide time within the teachers’ contracted school
day specifically for the purpose of collaboration. Either providing teachers non-student days
throughout the school year or banking minutes each day to generate collaboration time can
achieve this. All 46 elementary schools in the district of study bank instructional minutes five
days a week to generate a 50-minute collaboration session one time per week. As per the district
of study teachers’ contract the purpose of collaboration time is for teachers to work together
toward the improvement of student achievement. The District and Association agree that all
teachers, K-12, will collaborate on a regular and ongoing basis. The teachers and administrators
will mutually determine the topics for collaboration time (Garden Grove Unified School District
Contract, 2011-2012).
During the 2011-2012 school year a collaboration consult convened as a joint effort
between Garden Grove Education Association (GGEA) and Garden Grove Unified School
District (GGUSD) as a result of teacher requests for assistance with effective site collaboration.
It consisted of over 100 participants made up of K-12 teachers, instructional coaches, TOSA’s,
GGEA, administrators and district personnel. Over the two-day consult, members discussed
various readings on best practices for effective collaboration. Consult members shared ideas and
reviewed feedback from surveys that each school submitted to develop guiding principles for
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effective collaboration in Garden Grove Unified School District. A binder created to provide
ongoing support and resources for effective collaboration that is maintained at each school site
represents the reflection, development and collaborative work that occurred during the consult.
The principal maintains the primary responsibility for ensuring that collaboration is
effectively occurring on his/her campus. Principals have instituted a variety of measures to
ensure effective collaboration on their sites. These measures include; requiring that meeting
notes of topics that were discussed during collaboration be submitted on a weekly basis,
attending collaboration meetings on a regular basis, and ensuring that the mutually determined
agenda items for collaboration are focused on student achievement. The district of study has
been banking instructional minutes to allot time for collaboration on a weekly basis for the past
seven years. Each year a vote must be taken to determine if each site will continue to bank
minutes to use toward collaboration and every year all 46 elementary schools in the district of
study has voted to maintain collaboration time. This fact confirms that teachers in the district of
study value collaboration time.
Professional development. The final strategy suggested by research to increase student
achievement is professional development. According to Togneri and Anderson (2003) districts
that adopted new approaches to professional development that involved a coherent and district-
organized set of strategies to improve instruction realized improved student scores in math and
reading and marked evidence of closing the achievement gap. Resource allocation must focus on
the importance of executing successful professional development programs, allotting teacher
release time for professional development including providing substitute teachers when
necessary, and hiring instructional coaches. The district of study is committed to providing
teachers opportunities through professional development a coherent and district-organized set of
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strategies to improve instruction. Professional development opportunities in the district of study
occur at various times including prior to the beginning of the school year, during the school day,
and after school. There are both mandatory and optional trainings offered by the district. The
principal has the authority to require teachers to receive any professional development trainings
offered by the district by providing a substitute teacher for the teacher being trained to attend the
training on campus provided by the district TOSA during the school day.
All professional development trainings offered by the district of study are developed and
delivered by district personnel in the Office of K-6 Instruction. The Office of K-6 Instruction is
comprised of a Director and 22 TOSA’s. Within the office there are four teams focusing on
English Language Arts, Math, Special Education, or Differentiation. Each TOSA is required to
be a participant of two teams. Each team develops the professional development trainings
specific to their area of expertise. All district created trainings are research-based and developed
based on teacher and administrator survey feedback. Once a team has developed a training, it is
delivered to the entire Office of K-6 Instruction during a staff meeting to ensure all TOSA’s are
equipped to deliver the training at their designated school sites as per the principal’s request.
Professional development trainings in the district of study are offered prior to the school
year’s beginning, during the school day, and after school. Only the trainings that are offered
during the school day are mandatory. The entire week prior to the teacher’s first contracted day,
professional development half-day trainings are offered. Teachers must sign up for the trainings
in advance and receive a stipend for attendance. Principals are strongly encouraged to attend as
many trainings as possible during this week. Both at the principals’ and at the Office of K-6
Instruction’s discretion, professional development trainings occur during the school day on a
mandatory basis. The Office of K-6 Instruction usually delivers two mandatory trainings during
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the school day each year, one in English Language Arts and one in math. Principals can request
as many training opportunities at their school site as their budget affords and that are aligned
with their School Action Plan. The after school trainings that are offered by the district consist
of 1½ hour trainings that are typically follow-ups to previous trainings. These trainings are
optional and teachers receive a stipend for attending. In the 2011-2012 school year, the teachers’
contract was adjusted from a maximum of 15 hours of stipend time per school year to no
maximum amount of hours per school year for professional development. This adjustment was
proposed by the district in an effort to encourage teachers to attend as many professional
development opportunities as they believe would be beneficial.
Summary and recommendations. The findings from the district of study as they relate
to research question one reveal numerous strengths in all four areas of leadership, assessment
and data based decision-making, collaboration, and professional development. Resources have
been strategically allocated by the district in each of these four areas. Recommendations for
improvement are being made in the areas of leadership and assessment.
In the area of leadership the researcher asserts that since there are 46 elementary schools
in the district of study, it would be challenging for the Assistant Superintendent of Elementary
Education to visit each school site more than once per year, therefore it is recommended that the
principals receive a second site visit from the Director of K-6 Instruction. In addition, the
principals should be required to submit written evidence with examples of leadership training
implementation as a requirement of the evaluation process occurring each year.
The researcher recommends that in the area of assessments standards-based assessments
in English Language Arts must become a priority in the district of study. Since the district of
study is experiencing a fiscal crisis and cannot fund an outside agency to develop the
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assessments, TOSA’s on the English Language Arts team should be required to develop the
assessments. Even though this may prohibit the team from developing teacher training,
implementing standards-based assessments should be a priority. Since there is stringent federal
and state legislation focused on standards-based student achievement, it is imperative that
teachers in the district of study have a tool that provides them with accurate data on how their
students are performing on grade-level standards.
The researcher believes that these recommendations are focused on increasing student
achievement. Though acting upon these suggestions may not seem feasible given the current
state of educational funding in California, the remainder of this chapter will discuss current
resource allocation patterns of the district of study and how resource reallocations can be made
which would allow for some or all of these recommendations to be put into practice.
Data for Research Question Two
Research question two: How are human resources allocated across Garden Grove
Unified School District’s (GGUSD) elementary schools?
Elementary school human resource allocation data from the district of study (GGUSD)
were collected and input into the Staff Input Tab of the school simulation model. The following
section will provide a description of the human resource allocation categories as per the
Evidence-Based Model and as per GGUSD’s interpretation of these same categories. The
recommended human resource allocations as per the Evidence-Based Model and the current
allocations in the district of study will also be discussed.
The Staff Input Tab of the school simulation model allowed the researcher to record the
total amounts of both certificated and classified staff members for the 2011-2012 in the district
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of study. Table 7 details the total number of teachers in GGUSD separated by school, grade
level, core and specialist teachers.
Certificated staff. The district of study does not allocate core teachers in the same
proportions as the Evidence Model (EBM) recommends. For example, 11 of the 46 elementary
schools offer a state preschool program on campus. Each preschool class can enroll up to 24
students and due to the demand for the program all classes are at capacity. The Evidence-Based
Model recommends 15 students per teacher in Pre-K. To accommodate the larger number of
classrooms required due to the institution of class size reduction in 1996, the district of study
reduced the size of all primary rooms by converting three classrooms into four. Due to a
reduction in funding, the district of study eliminated class-size reduction in grades 1-3 in the
2008-2009 school year but classrooms remained reduced in size. Current class sizes are as
follows: Kindgerten-33 students, Grades 1 through 3 in Class-Size Reduction classrooms-25
students, Grades 1 through 3 in non Class-Size Reduction classrooms-34 students, and Grades 4
through 6 classrooms-36 students. If a teacher teaches a combination class, then their position
count is split in half between the two grade levels. The EBM recommendations are as follows:
Kindergarten through Grade 3 classrooms-15 students and Grades 4 through 6 classrooms-25
students.
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Table 7
District’s Allocation of Core Teachers and Specialist Teachers by School Allocated by Grade
Level

Note. Source School district data input into the model developed by Picus and Knight (2012)
As per the EBM, specialist teacher positions are funded to ensure a rich liberal arts
program that includes art, music and physical education, and to provide planning and
collaboration time for core teachers (Odden & Picus, 2008). The EBM recommends the amount
of specialist teachers be equal to 20% of the core teachers. In GGUSD, there are only music
specialist teachers at the elementary school level. There are both choral/vocal music and
instrumental music teachers that provide instruction to elementary school classrooms one to two
times per week in thirty-minute increments. In grades 1 through 3 in GGUSD, a specialist
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teacher teaches vocal music every other week for one 30-minute session. In grades 4 through 6,
a specialist teacher teaches vocal music for 30 minutes one time per week. Teachers receive
prep time during vocal music instruction.
Choral music is taught one time per week for 35 minutes by the same specialist teacher
that teaches vocal music but is only available on an elective basis to students in grades 4 through
6 and, therefore not all students participate in this program so teachers do not receive prep time.
Instrumental music is structured similarly to choral music. It is taught to students on an elective
basis and is only available to students in grades 4 through 6. A specialist teacher teaches
instrumental music twice a week for 25 minutes. Not all students in grades 4 through 6
participate; therefore it does not allow prep time for teachers.
The district employs 15 choral music teachers and 16 instrumental music teachers that are
distributed across all of the 46 elementary schools. Depending on enrollment, each elementary
school site receives .40%, .60%, .80%, or 1 full time credentialed choral music teacher. Of the
16 instrumental music teachers, six serve only elementary schools and 10 split their time
between an intermediate school and elementary schools. Each elementary school site regardless
of enrollment receives .20% of a full time instrumental music teacher. Therefore an elementary
school can range from having .60% of a specialist teacher to 1.20% of a specialist teacher
depending upon enrollment. The elementary site that has two separate schools on one campus
has 1.40% of a specialist because each of the two schools receives instrumental music time.
In addition to core and specialist teachers there are other types of certificated teachers
included in the Evidence Based school simulation model that support students at school sites.
These positions are organized into the following categories: instructional coaches, department
chairs, academic extra help staff, ELL staff, extended day staff, summer school staff, SPED staff,
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librarians, and non-academic pupil extra support staff. Figure 6 details human resource
allocations in the district of study for these certificated positions.
GGUSD employed 34 instructional coaches during the 2011-2012 school year. Eleven of
these instructional coaches were each assigned to support one school site. The 11 lowest
achieving elementary schools in the district of study as per the CST were the sites that received a
full time instructional coach. In addition to these 11 site-specific coaches, each elementary
school site receives a TOSA, serving as a site’s instructional coach, two days per week. The
EBM recommends one instructional coach for every 200 students at the elementary school level.
According to this ratio, GGUSD should have 126 instructional coaches. With the district of
study’s 2011-2012 elementary school enrollment exceeding 25,000 students, the allocation of
instructional coaches was 1 per approximately every 740 students.
GGUSD employed 19 part-time categorical teachers that each served one school site
during the 2011-2012 school year. The part-time categorical teachers worked at school sites
where the principals requested an additional certificated position to reduce class large class sizes
in particular grades for a portion of the day by providing intervention to at risk students and the
site’s budget was able to fund the additional position. Even though every principal in the district
of study expressed the desire to have a PTCT during the 2011-2012 school year, only the schools
that had a large Title I budget were able to afford one. Due to further budgetary cuts in the 2012-
2013 school year, no school sites in GGUSD are able to employ part-time categorical teachers.
The EBM recommends one full time academic extra help certificated teacher or tutor for every
100 “at risk” students at the elementary school level. A PTCT is comparable to the EBM’s
academic extra help staff member but is only employed part time. The district of study has one
PTCT for approximately every 1900 “at risk” students at the elementary school level.
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As per the EBM, ELL staff members are certificated Bilingual teachers that provide
instruction to ESL (English as a Second Language) students. The model recommends one ELL
staff member for every 100 ESL students at an elementary school. The district of study does not
employ staff members that only teach ELL at the elementary school level. General education
elementary school credentialed teachers in the district of study are certified with either the
CLAD (cross-cultural language, and academic development) certificate or the Bilingual-CLAD
(or BCLAD) certificate, which authorizes them to deliver instruction to English learners in
California. Specifically, the CLAD authorizes teachers to implement ELD instruction and use
SDAIE (Specially Designed Academic Instruction in English) techniques and the BCLAD
further authorizes content instruction in the student’s primary language.
ELD is instruction in English as a second language appropriate to the student's identified
language proficiency level. ELD instruction is designed to promote second language acquisition
according to the age and grade level of the student (EdSource, May 2007). All elementary
school ESL students in the district of study receive 30 minutes of ELD instruction from a
qualified credentialed general education teacher during the school day. The 30 minutes of ELL
instruction that occurs at all of the elementary schools on a daily basis in the district of study
equates to 0.06% of a full time ELL teacher at each school site.
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Table 8
District’s Allocation of Certificated Staff

Note. Source School district data input into the model developed by Picus and Knight (2012)
Extended day staff and summer school staff provide “at risk” students extra support
outside of the typical school day/year. As per the EBM, there should be .883% of a full time
teacher per every 100 “at risk” students to provide extended day support and .833% of a full time
teacher per every 100 “at risk” students to provide summer school support. In the district of
study, each elementary school has one extended day staff member per grade level in grades 1
through 6 that provides extended day intervention two days per week for 45 minutes per session.
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This equates to .19% of a full time teacher. In GGUSD, the summer school staff allocation is
two teachers per elementary school site regardless of school size to teach a 3-½ hour per day
two-week program. This equates to .08% of a full time teacher.
Nurses and non-academic pupil support staff are two certificated categories in which staff
members do not provide academic support to students. The EBM recommends one certificated
nurse for every 750 elementary school students. GGUSD employs 14 total certificated nurses to
serve all elementary schools in the district; therefore each site receives nurse services one day
per week equating to .20% of a full time position regardless of enrollment size. The student to
nurse ratio in GGUSD elementary schools is one certificated nurse for approximately every
1,800 students. Non-academic pupil support staff consists of guidance counselors, school
psychologists and social workers. According to the EMB, there should be one non-academic
pupil support staff member for every 450 elementary school students. GGUSD elementary
schools have school psychologists that serve several schools each. Depending on the size of the
school, the school psychologist spends between 1-3 days per week at a school site equaling .20%
to .60% of a full time certificated position.
The total number of certificated staff members for each school site in the district of study
can be referenced in “total certificated staff” column of Table 8.
Classified staff. Classified staff members are non-certificated employees that provide
support to certificated staff members and students. The five categories of classified employees
in the school simulation model are: Special Education aides, instructional aides, non-
instructional aides, library technicians, and library paraprofessionals. Table 9 displays the staff
counts of the classified personnel of the 46 elementary schools in the district of study.
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For the purposes of the school simulation model, only Special Education aides that
support students who are mainstreamed into the general education population are counted.
Special Education aides that support students that spend the entire school day in a special day
class (SDC) are not considered in the model. In GGUSD, the two types of Special Education
aides that provide mainstreamed students support are Resource Specialist Program (RSP)
teachers and Mild/Moderate (M/M) Special Education teachers. The Special Education students
that they serve spend at least half of the school day in a general education setting. For each site
in the district of study that has a RSP program or a M/M program, one instructional aide is
assigned per teacher. The EBM recommends one Special Education aide for every 300
elementary school students.
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Table 9
District’s Allocation of Classified Staff
Note. Source School district data input into the model developed by Picus and Knight (2012)

The EMB defines instructional aides as paraprofessional tutors, intervention specialists,
and reading teachers. Due to financial constraints GGUSD has laid off the majority of the
instructional aides in the district. The only instructional aides that are still employed are Title 1
or bilingual aides. These aides’ hours were reduced in the 2010-2011 school year from 3 hours
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per day to 1-½ hours per day and as their positions become vacant, they are not being filled. The
EBM recommends one instructional aide for every 15 preschool students.
Non-instructional aides in the district of study include: noon duty supervisors, health
clerks, and community liaisons. All prototypical elementary schools in GGUSD with 500
students or less are allocated 6 hours per day of noon duty supervision. Each elementary school
has one health aide that works 3 ½ hours per day. Beside the three English-only speaking school
sites, all other school have at least one and often times two community liaisons. Community
liaisons work 3 ½ hours per day and speak either Vietnamese or Spanish. The EBM
recommends two non-instructional aides per elementary school site.
Library technicians are the non-certificated staff in charge of maintenance for audio-
visual equipment and computers. In GGUSD library technicians are called computer resource
assistants (CRA) and are a 3½-hour per day employee. Approximately half of the schools
employ a CRA and their employment is based on principal request and the ability to fund the
position out of the Title 1 budget. Schools with larger Title 1 budgets are the schools that have
been able to employ CRA’s. The EBM does not recommend any library technicians in its model.
Library paraprofessionals are non-certificated staff that assists in checking in and out
books and other related tasks. The EBM does not recommend library paraprofessionals at the
elementary level because it recommends one full-time certificated librarian per elementary
school site. GGUSD employs library paraprofessionals to assume the role of a full-time
credentialed librarian. Prior to the 2009-2010 school year, library paraprofessionals were full-
time employees. Due to budgetary constraints their hours were reduced to 3 ½ hours per day.
Currently principals have the option to employ them for 1 ½ hours or 3 ½ hours per day.
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Office staff assumes the functions of running the school on a daily basis. Table 10 details
the personnel in this crucial area in the district of study. Each elementary school in the study
district has one full time secretary regardless of total student enrollment. School sites that have
enrollment larger than 700 students are provided a full time clerk to support the secretary and are
counted in the secretary category. In addition to office clerks, GGUSD also employs testing
clerks that are all counted as personnel in the secretary category. Each elementary site receives
testing clerk support one day per week. The EBM recommends two full time secretaries per
prototypical elementary school site.
The two largest elementary school sites in the district of study had an assistant principal
in the 2011-2012 school year. Each of these sites had enrollment of nearly 1,000 students. Due
to continuous budgetary cuts, neither of the two school have an assistant principal for the 2012-
2013 school year. The EBM recommends one assistant principal per 400 students at the
elementary school level.
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Table 10
District’s Allocation of Office Staff

Note. Source School district data input into the model developed by Picus and Knight (2012)

Summary. The total number of certificated and classified staff members in the district of
study (GGUSD) has been detailed and discussed. Additionally, the recommended human
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resource allocations as per the Evidence-Based Model for these staff members were described.
Since there are differences between the study district’s actual human resource allocation and the
recommendations of the EBM gaps will be created between the two allocations. These gaps will
be identified and addressed in the next section.
Data for Research Question Three
Research question three: Is there a gap between current human resource allocation
practices and what the research suggests is most effective?
Due to the fact that California is one of the lowest per pupil funded states in the nation
and to meet the personnel and fiscal elements of the Evidence-Based Model higher per pupil
funding would be required in California, it is not surprising to find that there are deficit gaps
between the current human resource allocations and the Evidence-Based Model recommended
human resource allocations in almost every category. The few positive gaps that do exist
between the current allocations and the recommendations of the EBM will be addressed in
research question four. In 2011-2012, California spent $8,852 per pupil, with a national ranking
of 43
rd
in spending adjusted for regional cost-of-living variations (Baron, 2012). Table 11
displays the human resource allocation gaps between current allocations and desired allocations
of the district of study and current allocations of the district of study and recommendations of the
Evidence-Based Model.
This section will address the most significant gaps between the study district’s current
human resource allocations and the recommendations of the Evidence-Based Model. An
analysis of these gaps utilizing Clark and Estes 2002, gap analysis framework will also be
conducted to determine if the potential causes of these gaps are due to lack of motivation, lack of
knowledge/skills, or organizational barriers.
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The first and most significant gap is the deficit of core teachers in GGUSD compared to
what the EBM recommends. The district falls short by 567.5 teachers. When considering the
district’s desired allocations to what the EBM recommends, there is still a 240.2 teacher deficit.
Current teacher to student ratios at the elementary school level are at the highest in over 10 years
due to the budgetary crisis that GGUSD is experiencing. Average class sizes in GGUSD primary
grades (K-3) are 33 students, and upper grades (4-6) are 36 students while the EBM recommends
15 students in grades (K-3) and 25 students in grades (4-6). Since there is a budgetary crisis and
since the district does not want to lay off any classified employees or specialist teachers to drive
down class sizes, this gap can be attributed to organizational barriers.
Anecdotal notes generated during an interview with the district Superintendent revealed
that desired elementary class sizes for the district would be that of what they were during the
institution of class size reduction. Average class sizes during the time of class size reduction
were 20 students in grades (1-3) and 28 students in grades (K, 4-6). Based on the fact that the
desired core teacher allocation is still at a deficit of 240.2 teachers as per the EBM
recommendations further supports the fact that this gap is due to organizational barriers and a
lack of motivation by the district office administrative team to prioritize smaller class sizes at the
elementary school level.
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Table 11
Total Human Resource Allocation Gaps for 46 Elementary Schools in the Study

District Total
Position Counts Gap

Title

Current

Desired

EB
Current –
Desired
Current –
EB
Principals 46.0 46.0 46.0 0.0 0.0
Assistant Principals 2.0 10.0 11.7 (8.0) (9.7)
Instructional Coaches 29.4 46.0 125.9 (16.6) (96.5)
Core teachers 826.7 1066.9 1394.2 (240.2) (567.5)
Specialist teachers 43.8 70.5 278.8 (26.7) (235.0)
SPED teachers 119.0 167.9 167.9 (48.9) (48.9)
ELL teachers 52.8 46.0 139.8 6.8 (87.0)
Academic extra help staff 12.3 23.0 166.1 (10.7) (153.7)
Non-academic pupil support 13.8 146.7 237.4 (132.9) (223.6)
Nurses 10.2 23.0 33.6 (12.8) (23.4)
Extended day/summer school staff 12.3 138.4 302.5 (126.1) (290.2)
Instructional aides 45.5 266.7 31.8 (221.2) 13.7
Supervisory aides 121.9 111.9 111.9 10.0 10.0
SPED aides 82.2 83.9 83.9 (1.7) (1.7)
Librarians 0.0 46.0 46.0 (46.0) (46.0)
Library Technicians 10.3 46.0 0.0 (35.7) 10.3
Library Paraprofessionals 18.8 0.0 0.0 18.8 18.8
Secretaries/clerks 81.0 83.9 111.9 (3.0) (31.0)
Note. Source School district data input into the model developed by Picus and Knight (2012)
Extended day/Summer School is the next significant gap between the district of study’s
current allocations and the recommendations of the EBM. There is a deficit of 290.2 teachers.
GGUSD employs on average one teacher per grade level in grades 1 through 6 to provided
extended day intervention at each elementary school in the district. This is partially due to
limited funds available for afterschool intervention but primarily due to the fact that a site only
has the opportunity to employ as many teachers as volunteer to teach afterschool. At many
school sites across the district, principals experience difficulty obtaining one teacher per grade
level that is willing to commit to the position. A compounding factor is that teachers do not want
to teach a grade level afterschool that is different from their regular teaching assignment. The
same issues exist pertaining to summer school. Since a summer school teaching assignment is an
PERSONNEL RESOURCE ALLOCATION 114

elective position that is only two weeks long, the district often has difficulty filling all the
teaching positions. Teachers have expressed that a two-week program is not long enough.
These factors led the researcher to suggest that this gap is due to a lack of motivation by teachers
in the district of study.
The final gap that will be addressed is the gap that exists between GGUSD’s actual
allocation of specialist teachers and the EBM’s recommendations. GGUSD employs 235 less
specialist teachers than what is recommended. As per the EBM, specialist teachers are to ensure
a rich liberal arts program of arts, music, and physical education, and to provide planning and
collaborative time for core teachers (Odden & Picus, 2008). Specialist teachers should be
allocated at 20% of the amount of core teachers. GGUSD provides a specialist teacher to each
elementary school site three days per week to teach vocal and instrumental music to students.
This equates to 30 minutes of release time every other week for teachers in grades 1-3 and 40
minutes of release time every week for teachers in grades 4-6.
Instrumental music is optional and only available to students in grades 4-6, therefore
teachers may have very reduced class sizes during the 45 minute instrumental music session one
time a week. Because the district of study values release time for teachers but cannot afford to
employ more specialist teachers to provide release time, teachers receive release days during the
school year. Teachers in grades 1-3 receive one release day per semester and teachers in grades
4-6 receive two release days per semester. Substitute teachers are employed during release days
which is less expensive than hiring more specialist teachers. Therefore, the cause of this gap is
due to organizational barriers. As much as GGUSD values release time for teachers, they are not
willing to employ more specialist teachers.
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Summary. Significant deficits between the district of study and recommendations of the
Evidence-Based Model in human resource allocations have been identified and analyzed. The
school simulation model provided the vehicle that identified these gaps and Clark and Estes’ gap
analysis model provided the researcher framework for identifying the causes of these gaps. The
following section will address the study district’s positive gaps generated in the model to
determine how the surplus of human resource allocations should be reallocated to other areas
aligned with strategies to increase student achievement.
Data for Research Question Four
Research question four: How can human resources be strategically re-allocated to align
with strategies that improve student achievement?
The first step in strategically budgeting for improved student performance requires a
district to identify uses of dollars that focus on student learning. Step two is to reiterate the goals
driving the budget process and the overall school system. These goals should be that students
meet proficiency standards for mathematics, science, reading, writing and history (Odden &
Picus, 2008). They further recommend that each district and school set specific, ambitious and
numerical goals for increasing student achievement in core academic areas. Without such
specific goals, it is impossible to track progress or make tough budget decisions to prioritize
programs and strategies. Table 4.7 displays the human resource allocation gaps between the
actual and desired and the actual and the recommendations of the EBM of the 46 elementary
schools in the study.
Since improved student achievement must be at the forefront of all human resource
reallocation decisions, funding strategies that ensure core instruction is as effective as possible
must be instituted. To address this notion the researcher recommends that the district of study
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reallocate resources toward increasing the amount of instructional coaches, reassigning
credentialed teachers to support “at risk” students, and allocating additional time during the
school day for teacher collaboration.
Increasing the number of instructional coaches is the first recommendation for
reallocation of human resources focused on increased student achievement. The district of study
employed 11 instructional coaches that were each assigned to one low academically achieving
elementary school in the 2011-2012 school year. In addition the district employed 18 TOSA’s
that each served two elementary schools two days per week as an instructional coach generating
a total of 29 instructional coaches that each could be assigned to one school site. The desired
allocation would be to have 46 instructional coaches; one instructional coach at each elementary
school site equating to approximately 17 more instructional coaches than what is currently
allocated. Each site’s instructional coach would provide direct support to core teachers by
delivering on-site professional development curtailed to school site goals, co/plan co/teach
opportunities including teacher feedback and follow-up, and classroom demonstration lessons.
Although the elementary school principals in the GGUSD are instructional leaders that have
been effectively trained in Instructional Supervision, they require an instructional coach to
ensure that teachers are receiving the necessary support to implement the curriculum and
instructional recommendations provided by the principal.
The researcher suggests two reallocation options to generate the additional 17
instructional coach positions. The first option would be to reduce the 45 instructional aides by
not filling vacancies as they become available, which would equate to 15 FTE teaching positions
to be utilized for instructional coaches. The district of study generated a surplus of 14
instructional aides when compared to the recommendations of the EBM. The rationale for
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phasing out the instructional aides is that often times they are not working directly with students
but are preparing materials for the teacher. Odden and Picus (2008) state research does not find
a link between aides and improved student performance, which is why instructional aides are not
include in the Evidence Based Model.
If the focus of human resource allocation is going to be direct toward increasing student
achievement, then eliminating instructional aides to generate 15 instructional coaching positions
which have proven to directly impact student achievement is a sensible trade. The additional two
coaching positions would be generated from reducing the district’s supervisory aides from 122
employees to 116. The EBM recommends 112 supervisory aides; therefore the district would
still exceed the EBM recommendations even with the reduction.
The second resource reallocation simulation to generate 17 additional instructional coach
positions is to increase class sizes. Average class sizes in the district of study were as follows:
Preschool-24 students, K-3
rd
Grade-33 students, and 4
th
-6
th
grade-36 students. Increasing class
sizes in Preschool through 6
th
grade by one student would generate 27 teaching positions in
which 17 would be assigned to instructional coaches. The additional 10 generated teaching
positions could be utilized as TOSA positions. TOSA’s could provide support to the
instructional coaches in the areas of curriculum and instruction. A variation to this
recommendation would be to only increase class sizes in certain grades. For example increasing
class sizes by 1.5 students in Preschool and 4
th
-6
th
grade would generate approximately 17
teaching positions. Either way, slightly increasing class size to provide each elementary site with
a full-time instructional coach is reallocating resources toward increasing student achievement.
Reallocating resources toward generating additional teachers to provide academic extra
help is a strategy that must receive priority when focusing on increasing student achievement.
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Extra help for struggling students must be provided to expand instructional time while
maintaining rigorous performance standards. The EBM recommends one academic extra help
staff member for every 100 “at risk” student. A significant gap of 154 teachers exists between
the district of study’s current allocation and what the EBM recommends. The researcher
recommends a 50 percent of the time academic extra support staff member for each elementary
school in the district equating to 23 staff members. Since the district currently has 12 academic
support staff, 11 more positions must be generated. The recommendation is to reassign the
instrumental music teaching positions, which would generate 11 positions that could be allocated
to academic extra support staff. Since instrumental music is only offered in Grades 4-6 and on a
voluntary basis, not all students benefit from the program nor do teachers receive release time
during instruction. Students will still be receiving choral music as an enrichment to core
curriculum. Reallocating resources to increase the amount of academic extra help staff members
is aligned with the district’s goal of increasing student achievement.
The final reallocation recommendation is to increase the amount of teacher collaboration
time. Currently teachers in the district of study receive 50 minutes of collaboration per week due
to the banking of 10 instructional minutes per day. If student performance goals are going to be
preeminent and assume priority over other goals, then the research recommends that teachers
receive at least three 45-minute collaboration sessions per week. Several categories of classified
staff positions should be transitioned including library technicians, library paraprofessionals and
testing clerks. Eliminating these positions would generate 13 teaching positions equating to one
additional 45-minute collaboration period weekly and a second additional 45 minute period bi-
monthly for each elementary school teacher.
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The researcher believes this is a favorable trade because the teacher can assume the roles
of all three of certificated positions proposed for elimination. If the district of study wanted to
ensure that teachers received a minimum of three 45-minute collaboration sessions per week, an
additional simulation option would be to increase class sizes by an average of .5 students. This
slight class size increase would generate an additional 14 teachers that could be utilized in a
variety of capacities to release core teachers for collaboration. Whichever of the two options that
is implemented would be a worthwhile strategy directed toward increased student achievement.
Reallocating current human resources for the purposes of increasing the amount of
instructional coaches, increasing the amount of intervention teachers, and providing teachers
with additional collaboration time would directly impact student achievement and support in
closing the achievement gap. Since the district of study has limited opportunities to reallocate
resources due to significant deficits between current human resource allocations and
recommendations of the Evidence-Based Model, the researcher proposed the reallocations that
would be most effective in increasing student achievement even if they cannot be implemented
on the recommended scale due to the budgetary crisis that all schools in California are currently
facing.
Summary. This chapter presented the findings as they pertain to each of the four
research questions proposed in the study. The researcher presented information collected from
interviews with district office administrators to describe the research based human resource
allocation strategies that are utilized at the elementary school level to improve student
achievement. Four strategies for increasing student achievement were presented along with a
description of how elementary schools in the district of study allocated various human resources
to support them. Current and desired human resource allocations from the district of study were
PERSONNEL RESOURCE ALLOCATION 120

compared as well as current and EBM recommendations were also compared. A school
simulation model was used to determine gaps in human resource allocations and an analysis was
conducted utilizing Clark and Estes (2002) gap analysis model as a framework to determine
potential causes. Human resource reallocation recommendations were made by the researcher
that was directed at increasing student achievement in the district of study. The study is
summarized in Chapter Five with conclusions, recommendations and implications for further
research.
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Chapter 5: Conclusions
Overview of the Study
This study focused on school improvement strategies, allocation and use of human
resources, limited resources and fiscal constraints, and gap analysis. All of these topics were
shown to influence whether or not a district or school will be successful as determined by
standards-based accountability measures, and all were subsequently used as the basis for the
researcher to devise the research questions for the study.
The Evidence-Based Model by Odden and Picus (2008) and the gap analysis framework
by Clark and Estes (2002) were the primary frameworks utilized in this study. The Evidence-
Based Model served as a gauge in determining how districts and schools allocate human
resources compared to a desired human resource allocation model. Conducting a gap analysis
revealed where there were shortcomings in resource allocation as per the Evidence-Based Model
recommendations. This led to suggestions on how district administrators can reallocate current
human resources to improve student performance.
Purpose of the Study
Education reform has catapulted student achievement accountability measures to the
forefront of educational decision-making. Education researchers who studied the impact of
various human resource allocation strategies employed by districts and schools arrived at the
same conclusion; in order for a district or school to meet the rigorous standards set forth by both
federal and state reform, implemented strategies must be directly linked to student achievement
(Odden & Archibald, 2009; Duke, 2006; Reeves, 2003). As a result the allocation of human
resources with the intent of increasing student achievement has become increasingly important.
The purpose of this study was to collect, analyze, and make reallocation recommendations for
PERSONNEL RESOURCE ALLOCATION 122

human resource allocations at the elementary school level in the study district. The findings will
inform educational leaders as they make decisions on how to allocate human resources in the
most effective way to in support, maintain, and increase student achievement.
Importance of Study
Consistently diminishing financial resources and increasing levels of student performance
accountability characterize the current state of California public schools. Now more than ever,
those in charge of making school-based financial decisions must be informed on how to
effectively allocate limited resources to optimize student achievement. Analyzing the gap
between current use of human resources and the recommendations presented in the Evidence-
Based Model by Odden and Picus (2008) will be insightful for educational leaders. The results
from this study will provide a framework for site administrators to use when faced with making
difficult decisions of how to effectively allocate limited human resources.
Methodology
A mixed methods approach was utilized to conduct the study and analysis of the 47
elementary schools in an urban district located in California. Qualitative in nature, interviews
with various district office personnel were conducted to determine instructional practices,
resource utilization, and the school’s and district’s current focus and overall vision. Quantitative
data derived from the district office was analyzed to determine site-based allocations of
personnel, professional development, and any other human resource expenses associated with
student achievement. This quantitative data was input into a database and compared against the
district’s ideal allocations and to the human resource allocations of the Evidence-Based Model.
Each school was analyzed and used to make comparisons of the similarities and differences in
human resource use across schools. More importantly, the data was utilized to indentify how the
PERSONNEL RESOURCE ALLOCATION 123

overall district’s resource use was aligned to the recommended allocations of the Evidence-
Based Model.
Sample and population. Using a purposive sampling method, this study focused on 47
elementary schools with varying API scores, AYP percentages/Program Improvement (PI)
statuses, and student demographics in one urban school district in Southern California.
Notorious for being a high-achieving urban district that is on the forefront of implementing
effective research-based instructional strategies with limited human resources, this district was
chosen because the researcher was interested in learning more about the rationale for resource
allocations across the elementary schools in the district. All elementary schools in the district
were selected for the sample to ensure a variety of school profiles based on academic
achievement and student demographics. The researcher purposefully chose a wide range of
cases to acquire variation on dimensions of interest, as in this case schools that have a variety of
API scores and student characteristics.
This study chose to examine the funding levels comprised from both federal and state
allocations for 47 elementary schools, which considered all sources of funding and how these
funds were allocated at school sites across the district. The majority of schools in the sample
received some level of federal Title I funding to support their students but this funding level
varied depending upon student population. Schools qualify for Title 1 funding when there is
40% or more of students who receive free or reduced lunch. This variation in funding support
was utilized to make comparisons between schools in terms of the effects of funding levels on
human resource allocation and student achievement. Additionally, this study included all
elementary schools from a single school district to derive a comprehensive understanding of the
role a district assumes in the allocation of human resources across elementary school sites.
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All schools in this sample with the exception of four are traditional public schools
severing student in grades K-6. Two of the schools serve GATE (Gifted and Talented
Education) students in grades 1-6 and the other two schools serve GATE students on the same
site but are separated into two campuses, one serving grades K-3 and the other serving grades 4-
6. For the purposes of this study, these two campuses will be reported as one K-6 campus to
provide consistency for comparison. Due to this modification, data was reported for 46 schools
instead of 47 schools.
Summary of the Findings
This section provides a summary of the study findings as they relate to each of the four
research questions. The findings were derived from a combination of what research-based
characteristics of high-performing schools and data collected from the 46 elementary schools that
participated in the study.
Findings for Research Question One
Research question one: What research based human resource allocation strategies
improve student achievement?
A synthesis of the literature reviewed in Chapter 2 consistently revealed the following
four strategies for increasing student achievement: leadership, assessment and data-based
decision making, collaboration, and professional development. The findings from the district as
they relate to research question one reveal numerous strengths in all four areas of leadership,
assessment and data based decision-making, collaboration, and professional development.
Resources have been strategically allocated by the district in each of these four areas.
Recommendations for improvement were made in the areas of leadership and assessment.
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In the area of leadership the researcher asserts that since there are 46 elementary schools
in the district of study, it would be challenging for the Assistant Superintendent of Elementary
Education to visit each school site more than once per year, therefore it is recommended that the
principals receive a second site visit from the Director of K-6 Instruction. In addition, the
principals should be required to submit evidence with examples of leadership training
implementation as a component of his/her evaluation process.
The researcher recommends that in the area of assessments, standards-based assessments
in English Language Arts must become a priority in the district. Since the district is
experiencing a fiscal crisis and cannot fund an outside agency to develop the assessments, the
Teachers on Special Assignment (TOSA)’s on the English Language Arts team should be
required to develop the assessments. Even though this may prohibit the team from developing
teacher training, implementing standards-based assessments should be a priority. There is
stringent federal and state legislation focused on standards-based student achievement, therefore
it is imperative that teachers have a tool that provides them with accurate data on how their
students are performing on grade-level standards.
Findings for Research Question Two
Research question two: How are human resources allocated across Garden Grove
Unified School District’s (GGUSD) elementary schools?
Elementary school human resource allocation data from the district of study (GGUSD)
were collected and input into the Staff Input Tab of the school simulation model. The total
number of certificated and classified staff members in GGUSD has been detailed and discussed.
Additionally, the recommended human resource allocations as per the Evidence-Based Model for
these staff members were described. There were differences between the district’s actual human
PERSONNEL RESOURCE ALLOCATION 126

resource allocations and the recommendations of the Evidence-Based Model, therefore gaps
occurred between the two allocations. Since California has one of the lowest per pupil funding
allocations in the nation, the district realized negative gaps or deficits in the majority of
categories. These gaps were identified and addressed in research question three.
Findings for Research Question Three
Research question three: Is there a gap between current human resource allocation
practices and what the research suggests is most effective?
Significant gaps were identified between current resource allocations in the district of
study and the Evidence-Based Model (EBM) recommendations that can primarily be attributed
to the low funding levels in California. Theses significant deficits between the district of study
and recommendations of the Evidence-Based Model in human resource allocations were
identified and analyzed. The school simulation model provided the vehicle that highlighted these
gaps and Clark and Estes’ (2002) gap analysis model provided the researcher framework for
identifying the causes of these gaps. The few positive gaps , generated when resources allocated
to a category exceed the allocation proposed by the EBM that did exist between the current
allocations and the recommendations of the EBM will be addressed in research question four.
Finding for Research Question Four
Research question four: How can human resources be strategically re-allocated to align
with strategies that improve student achievement?
Since improved student achievement must be at the forefront of all human resource
reallocation decisions, funding strategies that ensure core instruction is as effective as possible
must be instituted. To address this notion the researcher recommended that the district of study
reallocate resources toward increasing the amount of instructional coaches, reassigning
PERSONNEL RESOURCE ALLOCATION 127

credentialed teachers to support “at risk” students, and allocating additional time during the
school day for teacher collaboration.
Increasing the number of instructional coaches was the first recommendation for
reallocation of human resources focused on increased student achievement. The district of study
employed 11 instructional coaches that were each assigned to one low academically achieving
elementary school in the 2011-2012 school year. In addition the district employed 18 TOSA’s
that each served two elementary schools two days per week as an instructional coach generating
a total of 29 instructional coaches that each could be assigned to one school site. The desired
allocation would be to have 46 instructional coaches; one instructional coach at each elementary
school site equating to approximately 17 more instructional coaches than what is currently
allocated. Each site’s instructional coach would provide direct support to core teachers by
delivering on-site professional development curtailed to school site goals, co/plan co/teach
opportunities including teacher feedback and follow-up, and classroom demonstration lessons.
The researcher suggested two reallocation options to generate the additional 17
instructional coach positions. The first option would be to reduce the 45 instructional aides by
not filling vacancies as they become available, which would equate to 15 FTE teaching positions
to be utilized for instructional coaches. The district of study generated a surplus of 14
instructional aides when compared to the recommendations of the EBM. The rationale for
eliminating the instructional aides is that often they are not working directly with students but are
preparing materials for the teacher. Odden and Picus (2008) state research does not find a link
between aides and improved student performance, which is why instructional aides are not
include in the Evidence Based Model. If the focus of human resource allocation is going to be
directed toward increasing student achievement, then eliminating instructional aides to generate
PERSONNEL RESOURCE ALLOCATION 128

15 instructional coaching positions which have proven to directly impact student achievement is
a sensible trade. The additional two coaching positions would be generated from reducing the
district’s supervisory aides from 122 employees to 116. The EBM recommends 112 supervisory
aides; therefore the district would still exceed the EBM recommendations even with the
reduction.
The second resource reallocation simulation to generate 17 additional instructional coach
positions was to increase class sizes. Average class sizes in the district of study were as follows:
Preschool-24 students, K-3
rd
Grade-33 students, and 4
th
-6
th
grade-36 students. Increasing class
sizes in Preschool through 6
th
grade by one student would generate 27 teaching positions in
which 17 would be assigned to instructional coaches. The additional 10 generated teaching
positions could be utilized as TOSA positions. TOSA’s could provide support to the
instructional coaches in the areas of curriculum and instruction. A variation to this
recommendation would be to only increase class sizes in certain grades. For example increasing
class sizes by 1.5 students in Preschool and 4
th
-6
th
grade would generate approximately 17
teaching positions. Either way, slightly increasing class size to provide each elementary site with
a full-time instructional coach is reallocating resources toward increasing student achievement.
Reallocating resources toward generating additional teachers to provide academic extra
help is a strategy that must receive priority when focusing on increasing student achievement.
Extra help for struggling students must be provided to expand instructional time while
maintaining rigorous performance standards. The EBM recommends one academic extra help
staff member for every 100 “at risk” student. A significant gap of 154 teachers exists between
the district of study’s current allocation and what the EBM recommends. The researcher
recommended a 50 percent of the time academic extra support staff member for each elementary
PERSONNEL RESOURCE ALLOCATION 129

school in the district equating to 23 staff members. Since the district currently has 12 academic
support staff, 11 more positions must be generated. The recommendation was to reassign the
instrumental music teaching positions, which would generate 11 positions that could be allocated
to academic extra support staff. Since instrumental music is only offered in Grades 4-6 and on a
voluntary basis, not all students benefit from the program nor do teachers receive release time
during instruction. Students will still be receiving choral music as an enrichment to core
curriculum. Reallocating resources to increase the amount of academic extra help staff members
is aligned with the district’s goal of increasing student achievement.
The final reallocation recommendation is to increase the amount of teacher collaboration
time. Currently teachers in the district of study receive 50 minutes of collaboration per week due
to the banking of 10 instructional minutes per day. Since student performance goals are
preeminent and assume priority over other goals, the research recommended that teachers receive
at least three 45-minute collaboration sessions per week. Several categories of classified staff
positions should be transitioned including library technicians, library paraprofessionals and
testing clerks. Eliminating these positions would generate 13 teaching positions equating to one
additional 45-minute collaboration period weekly and a second additional 45 minute period bi-
monthly for each elementary school teacher.
The researcher believes this is a favorable trade because the teacher can assume the roles
of all three of certificated positions proposed for elimination. If the district of study wanted to
ensure that teachers received a minimum of three 45-minute collaboration sessions per week, an
additional simulation option would be to increase class sizes by an average of .5 students. This
slight class size increase would generate an additional 14 teachers that could be utilized in a
PERSONNEL RESOURCE ALLOCATION 130

variety of capacities to release core teachers for collaboration. Whichever of the two options that
is implemented would be a worthwhile strategy directed toward increased student achievement.
Reallocating current human resources for the purposes of increasing the amount of
instructional coaches, increasing the amount of intervention teachers, and providing teachers
with additional collaboration time would directly impact student achievement and support in
closing the achievement gap. The district had limited opportunities to reallocate resources due to
significant deficits between current human resource allocations and recommendations of the
Evidence-Based Model. As a result, the researcher proposed the reallocations in theses areas
since they would most effectively impact increasing student achievement. Even if the district
cannot implement the reallocations on the recommended scale due to the current budgetary crisis,
prioritizing reallocation toward the recommendations will positively impact student achievement.
Limitations
The following limitations were present in the study:
• Due to the size of the sample, the findings may not be generalized to other school and
student populations, especially those with different student demographics.
• Only elementary schools participated in the study, therefore the findings cannot be
generalized to intermediate schools or high schools.
• A portion of the data collection was based on anecdotal accounts from interviews that
contained structured and semi-structured questions; therefore there is the possibility of
subjectivity.
• The information obtained from interviewing the four district office administrators may
not constitute a representative sample of all other district administrators.
PERSONNEL RESOURCE ALLOCATION 131

• Due to the enactment of the American Recovery and Reinvestment Act (AARA) in
February 2009, school districts across the nation received one-time additional federal
funding that was required to be spent by September 2011. The outcomes of this study
may have been skewed as a result of how these additional one-time monies were
allocated in various ways across the district during the 2011-2012 school year.
• With the state of California announcing in February 2009 new flexibility for once
restricted categorical programs, district were able to spend their budgets more freely.
The 40 categorical programs equating to 30 percent of all categorical funds that were
once restricted gained flexibility in allocation options due to this provision (Weston,
2011). The outcomes of this study may have been skewed as a result of the various
reallocation appropriations of these once restricted funds.
Implications for Practice
Public schools administrators in California are currently facing the reality of dwindling
annual budgets that have been consistently cut over the past five years at the state and federal
level. In 2011-2012, California received a national ranking of 43
rd
in per-pupil spending adjusted
for regional cost-of-living variations (Baron, 2012). To further complicate matters, state and
federal legislation has instituted stringent academic accountability measures that districts are
valiantly trying to meet.
Now more than ever, those in charge of making school-based financial decisions must be
informed on how to effectively allocate limited resources to optimize student achievement. This
study reports the human resource allocations for 46 elementary schools in a large urban school
district in California. It compares the actual human resource allocations to the recommendations
of the Evidence Based Model and the actual to the desired human resource allocation of the
PERSONNEL RESOURCE ALLOCATION 132

study district. Gaps between these two resource allocation comparisons were identified and
analyzed via the utilization of the Gap Analysis model (Clark & Estes, 2002). Finally, resource
reallocation recommendations directed toward increased student achievement were made.
The Evidence-Based Model (EBM) is based on a cost reporting expenditure structure
developed by Odden, Archibald, Fermanich, and Gross (2003) that allows for the examination of
school-level spending. The EBM focuses exclusively on resource allocations that have been
proven by research to be the most effective in increasing student achievement. The six
instructional categories and three non-instructional categories arrange spending according to its
educational strategy or purpose, enabling a study of the school’s budget to reflect it educational
programs and priorities at the same time. Items in this structure are drawn from research on high
performing schools and the structure reflects a commitment to the core subjects and to
supporting the learning of all students in those subjects, reflected by minimal time spent in low-
level and elective classes especially at the high school level.
The gap between desired and actual performance must be assessed and closed if
organizational goals are to be achieved (Clark & Estes, 2002). In order to determine the
difference or “gap” between actual human resource allocations of a school district, the optimal
allocations of that same school district, and the recommended human resource allocation as per
the Evidence-Based Model, a gap analysis was conducted. Gap analysis diagnoses the human
causes behind performance gaps. Conducting a gap analysis not only reveals the size of the gap
but also can help in determining the potential causes of the gap. Once this information is
unveiled, a school district can make the necessary changes that will move them toward closing
the identified gap.
PERSONNEL RESOURCE ALLOCATION 133

Even though under California’s current funding formula it is not feasible to implement
resource reallocations to the recommended ratios of the Evidence-Based Model, the researcher’s
reallocation recommendations more acutely aligned the district’s limited resources to that of the
Evidence-Based Model. The results from this study along with the reallocation
recommendations will provide a framework for site administrators to use when faced with
making difficult decisions of how to effectively allocate limited human resources to support,
maintain, and increase student achievement.
Recommendations for Future Research
This study supplements the body of research on district and elementary school-level human
resource allocation, recommended allocation and effective strategies to improve student
achievement. The focus of this study was to analyze all 46 elementary schools within one large
urban unified district. Based on the findings of this study, the following recommendations for
future research are:
• Expand the study to a larger population that includes all schools in the district including
the intermediate and high schools instead of only the elementary schools. This study
could assist in determining whether or not human resource allocation patterns and the
implementation of research-based effective strategies are consistent across all school in
the district regardless of whether it is an elementary, intermediate, or high school. Once
this is determined, an analysis of whether or not differences or similarities in human
resource allocation and the level of implementation of effective strategies have an impact
on student achievement.
• Since the district of study is notably a high achieving urban district, it would be insightful
to study additional districts with similar demographics and characteristics that may or
PERSONNEL RESOURCE ALLOCATION 134

may not share the same achievement levels. The similarities or differences in human
resource allocation can be analyzed to determine whether or not there is a correlation
between the way human resources are allocated and the level of academic achievement.
The same analysis can be conducted with the level of implementation of effective
research-based strategies and the level of student success.
• Further investigation into the flexibility of elementary school site administrators in other
districts to make decisions about human resource allocation and implementation of
effective strategies at the site level and what type of impact this have on student
achievement. Also, unveiling what level of leadership training and support these
administrators receive and how that impacts student achievement would be insightful.
All elementary school principals within the study received extensive leadership training
paired with accountability measures guiding them and assisting them in making critical
decisions regarding human resource allocation and the implementation of effective
research-based strategies at the school site level.
Conclusion
Public schools in the state of California are currently contending with both federal and state
accountability measures that recognize a school’s academic success in often different and
sometimes conflicting manners. The NCLB federal legislation utilizes a status model by
charting a school’s Adequate Yearly Progress (AYP) that increases each year and culminates in
2014 when 100 percent of all students are required to be proficient in ELA and math in state
content standards. California’s accountability system utilizes a performance indicator known as
the Academic Performance Index (API) to rate schools along a scale ranging from 200 to 1,000.
This system uses a growth model for accountability, rewarding schools that display meaningful
PERSONNEL RESOURCE ALLOCATION 135

progress regardless of their starting point. The federal system provides little credit for growth
(Edsource, 2005). These rigorous federal and state accountability measures coupled with a
significant reduction in financial resource allocations to California public schools, creates a sense
of urgency for education leaders to optimize limited human resources toward increasing student
achievement.

This study served to inform education practitioners and policymakers of the actual human
resource allocations of to the 46 elementary schools that participated in the study and how these
allocations compared to the recommendations of the Evidence Based Model and to the desired
district allocations. The allocation gaps that were revealed in the two comparisons were
analyzed utilizing Clark and Este’s (2002) gap analysis framework to determine the causes of the
gaps. A synthesis of the literature reviewed in Chapter 2 consistently revealed the following four
strategies for increasing student achievement: leadership, assessment and data-based decision
making, collaboration, and professional development. The findings from the study can also
inform education leaders of how human resources were allocated across the district of study to
target each of these four areas. An educational leader can utilize the instructional improvement
strategies, resource allocation patterns, and resource reallocation recommendations described in
this study as a guide to utilize when making difficult allocation decisions focused on increasing
student achievement.
In reviewing the findings of this study, the researcher recommends that any school leader
implement Odden and Archibald’s (2009) 10 Strategies for Doubling Student Performance to the
greatest degree possible if not already doing so. A synthesis of the literature reviewed in this
study consistently revealed that the following four strategies support increased student
achievement: leadership, assessment and data-based decision making, collaboration, and
PERSONNEL RESOURCE ALLOCATION 136

professional development. Therefore, if an education leader must prioritize the 10 strategies, the
researcher recommends that these four strategies be the main focus.
The study revealed that the 46 elementary schools participating in this study experienced
significant deficits in the majority of Odden and Picus’ (2008) suggestions of the Evidence-
Based Model for allocating human resources. Many of the Evidence-Based Model allocation
suggestions were proven to assist in increasing student achievement, such as; interventions for
struggling students, time for collaboration among teachers to discuss instruction and monitor
student progress, and providing additional staff to coach teachers and provide support. In light
of the current financial crisis nationwide and especially in California, it is highly unlikely that
any district or school in California will be able to fully fund the model. Therefore, schools
leaders should prioritize their limited resources toward allocations that have proven to increase
student achievement as revealed in the 10 Strategies for Doubling Student Achievement and the
Evidence-Based Model.
PERSONNEL RESOURCE ALLOCATION 137

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Abstract (if available)

Abstract The purpose of this study was to analyze human resource allocation data for all elementary schools in large urban school district to determine whether resources were allocated in ways in that research suggests can lead to improved student achievement. Data from all 46 elementary schools that participated in the study were compared to the allocation recommendations of the Evidence-Based Model to identify allocation gaps. The following four research questions guided this study: (1) What research based human resource allocation strategies improve student achievement? (2) How are human resources allocated across Garden Grove Unified School District’s elementary schools? (3) Is there a gap between current human resource allocation practices and what the research suggests is most effective? (4) How can human resources be strategically re-allocated to align with strategies that improve student achievement? The findings suggest that the 46 elementary schools in Southern California do not have the financial resources available to allocate personnel at the levels recommended by the Evidence-Based Model, therefore educational leaders must be strategic in allocating their limited human resources toward areas that research asserts will directly contribute to increased student achievement. To address this notion recommendations were made to reallocate resources toward increasing the amount of instructional coaches, reassigning credentialed teachers to support “at risk” students, and allocating additional time during the school day for teacher collaboration. Results from this study will support educational leaders in making more informed decisions pertaining to human resource allocation and reallocation to support increased student achievement.

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Asset Metadata

Creator Sais, Melissa Marie (author)

Core Title Personnel resource allocation strategies in a time of fiscal crisis: case study of elementary schools in a California school district

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Defense Date 02/11/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag California school district,Elementary schools,OAI-PMH Harvest,personnel resource allocation strategies

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Picus, Lawrence O. (committee chair), Donavan, Frank (committee member), Escalante, Michael F. (committee member)

Creator Email msais@ggusd.us,sais@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-224222

Unique identifier UC11292805

Identifier usctheses-c3-224222 (legacy record id)

Legacy Identifier etd-SaisMeliss-1465.pdf

Dmrecord 224222

Document Type Dissertation

Rights Sais, Melissa Marie

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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