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Allocating human capital resources for high performance in schools: a case study of a large, urban school district

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Allocating human capital resources for high performance in schools: a case study of a large, urban school district

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Content Running Head: ALLOCATING HUMAN CAPITAL RESOURCES 1
ALLOCATING HUMAN CAPITAL RESOURCES FOR HIGH PERFORMANCE IN
SCHOOLS: A CASE STUDY OF A LARGE, URBAN SCHOOL DISTRICT

by

Jonathan Edward Swanson

A Dissertation Submitted to the
FACULTY OF THE USC ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
in Partial Fulfillment of the
Requirements for the Degree,
DOCTOR OF EDUCATION

May 2013

Copyright 2013 Jonathan Edward Swanson
ALLOCATING HUMAN CAPITAL RESOURCES 2
Dedication
This dissertation is dedicated to my wife, Christina. Thank you for your support,
demonstrated through encouragement, patience, and a listening ear. I also dedicate this
dissertation to my kids, Joshua and Bella. I love you all.

ALLOCATING HUMAN CAPITAL RESOURCES 3
Acknowledgements
I would like to thank Dr. Larry Picus for serving as chair of my dissertation committee
and guiding me throughout the process. Thank you for the time and effort you put into providing
me with valuable and timely feedback.
I would like to thank Dr. Michael Escalante and Dr. Frank Donavan for serving as
members of my dissertation committee. I appreciate the time you have dedicated so that we
might progress in our own knowledge and profession.
I would like to thank my USC cohort and dissertation group. It has been a rewarding
journey to reach this point, and I have enjoyed getting to know each of you. I look forward to
crossing paths again.

ALLOCATING HUMAN CAPITAL RESOURCES 4
Table of Contents
Dedication 2

Acknowledgements 3

List of Tables 6

List of Figures 7

Abstract 4

Chapter 1: Overview of the Study 10
Introduction 10
Statement of the Problem 14
Purpose of the Study 15
Importance of the Study 16
Limitations 17
Delimitations 17
Assumptions 17
Definitions 18

Chapter 2: Literature Review 20
Introduction 20
Effective Practices for Improving Student Achievement 21
Allocation and Use of Human Resources 37
Limited Resources and Fiscal Constraints 51
Gap Analysis 53

Chapter 3: Methodology 55
Introduction 55
Purpose of the Study/Research Questions 56
Sample and Population 57
Instrumentation 58
Data Collection 60
Data Analysis 61

ALLOCATING HUMAN CAPITAL RESOURCES 5
Chapter 4: Findings 64
Introduction 64
Study District Data Overview 64
General Student Population 65
Study District Achievement Data 68
Research Question #1: Odden’s 10 Strategies for Doubling Student Performance 76
Human Resource Allocation Recording Sheet 87
Research Question #2: Study District Human Resource Allocation 88
Research Question #3: Human Resource Allocation Gap 94
Research Question #4: Human Resource Additional Gaps and Tradeoff Proposal 106

Chapter 5: Discussion 117
Purpose of the Study 117
Summary of Study Process 117
Summary of Findings 120
Limitations 123
Concluding Comments 124

References 125

Appendix: Personnel Allocation (by level and school) 132

ALLOCATING HUMAN CAPITAL RESOURCES 6
List of Tables
Table 2.1: 21 Key Leadership Responsibilities Correlated with Higher Student Achievement 29
Table 2.2: Staff Employed in the Public Schools (% by category), 1960–2000 39
Table 2.3: School Expenditure Structure and Resource Indicators 45
Table 2.4: Adequate Resources for Prototypical Elementary, Middle, and High Schools 48
Table 2.5: Estimated Effect Sizes of Major Recommendations 50
Table 4.1: Middle School Personnel Allocation (by Category) 90
Table 4.2: High School Personnel Allocation (by Category) 91
Table 4.3: Alternative School Purpose and Student Population 92
Table 4.4: Alternative School Personnel Allocation (by Category) 93
Table 4.5: All Sample School Personnel Allocation (by Category) 94
Table 4.6: Middle School Personnel Comparison (by Category) 97
Table 4.7: High School Personnel Comparison (by Category) 98
Table 4.8: Alternative School Personnel Comparison (by Category) 100
Table 4.9: All Sample School Personnel Comparison (by Category) 101
Table 4.10: Estimated Effect Sizes of Major Recommendations 109
Table 4.11: Summary of Added Positions 111
Table 4.12: Summary of Reduced Positions 112
Table 4.13: All Sample School Personnel Hypothetical Model (by Category) 114

ALLOCATING HUMAN CAPITAL RESOURCES 7
List of Figures
Figure 1.1: California K-12 Education Revenues 13
Figure 2.1: Professional Community and Teacher Responsibility for Student Learning:
Analytic Framework 37

Figure 2.2: The Evidence-Based Model 47

Figure 2.3: California K-12 Education Revenues 51
Figure 3.1: Visual Model of Gap Analysis 59
Figure 3.2: Visual Model of Data Analysis 62
Figure 4.1: General Student Population by Ethnicity (2011-2012) 65
Figure 4.2: General Student Population by English Learner Status (2011-2012) 66
Figure 4.3: Percent of Students Qualifying for Free and Reduced-Price Lunch (2010-2011) 67
Figure 4.4: ELA CST Results: All Students 69
Figure 4.5: ELA CST Results: Students with Disabilities 69
Figure 4.6: ELA CST Results: English Learners 70
Figure 4.7: ELA CST Results: SES Disadvantaged 70
Figure 4.8: Math CST Results: All Students 71
Figure 4.9: Math CST Results: Students with Disabilities 71
Figure 4.10: Math CST Results: English Learners 72
Figure 4.11: Math CST Results: SES Disadvantaged 72
Figure 4.12: Study District Middle School API Score Longitudinal Summary 74
Figure 4.13: Study District High School API Score Longitudinal Summary 75
Figure 4.14: API Score Longitudinal Summary 76
Figure 4.15: Visual Model of Data Analysis 95

ALLOCATING HUMAN CAPITAL RESOURCES 8
Figure 4.16: Visual Model of Data Analysis 107
Figure 5.1.:Visual Model of Gap Analysis 120

ALLOCATING HUMAN CAPITAL RESOURCES 9
Abstract
This study used qualitative methods to understand the extent to which one district
allocated human resources toward research-based strategies for school improvement. The study
focused on one large urban school district in Southern California. The study district provided
actual human resource allocation data for each of its secondary schools for an examination as to
the manner in which the district has handled a limited budget. The practices of the study district
were viewed through the lens of the Evidence-Based Model (Odden, 2003) and Odden’s (2009)
Ten Strategies for Doubling Student Performance. The human resource allocation outlined in
the Evidence-Based Model was compared to the actual allocation of human resources at the
secondary level in the study district. A gap analysis was conducted to compare three points: the
current allocation of human resources in the study district, the allocation of human resources
according to the specifications of the Evidence-Based Model, and a proposed, hypothetical
allocation of human resources for the study district.
Findings from the study demonstrated alignment between the practices of the study
district and Odden’s (2009) ten research-based strategies for improving student achievement.
Regarding personnel, the study district did not have the level of human resources recommended
by the Evidence-Based Model. The study district was funded at a level significantly below what
is recommended by the Evidence-Based Model. Therefore, the human resource allocation of the
study district was not aligned to the Evidence-Based Model. Even so, recommendations were
made to increase instructional coach and academic extra help positions as well as to reduce the
ratio of specialist to core teachers. The study outlines how the recommendations could be
followed, moving the study district towards the Evidence-Based Model, without incurring
additional costs.
ALLOCATING HUMAN CAPITAL RESOURCES 10
Chapter 1: Overview of the Study
Introduction
Historically, education has been at the forefront of the public mind. Districts, schools,
and teachers are under public pressure to improve the academic achievement of students. The
No Child Left Behind Act (NCLB) was the federal government’s way of standardizing
expectations for states across the country. The act was first proposed in early 2001,
overwhelmingly passed in both the House and the Senate, and, finally, was signed into law in
early 2002. It took less than one year to become a law, partially due to the support it received
from both the Democratic and Republican parties. The most well-known characteristic of NCLB
is the set of proficiency targets which increase each year. While states decide how the
proficiency rates increase, 100% of students are supposed to be proficient or advanced by the end
of the 2013-2014 academic year. To that end, each state also decided on academic standards, an
accompanying standardized test for certain grade levels, and cut rates for proficiency for each of
the tests. In terms of accountability, schools are also required to meet Adequate Yearly Progress
(AYP) for each subgroup that is tested. This requirement brought voice to underperforming, and
sometimes overlooked, subgroups.
The impact of the No Child Left Behind Act on the educational system takes many forms.
Two of the most significant ways that NCLB affected schools is through the development of
state standards and the use of state standardized assessments for accountability. One of the
unique components of NCLB is the flexibility given to the states to decide on the level of rigor
associated with the standards, assessments, and proficiency levels for assessments. In addition,
NCLB requires that all students demonstrate proficiency on set standards by the end of the 2013-
2014 school year.
ALLOCATING HUMAN CAPITAL RESOURCES 11
In terms of intention, the designers of NCLB thought that the implementation of state
standards, standardized tests, and proficiency levels would lead to better educated students. With
the adoption of state standards, each state would decide upon a pre-determined set of knowledge
that each student should gain by the end of the year. Standardized tests would allow for
comparison among counties, districts, schools, grade levels, and teachers, thereby increasing
competition and, hopefully, achievement. This measurement constitutes a driving force for
NCLB; the implied notion is that, if students are tested regularly and the scores are published,
enough pressure will be exerted for teachers, schools, and districts to improve. In addition, the
designers stressed the importance of a criterion-referenced test in which students are not
compared to each other, but, rather, to a level of achievement. Each state also decided on
proficiency levels, which the designers of NCLB thought would serve as a goal for teachers and
students, giving them something to internalize and aim for.
Districts and schools responded to NCLB in the ten years since its implementation in a
variety of ways. Districts generally discussed instructional strategies, professional development,
and academic rigor, among other topics. One topic often left out of this discussion is the way in
which districts spend money. Schools have long focused discussions on school finance in terms
of equity. Others shifted the conversation to adequacy. In other words, debates center upon the
resources necessary to ensure all students achieve at a high level.
Adequacy has traditionally been determined through four distinct methods: 1) the
successful district approach, 2) the cost function approach, 3) the professional judgment
approach, and 4) the evidence-based approach (Odden, 2003; Rebell, 2007). The successful
district approach identifies districts in which students have high levels of standards achievement.
ALLOCATING HUMAN CAPITAL RESOURCES 12
After removing any outlier districts, “it then sets the adequacy level at the weighted average of
the expenditures per pupil of those districts” (Odden, 2003, p. 122).
The cost function approach uses regression analysis with spending per pupil as the
dependent variable and includes student/district characteristics and desired achievement levels as
the independent variables (Odden, 2003). This method produces an average expenditure level
for each student, depending upon the desired level of performance. Both the successful districts
approach and cost function approach determine funding for adequacy. However, they fail to
identify strategies for improving student achievement levels.
The third approach, the professional judgment approach, relies on educational experts to
determine the most effective strategies for K-12 education. The experts then determine the cost
of each of the strategies, yielding a funding cost per pupil. This approach does address the need
for instructional strategies in any improvement process and takes the accompanying cost into
consideration. However, educational experts do not always agree with what strategies are most
effective. In addition, the strategies may or may not be linked to actual student achievement
results.
The fourth approach for determining adequate levels of funding is called the evidence-
based approach. This approach identified a research-based set of effective educational strategies
for use in K-12 districts. Each of the strategies carries an appropriate ratio and/or cost with it.
The expenditures are grounded in research and best practice. For purposes of this study, the
Evidence-Based Model was employed as the method for achieving levels of adequacy.
An adequate level of funding is the desired state for K-12 education. The reality is that
funding is sparse; the economy has not allowed for a fully-funded adequacy model in California.
In fact, California K-12 educational revenues have decreased in recent years. From an operating
ALLOCATING HUMAN CAPITAL RESOURCES 13
funds standpoint, California K-12 education revenues totaled $56.7 billion in 2010-2011, down
from $62.9 billion in 2007-2008 (Edwards, 2011).

Figure 1.1. California K-12 Education Revenues
The economic downturn forced district decision-makers to strategize the best use of
funds; districts and school sites find they have to do more with less. As districts decide how to
best use available funds, questions remain regarding what the research says about the effect that
the allocation of resources has on student achievement. As a result of the economy, districts had
to make difficult decisions related to budgeting and staffing. While the Evidence-Based Model
has, in some respects, been unattainable in past years, it is even less attainable given the
economic downturn.
With districts typically spending 75% to 85% of their budgets on personnel, the
allocation of resources, specifically human resources, becomes vital (Webb & Norton, 2009).
While the Evidence-Based Model may be unattainable, there are elements of human resource
allocation within it that may still be of some use. Districts find funding sources for professional
development, instructional coaches, and tutors. With class size reduction in the balance, the
$53.0
$54.0
$55.0
$56.0
$57.0
$58.0
$59.0
$60.0
$61.0
$62.0
$63.0
$64.0
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Dollars (in billions)
Source: EdSource
ALLOCATING HUMAN CAPITAL RESOURCES 14
strategic use of resources may determine how well districts improve student achievement and,
therefore, meet the demands of NCLB.
Statement of the Problem
Schools and districts face many competing demands. One of the most significant
pressures is a result of NCLB. Schools seek higher test scores in an attempt to appease the
powers that be. Districts examine their policies, practices, and efforts in the pursuit of student
achievement. One of the most significant decisions a district or school can make is determining
how resources should be allocated.
Even though significant amounts of money are invested into the United States’ education
system, the funds have been distributed unfairly and not been effective in their use (Odden,
Monk, Nakib, & Picus, 1995). The amount spent on education varies substantially among states;
however, district spending patterns and allocations within California remain fairly consistent
(Picus, 1993). Within the realm of human resources, districts and schools have choices as to
how they spend their money. These decisions have an impact on daily operations within a
school and district.
Research indicates that best practices exist, related to resource allocation, when it comes
to student achievement (Knight, 2006; Miles & Frank, 2008; Odden, 2009; Odden & Archibald,
2009). Many times, the changes associated with said best practices result in a tradeoff. As an
example, research indicates that instructional coaches and tutors are two positions with the most
impact when it comes to student achievement (Knight, 2006; Odden & Picus, 2008). However,
many districts shy away from spending money in these categories because it often means an
increase in class sizes. Given that reluctance, there is a growing trend in the use of instructional
ALLOCATING HUMAN CAPITAL RESOURCES 15
coaches (Knight, 2006). Some districts choose to exchange lower class sizes for instructional
coaches.
A philosophical dilemma exists. The amount of money available to districts is limited,
yet the demands are many. The public wants higher test scores. Parents want their children to
learn and achieve at high levels. Many people, who would like to keep their jobs, are employed
in positions that have not demonstrated an impact on student achievement. Teachers want
smaller class sizes. Teacher unions want to support teachers with respect to pay and working
conditions. Each of these demands, and others, are taken into account in any human resources
decision.
While research regarding human resource allocation may be clear, it is not possible, in
some cases, to fully fund the suggested models, such as the Evidence-Based Model. Moving
toward the Evidence-Based Model requires exchanges that affect stakeholders in the educational
process. Some of these tradeoffs have a positive impact on student achievement, but it is
difficult to tell which tradeoffs have the greatest impact.
Purpose of the Study
The purpose of this study was to research the extent to which one district allocated
human resources towards research-based strategies for school improvement. Because of the
economic downturn, the study focused on one large urban school district in Southern California,
herein called the study district. The study district provided actual human resource allocation data
for each of its secondary schools for an examination as to how it handles its limited budget.
Comparisons were made between the Evidence-Based Model and the actual allocation of human
resources at the secondary level in the study district.
ALLOCATING HUMAN CAPITAL RESOURCES 16
To that end, this study aimed to provide insight into one district’s management of human
resources and the extent to which it meets the expectations of the Evidence-Based Model. The
data indicate which tradeoffs the study district made in order to design instruction around
strategies that are likely to maximize student achievement as well as which tradeoffs the district
would like made but had not yet been possible. This study aimed to contribute to the field of
research related to allocation of human resources in a fiscally difficult time, specifically with
relation to student achievement.
This study focused on the following questions:
1) What research-based human resource allocation strategies improve student achievement?
2) How are human resources allocated across the study district and its 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?
Importance of the Study
In years prior to this study, revenues for California K-12 school districts declined
(Edwards, 2011). Schools no longer have a cushion of money to rely upon and, in some cases,
experiment with. This study examined one district’s human resource allocation from a research-
based and strategic point of view.
From a foundational perspective, this study aimed to inspect the shift from equity to
adequacy. The study district was analyzed through the lens of the Evidence-Based Model, one
of four significant adequacy methods. This study may help inform leaders of districts and/or
schools who attempt to make the most of their resources, as many districts face decisions related
ALLOCATING HUMAN CAPITAL RESOURCES 17
to funding positions. Policymakers may find the study district data to be informative to their
decision-making, specifically with regards to school finance and funding. Finally, the study will
contribute to the academic literature concerning human resource allocation related to student
achievement.
Limitations
The data collected for use in this study were limited to the secondary schools within one
large urban school district in Southern California. The results from this study may not be
generalizable to other school districts. In addition, the data collected for this study came from
the 2012-2013 school year. The data is not longitudinal, but is, rather, a snapshot of human
resource allocation. The data do not demonstrate trends or patterns over time.
Delimitations
The delimitations of this study were related to the study district. The study district was
selected due to geographic location and its standing as a large urban school district. The design
of this study included one district and its schools. The data collected were limited to human
resource allocation; data related to resource allocation for other expenditures was not collected.
As a result of these limitations and delimitations, the results may only apply to districts in with
similar standing, funding, and pressures.
Assumptions
This study was based on several underlying assumptions. The first assumption was that
the human resource data provided by the study district was accurate and complete. The second
assumption was that the actual personnel positions match those reported by the study district.
The third, and final, assumption was that the study district’s first priority was to improve student
achievement.
ALLOCATING HUMAN CAPITAL RESOURCES 18
Definitions
Adequacy: The concept of providing educational programs and services so that all, or almost all,
children have an equal opportunity to meet high learning goals (Odden et al., 2005).
Adequate Yearly Progress (AYP): AYP is a set of annual goals based on the performance of each
school, district, and the State of California. Each of these entities met AYP if they met the
annual performance goals (California Department of Education, 2010).
Allocation: To set aside for a particular purpose.
California English Language Development Test (CELDT): “The California English Language
Development Test (CELDT) is given as an initial assessment to newly enrolled students whose
primary language is not English and as an annual assessment to English learners enrolled in
transitional kindergarten through grade twelve in California public schools” (Educational Data
Systems, 2012).
Equity: Equality and fairness.
Expenditure: A cost; an amount of money that is spent.
Formative Assessment: “All those activities undertaken by teachers, and/or by their students,
which provide information to be used as feedback to modify the teaching and learning activities
in which they are engaged” (Black & William, 1998, p. 8).
Human Resources: Individuals who work for a business or organization.
Instructional Coach: “An on-site professional developer who teaches educators how to use
proven teaching methods” (Knight, 2005, p. 17).
Leadership: “A process whereby an individual influences a group of individuals to achieve a
common goal” (Northouse, 2010, p. 3).
ALLOCATING HUMAN CAPITAL RESOURCES 19
NCLB (No Child Left Behind Act of 2001): Also known as the Elementary and Secondary
Education Act (ESEA), this legislation intends to increase achievement through standards-based
reform and a focus on assessment and accountability.
Recalibration: Adjusting the basket of educational goods and determining the cost of providing
them for all public school children (Odden et al., 2005).
Reliability: The extent to which an assessment gives consistent results.
Summative Assessment: All those activities undertaken by teachers, and/or by their students,
which provide information as to what students know or do not know.
Validity: The accuracy of an assessment; the extent to which an assessment measures what it was
designed to measure.

ALLOCATING HUMAN CAPITAL RESOURCES 20
Chapter 2: Literature Review
Introduction
Chapter two of this study contains the literature review. The literature review was
designed to explore previous research in relation to improving student achievement, human
resource allocation, the financial state at the time of this study, and gap analysis. These general
topics were chosen because of their significance to the study. The review of literature provides
the background to contextualize the study.
This chapter contains four sections specifically outlining the following topics: effective
practices for improving student achievement, allocation and use of human resources, limited
resources and fiscal constraints, and gap analysis. Each of these areas is a distinct topic or area
of research related to the utilization of resources. Some of the topics have a direct and
immediate relationship with the allocation of human resources.
The first section, titled “Effective Practices for Improving Student Achievement” is a
broad overview of specific practices that affect student results in a positive way. This section
also investigates three of the means by which effective practices are achieved, namely
leadership, professional development, and professional learning communities. This section
outlines research related to each of these three areas.
The second section discusses the allocation and use of human resources. A history of
school expenditures is provided with reference to changes in resource levels and in the use of
resources. In addition, the four primary methods for determining adequacy are defined and
described. Last is a discussion of the rationale behind the selection of the method that serves as
the comparison model for the purposes of this study: the Evidence-Based Model.
ALLOCATING HUMAN CAPITAL RESOURCES 21
The third section is titled “Limited Resources and Fiscal Constraints.” This section
places a lens on the financial situation in the state of California. In addition, this section reviews
the role of federal and state funding. District and local constraints are also described.
The fourth, and final, section in this chapter reviews Clark and Estes’ (2008) gap analysis
process. Specifically, the section explores the causes of gaps and describes the process for
arriving at a systematic solution. The gap analysis process was used multiple times within this
study; the fourth section presents examples of gap analysis.
Effective Practices for Improving Student Achievement
This section describes effective practices for improving student achievement. Authors
and researchers present different beliefs as to improving student outcomes. Professional
developers, consultants, and educational leaders subscribe to a variety of strategies for raising
results. For this reason, it is important for the study to be grounded in research, especially in
examining the global and general topic at hand. Within this section, the focus is not on the
specific allocation of human resources, but, rather, on a broad set of school improvement
strategies.
Odden’s 10 Strategies for Doubling Student Performance. Odden presented ten
research-based strategies known to boost student achievement (Odden, 2009; Odden &
Archibald, 2009). These strategies were discovered through an examination of schools that
showed great improvement and their commonalities. The ten strategies are:
1) Understanding the performance problem and challenge
2) Set ambitious goals
3) Change the curriculum program and create a new instructional vision
4) Formative assessments and data-based decision making
ALLOCATING HUMAN CAPITAL RESOURCES 22
5) Ongoing, intensive professional development
6) Using time efficiently and effectively
7) Extending learning time for struggling students
8) Collaborative, professional culture
9) Widespread and distributed instructional leadership
10) Professional and best practices
The first strategy, “understanding the performance problem and challenge,” speaks to the
importance of knowing the situation at hand. In many cases, this means looking at student data,
particularly the state test results. The data are analyzed at multiple levels, including student
subgroup results.
The second strategy deals with setting ambitious goals. Odden provided three guidelines
regarding ambitious goals (2009): 1) the successful districts and schools set high goals, beyond
what most would know as “stretch” goals, 2) the goals that were written applied to all students
and all subgroups, and 3) the schools that did not achieve their goals still showed significant
improvement.
The next strategy relates to changing the curriculum program and creating a new
instructional vision. In this study, the represented schools that showed the most improvement
chose or created a new curriculum, generally in the areas of math or language arts. This allowed
these schools to move past some of the limitations of the prior programs. In addition, the schools
and districts began to articulate what good instruction looks like. This shared vision of
instruction gave these schools a common base for dialogue and decision making.
“Formative assessments and data-based decision making,” the fourth strategy, play a
crucial role in school improvement. Formative assessments are assessments that inform the
ALLOCATING HUMAN CAPITAL RESOURCES 23
teachers of students’ strengths and misconceptions. Most importantly, teachers use the results of
these assessments to adjust instruction, which, by nature, means making decisions based on data.
Formative assessments can take many forms. Some of the more common forms, when used to
inform instructional decisions, are benchmark exams and common assessments.
The fifth strategy is “ongoing, intensive professional development.” In each of the
models, professional development was used to implement the other nine steps presented above.
Odden and Archibald (2009) outlined three primary costs of professional development:
professional development days without students, trainers, and instructional coaches.
Instructional coaches play a critical role in professional development.
The next strategy involves the efficient and effective use of time. This strategy entails
designing an effective master schedule, both in terms of the time students have in core classes as
well as designated time for teacher collaboration. At the elementary level, this may include class
size reduction.
“Extending learning time for struggling students” is the seventh strategy. This extended
time comes in several forms: during the school day, before or after school, on Saturdays, and
during summer school. Each of these options requires a clear, academic focus and is meant as
intervention when students struggle with a particular topic or content area. Because time is a
fixed resource, adding instructional time for a student outside of the traditional school day can
help increase achievement.
The eighth strategy entails establishing a collaborative, professional culture. This is
sometimes known as a “professional learning community.” Teams of teachers work together to
improve instruction by planning lessons, talking about instruction and best practice, creating
formative common assessments, using the results of the assessments to drive instruction, and
ALLOCATING HUMAN CAPITAL RESOURCES 24
working as a team. One of the byproducts of creating a collaborative, professional culture is the
deprivatization of instruction.
The ninth strategy is the implementation of “widespread and distributed instructional
leadership.” Because the principal cannot usually provide all of the necessary leadership by
him/herself, it is important to build capacity and groom leaders from within the school. Different
members of the school assist in leading through distinct avenues. Instructional leadership may
be embodied in department chairs, instructional coaches, grade-level leads, or other teachers
without an official role or title. The schools described by Odden and Archibald (2009) built
capacity and distributed leadership, and, thereby, they created a strong and collaborative culture.
The tenth, and final, strategy is “professional and best practices.” This strategy refers to
the importance of using research-based instructional strategies. Often, when faced with a
decision, school leaders may not know the most appropriate action to take. Leaders of schools
with improved achievement results look outside of themselves for research and best practices.
These schools use professional literature and experts to help guide their actions.
Strategies for improving low-performing schools. Duke (2006) reviewed five critical
studies related to improving low-performing schools. After reviewing the studies, Duke
compiled a list of the most common strategies or factors that play a part in schools’ turnaround
processes. The strategies are listed below:
1) Assistance
2) Collaboration
3) Data-driven Decision Making
4) Leadership
5) Organizational Structure
ALLOCATING HUMAN CAPITAL RESOURCES 25
6) Staff Development
7) Alignment
8) Assessment
9) High Expectations
10) Parent Involvement
11) Scheduling
Several of the strategies or factors presented by Duke overlap with Odden’s 10 strategies.
The first strategy, titled “Assistance,” refers to the school’s being responsive to students who
experience difficulty with learning. These schools do not wait for report cards or parent
meetings, but find ways to respond and assist immediately. “Collaboration” means teachers
work as a team in order to plan effective lessons and provide the assistance listed as the first
strategy. Collaboration is one of the building blocks of professional learning communities.
“Data-driven Decision Making” refers to the way decisions are made. In a school setting,
it is not uncommon for decisions to be made based upon feel or personal experience.
Notwithstanding these approaches, this strategy refers to the importance of using data to ground
decisions in what is proven to work. In a school setting, the most common area for making
decisions is instruction. The use of formative assessments assists with instructional decisions,
especially in determining whether students learned a particular topic.
“Leadership” is the next factor related to school improvement. Leadership sets the stage
for all other progress to be made. Leadership is also nebulous; it is often not prescribed or exact.
Even so, according to Duke (2006), it is one of the critical factors. The school’s organizational
structure has to do with how teams are assembled, who plays a leadership role, and what systems
are in place to support improvement efforts.
ALLOCATING HUMAN CAPITAL RESOURCES 26
“Staff Development” is another critical component of the improvement process. Staff
development refers to the training that teachers and other staff receive in order to perform their
job well. Staff development, in this context, is ongoing in nature.
In a school seeking improvement, curriculum, instruction, and assessment should be
aligned. In California, at the time of this study, alignment to the state standards was critical for
student achievement purposes, and students were assessed regularly at the school site.
Assessment results lead to decisions about instruction and support. Without regular assessment,
these decisions are uninformed and less strategic.
Duke (2006) also spoke to the importance of high expectations and parent involvement.
At a school that demonstrates high expectations, teachers believe that students can complete
rigorous work. Therefore, the teachers support the students as necessary in order to reach that
expectation. Parents are also involved in their children’s academics. These parents are
supportive of the school’s efforts and help their children succeed.
The final strategy relates to scheduling. Those who maximize the outcomes of this
strategy create a master schedule that supports the core subjects, emphasizing language arts and
mathematics. Other shifts in teacher schedules, preparation periods, and teaching assignment
can also have a major effect on student achievement.
90/90/90 schools. The term “90/90/90 schools” originates from the work of Doug
Reeves (2003). Reeves studied schools in which 90% of the students were eligible for free or
reduced-price lunch, 90% of the students were minority students, and 90% of students
demonstrated proficiency. As he studied these “90/90/90 schools,” several commonalities
surfaced. These five common characteristics are known to be positive indicators of student
achievement. Reeves (2003) described the characteristics (p. 3):
ALLOCATING HUMAN CAPITAL RESOURCES 27
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
5) Collaborative scoring of student work
The first of the five characteristics, which focuses on academic achievement, refers to the
schools’ emphasis on high quality work and evidence of student achievement. In these schools,
improvement is valued and students receive the message that effort is important. Teachers at
90/90/90 schools are well-versed in student and classroom data.
The next characteristic involves making “clear curriculum choices.” The 90/90/90
schools place emphasis on the core subjects, specifically reading, writing, and mathematics.
This emphasis is displayed in the form of allocation of instructional minutes. Many times, this
decision resulted in high scores in other areas as well.
Frequent assessment combined with multiple opportunities for improvement is another
characteristic of 90/90/90 schools. Teachers in these schools do not move on to the next unit
after each assessment. Rather, they treat the assessments as formative assessments, using the
data to inform remediation. The students who fail an assessment in these schools have multiple
opportunities to display proficiency. In other words, students may not do well on the first try,
but the timing of proficiency does not matter as much as the eventual achievement of
proficiency.
Nonfiction writing is the fourth characteristic of these effective schools. Teachers in
these schools regularly incorporate informative writing and score the writing with the use of a
common rubric. Teachers expect high quality writing, regardless of the content area, and yield
ALLOCATING HUMAN CAPITAL RESOURCES 28
benefits such as clarification of the student’s thoughts and thought process as well as diagnostic
information provided to the teacher through a written response, as opposed to a multiple choice
answer.
The fifth characteristic is the “collaborative scoring of student work.” In 90/90/90
schools, as assessments or writing assignments were given, it is not uncommon for teachers to
grade each other’s’ papers in order to increase “inter-rater reliability.” Inter-rater reliability
demonstrates that a student earns the same grade regardless of whose class he or she is in.
Besides the use of a rubric, this process helps teachers articulate the desired outcomes for an
assignment.
With reference to the five characteristics described above, Reeves (2003) referred to the
techniques implemented by these schools as persistent, replicable, and consistent. These schools
persist, as do the students within them. The techniques are replicable; other schools can
implement the same techniques if they so desire. Last, these schools are consistent in their
approach. There is no effort to jump from initiative to initiative. These schools chose to work at
what they do and have been rewarded because of that.
Leadership. In two of the three cited sources outlining effective strategies for
instructional improvement, leadership was a critical factor (Odden, 2009; Duke, 2006). Many
theories of leadership exist and are touted in both business and social sectors. Many believe that
leadership is one of the most important attributes in an organization (Bolman & Deal, 2008;
Kouzes & Posner, 2008; Marzano, Waters, & McNulty, 2005; Northouse, 2010). This section
summarizes key points from four books about leadership; each provides unique perspective
about the topic.
ALLOCATING HUMAN CAPITAL RESOURCES 29
Marzano, Waters, and McNulty (2005) studied leadership as it relates to correlation with
higher student achievement. They concluded that there are 21 “key leadership responsibilities”
that relate to achievement. Table 2.1 provides a complete list as well as each responsibility’s
effect. The five most effective responsibilities are situational awareness, flexibility, discipline,
monitoring/evaluation, and outreach.
Table 2.1
21 Key Leadership Responsibilities Correlated with Higher Student Achievement
Responsibility The extent to which the
principal…
Average
r
Number of
Studies
Number of
Schools
Situational Awareness Is aware of the details and
undercurrents in the
running of the school and
uses this information to
address current and
potential problems
.33 5 91
Flexibility Adapts his or her
leadership behavior to the
needs of the current
situation and is comfortable
with descent
.28 6 277
Discipline Protects teachers from
issues and influences that
would detract from their
teaching time or focus
.27 12 437
Monitoring/Evaluation Monitors the effectiveness
of school practices and
their impact on student
learning
.27 31 1129
Outreach Is an advocate and
spokesperson for the school
to all stakeholders
.27 14 478
Change Agent Is willing to and actively
challenges the status quo
.25 6 466
Culture Fosters shared beliefs and a
sense of community and
cooperation
.25 15 809

ALLOCATING HUMAN CAPITAL RESOURCES 30
Table 2.1, continued
Input Involves teachers in the
design and implementation
of important decisions and
policies
.25 16 669
Knowledge or
Curriculum,
Instruction, and
Assessment
Is knowledgeable about
current curriculum,
instruction, and assessment
practices
.25 10 368
Order Establishes a set of
standard operating
procedures and routines
.25 17 456
Resources Provides teachers with
materials and professional
development necessary for
the successful execution of
their jobs
.25 17 571
Contingent Rewards Recognizes and rewards
individual
accomplishments
.24 9 465
Focus Establishes clear goals and
keeps those goals in the
forefront of the school's
attention
.24 44 1619
Intellectual
Stimulation
Ensures that faculty and
staff are aware of the most
current theories and
practices and makes the
discussion of these a
regular aspect of the
school's culture
.24 4 302
Communication Establishes strong lines of
communication with
teachers and among
students
.23 11 299
Ideal Beliefs Communicates and
operates from strong ideals
and beliefs about schooling
.22 7 513
Involvement in
Curriculum,
Instruction, and
Assessment
Is directly involved in the
design and implementation
of curriculum, instruction,
and assessment practices
.20 23 826
Visibility Has quality contact and
interactions with teachers
and students
.20 13 477

ALLOCATING HUMAN CAPITAL RESOURCES 31
Table 2.1, continued
Optimizer Inspires and leads new
challenging innovations
.20 17 724
Affirmation Recognizes and celebrates
school accomplishments
and acknowledges failures
.19 6 332
Relationships Demonstrates an awareness
of the personal aspects of
teachers and staff
.18 11 505
Source: School Leadership that Works: From Research to Results (Marzano, Waters, & McNulty
2005)
An additional perspective on effective leadership is provided by Kouzes and Posner
(2008). These authors stressed the importance of five specific practices that leaders engage in: 1)
model the way, 2) inspire a shared vision, 3) challenge the process, 4) enable others to act, and 5)
encourage the heart. While these tend to be business-based leadership attributes, several of the
practices align with the responsibilities presented in Table 2.1.
Bolman and Deal (2008) described four leadership categories, or styles. The four
leadership categories presented by Bolman and Deal are structural, human resource, political,
and symbolic. Each of these categories is unique in its approach to leadership. The structural
leader is analytical and understands structure and systems. The human resource leader looks to
hire good people and motivate them through relationships. The political leader uses his/her
network to navigate conflict and fight for resources. The symbolic leader casts vision, inspiring
others through stories and charisma.
Northouse (2010) described leadership through the lens of one’s traits. The theory
behind this viewpoint is that leaders are special people who stand out because of their traits.
However, the question then becomes, “What are the traits of a leader?” Northouse gave an
example of a traits test called the “Leadership Trait Questionnaire,” which is a self-appraisal of
fourteen traits commonly associated with leadership. The fourteen leadership traits are listed
below:
ALLOCATING HUMAN CAPITAL RESOURCES 32
1) Articulate
2) Perceptive
3) Self-Confident
4) Self-Assured
5) Persistent
6) Determined
7) Trustworthy
8) Dependable
9) Friendly
10) Outgoing
11) Conscientious
12) Diligent
13) Sensitive
14) Empathic
The authors cited in this section provided varying perspectives on leadership, as is
gleaned from the examples. These leadership qualities, characteristics, responsibilities, and traits
may assist leaders in their quest to improve student achievement through the use of effective
strategies.
Professional development. NCLB affected education in many ways. Two of those
ways are through the articulation of standards and the increase of applied accountability.
Because of these, schools had to change the way they do business. Teachers required a new
skillset and, often, new knowledge. The path to this knowledge is through professional
development, which is a central component to improving student achievement
ALLOCATING HUMAN CAPITAL RESOURCES 33
As one of the principal vehicles used for improvement of instruction, professional
development is costly (Corcoran, 1995). Garet, Porter, Desimone, Birman, and Yoon (2001)
estimated the cost of high quality professional development to be approximately $512 per
teacher. Because of district accounting systems, and the variety of methods used to measure the
cost of professional development, many districts actually underestimate the amount of money
they spend on professional development (Miles, Odden, Fermanich & Archibald, 2004; Odden,
Archibald, Fermanich, & Gallagher, 2002). Also, the effects of professional development can be
difficult to measure due, in part, to the fact that approximately 82% of districts do not collect
data regarding whether their professional development is effective (Desimone, Porter, Garet,
Yoon, & Birman, 2002).
The research on professional development shows the importance of several factors: form,
duration, collective participation, content, active learning, and coherence (Birman, Desimone,
Porter & Garet, 2000; Supovitz & Turner, 2000). Birman et al. used each of these factors to
describe effective professional development (2000). In form, professional development should
be supported by coaching, observations, and modeling. The results of professional development
are amplified when accountability is an active part of the process (Desimone, Porter, Birman,
Garet, & Yoon, 2002). In addition, the duration of professional development should be
extended. When considering participation in professional development, it is best if teams of
teachers participate in the program, whether as an entire school team, a course team, a grade
level team, or a department. In terms of content, professional development is most effective
when it focuses on a content area as opposed to general teaching strategies. Similar to students,
adult learners learn best when professional development incorporates elements of active learning.
Last, program coherence allows for relationships between the learning and what is being done at
ALLOCATING HUMAN CAPITAL RESOURCES 34
the site. The content relies on other elements of the training and the real world in order to draw
connections.
Elmore and Burney offered several distinct models for professional development
including a “professional development laboratory,” outside instructional coaching, visits between
classes and schools, and training offsite (2002). The most innovative of these ideas is the
professional development laboratory, or PDL. In the PDL, teachers allow other teachers to visit
them for an extended amount of time. In this model, teachers spend time working together and
discussing instructional practice. In some ways, this model parallels the lesson study model.
Joyce and Calhoun (1996) suggest that professional development include an inquiry process with
a lens on effective curriculum and instruction. This results in increased teacher buy-in as well as
student achievement.
Joyce and Showers (2002) listed four key stages, or components, of professional
development: developing knowledge, demonstrating a particular skill, practicing the skill, and
participating in peer coaching. This model provides the initial training, opportunities to practice,
and support through a coaching model. Professional development efforts should strive to include
these four elements in order to maximize success.
Professional Learning Communities. Professional Learning Communities (PLCs) are
viewed as one of the principal ways that a school can harness its resources to affect student
achievement in a collaborative fashion. The professional literature narrowed down Professional
Learning Communities to six characteristics (DuFour & Eaker, 1998; DuFour, DuFour, Eaker &
Karhanek, 2004):
1) Shared mission, vision, and values
2) Collective inquiry
ALLOCATING HUMAN CAPITAL RESOURCES 35
3) Collaborative teams
4) Action orientation and experimentation
5) Continuous improvement
6) Results orientation
“Shared mission, vision, and values” refers to a commitment to what the school site
articulated as a direction for improvement. In line with this commitment, the entire staff
understands these foundational beliefs and supports them as a whole. In many cases, the
mission, vision, and values are created together, rather than being handed down by leadership.
“Collective inquiry” is a mindset. The mindset of collective inquiry seeks to challenge
current practice in order to find another, more effective, practice. Professional learning
communities shift the way business is done by continually seeking out best practice.
One of the hallmarks of Professional Learning Communities is collaborative teams.
PLCs understand that the work is better done in a collaborative manner as opposed to
individually. Working together also allows for systematic change and learning on a more
effective level.
“Action orientation and experimentation” speaks to the importance of doing rather than
talking. Professional Learning Communities continually act, try new things, and do not remain
stagnant. The members of PLCs refuse the tendency to stop and talk about proposed actions for
long periods of time. Rather, they prefer to get ideas on the table and try them out. As a result,
some of the attempted actions fail, providing group members with experience and added
knowledge.
The fifth characteristic is “continuous improvement,” and works hand in hand with some
of the previous characteristics. Those who value continuous improvement are not satisfied with
ALLOCATING HUMAN CAPITAL RESOURCES 36
the status quo, regardless of how well the organization performs. “Continuous improvement” is
a commitment to a continual process of searching for improving the way things are done.
The final characteristic is “results orientation.” A results orientation looks at available
data to guide the decision-making process. Decisions are not made by gut feeling or by what
feels right. Rather, after an initiative is tried or a risk is taken, members of a PLC look at the
results of the action. If the results/data affirm the decision, the action continues. If the
results/data do not affirm the decision, the action is adjusted or left behind.
Other researchers also supported the notion of professional learning communities/
professional community. Findings were that both professional community and social support for
achievement have a positive relationship with student performance (Louis & Marks, 1998).
According to Louis, Marks, and Kruse (1996), the five pillars of professional community are
shared values, a focus on student learning, collaboration, deprivatized practice, and reflective
dialogue. Some of the factors that support school-wide professional community are working in
concert, structural conditions (size, part/time faculty, collaboration time built into schedule,
flexible governance) and human/social resources. Figure 2.1 provides a framework for
professional community in relation to these factors.

ALLOCATING HUMAN CAPITAL RESOURCES 37

Figure 2.1. Professional Community and Teacher Responsibility for Student Learning: Analytic
Framework. Source: Louis, K., Marks, H., & Kruse, S.D. (1996).
Allocation and Use of Human Resources
Allocation and use of human resources is a broad topic. This study examined the history
of resources and expenditures in education. In addition, the study reviewed adequacy as it
relates to human resource allocation and the four principal methods of determining adequacy
with a focus on the Evidence-Based Model. The primary focus of the Evidence-Based Model,
within this study, was the allocation of personnel.
History of expenditures. Spending in education changed over the 100 years prior to this
study. Funding also changed during that time period. During the century between 1890 and
1990, spending on education increased an average of 3.5% per year (Hanushek & Rivkin, 1997).
Hanushek and Rivkin (1997) found that, during the same time frame, spending rose from $2
billion, or $164 per pupil, in 1890, to $187 billion, or $4,622 per pupil in 1990. As of 2010, that
ALLOCATING HUMAN CAPITAL RESOURCES 38
number had risen to a nationwide average of $10,586 per pupil (National Education Association,
2010).
With such a significant raise in expenditures over 100 years, questions arise as to how
increases in expenditures were allocated. In their work, Hanushek and Rivkin (1997) also
explored three major time periods in which critical shifts occurred. The three time periods are
“the great expansion” (1890-1940), “the baby-boom” (1940-1970), and “the great
intensification” (1970-1990).
During the great expansion and the fifty years allocated to the time period, the school
year increased by 40 days, and the enrollment rate, school age population, and percent of
students attending public school all rose. These shifts accounted for an increase of 12.7 million
students. The end of the great expansion led into the baby-boom. During the baby-boom era,
instructional staff pay almost doubled. In 1940, instructional staff earned approximately $83 per
day. In 1970, pay increased to approximately $155 per day. The great intensification was the
next time period, covering the years from 1970 to 1990. During the great intensification,
instructional per-day staff pay dropped by ten dollars between 1970 and 1980. Then, between
1980 and 1990, per-day pay for instructional staff increased by $40. This sudden increase over a
short period of time placed pressure on educational finance systems. Odden et al. (1995) found
that, by the end of the 1980s, the Federal portion of education revenues declined from 8% to
6.5%. Since the passage of NCLB, that amount increased to approximately 7%. Table 2.2
displays the numbers of staff employed in public schools, as a percent by category, at the decade
marks between 1960 and 2000.

ALLOCATING HUMAN CAPITAL RESOURCES 39
Table 2.2
Staff Employed in the Public Schools (% by category), 1960–2000
Staff 1960 1970 1980 1990 2000
District
administrators
2.0% 1.9% 1.9% 1.7% 1.7%
Instructional staff 69.8 68.0 68.6 67.9 67.9
Site administrators 3.0 2.7 2.6 2.8 2.5
Teachers 64.8 60.0 52.4 53.4 51.6
Teacher aides — 1.7 7.8 8.8 11.2
Counselors 0.8 1.7 1.8 1.8 1.7
Librarians 0.8 1.3 1.2 1.1 0.9
Support staff 28.1 30.1 29.5 30.4 30.4
Source: Odden & Picus (2008) from National Center for Education Statistics, 2006, Table 80.
Retrieved from nces.ed.gov/programs/digest/d05/tables/dt05_080.asp on July 25, 2006.
In more recent years, the increase in funding was allocated to additional teaching staff,
for the purpose of elective classes, lower class sizes, or other services. Special education
services also increased. Teacher salaries grew, although not at the pace of those provided in
other similar occupations (Odden & Picus, 2008). In another study, Hannaway, McKay, and
Nakib (2002) reviewed school expenditure data from the 1990s. The data showed that, of the
increased revenues, a limited amount was allocated to instructional improvement and support.
The largest increase was to pupil support services, including special education. In that time,
district administration decreased. Lankford and Wyckoff (1995), in an additional study
completed in New York, reported that special education services were the recipients of the
largest increase in funding during the same approximate time period.
Odden and Picus (2008) summarized eight key findings regarding allocation of resources
at the school site level. The first finding was the previously discussed increase in resources from
ALLOCATING HUMAN CAPITAL RESOURCES 40
1890 through 1990. The second finding was a recommendation of use for educational resources.
Odden and Picus suggested that 30% to 40% of resources be allocated to core classes,
professional development, and a site principal. Another 30% to 40% should be used for support
services for students. The remaining 20% to 30% should be spent on central administration,
operations, transportation, and other behind-the-scenes systemic support. The third finding was
that the majority of additional dollars given to education were used to increase educational
services outside of the core program. Because of this, teacher salaries did not rise at the same
rate as similar non-education professions. The fourth finding was similar to the third finding:
much of the new funding given to schools was put toward special education, planning periods,
non-core subjects, and reducing class sizes. Related to this point, it is apparent that special
education, planning periods, and non-core subjects received more resources than class size
reduction. This was the fifth finding. The sixth finding was that those students who received
pull-out services through special education did not show an increase in results. The seventh
finding was related to professional development; several large districts spent more than
suggested on professional development, some up to $5,000 per teacher. The eighth, and last,
finding was that trend data was available for the study of resources, and more detailed staffing
information is necessary in order to arrive at this level of analysis.
Adequacy. As was previously outlined in Chapter 1 of this study, adequacy has
traditionally been determined through four distinct methods: 1) the successful district approach,
2) the cost function approach, 3) the professional judgment approach, and 4) the evidence-based
approach (Odden, 2003; Rebell, 2007). The successful district approach identifies districts in
which students have high levels of standards achievement. After removing outlier districts, “it
ALLOCATING HUMAN CAPITAL RESOURCES 41
then sets the adequacy level at the weighted average of the expenditures per pupil of those
districts” (Odden, 2003, p. 122).
The cost function approach uses regression analysis with spending per pupil as the
dependent variable and includes student/district characteristics and desired achievement levels as
the independent variables (Odden, 2003). This method produces an average expenditure level
for each student, depending upon the desired level of performance. Both the successful districts
approach and the cost function approach determine funding for adequacy. However, they fail to
identify strategies for improving student achievement levels.
The third approach, known as the professional judgment approach, relies on educational
experts to determine the most effective strategies for K-12 education. The experts then
determine the cost of each of the strategies, yielding a funding cost per pupil. This approach
does address the need for instructional strategies in any improvement process and takes the
accompanying cost into consideration. However, educational experts are not always in
agreement with what strategies are most effective. In addition, the strategies may or may not be
linked to actual student achievement results.
The fourth approach for determining adequate levels of funding is called the evidence-
based approach. This approach identified a research-based set of effective educational strategies
for use in K-12 districts. Each of the strategies carries an appropriate ratio and/or cost with it.
The expenditures are grounded in research and best practice.
One example of a position from the Evidence-Based Model that is underutilized in
education, yet grounded in research, is the instructional coach position. Because of its
significance and potential impact within the Evidence-Based Model, and for achieving adequacy,
further research on the position is presented below.
ALLOCATING HUMAN CAPITAL RESOURCES 42
The instructional coach position is one that can lead to an increase in student
achievement. Odden and Picus (2008) note that research found an effect size of between 1.25
and 2.71 for improving student learning in schools that use instructional coaches. It must be
made clear that these effect sizes do not happen by simply placing an instructional coach on a
campus. Rather, the instructional coach performs specific tasks to reinforce professional
development and support teachers.
With relation to professional development, the term “instructional coach” can have
multiple meanings. The role and purpose of an instructional coach is often defined by the site
principal and often changes in meaning from school to school. It is for this reason that it is
necessary to define the term. Dennen (2003) described coaches as “people who are hired to
provide guidance on a particular task at the individual or organizational level” (p. 817). Knight
(2005) described an instructional coach as “an on-site professional developer who teaches
educators how to use proven teaching methods” (p. 17). This second definition includes the
function of the coach and his/her primary activity.
As a general note, an instructional coach is only as effective as the activities he or she
completes. At the same time, those who seek to hire for the position should look for a set of
skills and qualities that will assist the coach in the successful completion of his or her duties.
Two of these qualities are the demonstration of success in a classroom and the ability to build
relationships. The chances of an instructional coach’s being successful are greatly diminished
when the wrong person is hired for the job. Collins (2001) refers to this concept as having the
right people on the bus. In other words, it is critical to have the right personnel for the job before
proceeding. In a position like instructional coach, it is that much more crucial to have the right
person in the position because of the impact that person will have on the school site.
ALLOCATING HUMAN CAPITAL RESOURCES 43
Odden and Picus (2008) described the effect size of having instructional coaches to be a
robust number between 1.25 and 2.71. These numbers rely on the coaches’ delivering and
supporting professional development. One research study from Kansas showed an 85%
implementation rate for staff development participants who had the support of an instructional
coach (Knight, 2006). In contrast, Showers and Joyce (1996) asserted that traditional inservices
without support typically resulted in a 10% implementation rate. The traditional form of
professional development, centered on single inservices, is ineffective when related to student
achievement results (Knight, 2006). An 85% implementation rate builds the case for
understanding how those who are successful in this role use their time and energy. In other
words, it makes sense to find out what successful instructional coaches do.
One of the primary responsibilities of an instructional coach is the support, and
sometimes delivery, of professional development. The characteristics of effective professional
development include teacher collaboration, active learning, goal-setting aligned with student
achievement, content related to student thinking, instructional strategies, curriculum, and a time
for observation and reflection.
Odden, Picus, and Fermanich (2003) explained the primary responsibilities of coaching
to be providing job-embedded professional development, mentoring, and assisting with the
coordination of an instructional program. Professional development should support an
established instructional focus aligned to standards and assessment. While the most important
factor related to students’ achieving at a higher rate is the teacher, professional development is
significant because it affects the way teachers teach. Instructional coaching has an impact on
attitudes, practices, belief systems, efficacy, and, ultimately, student achievement (Cornett and
Knight, 2008).
ALLOCATING HUMAN CAPITAL RESOURCES 44
Knight (2004) outlines the key responsibilities of the instructional: 1) “Meet with
departments or teams,” 2) “Meet one-on-one with interested teachers,” 3) “Work on real
content,” 4) “Model lessons in each teacher’s classroom,” 5) “Make it as easy as possible,” and
6) “Respond quickly to teacher requests” (p. 33-34). On a broader scale, Wong and Nicotera
(2003) list several areas of support necessary for a successful coaching model. These areas
include establishing relationships of trust, receiving administrative support, building a need for
improvement, having clear expectations, measuring the success of the model, and funding for
training, personnel, and release time.
Due to the economic climate, the strategic use of instructional coaches became more
relevant. Picus, Odden, and Goetz (2009) referred to professional development as one of the
“critical components of a successful school” (p. 21). Garet, Porter, Desimone, Birman, and
Yoon (2001) asserted that effective professional development is expensive, with an estimated
expense of approximately $512 per teacher. Schools can save financial resources by asking
instructional coaches to provide professional development and support these initiatives from
within. This not only serves the schools and district by saving money, but the instructional
coaches/leaders can develop their skills and hone their craft. The development and growth of
district personnel serves both the individual and the district in the long run.
At times, positions such as the instructional coach come at the expense of other district
initiatives. One of the district initiatives often in competition for funding with the instructional
coach position is class size reduction. Odden and Picus (2011) reported the results of the
Tennessee STAR experiment, an experiment of class size reduction. The experiment found that
class size reduction was beneficial only for students in grades K-3.
ALLOCATING HUMAN CAPITAL RESOURCES 45
From a financial perspective, one of the principal methods of measuring priorities is
through district or school expenditures. Table 2.3 presents a list of expenditure categories and
indicators is outlined. These expenditure categories allow for analysis of both the priorities of
the school and of whether a school’s expenditures align with its vision. These are also some of
the categories to consider when determining levels of adequacy.
Table 2.3
School Expenditure Structure and Resource Indicators
School Resource Indicators
School Building Size Length of Reading Class (Elementary)
School Unit Size Length of Mathematics Class (Elementary)
Percent Low Income Reading Class Size (Elementary)
Percent Special Education Mathematics Class Size (Elementary)
Percent ESL/LEP Regular Class Size (Elementary)
Expenditures Per Pupil Length of Core* Class Periods (Secondary)
Professional Development Core Class Size (Secondary)
Expenditures Per Teacher
Special Academic Focus of School/Unit Non-Core Class Size (Secondary)
Length of Instructional Day Percent Core Teachers
Length of Class Periods Math, English/LA, Science, & Social Studies

School Expenditure Structure
Instructional 1. Core Academic Teachers
-English/Reading/Language Arts
-History/Social Studies
-Math
-Science
2. Specialist and Elective Teachers/Planning and
Preparation
-Science
-Art, music, physical education, etc.
-Academic Focus with or without Special Funding
-Vocational
-Drivers Education
-Librarians
3. Extra Help
-Tutors
-Extra Help Laboratories
-Resource Rooms (Title I, special education
or other part-day pullout programs)
ALLOCATING HUMAN CAPITAL RESOURCES 46
-Inclusion Teachers
-English as a second language classes
-Special Education self-contained classes for
severely disabled students (Including aides)
-Extended Day and Summer School
-District-Initiated Alternative Programs
4. Professional Development
-Teacher Time - Substitutes and Stipends
-Trainers and Coaches
-Administration
-Materials, Equipment and Facilities
-Travel & Transportation
-Tuition and Conference Fees
5. Other Non-Classroom Instructional Staff
-Coordinators and Teachers on Special Assignment
-Building Substitutes and Other Substitutes
-Instructional Aides
6. Instructional Materials and Equipment
-Supplies, Materials and Equipment
-Computers (hardware, software, peripherals)
7. Student Support
-Counselors
-Nurses
-Psychologists
-Social Workers
-Extra-Curricular and Athletics
Non-Instructional 8. Administration
9. Operations and Maintenance
-Custodial
-Utilities
-Security
-Food Service
______________________________________________________________________________
Source: Odden, A., Archibald, S., Fermanich, M., & Gross, B. (2003).

The expenditure categories mentioned above not only lead to a discussion of adequacy,
but they can also be used for accurate accounting purposes. As researchers investigate what
works and what does not, processes for expenditure recording and reporting often do not yield
the level of detail that is necessary for informed decisions. If schools were to consistently record
expenditures according to the categories listed above, a more meaningful dialogue could occur.
ALLOCATING HUMAN CAPITAL RESOURCES 47
Evidence-Based Model. For the purposes of this study, the Evidence-Based Model was
used for determining adequacy. While there are other options, this approach was chosen because
it is grounded in research and best practice. Also, the model carries with it a desired ratio for the
human resources that work within a school. Figure 2.2 provides a visual representation of the
Evidence-Based Model.

Figure 2.2. The Evidence-Based Model. Source: Odden, A., Picus, L. O., Goetz, M., Mangan,
M. T., & Fermanich, M., (2006).
Within the Evidence-Based Model, one of the key ratios is that within the classroom.
The K-3 student -to-teacher ratio is 15 to 1. The grade 4-12 ratio is 25 to 1. Tutors are funded at
one tutor for every 100 at-risk students. Instructional coaches are funded at one coach for every
Instructional
Materials
Pupil Support:
Parent/Community
Outreach/
Involvement
Gifted
Tutors and pupil support:
1 per 100 at risk
Elem
20%
Middle
20%
High School 33%
The Evidence Based Model:
A Research Driven Approach to Linking Resources to Student Performance
K-3: 15 to 1
4-12: 25 to 1
State and CESAs
District Admin
Site-based Leadership
Teacher
Compensation
ELL
1 per
100
Technology

ALLOCATING HUMAN CAPITAL RESOURCES 48
200 students. Table 2.4 presents a more comprehensive description of the Evidence-Based
Model.
Table 2.4
Adequate Resources for Prototypical Elementary, Middle, and High Schools
School Element Elementary Schools Middle Schools High Schools
School Characteristics
School configuration K-5 6–8 9–12
Prototypic school size 432 450 600
Class size K-3: 15 25 25
4–5: 25
Full-day kindergarten Yes NA NA
Number of teacher work days 200 teacher work days,
including 10 days for
intensive training
200 teacher work days,
including 10 days for
intensive training
200 teacher work
days, including 10
days for intensive
training
% disabled 12% 12% 12%
% poverty (free and 50% 50% 50%
reduced-price lunch)
% ELL 10% 10% 10%
% minority 30% 30% 30%
Personnel Resources
1. Core teachers 24 18 24
2. Specialist teachers 20% more: 4.8 20% more: 3.6 33% more: 8.0
3. Instructional
facilitators/mentors
2.2 2.25 3.0
4. Tutors for struggling
students
One for every 100
poverty students: 2.16
One for every 100
poverty students: 2.25
One for every 100
poverty students:
3.0

ALLOCATING HUMAN CAPITAL RESOURCES 49
Table 2.4, continued
5. Teachers for ELL students Additional 1.0 teachers
for every 100 ELL
students: 0.43
Additional 1.0 teachers
for every 100 ELL
students: 0.45
Additional 1.0
teachers for every
100 ELL students:
0.60
6. Extended-day 1.8 1.875 2.5
7. Summer school 1.8 1.875 2.5
8a. Learning and mildly
disabled students
Additional 3 professional
teacher positions
Additional 3 professional
teacher positions
Additional 4
professional
teacher positions
8b. Severely disabled students 100% state
reimbursement minus
federal funds
100% state
reimbursement minus
federal funds
100% state
reimbursement
minus federal
funds
9. Teachers for gifted students $25/student $25/student $25/student
10. Vocational education NA NA No extra cost
11. Substitutes 5% of lines 1–11 5% of lines 1–11 5% of lines 1–11
12. Pupil support staff 1 for every 100 poverty
students: 2.16
1 for every 100 poverty
students plus 1.0
guidance/250 students:
3.25
1 for every 100
poverty students
plus 1.0
guidance/250
students: 5.4
13. Non-instructional aides 2.0 2.0 3.0
14. Librarians/media
specialists
1.0 1.0 1.0 librarian
1.0 library
technician
15. Principal 1 1 1
16. School site secretary 2.0 2.0 3.0
17. Professional development Included above:
Instructional facilitators
Planning and prep time
10 summer days
Additional: $100/pupil
for other PD expenses—
trainers, conferences,
travel, etc.
Included above:
Instructional facilitators
Planning and prep time
10 summer days
Additional: $100/pupil
for other PD expenses—
trainers, conferences,
travel, etc.
Included above:
Instructional
coaches Planning
and prep time 10
summer days
Additional:
$50/pupil for other
PD expenses—
trainers,
conferences, travel,
etc.
ALLOCATING HUMAN CAPITAL RESOURCES 50
Table 2.4, continued
18. Technology $250/pupil $250/pupil $250/pupil
19. Instructional materials $140/pupil $140/pupil $175/pupil
20. Student activities $200/pupil $200/pupil $250/pupil
Source: Odden & Picus, 2008.
As previously described, one of the benefits of the Evidence-Based Model is its research
base. The theory behind the Evidence-Based Model is that schools should engage in activities
that will have the largest impact on student achievement. The programs described in Table 2.5
each correlate with a positive effect size when it comes to student achievement, and provide the
rationale for selecting the Evidence-Based Model.
Table 2.5
Estimated Effect Sizes of Major Recommendations
Recommended Program Effect Size
Full Day Kindergarten 0.77
Class Size of 15/16 in Grades K-3
Overall
Low Income and Minority Students

0.25
0.50
Multi-Age Classrooms
Multi-Grade Classrooms
Multi-Age Classrooms

Professional Development with Classroom
Instructional Coaches
1.25 to 2.70
Tutoring, 1-1 0.4 to 2.5
English-Language Learners 0.45
Extended-Day Programs
Structured Academic Focused Summer School 0.45
Embedded Technology 0.30 to 0.38
Gifted and Talented
Accelerated Instruction or Grade Skipping
Enrichment Programs

0.5 to 1.0
0.4 to 0.7
Source: Odden et al., 2005.

Critics of the Evidence-Based Model cite unrealistic effect sizes and estimates for
potential achievement as well as a high cost for full implementation of the model (Hanushek,
2007). Hanushek (2007) criticized one report, prepared for Washington State, by stating that
ALLOCATING HUMAN CAPITAL RESOURCES 51
Evidence-Based Model, if fully implemented, would cost up to an additional $2,760 per student.
He also challenged the potential results of a fully-funded Evidence-Based Model.
Limited Resources and Fiscal Constraints
An adequate level of funding is the desired state for K-12 education, but reality is that
funding is sparse; the economy, at the time of this study, did not allow for a fully-funded
adequacy model in California, regardless of the chosen model. Figure 2.3 presents a summary of
K-12 revenues in the State of California over years prior to this study.

Figure 2.3. California K-12 Education Revenues
The economic downturn led to several one-time or short-term monies given to education.
Examples of these are Quality Education Investment Act (QEIA) and American Recovery and
Reinvestment Act (ARRA) funds. While these funding sources helped in the short-term, they
did not assist in the pursuit of an adequacy model to allow all students to achieve at high levels.
According to the California Department of Education (2012), the Quality Education
Investment Act was approved and signed into law through Senate Bill 1133 in 2006. According
to the Sacramento County Office of Education and Los Angeles County Office of Education,
$53.0
$54.0
$55.0
$56.0
$57.0
$58.0
$59.0
$60.0
$61.0
$62.0
$63.0
$64.0
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11
Dollars (in billions)
Source: EdSource
ALLOCATING HUMAN CAPITAL RESOURCES 52
QEIA specifically supported California schools in the two lowest deciles of the state’s API as of
2005. This amounted to financial support to 488 schools, typically with high amounts of low-
income and English Learners. The amounts given to the different grade levels are tiered: $500
per student in K-3, $900 per student in grades 4-8, and $1000 per student in grades 9-12. The
intent of QEIA funding was to improve student achievement by reducing class sizes, adding
counselors at the high school level, and improving staff development for teachers and principals.
As of 2012, the funding for QEIA was coming to a close.
Funds from American Recovery and Reinvestment Act (ARRA) were another example of
a short-term fix for the economic problems facing education. President Obama signed this act
into law in February of 2009. The purpose of these funds, not limited to the educational arena,
was to help a struggling economy, save jobs, and provide funding toward a brighter future.
According to the U.S. Department of Education (2010), $97.4 billion were awarded to
educational entities across the United States of America. Approximately 275,000 jobs in the area
of education were created or saved as a result of this funding. As of 2012, this funding had also
ended.
As can be seen, short-term solutions appeared from time to time, but these failed to
address the larger issue of the economy. An adequacy model such as the Evidence-Based Model
was not financially possible or feasible at the time of this study. Even so, schools found ways to
be successful despite the financial shortcomings, and one of those ways involved creativity.
Miles and Darling-Hammond (1997) conducted a study on resource allocation related to high-
performing schools. The authors found that the high performing schools re-allocated funds to
support the following six concepts:

ALLOCATING HUMAN CAPITAL RESOURCES 53
 An increased amount of special education students in mainstream classes
 Flexible grouping for students
 Structures that allow for and encourage personalized learning environments
 Longer, as well as varied, blocks of instructional time
 Common planning time
 Creativity regarding staff roles and schedules
In summary, full implementation of the Evidence-Based Model may not be financially
possible. However, with creative application and choice, elements of the Evidence-Based Model
may be possible, and the goal remains that the strategic choice of these elements may lead to
increased student achievement.
Gap Analysis
A gap analysis is typically used to measure the gap between the current state and the
desired state. This study measured two different gaps. The first gap was the difference between
current human resource allocation across all schools in the study district and the
recommendations of the Evidence-Based Model. As has already been stated, due to the
economic situation in the state of California, a fully funded Evidence-Based Model was unlikely.
Even so, the gap was measured for each of the designated positions within the Evidence-Based
Model framework.
The second gap measured was that between the actual human resource allocation in the
study district and the hypothetical allocation of personnel in the study district. The hypothetical
allocation provides an alternative way to allocate resources in order to move towards the
Evidence-Based Model. These two sets of gaps provide the framework for the gap analysis in
ALLOCATING HUMAN CAPITAL RESOURCES 54
this study. The measuring and the size of each gap allowed for analysis of regarding the
placement of priorities at the state, district, and school levels.
Gap analysis causes. To analyze performance gaps, Clark and Estes (2008) listed three
critical factors: 1) Knowledge and skills, 2) Motivation, and 3) Organizational barriers (p. 43).
Because of the complexity of “diagnosing performance gaps,” one must take all three factors into
consideration. Failing to acknowledge one of the factors might lead to a breakdown in the
process whereby a major cause of the performance gap is not addressed.
Knowledge and Skills. The knowledge and skills factor refers to the information needed
to accomplish the task, and is typically addressed through professional development or training.
In this study, this phrase refers to the knowledge and skills those within the study district must
have in order to close the gap between current human resource allocation and either the
hypothetical allocation of personnel or that proposed by the Evidence-Based Model.
Motivation. According to Clark and Estes (2008), the motivational barrier is, in essence,
“choosing to work towards a goal,” “persisting at it until it is achieved,” and investing the mental
effort necessary to get the job done (p. 44). This factor refers to personnel’s being motivated
enough to continue working towards a goal until it is accomplished.
Organizational barriers. Organization barriers refer to a lack of funding, structures, and
resources; this can also indicate systems that do not work well. These structural barriers inhibit
progress as it relates to closing the defined gap. This study examined whether there were
funding, structural, or resource-related barriers that held the study district back from achieving
the hypothetical allocation of human resources.

ALLOCATING HUMAN CAPITAL RESOURCES 55
Chapter 3: Methodology
Introduction
This chapter presents an outline of the methodology for this qualitative study and reviews
five areas: the research questions, the sample and population, the instrumentation, the data
collection, and the data analysis. While human resource allocation data was collected from a
study district, much of the study consisted of analysis and interpretation of the data. This study
was replicated across seventeen districts by seventeen doctoral students across Southern
California, and each utilized the same instrumentation, data collection, and analysis procedures.
Odden’s 10 Strategies for Doubling Student Performance. As described in Chapter 2,
Odden presented ten research-based strategies known to boost student achievement (Odden,
2009; Odden & Archibald, 2009):
1) Understanding the performance problem and challenge
2) Set ambitious goals
3) Change the curriculum program and create a new instructional vision
4) Formative assessments and data-based decision making
5) Ongoing, intensive professional development
6) Using time efficiently and effectively
7) Extending learning time for struggling students
8) Collaborative, professional culture
9) Widespread and distributed instructional leadership
10) Professional and best practices
While the Evidence-Based Model is not explicitly stated within the ten strategies, some
of the underlying motives are present. As an example, professional development is one of the
ALLOCATING HUMAN CAPITAL RESOURCES 56
driving forces behind the instructional coach position. Summer school funding, and after school
personnel dedicated to helping students, are found in point number seven: extending learning
time for struggling students. Therefore, Odden’s ten strategies serve as the foundation for
student achievement in this study.
Purpose of the Study/Research Questions
The purpose of this study was to research the extent to which one district allocated
human resources towards research-based strategies for school improvement. Because of the
economic downturn, the study focused on one large urban school district in Southern California.
The study district provided actual human resource allocation data for each of its schools, in the
form of a “people book,” in order to examine how it handled a limited budget. Comparisons
were made between the Evidence-Based Model and the actual allocation of human resources in
the study district.
To that end, this study sought to provide insight into one district’s management of human
resources and the extent to which it met the expectations of the Evidence-Based Model. The
data indicated which tradeoffs the study district made in order to design instruction around
strategies that are likely to maximize student achievement as well as which tradeoffs the district
would like made but, perhaps, had not yet been possible. This study also aimed to contribute to
the field of research related to allocation of human resources in a fiscally difficult time,
specifically with relation to student achievement.
The research questions, previously presented in Chapter 1, are as follows:
1) What research-based human resource allocation strategies improve student achievement?
2) How are human resources allocated across the study district and its schools?
ALLOCATING HUMAN CAPITAL RESOURCES 57
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?
Sample and Population
The study district was located in Southern California and was one of the ten largest
school districts in the state, as it enrolled approximately 60,000 students. In order to serve
60,000 students, the study district oversaw and operated 36 elementary schools, nine
intermediate schools, seven high schools, and three alternative schools. Approximately 4,500
employees worked for the district at the time of this study.
The district was founded in 1888, and, at the time of this study, served a high minority
population; about 95% of the student population was Hispanic, 3% was Asian, and 2% was
White. Approximately 60% of the students were categorized as English Learners, and 80%
received free or reduced-price lunch. Similar to other districts in the area, the student population
shifted dramatically over the previous several decades from a primarily White population to a
Hispanic population that includes a significant number of English Learners. This shift led to a
need for instructional reform at all levels; the students’ needs changed, and the previous practices
were no longer effective for all students.
From a district leadership and oversight perspective, the school board at the study district
had been relatively stable and was responsible for a budget of $483 million. As a five person
board, its newest member took office in 2008. The most experienced board member was elected
in 1987. The superintendent held the post since 2011 and was the second female to occupy the
ALLOCATING HUMAN CAPITAL RESOURCES 58
position in the district’s history. All of the district’s 36 elementary schools recently surpassed an
API score of 700, translating to a bright future for the schools of the study district.
From a secondary perspective, four of the six district comprehensive high schools were
labeled “Persistently Low Achieving Schools” (PLAS) by the State of California. The other two
comprehensive high schools surpassed the 800 API mark despite similar demographics. In
addition, two of the nine intermediate schools were given the same PLAS label. The district
itself was labeled a “District Assistance and Intervention Team,” also known as a DAIT district.
Both the PLAS and DAIT designations increased funding and accountability at the district and
site levels.
The secondary schools within the study district served as the sample for this study, and
human resource allocations were observed and recorded for all middle and high schools within
the district. This study was one of seventeen completed using school districts across Southern
California. When combined, the variety of size, demographics, and location for each of the
seventeen studies provide a more complete and representative sample. The district studied here
was chosen because of its relatively large size, diverse population, and location.
Instrumentation
The instrumentation was a Microsoft Excel-based model developed by David Knight and
Dr. Lawrence O. Picus (Odden & Picus, forthcoming) to record school level personnel
allocations and allow the user to compare those allocations to both the Evidence-Based Model
and to any other model for distribution of resources. The purpose of the human resource
allocation recording sheet was to record the number of personnel in different roles at each school
site within the study district. The instrument was designed to assist with research questions two,
three, and four, shown below:
ALLOCATING HUMAN CAPITAL RESOURCES 59
2) How are human resources allocated across the study district and its 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?
Research question number two was answered by tallying the number of personnel within
each role in the human resource allocation recording sheet. There were two other points of
reference within the human resource allocation recording sheet. The first of these was the
Evidence-Based Model. The exact specifications of the Evidence-Based Model were input into
the recording sheet. The second point of reference was a hypothetical allocation of human
resources. These two points of reference, along with the actual human resource allocation
practices of the study district, served as the primary points of data. The recording sheet tabulated
the differences among these three points as a basis for discussion and analysis. These differences
address the third research question (Figure 3.1).

Figure 3.1. Visual Model of Gap Analysis
Study District's Actual
Human Resource
Allocation
Study District's
Hypothetical Allocation
of Personnel
Evidence-Based Model
Specifications
ALLOCATING HUMAN CAPITAL RESOURCES 60
The human resource allocation recording sheet also allowed for hypothetical situations.
In other words, the researcher was able examine the possibility of removing a teacher at a school
site in exchange for the addition of an instructional coach. The recording sheet revealed the
impact that a move such would have on any differences between the current human resource
allocation and that proposed by the Evidence-Based Model. This flexibility within the model
allowed for a discussion regarding the ways in which human resources might be re-allocated to
better meet student needs in order to increase achievement levels. This discussion, in turn,
addresses research question number four.
During the summer of 2012, Dr. Lawrence O. Picus trained the seventeen individual
researchers on the proper use of the human resource allocation recording sheet. At that time,
protocols were discussed. In addition, the interview questions for district leadership were
finalized so that each researcher would be able to determine the hypothetical allocation of
personnel for his/her study district. For this study, the researcher interviewed the assistant
superintendent of secondary education and secondary site principals.
Data Collection
The data collection process consisted of requesting the “people book” from the study
district. The “people book” was a file listing the number of personnel in each role at each school
site within the district. The data from the “people book,” were all transferred into the human
resource allocation recording sheet and recorded according to school site.
Other data examined consisted of the study district’s hypothetical allocation of personnel
numbers. These data were recommended as a result of interviews with the study district’s
assistant superintendent of secondary education and secondary site principals.
ALLOCATING HUMAN CAPITAL RESOURCES 61
The design of the project, overseen by Dr. Lawrence O. Picus, called for data to be
collected for seventeen districts in Southern California. The data were collected in a consistent
manner in each of the seventeen individual studies, potentially allowing for further analysis
between districts. To ensure consistency in data collection, data entry, and other procedures, Dr.
Lawrence O. Picus provide training on the data collection process and human resource allocation
recording sheet during the summer of 2012.
Data Analysis
Data analysis is the final step described within the methodology chapter of this study.
The data analysis stage follows completion of all data collection and data entry into the human
resource allocation recording sheet.
Data analysis, in the context of this study, consisted of a gap analysis process conducted
for three reference points: 1) the study district’s actual human resource allocation, 2) the
Evidence-Based Model specifications, and 3) the study district’s hypothetical allocation of
personnel. The primary source of data was the study district’s actual human resource allocation.
This allocation was compared to the Evidence-Based Model’s specification as well as to the
study district’s hypothetical allocation of personnel. This resulted in two distinct gaps. The
third, and final gap, came as a result of the comparison between the Evidence-Based Model’s
specifications and the study district’s hypothetical allocation of personnel.
Phase one of the data analysis consisted of an examination of the three gaps to seek both
large and small differences between human resource allocation numbers. A large difference
meant that two of the data sources were not aligned within a specific personnel position. A small
difference meant that two of the data sources were aligned within a specific personnel position.
ALLOCATING HUMAN CAPITAL RESOURCES 62
These differences speak to the alignment of human resource allocation philosophies and actual
practice.
Phase two of data analysis consisted of determining the reasons behind the size of the
gaps outlined in phase one. It was presumed that, in some cases, the gaps would be due to a
predetermined staffing ratio, and, in others, to decisions made by the study district. The third
phase of data analysis entailed analysis of potential human resource tradeoffs with the intent of
placing personnel in positions that would have the greatest impact on student achievement. The
potential impact of these tradeoffs was also analyzed.
Phase four of the data analysis consisted of determining whether the tradeoffs described
in phase three were possible and feasible. These four phases of data analysis provided a picture
of current practice, potential tradeoffs, and possibilities for improvement in the area of human
resource allocation.

Figure 3.2. Visual Model of Data Analysis
Phase 1:
Examination of
Gaps
Phase 2:
Examination of
Gap Causes
Phase 3:
Potential
Human
Resource
Tradeoffs
Phase 4:
Tradeoff
Possibilities/
Feasibility
ALLOCATING HUMAN CAPITAL RESOURCES 63
Chapter 4 of this study presents the results of the data collection and analysis, and
Chapter 5 delves into recommendations for the study district.

ALLOCATING HUMAN CAPITAL RESOURCES 64
Chapter 4: Findings
Introduction
This study was conducted to review the relationship between human resource allocation
and student achievement in a large, urban school district. This chapter discusses an overview
and a longitudinal summary of the study district’s performance data. It also revisits and reviews
the purpose and function of the human resource allocation recording sheet described in Chapter
3. This chapter also presents the findings from the study as they pertain to the four research
questions:
1) What research-based human resource allocation strategies improve student achievement?
2) How are human resources allocated across the study district and its 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 first research question can be answered in a multitude of ways. For the purposes of
this study, the primary source was Odden’s ten research-based strategies known to boost student
achievement (Odden, 2009; Odden & Archibald, 2009). This chapter explores these strategies
along with the study district’s approach to each.
Study District Data Overview
As a point of reference, the study district will be compared to the county to which it
belongs as well as the state of California. In order to keep the identity of the district confidential,
the county will be referred to as “XYZ County” from here forward. The data being provided
ALLOCATING HUMAN CAPITAL RESOURCES 65
include ethnicity data, socioeconomic status, achievement data, and other relevant data. The data
can be broken up into the following categories: general student population and achievement data.
General Student Population
This section provides a comparison of the general student population of the study district,
the county within which it is located, and the state of California. For purposes of this study, the
pseudonym “XYZ County” is used in place of the county’s true name. The comparison data
include data on student ethnicity, English fluency, and socioeconomic status. As can be seen in
the figures below, the population of the study district does not resemble the population of XYZ
County or the state of California. Figure 4.1 presents a summary of the student population by
ethnicity.

Figure 4.1. General Student Population by Ethnicity (2011-2012). Source: DataQuest Website,
California Department of Education
The study district serves a higher percentage of Latino students than is typically found in
XYZ County and across California – approximately 93.1%. The study district has similar
percentages of White and Asian/Pacific Islander/Filipino students at 2.8% and 3.1%,
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Study District XYZ County California
Other
African American
Asian/Pacific
Islander/Filipino
White
Latino
ALLOCATING HUMAN CAPITAL RESOURCES 66
respectively. Interestingly, the student populations of XYZ County and of California appear to
be quite similar. Comparing XYZ County and the state of California, California has
approximately 4% more Latino students and 4% less White students than does the county.
Similarly, California has approximately 5% more African-American students and 5% less
Asian/Pacific Islander/Filipino students than does the county.
The next segment of the student population to be examined was comprised of those with
limited English fluency. At the time of this study, the study district served a higher percentage
of English learner students when compared to the county and state levels. Approximately one
half of the students attending the study district were categorized as English learners as measured
by the California English Language Development Test (CELDT). In contrast, approximately
26% of students in the county were English learners and 22% of California students were
English learners. The data shown in Figure 4.2 depict the general student population in the study
district, XYZ County, and the state of California. The English learner status data are similar to
the ethnicity data previously found in Figure 4.1 in that the county data resemble the state data.

Figure 4.2. General Student Population by English Learner Status (2011-2012). Source:
DataQuest Website, California Department of Education
0%
20%
40%
60%
80%
100%
120%
Study District XYZ County California
Non English Learners
English Learners
ALLOCATING HUMAN CAPITAL RESOURCES 67
The last subgroup to be examined is that comprised of students from populations with
low socioeconomic status. The criterion by which this characteristic is measured is the free and
reduced-price lunch count. The free and reduced-price lunch count numbers are shown in Figure
4.3. The data are summarized for the study district, XYZ County, and the state of California.

Figure 4.3. Percent of Students Qualifying for Free and Reduced-Price Lunch (2010-2011).
Source: DataQuest Website, California Department of Education
The schools within the study district were Title 1 schools, generally serving students from
lower socioeconomic status as evidenced by free and reduced-price lunch rates. As can be seen
in Figure 4.3, the study district contains a significantly higher segment of the population,
approximately 84.3%, who qualifies for free and reduced-price lunch. XYZ County had an
overall free and reduced-price lunch rate of 45.6%, and the state’s rate is 56.7%.
In summary, the study district, in many ways, did not, at the time of the study, serve a
population representative of the county or state. The study district had a more significant
English Learner population and a higher percentage of students qualifying for free and reduced-
price lunch. In addition, the district had a high percentage of Latino students when compared to
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
Study District XYZ County California
ALLOCATING HUMAN CAPITAL RESOURCES 68
the county and state levels. Despite these differences, the study district improved achievement as
measured by the California Standards Test.
Study District Achievement Data
The study district demonstrated perseverance and a commitment to continuous
improvement as evidenced by the data shown in figures 4.4 through 4.11. The figures below
present longitudinal data for the English-Language Arts (ELA) section of the CST, specifically
the percentage of students who achieved proficiency or above. The data span five years of
results and are separated into the following categories: all students, English Learners, students
with disabilities, and low socioeconomic status students as measured by qualification for free and
reduced-price lunch. The English-Language Arts results can be seen in figures 4.4 through 4.7.
Immediately following the English-Language Arts results, the results for mathematics are
presented. The same format and categories are used as in the presentation of the English-
Language Arts results. The data span five years of results and include summary results for all
students: English Learners, students with disabilities, and students with low socioeconomic
status. The charts indicate the percentage of students who were deemed to be proficient or
advanced. The mathematics results are shown in figures 4.8 through 4.11.

ALLOCATING HUMAN CAPITAL RESOURCES 69

Figure 4.4. ELA CST Results: All Students. Source: DataQuest Website, California Department
of Education.

Figure 4.5. ELA CST Results: Students with Disabilities. All Students. Source: DataQuest
Website, California Department of Education.
0%
10%
20%
30%
40%
50%
6th
Grade
7th
Grade
8th
Grade
9th
Grade
10th
Grade
11th
Grade
% Proficient/Advanced
2008
2009
2010
2011
2012
0%
5%
10%
15%
20%
25%
30%
35%
40%
6th
Grade
7th
Grade
8th
Grade
9th
Grade
10th
Grade
11th
Grade
% Proficient/Advanced
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 70

Figure 4.6. ELA CST Results: English Learners. Source: DataQuest Website, California
Department of Education

Figure 4.7. ELA CST Results: SES Disadvantaged. All Students. Source: DataQuest Website,
California Department of Education.

0%
2%
4%
6%
8%
10%
12%
14%
6th
Grade
7th
Grade
8th
Grade
9th
Grade
10th
Grade
11th
Grade
% Proficient/Advanced
2008
2009
2010
2011
2012
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
6th
Grade
7th
Grade
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Grade
9th
Grade
10th
Grade
11th
Grade
% Proficient/Advanced
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 71

Figure 4.8. Math CST Results: All Students. Source: DataQuest Website, California Department
of Education.

Figure 4.9. Math CST Results: Students with Disabilities. Source: DataQuest Website,
California Department of Education.
0%
10%
20%
30%
40%
50%
60%
% Proficient/Advanced
2008
2009
2010
2011
2012
0%
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15%
20%
25%
30%
35%
40%
45%
% Proficient/Advanced
2008
2009
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2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 72

Figure 4.10. Math CST Results: English Learners. Source: DataQuest Website, California
Department of Education.

Figure 4.11. Math CST Results: SES Disadvantaged. Source: DataQuest Website, California
Department of Education.
The English-Language Arts results shown in Figures 4.4 through 4.7 demonstrate an
upward trend in the five years prior to this study, regardless of subgroup. While there are a few
anomalies across the data sets, particularly related to the 6
th
grade and 10
th
grade results in Figure
4.6 and the sharp increase in the results of Figure 4.5, the data consistently show two trends. The
first trend is that the achievement results rose over five years. The achievement scores in
English-Language Arts increased a minimum of three percentage points and a maximum of 37
0%
5%
10%
15%
20%
25%
30%
% Proficient/Advanced
2008
2009
2010
2011
2012
0%
10%
20%
30%
40%
50%
60%
% Proficient/Advanced
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 73
percentage points. The average five year ELA increase in achievement scores in the study
district was 14.6 percentage points. The second trend in the study district results is that scores
generally declined as students progressed through school. In other words, the 10
th
grade results
in English-Language Arts are lower than the 9
th
grade results for the same subject.
The mathematics results, seen in Figures 4.8 through 4.11, show similar trends. Again,
student achievement rose over five years. The smallest increase in scores was two percentage
points. The maximum increase in mathematics scores was 35 percentage points. The average
five year mathematics increase in the study district was 13.8 percentage points, slightly lower
than the ELA average increase. The second trend, scores declining as students progress, holds
generally true with two exceptions: 1) students in the study district score higher in 7
th
grade math
than 6
th
grade math, and 2) students in Geometry score lower than students in Algebra II.
Comparisons can also be made between the results of English-Language Arts and
mathematics. Interestingly, the results for all students, students with disabilities, and low
socioeconomic students show similar trends. In general, the scores for mathematics are higher in
grades six and seven. Beyond grade seven, the pattern is reversed and English-Language Arts
scores are higher.
The other subgroup of students, English Learners, does not follow the same trend.
English Learners consistently perform better in mathematics than in English-Language Arts, with
the exception of one mathematics course: Geometry. This, most likely, is due to the language
required to achieve proficiency in Geometry.
Regardless of the anomalies in the achievement data, it is evident that the study district
showed progress. The learning strategies used assisted with student results. These strategies
ALLOCATING HUMAN CAPITAL RESOURCES 74
were successful with all students, including English Learners, students with disabilities, and low
socioeconomic subgroups.
Another measure often used to measure school success is the Academic Performance
Index (API). Longitudinal API data are displayed in figures 4.12 through 4. Figures 4.12 and
4.13 present the API scores for all middle and high schools in the study district. The average
API score for the middle and high schools in the study district are also charted. Figure 4.14
compares longitudinal API data from the study district to the state API scores for different grade
level ranges.

Figure 4.12. Study District Middle School API Score Longitudinal Summary. Source: California
Department of Education Website

500
600
700
800
900
1000
Middle
School
"A"
Middle
School
"B"
Middle
School
"C"
Middle
School
"D"
Middle
School
"E"
Middle
School
"F"
Middle
School
"G"
Middle
School
"H"
Middle
School
"I"
Average
Middle
School
API Score
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 75

Figure 4.13. Study District High School API Score Longitudinal Summary. Source: California
Department of Education Website
The API results in Figures 4.12 and 4.13 show an upward trend for all middle and high
schools in the study district over five years. The middle school chart in Figure 4.12 has one
unique feature. The data between 2008 and 2009 show a decrease in API scores for middle
schools “B,” “D,” “H,” and “I.” Data for All other years demonstrate growth with few
exceptions. In Figure 4.13, the data show four high schools, high schools “J,” “M,” “N,” and
“P,” that are at similar achievement levels. High school “L” surpassed an API score of 900 for
the first time in 2012. All high schools in the study district show growth over the past five years
ranging from 35 points (school “O”) to 101 points (school “P”).

500
600
700
800
900
1000
High
School "J"
High
School "K"
High
School "L"
High
School
"M"
High
School "N"
High
School
"O"
High
School "P"
Average
High
School
API Score
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 76

Figure 4.14. API Score Longitudinal Summary. Source: California Department of Education
Website
The API results in Figure 4.14 show an increase for the entire study district as well as all
California grade levels. The API results for XYZ County were not available for comparison
purposes. While all segments showed increases, it is important to note that the study district
increased at a faster rate than did California. Over five years, the study district grew 69 points.
California grades seven and eight grew a total of 53 points. California high schools grew a total
of 50 points, and all California schools grew a total of 47 points.
Research Question #1: Odden’s 10 Strategies for Doubling Student Performance
As described in Chapter 2, Odden provided ten research-based strategies known to boost
student achievement (Odden, 2009; Odden & Archibald, 2009). The ten strategies are listed
below:
1) Understanding the performance problem and challenge
2) Set ambitious goals
3) Change the curriculum program and create a new instructional vision
600
620
640
660
680
700
720
740
760
780
800
Study
District 7-8
Study
District 9-
12
Study
District
California
7-8
California
9-12
California
Overall
Average API Score
2008
2009
2010
2011
2012
ALLOCATING HUMAN CAPITAL RESOURCES 77
4) Formative assessments and data-based decision making
5) Ongoing, intensive professional development
6) Using time efficiently and effectively
7) Extending learning time for struggling students
8) Collaborative, professional culture
9) Widespread and distributed instructional leadership
10) Professional and best practices
Study district approach to 10 Strategies. The study district approached each of
Odden’s ten strategies with a different speed, emphasis, and timeframe. In order to gain insight
into district level practices in relation to the ten strategies, the study district’s assistant
superintendent of secondary education was interviewed. The assistant superintendent shared
district and schoolwide areas of emphasis as they relate to student achievement. The feedback
from the assistant superintendent, along with other anecdotal evidence gathered from study
district principals, is categorized below by strategy.
Understanding the performance problem and challenge. One of the principal ways the
study district worked to understand its performance problem was through the use of data. The
data used to understand any successes and/or problems are generated both internally and
externally. Internally, the district examined benchmark data in the areas of English-Language
Arts, mathematics, social science, and science. Other internal measures include attendance data,
student discipline data, and grading data. External sources of data include CST results,
CAHSEE results, English Learner proficiency and reclassification as measured by CELDT,
Advanced Placement (AP) results, PSAT/SAT/ACT results, and A-G course completion.
ALLOCATING HUMAN CAPITAL RESOURCES 78
The use of data is significant in the study district. The study district discussed data in
many venues including its board meetings, cabinet meetings, principal meetings, department
chair meetings, and site-level meetings. The data were also used to make district and site-level
decisions. The ultimate goal of these conversations and decisions was that students would be
college and career ready.
Most recently, the study district was working to establish and articulate Key Performance
Indicators (KPIs). The KPIs for the study district were to be used as a constant reminder and a
series of checkpoints so that more students would be college and career ready. These indicators
would inform each school and grade level along the way as to how well students were being
supported. In some ways, this would assist the district and its schools in understanding what the
performance problem may be in an ongoing fashion.
The following are the nine Key Performance Indicators for the study district:
-Reading proficiency in grades 1, 3, and 6
-Writing proficiency in grades 4, 7, and 10
-EL proficiency and reclassification, grades 5 and 7
-Math proficiency in grade 4
-Algebra I proficiency in grade 8
-Algebra II proficiency in grade 11
-AP courses in grades 10-12
-PSAT/SAT/ACT in grades 10-12
-A-G course completion in grade 12
In terms of resources, the study district formed a Research and Evaluation department
whose role was to support the schools with data, testing, and professional development related to
ALLOCATING HUMAN CAPITAL RESOURCES 79
these areas. Over several years, the study district utilized Data Director as its primary program
for data analysis and reporting for teachers and administrators. In the summer prior to the 2012-
2013school year, the study district changed its provider for this service to a company called
Illuminate. District-wide, teachers and administrators were in the initial phase of professional
development so that accurate and strategic data reports could be created and used for
instructional purposes. Both of the mentioned programs assist with the task of understanding the
performance problem and challenge.
Set ambitious goals. In the realm of goal-setting, the study district had two types of
goals: goals set by outside agencies and goals internally set. The study district had goals
externally set by the state and federal governments. Through No Child Left Behind and the
accompanying Adequate Yearly Progress, the schools of the study district have a set target goal
of 78.0% for English-Language Arts and 78.2% for mathematics for the 2011-2012 school year.
The state of California also set an Academic Performance Index goal of 800 for all schools.
Once schools reach an API of 800, they were still expected to continue growing.
With regards to goals set within the district, many of the goals were improvement-
oriented. For example, while no specific and measureable goal was set related to the
aforementioned KPIs, there was an expectation that the measured outcomes for each indicator
improve over time. In addition, several of the schools within the study district set yearly
ambitious goals as a school and/or as departments. Many schools set internal goals related to
external measures such as CST and CAHSEE scores. Some set attendance goals, suspension
goals, or possibly expulsion goals.
Change the curriculum program and create a new instructional vision. At the time of
this study, the study district was in the process of undergoing a major curriculum program shift
ALLOCATING HUMAN CAPITAL RESOURCES 80
similar to most other districts. With the adoption of Common Core State Standards (CCSS), the
study district worked to realign curriculum and instructional practices to the expectations of
Common Core before the start of the 2013-2014 school year. Because of the philosophical
differences between the state standards and those of CCSS, districts were required by necessity
to create a new instructional vision. Not only would the content standards change in some
regard, but the instruction necessary to accomplish the standards would differ. Teachers would
need to change instructional practice in order to be successful. The superintendent of the study
district made the adjustment to Common Core standards and instruction a focal point of the
district for the 2012-2013 school year.
In addition to the changes presented through Common Core State Standards, the study
district adopted a new mathematics curriculum in 2007. In this adoption, the study district was
given the opportunity to reexamine its curriculum and instructional methods as suggested by
Odden. While the standards remained the same, the supporting curriculum for mathematics did
not. For English-Language Arts, the instructional materials did not change. While there was a
scheduled instructional materials adoption for ELA, the adoption was postponed across the state
primarily due to financial reasons. Also, the coming of the CCSS helped delay the adoption of
new instructional materials.
Formative assessments and data-based decision making. The use of formative
assessments in the study district had been an area of growth in years prior to this study. The study
district implemented quarterly benchmarks across four core subject areas: English-Language Arts,
mathematics, social science, and science. While assessments such as these are not automatically
used as formative assessments, many schools implemented “data chats.” Data chats are data-
centered conversations about common assessment results, many times from the district
ALLOCATING HUMAN CAPITAL RESOURCES 81
benchmarks. As teachers and departments analyze the data and adjust instruction as a result of
the conversation, the assessments become formative in nature as opposed to summative. In
addition to the district benchmark, many teachers implemented other forms of formative
assessment including quizzes, homework assignments, department common assessments, and the
use of remote personal response systems, or clickers.
In many cases, the study district utilized data to drive its decisions. The aforementioned
Key Performance Indicators serve as an example of indicators that guide decisions at the district
and site-levels. The district’s desire was that all key decisions be driven by what the data
suggest, not simply what sounds like the proper course of action. In this sense, the study
district’s philosophy on decision making was aligned with Odden’s philosophy.
Ongoing, intensive professional development. The study district subscribed to the
importance of ongoing, intensive professional development. In the study year, two of the
primary district professional development initiatives were preparing for Common Core State
Standards and implementing Positive Behavior Intervention Support (PBIS) district-wide. PBIS
is a disciplinary support system whose foundation is positive reinforcement. Both of these
initiatives were driven by the district office and aimed to have a significant impact on the way
schools did business. In addition to the two initiatives, the district placed emphasis on training
for mathematics teachers at the middle school level, counselor training, and leadership coaching.
The preparation and initial implementation of CCSS were supported by the district
through a group of approximately 40 teacher specialists whose role was to assist schools in the
transition to Common Core. Each specialist was assigned to multiple schools to support them
through professional development in staff meetings and individual teacher support. The
specialists were out of the classroom and spent much of their time at the schools to which they
ALLOCATING HUMAN CAPITAL RESOURCES 82
were assigned. They also spent time with the other specialists in order to create the staff
development that was to be delivered at the school sites. An initial year-long professional
development plan was presented to study district principals. This professional development was
to be presented on a monthly basis at site staff meetings. In terms of individual teacher support,
the specialists were available for encouragement, demo lessons, lesson planning, and other
support structures so that teachers felt more comfortable as they approached the implementation
of CCSS. In this model, the teacher specialists were seen as experts whose role was to support
school sites.
With regards to PBIS, the study district took a different approach. Schools created PBIS
teams, or committees, to learn about the philosophy behind Positive Behavior Intervention
Support and spearhead its creation at each school site. Professional development was provided
at the district through an outside consultant. As the teams learned about PBIS, they were tasked
with selling the vision of PBIS at their respective school sites. In some ways, this had
similarities to a train-the-trainer model of professional development. The key to this process was
the sales and articulation of the PBIS framework at each school site. In this model, the PBIS
committee was not necessarily seen as a group of experts, but, rather, as agents of change.
The study district also started a professional development initiative with mathematics
teachers, starting in grade six. Those attending studied instructional strategies, structures for
working as a course team, and Common Core training. Each year, the professional development
includes a new grade level. In its first year, which was the year 2011-2012, all sixth grade
teachers participated. In the 2012-2013 school year, seventh grade teachers were added to the
group of participants. The goal was to have a structured professional development system
ALLOCATING HUMAN CAPITAL RESOURCES 83
leading up to Algebra, whether at the high school or eighth grade level. This training was
supported by consultants from a local university.
Another key professional development area was aimed at counselors. Because the
district’s goal was to reach higher graduation rates and A-G completion rates, ultimately
preparing more students for college and career readiness, there was a strategic necessity to
examine the way counselors did business. This professional development intended to unite
counselors in having high expectations for students, regardless of ethnicity, socioeconomic
status, or English fluency levels.
Leadership is an area the study district decided to support through coaching and
professional development. Principals met with external consultant coaches, providing the
principals with a sounding board, an outside perspective, and, in some cases, a unified approach
to what to look for in classes. These coaches joined and facilitated classroom walkthroughs
while guiding discussions on best practices, walkthrough evidence, and the resulting feedback to
staff.
In previous years, the study district has had other professional development initiatives.
Some of the previous initiatives include Sheltered Instruction Observation Protocol (SIOP),
Thinking Maps, Instructional Rounds, and a general emphasis on student engagement. Each of
these still had a presence in the district, but there had been a move away from implementing
certain programs and a push towards implementing best practices, regardless of their titles.
There was also an organized effort to include classroom walkthroughs in administrative meetings
as a form of professional development. This has served as a norming protocol for administrators
at all levels.
ALLOCATING HUMAN CAPITAL RESOURCES 84
Using time efficiently and effectively. Using time efficiently and effectively, as
described by Odden, presents some challenges for the study district. One of the examples of the
effective use of time is designing an effective master schedule. Within the study district, there
were 19 secondary schools, each with its own master schedule and a different creator or group of
creators. Because of this, there was a range of efficiency within the created master schedules.
One strategy the study district used to fix this variety was that those responsible for creating the
master schedule would meet on a regular basis to share timelines, strategies, and other
information. In a sense, the group would become its own professional learning community.
Extending learning time for struggling students. In some unique cases, schools
received additional money due to their designation as Persistently Low Achieving Schools
(PLAS). At the time of this study, the study district contained six PLAS schools. Several of the
PLAS schools chose to add sixty instructional minutes to the school day using a portion of their
allotted money. While this option was not available to all schools, this strategy partially fulfills
Odden’s seventh strategy. The seventh strategy indicates that the extended learning time should
be used for struggling students. The PLAS schools that employed this strategy extended the
school day for all students. Also, the addition of sixty instructional minutes does not mean that
achievement will increase. The instructional strategies used within the sixty minutes should be
in line with what is known as best practice.
Collaborative, professional culture. The study district strove to better build a
collaborative, professional culture. In some ways, this is referred to as a “Professional Learning
Community” (PLC). Examples of structures put into place extend from district-level to site-level
practices.
ALLOCATING HUMAN CAPITAL RESOURCES 85
At the district-level, the study district moved toward vertical articulation through
“Learning Pathway Teams.” These teams were made up of principals at both the elementary and
secondary levels for student pathways, sometimes known as feeder patterns. In other words,
students in any given neighborhood within a district often travel from a group of elementary
schools to one or two middle schools to one high school. That set of schools could form one
learning pathway team. In previous years, these meetings existed but were often separated into
elementary and secondary groups. At the time of this study, the learning pathway teams were
grouped together at principal meetings to discuss key issues and also met additionally throughout
the year to discuss instruction and the district’s initiatives.
There were several other groups at the district-level that would consider themselves part
of a collaborative, professional culture. The teacher specialists mentioned in the fifth strategy
met once per week to discuss successes and share strategies. The district also coordinated an
administrative liaison group that led department chair meetings to move site leaders towards best
practices. Additional meeting groups to consider in this conversation include department chairs,
lead counselors, teachers on special assignment, assistant principals, English Language
Development (ELD) coordinators, and the Common Core Task Force. The study district put
many structures into place for a collaborative and professional culture.
At the site-level, there were also several examples of a collaborative, professional culture,
whether that culture was found in the entire site or in a subgroup within site. The extent of the
effectiveness and collaboration within the culture was greatly affected by the leader of the school
and the leader of the group. The study district schools operated using many of the same
collaborative structures that seen across the country. While they were not uncommon, the
structures varied in format, power, and effectiveness across the study district. These structures
ALLOCATING HUMAN CAPITAL RESOURCES 86
include the following groups: department, grade-level team, course team, leadership team, data
team, administrative team, cross-curricular teams, and a variety of committees.
Widespread and distributed instructional leadership. Similar to Odden’s eighth
strategy, this practice is determined by the leader of the site and/or group in question. Many site
principals and leaders within the study district employed a leadership style that is distributed in
nature. In other words, the principal was aware that he or she could not provide all of the
necessary leadership alone and chose to build capacity and groom leaders from within the
school. Instructional leadership takes on many different forms at a school site: instructional
coaches, department chairs, grade-level leads, course leads, and even some teachers without a
formal title or role. A principal can choose how to empower or disempower these leaders.
Those principals who empower their instructional leaders are aligned with Odden’s philosophy
on school leadership. Many site principals in the study district subscribe to this style of
leadership.
Professional and best practices. At the time of this study, leaders within the study
district understood the importance of researching professional and best practices. In some cases,
outside consultants or professional developers were brought into the study district to provide the
knowledge and support necessary to move towards best practice. Examples of this include the
district’s work on PBIS, the counseling initiative, and the professional development provided for
secondary mathematics teachers. Each of these was chosen for development because data
demonstrated they were strategic areas to focus on in order to move more students towards
college and career readiness.
The study district was not unique in that it had many leaders who gained an
understanding of best practice through professional literature, networking, classes, the Internet,
ALLOCATING HUMAN CAPITAL RESOURCES 87
and, in some cases, action research. Once the knowledge of best practice is gained, the following
step is to figure out how to strategize, prioritize, and provide the necessary support so that others
in the district are able to improve practice. Odden’s tenth strategy runs counter to what many
departments or groups commonly do, which is make the decision that sounds the best. It is the
failure to search out research and best practice that leads to this problem.
Human Resource Allocation Recording Sheet
The instrumentation to assess the allocation and use of human resources in the study
school district was a Microsoft Excel based model developed by David Knight and Dr. Lawrence
O. Picus that allows the user to record school level personnel allocations and make comparisons
(Odden & Picus, forthcoming). Comparisons can be made among the actual school personnel
allocations, the Evidence-Based Model, and any other model for the distribution of resources.
The researcher utilized the third option through a hypothetical distribution. The recording sheet
organized site-level human resource allocation into categories, facilitating the comparison
described above. The instrument was designed to assist with research questions two, three, and
four, shown below:
2) How are human resources allocated across the study district and its 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?
Research question number two was answered by tallying the number of personnel within
each role in the human resource allocation recording sheet. The study district provided these
data, and the following section provides a listing and discussion of these results.
ALLOCATING HUMAN CAPITAL RESOURCES 88
Research question number three was answered through an analysis of the gaps
demonstrated in the human resource allocation recording sheet. In addition to the personnel tally
listed in the recording sheet, there were two other points of reference. The first point of
reference was the Evidence-Based Model. The second point of reference was a hypothetical
allocation of personnel in order to move the schools within the district towards the Evidence-
Based Model and what is known to be best practice. These two points of reference, along with
the actual human resource allocation practices of the study district, served as the primary points
of data. The recording sheet tabulated the differences among these three points as a basis for
discussion and analysis. These differences serve to answer the third research question.
Research question number four centers on the hypothetical allocation of personnel. The
point of reference referred to as the hypothetical allocation of personnel allowed for a simulation
of human resource allocation. This flexibility of the model allowed for a discussion of how
human resources might be re-allocated to better meet student needs in order to increase
achievement levels.
Research Question #2: Study District Human Resource Allocation
Human resources were allocated across the study district and its schools in a fairly
uniform fashion. This section discusses the summary charts from the human resource allocation
recording sheet. The reports were divided into three subgroups: middle schools, high schools,
and alternative schools. Finally, this section provides a summary chart for all secondary schools
in the study district.
Table 4.1 presents the middle school personnel chart. There were nine middle schools in
the study district, and their student enrollment numbers ranged from 916 students to 1,600
students, with an average of 1,253 students per school. Each middle school had one principal,
ALLOCATING HUMAN CAPITAL RESOURCES 89
two assistant principals, and at least one instructional coach. This personnel package existed for
each middle school regardless of size. The instructional coaches were sometimes known as
teachers on special assignment (TOSA).
In terms of classified employees, the number of special education aides ranged from 1.4
to 9.1, depending on the number of special education students. The number of secretaries, or
clerks, ranged from 4.72 to 5.47. Only two of the nine schools offered a summer school
program. One area worth noting is the row titled “ELL teachers.” In the study district, there
were teachers who taught English Learner classes for English-Language Arts. For the purpose of
this study, these teachers were recorded as “core teachers.” Many classified employees did not
work eight hours per day; they were not full time employees. Table 4.1 presents a complete
summary of middle school personnel employed in the study district. More detailed personnel
allocation data, sorted by individual school, are listed in the Appendix.

ALLOCATING HUMAN CAPITAL RESOURCES 90
Table 4.1
Middle School Personnel Allocation (by Category)

Source: Odden & Picus, forthcoming
Table 4.2 provides the summary of personnel at the high school level. There were seven
high schools in the study district with enrollment ranging from 319 students to 2,849 students.
The school with 319 students was an outlier, as the next largest school served 1,877 students.
Including the outlier school with the smallest enrollment, the average school size was 2,056
students. Each school, except the school with 319 students, had one principal and either four or
five assistant principals. The number of teachers at each site, including core, specialist, and
special education teachers, ranged from 13 to 107.6. Contrary to middle school, each high
school and alternative school ran a summer school program allowing for credit recovery. With
the exception of the smallest high school, all other comprehensive high schools funded a teacher
Title Current
Middle Schools
Principals 9.0
Assistant principals 18.0
Instructional coaches 11.4
Core teachers 281.4
Specialist teachers 92.6
SPED teachers 62.0
ELL teachers 0.0
Academic extra help staff 0.0
Non-academic pupil support 23.8
Nurses 3.6
Extended day / summer school staff 2.4
Instructional aides 0.0
Supervisory aides 6.8
SPED aides 44.3
Librarians 0.0
Library technicians 6.8
Library paraprofessionals 6.8
Secretaries / clerks 45.1
ALLOCATING HUMAN CAPITAL RESOURCES 91
headcount between 20 and 30 teachers for summer school, resulting in a district-wide teacher full
time equivalent (FTE) count of 21.4 for the high schools in the study district; the outlier school
did not hold summer school. Similarly, each high school had between 6.9 and 12.8 special
education aides and between 8.16 and 10.3 secretaries; this does not include the smallest high
school with zero special education aides and three secretaries. Table 4.2 presents a complete
summary of high school personnel employed in the study district. More detailed personnel
allocation data, sorted by high school, is listed in the Appendix.
Table 4.2
High School Personnel Allocation (by Category)

Source: Odden & Picus, forthcoming
Table 4.3 lists the number of employees working at the three alternative high schools, not
including independent study. Two of the three schools were known as the district’s credit
Title Current
High Schools
Principals 7.0
Assistant principals 28.0
Instructional coaches 12.6
Core teachers 369.7
Specialist teachers 124.1
SPED teachers 72.0
ELL teachers 3.0
Academic extra help staff 0.0
Non-academic pupil support 51.4
Nurses 3.0
Extended day / summer school staff 21.4
Instructional aides 6.0
Supervisory aides 18.5
SPED aides 59.8
Librarians 6.0
Library technicians 4.5
Library paraprofessionals 4.5
Secretaries / clerks 56.3
ALLOCATING HUMAN CAPITAL RESOURCES 92
recovery schools where students temporarily attended school in order to get caught up with
credits. The third school was for students in grades seven through twelve who had been expelled
from their home school. The students who attended the third school generally attended for the
remainder of the semester or school year. Afterwards, they returned to a comprehensive setting.
There were 688 students among the three schools: fifty students in the smallest school and 334
students in the largest school.
Table 4.3
Alternative School Purpose and Student Population

Source: Odden & Picus, forthcoming
Within the alternative school umbrella in the study district, there was one principal who
oversaw all three alternative settings. There was also an assistant principal at each of the three
schools who oversaw daily operations. School “Q” had 12.5 teachers, school “R” had 13.9
teachers, and school “S” had 7 teachers. In comparison to the Evidence-Based Model
specifications, schools “Q” and “R” had an approximate student to teacher ratio of 24:1. School
“S”, on the other hand, met the recommendations of the Evidence-Based Model with an
approximate student to teacher ratio of 7:1. In addition, the summer school program employed a
teacher headcount range of 8 to 15 teachers per alternative site due to the number of students
needing credit recovery in the district. The FTE count for summer school teachers at the
alternative schools was 4.1. The additional support staff was limited due to the size of each
school. Three secretaries worked at each of the two larger schools and one secretary worked at
the smallest school. Table 4.4 presents a complete summary of alternative school personnel
Name of School Purpose of School Student Population
Alternative School "Q" Credit Recovery 304
Alternative School "R" Credit Recovery 334
Alternative School "S" Disciplinary 50
ALLOCATING HUMAN CAPITAL RESOURCES 93
employed in the study district. More detailed personnel allocation data, sorted by each
alternative school, can be found in the Appendix.
Table 4.4
Alternative School Personnel Allocation (by Category)

Source: Odden & Picus, forthcoming
Table 4.5 provides a summary of all secondary schools in the study district. The
summary table presents the sum total of the previous three tables listed in this section. The total
number of personnel accounted for in this human resource allocation recording sheet was 1,516
employees. These employees helped operationalize 19 secondary schools in the study district.
Table 4.5 presents a complete summary of all secondary school personnel employed in the study
district.

Title Current
Alternative Schools
Principals 1.0
Assistant principals 3.0
Instructional coaches 1.0
Core teachers 24.9
Specialist teachers 6.5
SPED teachers 2.0
ELL teachers 0.0
Academic extra help staff 0.0
Non-academic pupil support 1.0
Nurses 0.0
Extended day / summer school staff 4.1
Instructional aides 0.0
Supervisory aides 2.0
SPED aides 1.5
Librarians 0.0
Library technicians 0.0
Library paraprofessionals 0.0
Secretaries / clerks 7.0
ALLOCATING HUMAN CAPITAL RESOURCES 94
Table 4.5
All Sample School Personnel Allocation (by Category)

Source: Odden & Picus, forthcoming
The next section examines the gap between current practice in the study district and the
outlined human resource allocation through the Evidence-Based Model.
Research Question #3: Human Resource Allocation Gap
Data analysis, in the context of this question, consisted of a gap analysis process between
two reference points: the study district’s actual human resource allocation and the Evidence-
Based Model’s specifications. The primary source of data was the study district’s actual human
resource allocation. This source was compared to the Evidence-Based Model’s specifications.
Phases one and two of the data analysis, as shown in Figure 4.15, address the third
research question. Phase one was an examination of the gap, looking for both large and small
Title Current
All Sample Schools
Principals 17.0
Assistant principals 49.0
Instructional coaches 25.0
Core teachers 676.0
Specialist teachers 223.2
SPED teachers 136.0
ELL teachers 3.0
Academic extra help staff 0.0
Non-academic pupil support 76.2
Nurses 6.6
Extended day / summer school staff 27.9
Instructional aides 6.0
Supervisory aides 27.2
SPED aides 105.6
Librarians 6.0
Library technicians 11.3
Library paraprofessionals 11.3
Secretaries / clerks 108.4
ALLOCATING HUMAN CAPITAL RESOURCES 95
differences between human resource allocation numbers. A large difference meant that two of
the data sources were not aligned within a specific personnel position. A small difference meant
that two of the data sources were aligned within a specific personnel position. These differences,
whether large or small, speak to the alignment of human resource allocation philosophies and
actual practice. For this purpose, the researcher provided the ratio between the current human
resource allocation and the Evidence-Based Model. This ratio was recorded and expressed as a
percent in the tables that follow within this section.
Phase two of data analysis assisted in understanding why the given gaps outlined in phase
one were large or small. This reports the estimated causes of the more significant gaps. Phases
three and four will be addressed in the response to research question number four in the next
section.

Figure 4.15. Visual Model of Data Analysis
Gap size definition. During phase one, it was necessary to determine which gaps were
“large” or “small.” The ratio between actual human resource allocation and that proposed by the
Phase 1:
Examination of
Gaps
Phase 2:
Examination of
Gap Causes
Phase 3:
Potential
Human
Resource
Tradeoffs
Phase 4:
Tradeoff
Possibilities/
Feasibility
ALLOCATING HUMAN CAPITAL RESOURCES 96
Evidence-Based Model served as the primary indicator. This indicator is labeled as “Ratio of
Current/EB” in the tables below. For the purposes of this study, any percentage in this category
between 75% and 125% was considered a “small” gap, meaning that the two data sources were
aligned to a certain degree. In this case, a “small” gap is defined as falling within 25 percentage
points of the Evidence-Based Model. Any percentages below 75% or above 125% are
considered a “large” gap, meaning the two data sources were not aligned. In other words, a
“large” gap is defined as falling more than 25 percentage points away from the Evidence-Based
Model. In each of the tables, the cutoff point of 25% was chosen as an arbitrary number as a
way to assist in determining the extent of each gap. The ratios are recorded for each human
resource category as well as in a total position count at the bottom of the table.
Presentation of gap data. The gap data for all middle schools in the study district are
presented in Table 4.6 below. As can be seen in the far right column, counts in six of the
categories are yielded small gaps: two administrator categories, specialist teachers, special
education teachers, special education aides, and secretaries/clerks. Of the six positions, three
were funded beyond the recommendations of the Evidence-Based Model: assistant principals,
specialist teachers, and special education aides. These positions may prove useful when
considering potential shifts in position counts without compromising the Evidence-Based Model.
The remaining twelve categories of position counts and the “total position count” line item all
yielded large. The most notable position in the middle school analysis is the core teacher
position due to its importance and high number in comparison with the other human resource
categories. Separate from the core teacher position, the highest ratio for the categories with a
large gap is the nurse position, listed at 23.9%. In addition, the specialist teacher position count
ALLOCATING HUMAN CAPITAL RESOURCES 97
is about 33% of the size of the core teacher count. The total position count at the middle school
level is at 46.8% of the Evidence-Based Model (Table 4.6).
Table 4.6
Middle School Personnel Comparison (by Category)

Source: Odden & Picus, forthcoming
The gap data for all high schools in the study district are presented in Table 4.7 below.
From the far right column, six of the counts yielded small gaps. At the high school level, two of
the six small gap categories were overfunded when compared with the Evidence-Based Model.
The two overfunded positions were assistant principals and special education aides. The counts
that yielded small gaps were similar to those from found in the middle school table. The
difference between the two tables is that the specialist teacher gap is considered large at the high
Gap
Ratio of
Title Current EB Current/EB
Middle Schools
Principals 9.0 9.0 0.0 100.0%
Assistant principals 18.0 16.1 1.9 112.0%
Instructional coaches 11.4 56.4 (45.0) 20.2%
Core teachers 281.4 451.2 (169.8) 62.4%
Specialist teachers 92.6 90.2 2.4 102.6%
SPED teachers 62.0 75.2 (13.2) 82.4%
ELL teachers 0.0 39.1 (39.1) 0.0%
Academic extra help staff 0.0 100.0 (100.0) 0.0%
Non-academic pupil support 23.8 145.1 (121.3) 16.4%
Nurses 3.6 15.0 (11.4) 23.9%
Extended day / summer school staff 2.4 166.6 (164.2) 1.4%
Instructional aides 0.0 0.0 0.0 N/A
Supervisory aides 6.8 50.1 (43.4) 13.5%
SPED aides 44.3 37.6 6.7 117.9%
Librarians 0.0 9.0 (9.0) 0.0%
Library technicians 6.8 0.0 6.8 N/A
Library paraprofessionals 6.8 0.0 6.8 N/A
Secretaries / clerks 45.1 50.1 (5.0) 90.0%
Total Position Counts 613.9 1310.8 (679.3) 46.8%
Position Counts
Current - EB
ALLOCATING HUMAN CAPITAL RESOURCES 98
school. One new position count gap is small at the high school level: the librarian position. The
counts in the twelve remaining categories and the “total position count” line item, once again, all
yielded large gaps. Two of the three positions with the most personnel, core teachers and
specialist teachers, hover at approximately 65% of the Evidence-Based Model’s specifications.
Many positions were understaffed. At the high school level, the specialist teacher count was
approximately 34% of the core teacher count. The total position count at the high school level
was at 49.8% of the Evidence-Based Model’s specifications and about three percentage points
higher than at the middle school level.
Table 4.7
High School Personnel Comparison (by Category)

Source: Odden & Picus, forthcoming
Gap
Ratio of
Title Current EB Current/EB
High Schools
Principals 7.0 7.0 0.0 100.0%
Assistant principals 28.0 24.0 4.0 116.7%
Instructional coaches 12.6 72.0 (59.4) 17.5%
Core teachers 369.7 575.7 (206.0) 64.2%
Specialist teachers 124.1 190.0 (65.9) 65.3%
SPED teachers 72.0 95.9 (23.9) 75.0%
ELL teachers 3.0 47.9 (44.9) 6.3%
Academic extra help staff 0.0 113.4 (113.4) 0.0%
Non-academic pupil support 51.4 170.9 (119.5) 30.1%
Nurses 3.0 19.2 (16.2) 15.6%
Extended day / summer school staff 21.4 188.9 (167.5) 11.3%
Instructional aides 6.0 0.0 6.0 N/A
Supervisory aides 18.5 72.0 (53.5) 25.7%
SPED aides 59.8 48.0 11.8 124.6%
Librarians 6.0 7.0 (1.0) 85.7%
Library technicians 4.5 0.0 4.5 N/A
Library paraprofessionals 4.5 0.0 4.5 N/A
Secretaries / clerks 56.3 72.0 (15.7) 78.2%
Total Position Counts 847.7 1703.8 (856.1) 49.8%
Position Counts
Current - EB
ALLOCATING HUMAN CAPITAL RESOURCES 99
The gap data for the alternative schools in the study district are presented in Table 4.8. In
comparison to the Evidence-Based Model, all counts’ gaps are considered to be large. The two
underfunded positions, in reference to the Evidence-Based Model, were principals and core
teachers. All other positions were not funded in the Evidence-Based Model. Therefore, any
personnel allocated to the alternative school setting, with the exception of principals and core
teachers, would create a large gap. If that information is coupled with the smaller gap in the total
position count line item, one can conclude that the alternative schools in the study district did a
better job of funding positions. However, the positions funded were not the same positions
specified through the Evidence-Based Model. In the Evidence-Based Model, one core teacher
and one administrator are funded for every seven students. The total position count at the
alternative school level is 53.4% that of the Evidence-Based Model’s specifications and
approximately 4% more than the high school total and 7% more than the middle school total.

ALLOCATING HUMAN CAPITAL RESOURCES 100
Table 4.8
Alternative School Personnel Comparison (by Category)

Source: Odden & Picus, forthcoming
The gap data for all schools in the study district are shown in Table 4.9. This table most
closely aligns with the percentages found in the middle school table. Similar to the middle
school data, six of the count categories yielded small gaps: both administrator categories,
specialist teachers, special education teachers, special education aides, and secretaries/clerks.
Again, the remaining twelve position count categories all yielded large gaps. The specialist
teacher position count was approximately 33% of the core teacher position count. This is the
approximate proportion across schools and grade levels. Across all sample schools, two of the
positions were overfunded in relation to the Evidence-Based Model. The remaining positions
were underfunded. This mismatch of funding suggests either a philosophical or knowledge-
Gap
Ratio of
Title Current EB Current/EB
Alternative Schools
Principals 1.0 3.0 (2.0) 33.3%
Assistant principals 3.0 0.0 3.0 N/A
Instructional coaches 1.0 0.0 1.0 N/A
Core teachers 24.9 98.3 (73.4) 25.3%
Specialist teachers 6.5 0.0 6.5 N/A
SPED teachers 2.0 0.0 2.0 N/A
ELL teachers 0.0 0.0 0.0 N/A
Academic extra help staff 0.0 0.0 0.0 N/A
Non-academic pupil support 1.0 0.0 1.0 N/A
Nurses 0.0 0.0 0.0 N/A
Extended day / summer school staff 4.1 0.0 4.1 N/A
Instructional aides 0.0 0.0 0.0 N/A
Supervisory aides 2.0 0.0 2.0 N/A
SPED aides 1.5 0.0 1.5 N/A
Librarians 0.0 0.0 0.0 N/A
Library technicians 0.0 0.0 0.0 N/A
Library paraprofessionals 0.0 0.0 0.0 N/A
Secretaries / clerks 7.0 0.0 7.0 N/A
Total Position Counts 54.0 101.3 (47.2) 53.4%
Position Counts
Current - EB
ALLOCATING HUMAN CAPITAL RESOURCES 101
based misalignment with the Evidence-Based Model. The total position count for all schools in
the study district is 48.6% of the personnel allocated through the Evidence-Based Model. In
total, the Evidence-Based Model would require an additional 1,600 employees in order to
appropriately staff the secondary schools in the study district.
Table 4.9
All Sample School Personnel Comparison (by Category)

Source: Odden & Picus, forthcoming
Human resource gap analysis. To analyze any performance gap, in this case a human
resource allocation gap, Clark and Estes (2008) listed the three critical factors: 1) Knowledge
and skills, 2) Motivation, and 3) Organizational barriers (p. 43). All three factors must be taken
into consideration. Failing to acknowledge one of the factors could lead to a breakdown in the
Gap
Ratio of
Title Current EB Current/EB
All Sample Schools
Principals 17.0 19.0 (2.0) 89.5%
Assistant principals 49.0 40.1 8.9 122.3%
Instructional coaches 25.0 128.4 (103.4) 19.5%
Core teachers 676.0 1125.2 (449.2) 60.1%
Specialist teachers 223.2 280.2 (57.0) 79.7%
SPED teachers 136.0 171.2 (35.2) 79.5%
ELL teachers 3.0 87.0 (84.0) 3.5%
Academic extra help staff 0.0 213.3 (213.3) 0.0%
Non-academic pupil support 76.2 316.0 (239.9) 24.1%
Nurses 6.6 34.2 (27.6) 19.3%
Extended day / summer school staff 27.9 355.6 (327.6) 7.9%
Instructional aides 6.0 0.0 6.0 N/A
Supervisory aides 27.2 122.1 (94.9) 22.3%
SPED aides 105.6 85.6 20.0 123.4%
Librarians 6.0 16.0 (10.0) 37.5%
Library technicians 11.3 0.0 11.3 N/A
Library paraprofessionals 11.3 0.0 11.3 N/A
Secretaries / clerks 108.4 122.1 (13.7) 88.8%
Total Position Counts 1515.6 3115.9 (1600.3) 48.6%
Position Counts Current -
EB
ALLOCATING HUMAN CAPITAL RESOURCES 102
process, whereby a major cause of the performance gap is not addressed. The most obvious
recognized gap to consider is the actual count of necessary personnel required to reach the
specifications of the Evidence-Based Model. At the time of this study, approximately 1,516
people were employed at the secondary level in the study district. An additional 1,600
employees would be needed to match the requirements of the Evidence-Based Model. The
causes of this significant gap are addressed and explained below. After an outline of the gaps,
recommendations for the study district are made in the following section.
Knowledge and skills. The knowledge and skills factor refers to the information needed
in order to accomplish a task. The knowledge and skills factor is typically addressed through
professional development or training. This study sought to better understand the knowledge and
skills necessary for the organization to close the gap between current human resource allocation
and the Evidence-Based Model’s specifications.
In the case of the study district’s allocation of human resources, the Evidence-Based
Model is one model by which a site administrator could seek to increase student achievement. If
this were a chosen goal at a school site, those who allocate funding towards positions would need
to understand the Evidence-Based Model. Because district office personnel make decisions
about class sizes and funding allocations, it would benefit them to know how different human
resource assignments can affect student achievement. As previously mentioned, the framework
for best practice of human resource allocation utilized here is the Evidence-Based Model.
Addressing the knowledge barrier from another perspective, site administrators also need
to understand the Evidence-Based Model and how it can be applied at the school site given the
school’s financial and human resource context. Many site principals understand their school’s
financial circumstances, but they are unaware of how they can move towards the Evidence-
ALLOCATING HUMAN CAPITAL RESOURCES 103
Based Model or an alternate vision of school improvement related to human resources. Without
this knowledge, the human resources allocation gap might not be noticed or addressed, even on a
smaller scale.
At the time of this study, due to the fact that some positions were overfunded in
comparison to the Evidence-Based Model while others were underfunded, knowledge of the
Evidence-Based Model’s specifications appears to be one of the contributors to the gap at hand.
If that were not the case, all position categories would be underfunded. As seen in tables 4.6
through 4.9, several position categories were overfunded, even if to small degrees. There are
human resources tradeoffs that will allow districts and schools to support students towards higher
achievement. Some of these potential tradeoffs were reviewed through examining the answer to
research question number four in the next section. However, no knowledge or skills will provide
an additional 1,600 employees to match the Evidence-Based Model. This gap must be looked
analyzed considering the economic state, resulting in smaller, yet possible, shifts in personnel
towards the Evidence-Based Model and increased student achievement. It is necessary to
understand the specifications of the Evidence-Based Model and prioritize the first steps of
implementation, even though the shifts may be considered small.
Motivation. According to Clark and Estes (2008), the motivational barrier boils down to
“choosing to work toward a goal,” “persisting at it until it is achieved,” and investing the mental
effort necessary to get the job done (p. 44). This factor refers to being motivated enough to
continue working towards a goal until it is accomplished.
The motivational factor is one that should be examined through the eyes of the study
district’s decision makers. In the realm of the question at hand, the decision makers are district
office personnel and, to a lesser degree, school site principals. The district office personnel
ALLOCATING HUMAN CAPITAL RESOURCES 104
responsible for human resources allocation were motivated to increase student achievement
while remaining fiscally solvent. In other words, there was no lack of motivation for allocating
human resources in a manner that maximizes student achievement.
School site principals were also motivated to allocate human resources with the intent to
increase student achievement, as evidenced by conversations with site principals in the study
district. Principals were incentivized to regularly work towards offering professional
development, tutoring, and improving classroom instruction. These were some of the strategies
that principals generally used and dedicated their time to in order to increase student
achievement. The site principals were committed to their work and wanted to see the students
and teachers succeed.
From the reasons stated above, principals and district office personnel were likely
motivated to increase student achievement. It is not the motivation that is lacking, but the
knowledge and skills required to allocate human resources in the best possible manner. This
statement shows that site principals need additional training and knowledge when it comes to
best practice related to human resource allocation and not additional motivation.
Organizational barriers. Organization barriers refer to a lack of funding, structures, and
resources; this can also indicate systems that do not work well. These structural barriers inhibit
progress from being made as it relates to closing the defined gap. With reference to this study,
one might examine whether there were funding, structural, or resource-related barriers that are
holding the study district back from achieving the allocation of human resources outlined within
the Evidence-Based Model.
One of the organizational barriers that contribute to the gap between the current
allocation of human resources and the Evidence-Based Model is funding. As reviewed
ALLOCATING HUMAN CAPITAL RESOURCES 105
previously in this study, the economy suffered in years prior to this study, resulting in a reduction
of funding for public education. Because of the lack of funding, many site-based positions were
cut. At the same time, if public education had been fully funded in California, there would still
be a significant gap between actual practice of human resource allocation and the Evidence-
Based Model. Table 4.9 demonstrates a gap of 1,600 employees in order to adequately staff the
secondary schools of the study district at the levels of the Evidence-Based Model.
The second organizational barrier that contributes to the gap can be described as a lack of
alignment between the study district and the certificated and classified unions regarding most
beneficial placement of human resources. The district could simply say that it would reduce the
number of special education teachers by fifteen in order to add thirty supervisory aides. At the
time of this study, there were two significant issues with many hypothetical personnel shifts
similar to this one. First, the certificated union had a collective bargaining agreement with the
school district. The agreement, or contract, guided some decisions related to certificated
employees, especially relating to loss of employment. One of the union’s priorities was to
protect their members. Therefore, the union was involved in any situation in which an employee
might be terminated or let go. The district had to work in partnership with the unions in order to
make personnel changes. Second, any shift similar to the one described above might have
created a sense of friction between the two unions as well as between the unions and the school
district.
In summary, two of the three critical areas contributed to the gap between current human
resource allocation and the Evidence-Based Model. The two areas were knowledge and skills
and organizational barriers. The knowledge and skills area could be addressed through
professional development and dialogue among strategic individuals. The organizational barriers,
ALLOCATING HUMAN CAPITAL RESOURCES 106
as they were encountered, were not as easily influenced by the study district. Both the levels of
funding and the difficulty of reassigning positions and/or personnel were complicated and, in
some ways, beyond the scope of the district. Motivation was not considered to be a significant
contributor to the gap. The next section examines the fourth research question. Specifically,
human resources tradeoffs will be proposed in conjunction with the accompanying feasibility of
each tradeoff.
Research Question #4: Human Resource Additional Gaps and Tradeoff Proposal
The gap between the actual human resources allocation and the Evidence-Based Model
was explored in the third research question. This section identifies and describes two additional
gaps: 1) the gap between the current allocation of human resources and the study district’s
hypothetical allocation of personnel and 2) the gap between the study district’s hypothetical
allocation of personnel and the Evidence-Based Model. The fourth research question also
addresses the next stages of potential action as a result of the data and research brought to light.
Human resource tradeoffs are proposed and their feasibility discussed for the study district. In
reference to the visual model of data analysis, Figure 4.16, phases three and four will be
addressed.

ALLOCATING HUMAN CAPITAL RESOURCES 107

Figure 4.16. Visual Model of Data Analysis
The third phase of data analysis consisted of looking at potential human resource
tradeoffs with the intention of placing personnel in positions that would have the greatest impact
on student achievement. This could mean reducing the number of personnel in one position
while increasing the number of personnel in another position. The potential impact of these
tradeoffs is also discussed.
Phase four of the data analysis consisted of determining whether the tradeoffs described
in phase three were possible and feasible. In other words, after considering the outside
restrictions placed on the study district, are the tradeoffs possible? These four phases of data
analysis provide a picture of actual practice, potential tradeoffs, and possibilities for
improvement in the area of human resource allocation.
Potential human resource recommendations and tradeoffs. Personnel decisions
should be guided by research whenever possible. In reference to the study district and the task of
investigating potential human resource tradeoffs, Odden’s work on estimated effect sizes proves
Phase 1:
Examination of
Gaps
Phase 2:
Examination of
Gap Causes
Phase 3:
Potential
Human
Resource
Tradeoffs
Phase 4:
Tradeoff
Possibilities/
Feasibility
ALLOCATING HUMAN CAPITAL RESOURCES 108
to be useful. Odden et al. (2005) summarized estimated effect sizes of major recommendations
in Table 4.10 below. In consideration of the actual human resource allocation in the study
district, one must examine potential effect sizes of key positions as they relate to school settings.
The activities and daily duties of the positions with the highest effect sizes assist in providing the
largest student achievement increases. These effect sizes are a result of proper implementation
of the position’s job description. Therefore, it may prove beneficial for the study district to shift
funding from personnel positions with lower effect sizes, and, therefore, less effective activities
to those with higher effect sizes. This shift would create a human resource tradeoff, assuming
individuals in these positions had strong skills in implementing their responsibilities. As seen in
Table 4.10, two of the positions with the largest potential effect sizes were instructional coaches
and tutors, or academic extra help staff. As such, two of the three human resource
recommendations outlined below relate to these positions. The feasibility of these
recommendations in terms of finances, union issues, and other questions will be discussed in the
next section.

ALLOCATING HUMAN CAPITAL RESOURCES 109
Table 4.10
Estimated Effect Sizes of Major Recommendations
Recommended Program Effect Size
Full Day Kindergarten 0.77
Class Size of 15/16 in Grades K-3
Overall
Low Income and Minority Students

0.25
0.50
Multi-Age Classrooms
Multi-Grade Classrooms
Multi-Age Classrooms

Professional Development with Classroom
Instructional Coaches
1.25 to 2.70
Tutoring, 1-1 0.4 to 2.5
English-Language Learners 0.45
Extended-Day Programs
Structured Academic Focused Summer School 0.45
Embedded Technology 0.30 to 0.38
Gifted and Talented
Accelerated Instruction or Grade Skipping
Enrichment Programs

0.5 to 1.0
0.4 to 0.7
Source: Odden et al., 2005.
As a result of the data collected and research completed within this study, there are three
recommendations for the study district as they pertain to human resource allocation for
maximum student achievement given the fiscal climate. Following the initial description of the
three recommendations, a series of hypothetical actions is also described, allowing for fulfillment
of the three recommendations and a shift in human resources at no additional cost to the district.
The next section discusses the feasibility of these recommendations and accompanying
hypothetical actions.
Recommendation #1. Increase the number of instructional coaches allocated to each
school site. The effect size for the activities associated with instructional coaches ranged from
1.25 to 2.70 (Odden, 2005). A portion of the instructional coach effect size depends upon his or
her actions within the job. Much of the instructional coach’s role would be to provide
professional development and follow-up classroom support for teachers in alignment with
ALLOCATING HUMAN CAPITAL RESOURCES 110
district and site initiatives. Because it was not possible, at the time of this study, to fully fund the
number of instructional coaches recommended by the Evidence-Based Model, the study district
should take incremental steps by funding a smaller number of instructional coaches.
Recommendation #2. Increase the number of academic extra help staff. The effect size
for the activities associated with each academic extra help staff, or tutor, ranged from 0.4 to 2.5
(Odden, 2005). The role of the academic extra help staff would be to provide small group
targeted tutoring based on site needs. In the study district, the targeted tutoring would most
likely be for English learners and those struggling with core subject areas. Similar to the first
recommendation, it was not possible to fully fund the number of academic extra help staff
required by the Evidence-Based Model. Therefore, the second recommendation helps the study
district take incremental steps by funding a limited number of academic extra help staff.
Recommendation #3. Reduce the ratio of specialist teachers to core teachers. The actual
study district ratio between specialist teachers and core teachers was 33%. In other words, there
was one specialist teacher for every three core teachers. This recommendation would reduce the
ratio to 20%. In other words, in the hypothetical model, there would be one specialist teacher for
every five core teachers. This exchange shifts the focus of instruction to the core subject areas
while allowing for electives and non-core subjects on a smaller scale. In reference to the
specifications of the Evidence-Based Model, the ratio of specialist teachers to core teachers is set
to 33% at the high school level and 20% at the middle school level.
According to the three recommendations for the study district, there is a need to increase
the number of instructional coaches, academic extra help staff, and core teachers. The human
resource allocation recording sheet allowed for hypothetical situations based on the user’s
priorities. In this case, the recommendations listed above were inputted into the human resource
ALLOCATING HUMAN CAPITAL RESOURCES 111
allocation recording sheet, and the result was additional costs. The hypothetical model funded
54 instructional coach positions (addition of 29 positions). Each comprehensive middle and high
school was allocated three instructional coaches. The alternative schools were allocated two
instructional coaches each. The hypothetical model also funded 35 academic extra help staff
positions (addition of 35 positions). Each comprehensive middle and high school was allocated
two academic extra help positions while the alternative schools were allocated one academic
extra help position each. Last, the hypothetical model funded 736 core secondary teachers
(addition of 60 positions). Table 4.11 presents a summary of added positions.
Table 4.11
Summary of Added Positions
Position Title Hypothetical Count +/-
Instructional Coaches +29 positions
Academic Extra Help Staff +35 positions
Core Secondary Teachers +60 positions
Total Gained Positions +124 positions
Source: Odden & Picus, forthcoming
The human resource allocation recording sheet calculated costs of new personnel and
savings from reduced positions. In order to achieve each of the previous recommendations, a
series of hypothetical reductions was necessary. In the following position categories, positions
were cut because they were overfunded in comparison to the Evidence-Based Model: assistant
principals (loss of 6 positions), instructional aides (loss of 6 positions), special education aides
(loss of 21.6 positions), and library technicians (loss of 11.3 positions). These position counts
were cut to closely match the Evidence-Based Model due to the fact that they were considered to
be overfunded.
ALLOCATING HUMAN CAPITAL RESOURCES 112
The cuts described above were not significant enough to cover the added positions. In
order to properly fund the added positions, it was necessary to cut two additional position
categories: specialist teachers and special education teachers. Each of the comprehensive middle
and high schools lost one specialist teacher (loss of 16 positions) and one special education
teacher (loss of 16 positions). An additional 60 specialist teacher positions were exchanged (loss
of 60 positions) in order to fully fund the addition of core teachers described earlier. One
alternative would have been to increase class sizes in order to gain the flexibility of adding other
positions. This action would have resulted in a move away from the Evidence-Based Model,
specifically as it relates to core teachers. There were other alternatives that allowed for a shift
towards the Evidence-Based Model without sacrificing class sizes. Table 4.12 presents a
summary of reduced positions.
Table 4.12
Summary of Reduced Positions
Position Title Hypothetical Count +/-
Assistant Principals -6 positions
Instructional Aides -6 positions
Special Education Aides -21.6 positions
Library Technicians -11.3 positions
Special Education
Teachers -16 positions
Specialist Teachers -76 positions
Total Reduced Positions -136.9 positions
Source: Odden & Picus, forthcoming
A review of Tables 4.11 and 4.12 demonstrates the number of reduced positions is greater
than the number of added positions. This was mostly due to the cost of each position. The
salary and benefit total cost of the personnel in the two tables were approximately equal.
Therefore, this plan would result in no additional cost to the study district.
ALLOCATING HUMAN CAPITAL RESOURCES 113
Through the hypothetical cuts described above, each of the three outlined
recommendations was achieved. Table 4.13 provides a complete listing of position counts and
comparisons among the current allocation of human resources, the hypothetical model, and the
Evidence-Based Model. The ratio in Table 4.13 was slightly changed from previous tables in
order to demonstrate the ratio, or gap, between the hypothetical model and the Evidence-Based
Model instead of the ratio between the actual allocation and the Evidence-Based Model. In
Table 4.13, the column that best describes the hypothetical tradeoffs is the column titled “Gap:
Current – Hypothetical.” All of the changes moved towards an allocation of human resources
that would be more conducive to increased student achievement.
ALLOCATING HUMAN CAPITAL RESOURCES 114
Table 4.13
All Sample School Personnel Hypothetical Model (by Category)
Source: Odden & Picus, forthcoming
Tradeoffs feasibility. Changes in human resource allocations often result in a variety of
complications, especially when considering reduction of personnel and personal livelihood. The
numbers and tradeoffs listed thus far have benefits and drawbacks. The change in allocation
should be considered through three perspectives: a financial perspective, an academic
perspective, and a union perspective. Each of these perspectives allow for determination of the
feasibility of the hypothetical model.
The financial perspective is one of the perspectives that drive decisions in difficult
economic times. Districts want to know if proposed changes are affordable. If a proposal
increases costs, it is likely that it will be turned down. In the case of the hypothetical model, the
change in human resources allocations results in no additional costs or savings to the district.
Ratio of
Title Current Hypothetical EB Hyp/EB
All Sample Schools
Principals 17.0 17.0 19.0 0.0 (2.0) 89.5%
Assistant principals 49.0 43.0 40.1 6.0 8.9 107.4%
Instructional coaches 25.0 54.0 128.4 (29.0) (103.4) 42.1%
Core teachers 676.0 736.0 1125.2 (60.0) (449.2) 65.4%
Specialist teachers 223.2 147.2 280.2 76.0 (57.0) 52.5%
SPED teachers 136.0 120.0 171.2 16.0 (35.2) 70.1%
ELL teachers 3.0 3.0 87.0 0.0 (84.0) 3.5%
Academic extra help staff 0.0 35.0 213.3 (35.0) (213.3) 16.4%
Non-academic pupil support 76.2 76.2 316.0 0.0 (239.9) 24.1%
Nurses 6.6 6.6 34.2 0.0 (27.6) 19.3%
Extended day / summer school staff 27.9 27.9 355.6 0.0 (327.7) 7.8%
Instructional aides 6.0 0.0 0.0 6.0 6.0 N/A
Supervisory aides 27.2 27.2 122.1 0.0 (94.9) 22.3%
SPED aides 105.6 84.0 85.6 21.6 20.0 98.1%
Librarians 6.0 6.0 16.0 0.0 (10.0) 37.5%
Library technicians 11.3 0.0 0.0 11.3 11.3 N/A
Library paraprofessionals 11.3 11.3 0.0 0.0 11.3 N/A
Secretaries / clerks 108.4 108.4 122.1 0.0 (13.7) 88.8%
Total Position Counts 1515.6 1502.8 3115.9 12.9 (1600.3) 48.2%
Position Counts Current -
Hypothetical
Current -
EB
Gap
ALLOCATING HUMAN CAPITAL RESOURCES 115
The cost of the additional positions was approximately equal to the savings of the reduced
positions. Financially, the tradeoffs are considered to be feasible due to no additional costs.
From an academic perspective, the increases followed the research-based
recommendations presented earlier in this chapter. An increase in instructional coaches,
academic extra help staff, and core teachers would assist students from an academic perspective
in multiple ways. Instructional coaches would provide professional development and support to
teachers. Academic extra help staff would strategically provide small group and one on one
tutoring for students. Additional core teachers would shift the master schedule emphasis from
elective courses to core courses. Academically, the tradeoffs are considered to be feasible due to
the aforementioned benefits.
The third consideration is the perspective of the employee unions. In sum total, the
positions were lost in the following areas: management (loss of 6 positions), classified
employees (loss of 38.9 positions), and certificated employees (loss of 92 positions). The total
loss of positions, regardless of category, is 136.9 positions. At the same time, there was an
increase in the number of certificated employees in other areas (addition of 124 positions). From
a union perspective, administrators do not belong to a union. Therefore, a reduction of
administrative positions would not likely cause union-related problems. For the classified union,
38.9 positions were being cut while none were added. For that reason, the classified union
would be likely to oppose the hypothetical model. The certificated union would be pleased with
the net addition of 32 positions. However, that would only be possible in the eyes of the union if
no certificated employees lost their jobs. In other words, positional shifts would be necessary.
The union would also have concerns regarding class size increases. Many of the added
positions were not classroom teachers. Essentially, each comprehensive middle and high school
ALLOCATING HUMAN CAPITAL RESOURCES 116
would lose one specialist teacher and one special education teacher. At the time of this study,
most special education teachers in the study district operated in a “collaboration” model where
they joined regular education teachers in a supportive role. In this system, the special education
teachers were not assigned a group of students to teach on a daily basis. On the other hand,
specialist teachers were assigned students to teach. Therefore, classes would have to increase to
compensate for the loss of teaching staff. A loss of one teacher for each middle and high school
would result in a minor class size increase, depending on the staffing of the school. In the least
staffed school, a loss of one teacher would result in an increase of approximately two additional
students for all other classes. In the most well-staffed school, a loss of one teacher would result
in an increase of approximately 0.3 additional students for all other classes. One would have to
check the collective bargaining agreement regarding class sizes to see if any portion of the
contract would be broken through these changes. From a union perspective, the tradeoffs are
less feasible due to the loss of positions for classified employees and the shifted positions for
certificated employees.
The union complications are the one drawback regarding feasibility. However, there are
still options for moving forward with the hypothetical model over an extended period of time.
The recommendation for the study district, given the union limitations, would be to leave the
designated positions unfilled as employees retire or leave the district. Once the money becomes
available, the study district would begin to create additional positions and hire to fill the
positions. As a result, human resource allocations, and the underlying funding that supports the
positions, would be used in a manner that would increase student achievement for the secondary
schools in the study district. With these steps, human resources would be strategically re-
allocated to align with strategies that improve student achievement.
ALLOCATING HUMAN CAPITAL RESOURCES 117
Chapter 5: Discussion
The fifth, and final, chapter of this study provides an overview of the process, a summary
of the findings, and concluding comments.
Purpose of the Study
The purpose of this study was to research the extent to which one district allocated
human resources toward research-based strategies for school improvement. Because of the
economic downturn, the study focused on one large urban school district in Southern California,
referred to herein as the study district. The study district provided actual human resource
allocation data for each of its secondary schools and made it possible to examine how it allocated
personnel when facing the restrictions of increasingly limited budget resources. Comparisons
were made between the Evidence-Based Model (Odden & Picus, 2008) and the actual allocation
of human resources at the secondary level in the study district.
To that end, this study provides insight into one district’s management of human
resources and the extent to which it compares to the Evidence-Based Model. The data collected
indicate the decisions and tradeoffs the study district made in order to design instruction around
strategies that are likely to maximize student achievement. Also, the study examined which
tradeoffs the district could make based on a research-focused model. This study also contributed
to the field of research related to allocation of human resources in a fiscally difficult time,
specifically with relation to student achievement.
Summary of Study Process
Chapter 1 provides an overview of the current state of education as it relates to the
national economy as well California’s economy. Specifically, limited resources are presented as
a challenge for education. Four methods of determining adequacy are also explained (Odden,
ALLOCATING HUMAN CAPITAL RESOURCES 118
2003; Rebell, 2007). Chapter One also outlined four research questions to guide the remaining
work of the study.
The four research questions were:
1) What research-based human resource allocation strategies improve student achievement?
2) How are human resources allocated across the study district and its 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?
Chapter 2 reviews relevant literature outlining the following topics: effective practices for
improving student achievement, allocation and use of human resources, limited resources and
fiscal constraints, and gap analysis. Each of these areas is viewed through the lens of research
related to the utilization of resources. One effective practice for improving student achievement
that played a central role to the remainder of the study was Odden’s Ten Strategies for Doubling
Student Performance:
1) Understanding the performance problem and challenge
2) Set ambitious goals
3) Change the curriculum program and create a new instructional vision
4) Formative assessments and data-based decision making
5) Ongoing, intensive professional development
6) Using time efficiently and effectively
7) Extending learning time for struggling students
8) Collaborative, professional culture
ALLOCATING HUMAN CAPITAL RESOURCES 119
9) Widespread and distributed instructional leadership
10) Professional and best practices
Chapter Two also revealed that the Evidence-Based Model would serve as the desired
model for achieving adequacy for the purposes of this study. A detailed description of the
Evidence-Based Model was provided for elementary, middle, and high schools.
Chapter Three discussed the methodology of the study. The study district was described
and the instrumentation was outlined. In particular, the human resource allocation recording
sheet was provided as a tool to record school level personnel allocations and allow the user to
compare those allocations to both the Evidence-Based Model and to any desired distribution of
resources the user wished to consider. The primary purpose of the human resource allocation
recording sheet was assist with the response to research questions two, three, and four:
2) How are human resources allocated across the study district and its 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 study district provided its “people book” in order to populate the values of the human
resource allocation recording sheet. A gap analysis process was used to analyze the gaps among
the actual human resource allocation, the Evidence-Based Model, and the study district’s
hypothetical allocation of personnel. A visual model of the proposed gap analysis was also
provided (Figure 5.1).
ALLOCATING HUMAN CAPITAL RESOURCES 120

Figure 5.1. Visual Model of Gap Analysis
Chapter 4 describes the study district, the study district’s county, and the state of
California. Student demographic and achievement data provide context and background
information. Chapter 4 also addresses the findings from the study by providing the answers to
the four research questions. The study district is viewed through the lens of Odden’s Ten
Strategies for Doubling Student Performance. The gaps among the study district’s human
resource allocation, the Evidence-Based Model, and a hypothetical allocation of human
resources is analyzed and described. These results are presented along with three
recommendations for the study district.
Summary of Findings
This study was, at its simplest level, based on a gap analysis process using a human
resource allocation sheet. The most obvious recognized gap to consider was the actual count of
necessary personnel required to reach the specifications of the Evidence-Based Model. At the
time of this study, approximately 1,516 people were employed at the secondary level in the study
Study District's Actual
Human Resource
Allocation
Study District's
Hypothetical
Allocation of Personnel
Evidence-Based Model
Specifications
ALLOCATING HUMAN CAPITAL RESOURCES 121
district. An additional 1,600 employees would be needed to match the requirements of the
Evidence-Based Model. In other words, the study district was staffed at approximately 48.6% of
the Evidence-Based Model at the secondary level.
At the time of this study, due to the fact that some positions were overfunded in
comparison to the Evidence-Based Model while others were underfunded, knowledge of the
Evidence-Based Model specifications appeared to be one of the contributors to the gap at hand.
However, no knowledge or skills would provide an additional 1,600 employees to match the
Evidence-Based Model. This gap should be looked at while considering the economic state,
resulting in incremental shifts in personnel towards the Evidence-Based Model and increased
student achievement. Although it may not have been possible for the study district to meet the
personnel recommendations of the Evidence-Based Model, there are steps that could be taken to
move the district towards the model with a limited number of staff. In order for that to happen,
some resource reallocation would likely be needed.
Another significant barrier was the organizational barrier. The first organizational barrier
that contributed to the gap was funding. As reviewed previously in this study, the economy
suffered in years prior to this study, resulting in a reduction of funding for public education. At
the same time, if public education were fully funded in California, there would still be a
significant gap between current practice of human resource allocation and the Evidence-Based
Model. The second organizational barrier that contributed to the gap could be described as a
lack of alignment between the study district and the certificated and classified unions regarding
most beneficial placement of human resources. In summary, two of the three critical areas
contributed to the gap between current human resource allocation and the Evidence-Based
Model.
ALLOCATING HUMAN CAPITAL RESOURCES 122
Due to the economic difficulty within California at the time of this study, it was
unrealistic to recommend, or request funding for, a fully funded Evidence-Based Model.
However, the researcher provided three recommendations for the study district in order to better
align with the Evidence-Based Model at no additional cost to the district.
Recommendation #1. Increase the number of instructional coaches allocated to each
school site. The effect size for the activities associated with each instructional coach ranged
from 1.25 to 2.70 (Odden, 2005). A portion of the instructional coach effect size depends upon
his or her actions within the job. Much of the instructional coach’s role would be to provide
professional development and follow-up classroom support for teachers in alignment with
district and site initiatives. Because it was not possible to fully fund the number of instructional
coaches recommended by the Evidence-Based Model, the study district should take incremental
steps by funding a smaller number of instructional coaches.
Recommendation #2. Increase the number of academic extra help staff. The effect size
for the activities associated with each academic extra help staff, or tutor, ranged from 0.4 to 2.5
(Odden, 2005). The role of the academic extra help staff would be to provide small group
targeted tutoring based on site needs. In the study district, the targeted tutoring would most
likely be for English learners and those struggling with core subject areas. Similar to the first
recommendation, it was not possible to fully fund the number of academic extra help staff
required by the Evidence-Based Model. Therefore, the second recommendation helps the study
district take incremental steps by funding a limited number of academic extra help staff.
Recommendation #3. Reduce the ratio of specialist teachers to core teachers. The actual
study district ratio between specialist teachers and core teachers is 33%. In other words, there is
one specialist teacher for every three core teachers. This recommendation would reduce the ratio
ALLOCATING HUMAN CAPITAL RESOURCES 123
to 20%. In other words, in the hypothetical model, there would be one specialist teacher for
every five core teachers. This exchange would shift the focus of instruction to the core subject
areas while allowing for electives and non-core subjects on a smaller scale. The goal of this
recommendation was not to reduce the number of teachers as there were already relatively few
teachers when compared to the specifications of the Evidence-Based Model. A reduction of
teachers would have moved the study district further away from the Evidence-Based Model.
The financial perspective was one of the perspectives that drove decisions due to the
economy. Districts want to know if proposed changes are affordable. If a proposal increases
costs, it is likely that it would be turned down. In the case of the hypothetical model, the change
in human resources allocations resulted in no additional costs or savings to the district. The cost
of the additional positions was approximately equal to the savings of the reduced positions.
Financially, the tradeoffs are considered to be feasible due to no additional costs.
Limitations
The data collected for use in this study were limited to the secondary schools within one
large urban school district in Southern California. The results from this study may not be
generalizable to other school districts. In addition, the data collected for this study came from
the 2012-2013 school year. The data was not longitudinal, but it was, rather, a snapshot of
human resource allocation. The data do not demonstrate trends or patterns over time. Last, the
extent of district alignment to Odden’s Ten Strategies for Doubling Student Performance was
based on interviews with district office personnel and secondary site principals. As a result, the
data collected through the interviews could have been subjective in nature.

ALLOCATING HUMAN CAPITAL RESOURCES 124
Concluding Comments
At the time of this study, the budget crisis in the state of California, accentuated by the
troubled economy, resulted in significant cuts to public education. Districts and schools were
forced to do more with less. Accountability for education continued to play a prominent role in
the minds of policymakers and the general public, and results are still demanded despite
decreased funding.
Districts and schools were forced to be creative with their approach to educating students.
Because funding was limited, districts had to reflect, examining what they believed to be the best
use of the available money. The described dilemma increased the importance of this human
resources study. The available research gives clear guidelines as to what works when it comes to
human resource allocation.
Unfortunately, at the time of this study, a wide chasm separated the state of California
from the recommendations of the Evidence-Based Model, mostly due to the higher staffing
levels and, thus, greater expense of the recommendations of the Evidence-Based Model. Even
so, districts can use research to inform human resource decisions, inching closer to what could be
described as best practice. In many cases, including that of the study district, shifts can be made
using existing resources to better serve the needs of our students. As a result, more students will
graduate from school prepared for college, career, and the demands of the 21
st
century.

ALLOCATING HUMAN CAPITAL RESOURCES 125
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ALLOCATING HUMAN CAPITAL RESOURCES 132
Appendix
Personnel Allocation (by level and school)

Middle Schools School "A" School "B" School "C" School "D" School "E" School "F" School "G" School "H" School "I"
Principals 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Assistant principals 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0
Instructional coaches 1.0 1.0 1.0 1.0 1.0 3.0 1.0 1.4 1.0
Core teachers 32.2 39.8 28.6 34.8 34.4 29.4 25.4 30.6 26.2
Specialist teachers 13.2 8.8 11.2 10.4 9.2 8.6 10.0 12.0 9.2
SPED teachers 11.0 7.0 3.0 13.0 4.0 7.0 6.0 5.0 6.0
ELL teachers 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Academic extra help staff 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Non-academic pupil support 3.0 2.0 2.0 3.0 2.0 4.0 2.0 2.0 3.8
Nurses 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4
Extended day / summer school staff 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 1.2
Instructional aides 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Supervisory aides 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
SPED aides 9.1 4.5 1.4 9.1 2.9 6.9 3.0 2.7 4.8
Librarians 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Library technicians 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Library paraprofessionals 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Secretaries / clerks 5.5 4.7 4.7 5.2 4.7 5.4 4.7 4.7 5.4
Table A.1: Middle School Personnel Allocation (by school). Source: Odden & Picus, forthcoming
ALLOCATING HUMAN CAPITAL RESOURCES 133

High Schools School "J" School "K" School "L" School "M" School "N" School "O" School "P"
Principals 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Assistant principals 5.0 4.0 0.0 5.0 5.0 4.0 5.0
Instructional coaches 3.0 0.0 0.0 2.0 2.0 1.0 4.6
Core teachers 60.0 63.2 8.5 50.4 69.4 58.8 59.4
Specialist teachers 18.8 20.2 4.5 15.2 23.2 19.8 22.4
SPED teachers 11.0 9.0 0.0 13.0 15.0 7.0 17.0
ELL teachers 1.0 1.0 0.0 0.0 0.0 0.0 1.0
Academic extra help staff 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Non-academic pupil support 10.0 6.0 1.0 8.0 10.0 6.8 9.6
Nurses 0.5 0.5 0.0 0.5 0.5 0.5 0.5
Extended day / summer school staff 3.6 2.4 0.0 3.6 3.6 2.4 5.8
Instructional aides 1.0 1.0 0.0 1.0 1.0 1.0 1.0
Supervisory aides 3.5 3.0 0.0 3.0 3.0 3.0 3.0
SPED aides 9.3 8.7 0.0 9.7 12.8 6.9 12.4
Librarians 1.0 1.0 0.0 1.0 1.0 1.0 1.0
Library technicians 0.8 0.8 0.0 0.8 0.8 0.8 0.8
Library paraprofessionals 0.8 0.8 0.0 0.8 0.8 0.8 0.8
Secretaries / clerks 8.9 8.2 3.0 8.9 10.3 8.2 8.9
Table A.2: High School Personnel Allocation (by school). Source: Odden & Picus, forthcoming
ALLOCATING HUMAN CAPITAL RESOURCES 134
Table A.3: Alternative School Personnel Allocation (by school)

Source: Odden & Picus, forthcoming
Alternative Schools School "Q" School "R" School "S"
Principals 0.3 0.3 0.3
Assistant principals 1.0 1.0 1.0
Instructional coaches 1.0 0.0 0.0
Core teachers 10.0 10.4 4.5
Specialist teachers 2.0 3.0 1.5
SPED teachers 0.5 0.5 1.0
ELL teachers 0.0 0.0 0.0
Academic extra help staff 0.0 0.0 0.0
Non-academic pupil support 1.0 0.0 0.0
Nurses 0.0 0.0 0.0
Extended day / summer school staff 1.3 1.9 1.0
Instructional aides 0.0 0.0 0.0
Supervisory aides 1.0 1.0 0.0
SPED aides 0.5 0.5 0.5
Librarians 0.0 0.0 0.0
Library technicians 0.0 0.0 0.0
Library paraprofessionals 0.0 0.0 0.0
Secretaries / clerks 3.0 3.0 1.0

Abstract (if available)

Abstract This study used qualitative methods to understand the extent to which one district allocated human resources toward research-based strategies for school improvement. The study focused on one large urban school district in Southern California. The study district provided actual human resource allocation data for each of its secondary schools for an examination as to the manner in which the district has handled a limited budget. The practices of the study district were viewed through the lens of the Evidence-Based Model (Odden, 2003) and Odden’s (2009) Ten Strategies for Doubling Student Performance. The human resource allocation outlined in the Evidence-Based Model was compared to the actual allocation of human resources at the secondary level in the study district. A gap analysis was conducted to compare three points: the current allocation of human resources in the study district, the allocation of human resources according to the specifications of the Evidence-Based Model, and a proposed, hypothetical allocation of human resources for the study district. ❧ Findings from the study demonstrated alignment between the practices of the study district and Odden’s (2009) ten research-based strategies for improving student achievement. Regarding personnel, the study district did not have the level of human resources recommended by the Evidence-Based Model. The study district was funded at a level significantly below what is recommended by the Evidence-Based Model. Therefore, the human resource allocation of the study district was not aligned to the Evidence-Based Model. Even so, recommendations were made to increase instructional coach and academic extra help positions as well as to reduce the ratio of specialist to core teachers. The study outlines how the recommendations could be followed, moving the study district towards the Evidence-Based Model, without incurring additional costs.

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Asset Metadata

Creator Swanson, Jonathan Edward (author)

Core Title Allocating human capital resources for high performance in schools: a case study of a large, urban school district

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 03/05/2013

Defense Date 02/11/2013

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

Tag evidence-based model,human resources,OAI-PMH Harvest,Personnel,resource allocation

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 jeswanson@hotmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-223073

Unique identifier UC11292584

Identifier usctheses-c3-223073 (legacy record id)

Legacy Identifier etd-SwansonJon-1457.pdf

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Document Type Dissertation

Rights Swanson, Jonathan Edward

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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