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The implementation of data driven decision making to improve low-performing schools: an evaluation study of superintendents in the western United States

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The implementation of data driven decision making to improve low-performing schools: an evaluation study of superintendents in the western United States

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THE IMPLEMENTATION OF DATA DRIVEN DECISION MAKING TO IMPROVE
LOW-PERFORMING SCHOOLS: AN EVALUATION STUDY OF SUPERINTENDENTS IN
THE WESTERN UNITED STATES
by
Enrique Ruacho

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

December 2020

Copyright 2020 Enrique Ruacho

ii
Acknowledgements
I could not have completed this dissertation without the support, motivation, inspiration,
and feedback from several individuals. First and foremost, Dr. Daisy Gonzales, my better half,
supported, motivated, and inspired me to pursue this journey. Whether it was helping to edit,
teaching me about literature reviews, making sure I woke up for class, or bringing me a snack in
between classes, her support guided me in my quest to complete this dissertation. I am grateful
for her love, and her dedication to support my pursuit of a doctorate degree. I dedicate the
completion of this dissertation and degree to Dr. Daisy Gonzales.
I acknowledge my parents, whose sacrifice to immigrate to this country is a source of
motivation and inspiration. They have dedicated their entire lives to carve out a better future for
their children. This dissertation and degree aim to honor their journey and sacrifice for our
family. Thank you for all you have done and continue to do for us.
I also acknowledge my dissertation committee. I am thankful for the contributions of
each of my dissertation committee members. They included Dr. Helena Seli, who served as
Dissertation Committee Chair, and Dr. Jennifer Phillips and Dr. John Roach, who served as
Dissertation Committee Members. Each provided unique contributions that helped me progress
in my completion of this dissertation. Thank you for your work and contributions.
Finally, I also want to acknowledge my mentors who have always supported my efforts
to pursue educational and professional endeavors. Specifically, I acknowledge Leilani
Aguinaldo, Martha Chavez, and Lee Angela Reid who supported my application to USC’s
Rossier School of Education. Thank you for your support, mentorship, and friendship.

iii
TABLE OF CONTENTS
Acknowledgements ....................................................................................................................... ii
List of Tables ................................................................................................................................. v
List of Figures ............................................................................................................................... vi
Abstract ........................................................................................................................................ vii
Chapter One: Introduction of The Problem of Practice ........................................................... 1
Organizational Context and Mission .......................................................................................... 1
Importance of Addressing the Problem ...................................................................................... 2
Organizational Performance Status ............................................................................................ 2
Organizational Performance Goal .............................................................................................. 3
Description of Stakeholder Groups............................................................................................. 3
Stakeholder Group for the Study ................................................................................................ 4
Organizational and Stakeholder Groups’ Goals ......................................................................... 4
Purpose of the Study and Questions ........................................................................................... 5
Methodological Framework ........................................................................................................ 6
Definitions .................................................................................................................................. 6
Organization of the Study ........................................................................................................... 6
Chapter Two: Literature Review ................................................................................................ 7
Continuous Improvement and Data Driven Decision Making in Schools.................................. 7
Factors Influencing Low-Performing Schools .......................................................................... 10
Superintendents as Educational Leaders ................................................................................... 13
The Clark and Estes Gap Analytical Conceptual Framework .................................................. 17
Superintendents’ Knowledge, Motivation, and Organizational Influences .............................. 18
Conceptual Framework ............................................................................................................. 28
Conclusion ................................................................................................................................ 32
Chapter Three: Methods ............................................................................................................ 34
Participating Stakeholders ........................................................................................................ 34
Validity and Reliability ............................................................................................................. 37
Credibility and Trustworthiness................................................................................................ 38
Ethics ........................................................................................................................................ 39
Quantitative Data Collection and Instrumentation ................................................................... 41
Qualitative Data Collection and Instrumentation ..................................................................... 43
Data Analysis ............................................................................................................................ 45
Chapter Four: Results and Findings ......................................................................................... 47
Participating Stakeholders ........................................................................................................ 47
Knowledge Results and Findings ............................................................................................. 49
Motivation Results and Findings .............................................................................................. 55
Organizational Results and Findings ........................................................................................ 62
Summary of Results and Findings ............................................................................................ 69

iv
Chapter 5: Recommendations ................................................................................................... 71
Recommendations for Practice to Address KMO Influences ................................................... 71
Integrated Implementation and Evaluation Plan ....................................................................... 82
Implications for Practice ........................................................................................................... 93
Future Research ........................................................................................................................ 93
Conclusion ................................................................................................................................ 95
References .................................................................................................................................... 97
Appendix A: Survey Item ......................................................................................................... 102
Appendix B: Interview Protocol .............................................................................................. 109
Appendix C: Survey Responses ............................................................................................... 111
Appendix D: Informed Consent and Information Sheet ....................................................... 116
Appendix E: Program Implementation Evaluation Tool ...................................................... 118
Appendix F: Program Implementation Delayed Evaluation Tool ....................................... 119

v
List of Tables
Table 1 Organizational Mission, Organizational Global Goal, and Stakeholder Goal ................ 5
Table 2 Knowledge Influence, Knowledge Types, and Knowledge Assessment .......................... 20
Table 3 Motivational Influences and Motivational Influence Assessments ................................. 24
Table 4 Summary of Assumed Organizational Influences and Organizational Assessments ...... 28
Table 5 Survey Responses to Descriptive Questions .................................................................... 48
Table 6 Interview Participants by Position, Student Enrollment, and Low-Performing Schools 49
Table 7 Survey Responses to Self-Efficacy Related Statements ................................................... 57
Table 8 Survey Responses to Task Value Related Statements ...................................................... 60
Table 9 Survey Responses Related to Organizational Model Statements .................................... 64
Table 10 Survey Responses Related to Organizational Setting Statements ................................. 67
Table 11 Summary of Assumed Influence, Description, and Status as Gap or Asset .................. 69
Table 12 Summary of Knowledge Influences and Recommendations .......................................... 72
Table 13 Summary of Motivation Influences and Recommendations .......................................... 75
Table 14 Summary of Organization Influences and Recommendations ...................................... 78
Table 15 Outcomes, Metrics, and Methods for External and Internal Outcomes ....................... 84
Table 16 Critical Behaviors, Metrics, Methods, and Timing for Evaluation............................... 86
Table 17 Required Drivers to Support Critical Behaviors .......................................................... 87
Table 18 Evaluation of the Components of Learning for the Program ....................................... 89
Table 19 Components to Measure Reactions to the Program ..................................................... 90

vi
List of Figures
Figure 1 Conceptual framework .................................................................................................. 31
Figure 2 Dashboard Sample Result of Immediate Survey Evaluation for the First Statement .... 92

vii
Abstract
This study explored the implementation of data driven decision making (DDDM) among
superintendents to improve low-performing schools in the Western United States. Specifically,
this study utilized the Clark and Estes (2008) Gap Analytic Conceptual Framework to understand
superintendents’ knowledge, motivation, and organizational influences related to DDDM
implementation with the ultimate goal of the state reclassifying low-performing schools within
four years. A convergent mixed method design, consisting of a survey instrument and interviews
with superintendents, was utilized to collect data. The survey included agree-disagree Likert-type
items. This study surveyed 64 participants with a response rate of 14.6% and interviewed nine
participants. The results and findings demonstrated a high degree of value among
superintendents for DDDM implementation, and that superintendents used their personal stories
of adversity, their responsibility as a leader, and their role model status in the community as
sources of high self-efficacy. However, the results and findings also demonstrated a gap in the
procedural knowledge of superintendents, and an organizational influence gap related to the
State Department of Education providing professional development. Finally, this study
recommended an integrated implementation and evaluation plan, designed by applying the New
World Kirkpatrick Model (2016) to the performance goal of superintendents.

1
Chapter One: Introduction of The Problem of Practice
This study addresses the problem of low-performing schools contributing to inequities in
student opportunities and outcomes in the Western United States. The State recently identified
781 schools that are low-performing.
1
Low-performance is identified as a school with a
graduation rate of 67% or less, or as a result of poor performance among all the multiple measure
indicators in the State’s school data dashboard.
2
The evidence highlights this State is not
effectively addressing inequities in student performance and achievement, which is a priority
under recent State reforms and the Every Student Succeeds Act (ESSA, 2015). The problem is
important to address because inequities in academic performance are not evenly distributed
among schools, but rather, are concentrated in low-performing schools that typically serve low-
income and historically disadvantaged students (Wyckoff, 2006).
Organizational Context and Mission
This study uses school districts and the State Department of Education (SDE) in the
Western United States as the sites of examination. The SDE is tasked with conducting oversight
and evaluation of school districts with low-performing schools. Their mission is to provide all
students with a world-class education that spans from early childhood to adulthood.
Accountability is conducted through the use of a school data dashboard, which uses a multiple
measures performance system to identify low-performing schools and to satisfy State and federal
accountability requirements. This context and mission inform school districts and their efforts to
implement data driven decision-making to improve school performance and student outcomes.

1
Source is the State Department of Education’s letter noting public release of data.
2
Source is the State Department of Education’s memos to its State Board of Education.

2
Importance of Addressing the Problem
The problem of low-performing schools is important to address because inequities in
student opportunities and outcomes contribute to the widening gap in educational achievement
among students of color. For example, a qualitative study on the experiences of young African-
American males found that teacher negativity and classroom chaos hindered their ability to
successfully navigate the environment in a low-performing school (Fitzgerald et al., 2019). The
consequence for students who fail to acquire the necessary academic knowledge and skills
manifests in limited opportunities to complete college-level courses and to graduate from high
school. Adding to this problem, Royster et al. (2015) found that timing was important to ensure
that students are on track starting in middle school to be college-ready by high school. This study
suggested that a low-performing school environment has a cumulative effect on the academic
outcomes of students. A lack of college readiness preparation in high school impacts whether
students have the capacity to enter and complete a college education. Jackson and Kurlaender
(2014) conducted an analysis of longitudinal student data on college readiness in the California
State University system. Their analysis showed that college readiness is a predictor of college
outcomes for students. A low-performing school can negatively impact the opportunities and
outcomes of students, which is why it is important to address the problem.
Organizational Performance Status
The SDE measures the organizational performance status of school districts. The SDE
identifies schools that are considered low-performing, which currently totals 781 schools.
3

Among these schools, 300 are high schools with a graduation rate of 67% or less.
4
In comparison

3
Source is the State Department of Education’s memos to its State Board of Education.
4
Source is the State Department of Education’s memos to its State Board of Education.

3
to the total number of schools in California, approximately 7.4% are considered low-performing
schools.
5

Organizational Performance Goal
The organizational performance goal for this Western State is derived from the “Every
Student Succeeds Act” and from State reforms in school funding and accountability policy.
These reforms require education leaders to address inequities in student opportunities and
outcomes. As a result, the State has tasked its Department of Education, along with other
relevant State agencies, with developing and operating a system of support that integrates data
driven decision-making (DDDM). This system of support requires superintendents to implement
improvement practices that are informed by data and analysis to reduce inequities in educational
opportunities and to improve low-performing schools. The organizational performance goal for
this State is to ensure that 100% of school districts reclassify low-performing schools or student
subgroups within four years of identification. The success of this State’s system of support
depends on the capacity of superintendents to effectively implement DDDM to address inequities
in student outcomes.
Description of Stakeholder Groups
There are three key stakeholder groups that influence the capacity of school districts to
implement DDDM and that contribute to the goal of reclassifying 100% of low-performing
schools or student subgroups within four years of identification. The first group includes county
and school district superintendents. Under the State’s system of support, county and school
district superintendents are required to outline strategic objectives and resource allocations to
improve school performance. County superintendents are required to provide assistance, such as

5
Source is the State Department of Education’s memos to its State Board of Education.

4
technical support and guidance. School Board Members are the second group who have the
authority to set policies and approve contracts, which influences whether the school district
prioritizes DDDM to improve low-performing schools. School principals are a third stakeholder
group because they have direct local control over the daily activities at low-performing schools
and influence the implementation of DDDM.
Stakeholder Group for the Study
While a complete analysis would involve all stakeholder groups, for practical purposes,
this study focuses on school district superintendents (“superintendents”). The actions of
superintendents directly influence whether school districts have the capacity to sustain DDDM to
reclassify low-performing schools. State and federal reforms are key contributors that frame a
culture where school districts are now required to integrate DDDM. Achievement and progress
are determined through the use of the school data dashboard, which uses multiple measures to
track student achievement, such as graduation or A-G completion rates, suspension, and chronic
absenteeism. The lowest performing schools either have a graduation rate that is less than 67%,
or demonstrates a measure with all red indicators, five or more red indicators, or all red and
orange indicators. The stakeholder performance goal that this study examined was related to the
capacity of superintendents to implement DDDM to reclassify low-performing schools. DDDM
is defined by a process where data and analysis are used to inform local decision-making
(Haecker et al., 2017). The purpose of DDDM is to improve local practice. The superintendents
are ultimately held responsible for making progress with the lowest performing schools.
Organizational and Stakeholder Groups’ Goals
Table 1 lists the organizational mission, organizational global goal, and stakeholder
performance goal.

5
Table 1
Organizational Mission, Organizational Global Goal, and Stakeholder Goal
Organizational Mission
The State will provide a world-class education for all students, from early childhood to
adulthood.
Organizational Global Goal
The goal of this State is to ensure that 100% of school districts reclassify low-performing
schools or student subgroups within four years of identification.
Stakeholder Goal
By January 2020, superintendents will implement data driven decision-making to improve
low-performing schools.

Purpose of the Study and Questions
The purpose of this study was to explore the capacity of superintendents to implement
DDDM to contribute to the goal of reclassifying 100% of low-performing schools or student
subgroups within four years of identification. While a complete gap analysis would have focused
on all stakeholders, for practical purposes, the stakeholder of focus in this study were
superintendents. The questions that guided this study were as follows:
1. What are superintendents’ knowledge and motivation related to implementing data driven
decision-making to improve low-performing schools?
2. What is the interaction between organizational culture and context and superintendent
knowledge and motivation to implement data driven decision making to improve low-
performing schools?
3. What are the recommended knowledge and skills, motivation, and organizational
solutions?

6
Methodological Framework
This study utilized a mixed methodological approach to gather data and to conduct a gap
analysis. The goal of this study was to explore superintendents’ capacity to implement DDDM to
improve low-performing schools. This study utilized a convergent mixed method design that
incorporated both quantitative and qualitative components into the study (Creswell & Creswell,
2018). The quantitative approach consisted of a survey. The qualitative approach consisted of
semi-structured interviews. Finally, this study utilized the Clark and Estes’ (2008) analytical
framework to assess the knowledge and skills, motivation, and organizational influences of
superintendents in their respective school district.
Definitions
Continuous improvement: Continuous improvement is a cyclical process designed to assist
organizations or schools improve through goal setting, planning, evaluation, and
reflection (Lewis, 2015).
Data Driven Decision Making (DDDM): A process where data and analysis informs local
decision making and practice (Haecker et al., 2017).
Organization of the Study
This study was organized into five chapters. Chapter one discussed the problem of
practice in relation to the identification of the lowest performing schools. Chapter two provides a
literature review and an introduction of the Clark and Estes Gap Analytic Conceptual
Framework. Chapter three details the knowledge, motivation, and organizational influences to be
examined. Chapter four provides an analysis of the data and results. Chapter five provides
recommendations for practice to reinforce the assets and close gaps.

7
Chapter Two: Literature Review
This literature review examines the root causes of gaps in the implementation of data
driven decision making (DDDM) to improve low-performing schools. This review begins with
general research on the importance of continuous improvement in the school setting and its
nexus to DDDM. This is followed by an overview of the literature on low-performing schools in
the United States and the implementation of DDDM. Following the general research literature,
the review turns to the Clark Estes Gap Analytical Conceptual Framework, which analyzes
knowledge, motivation, and organizational influences of superintendents’ ability to implement
DDDM to improve low-performing schools.
Continuous Improvement and Data Driven Decision Making in Schools
The challenges associated with low-performing schools is not a new problem confronting
education leaders, but the adoption of continuous improvement strategies to raise student
achievement and improve school district management is a new paradigm (Bryk et al., 2015;
Lewis, 2015). Continuous improvement has its origins in industrial manufacturing and continued
its development in the health care industry, where it was used to improve the delivery and quality
of health care services (Junghans, 2018). In the field of education, continuous improvement
serves as an important foundation to improve the capacity of low-performing schools. Numerous
research studies have found that continuous improvement, such as DDDM and other micro-level
processes (i.e., professional learning communities and plan-do-study-act cycles), positively
contributes to the development of high capacity schools (Bennett et al., 2014; Lewis, 2015;
Marsh, 2012; Marsh & Farrell, 2015; Park et al., 2013; Tichnor-Wagner et al., 2017; Wohlstetter
et al., 2008). Specifically, this study examines DDDM as a continuous improvement practice.
Even though continuous improvement consists of different micro-level processes, this study

8
explores how superintendents implement DDDM to improve low-performing schools. However,
the implementation of DDDM is imperfect and presents its own set of challenges. Common
challenges include insufficient time and resources to implement DDDM and an overwhelming
number of conflicting school mandates and high leadership turnover (Bennett et al., 2014;
Marsh, 2012; Tichnor-Wagner et al., 2017). Nonetheless, DDDM remains a relevant strategy to
foster organizational change and leadership in a high accountability school environment (Park et
al., 2013). In fact, a recent study developed a survey tool to allow school district leaders to
evaluate perceptions of data informed practice (Jimerson, 2016). While the DDDM literature is
still developing, education leaders are adopting this paradigm in an effort to improve low-
performing schools and to address persistent educational equity gaps. The following sections
examine DDDM and its benefits and the challenges associated with implementation.
Data Driven Decision Making and its Benefits
Data driven decision making fits into the continuous improvement framework through its
iterative process of data and analysis. The purpose of continuous improvement is to build
organizational capacity, improve decision-making and performance, and to establish a culture of
improvement (Lewis, 2015). For example, Park et al. (2013) examined how local administrators
and teachers made sense of data use for equity and learning. Their findings demonstrated that the
adoption of DDDM led administrators to develop an iterative process of framing to facilitate
root-cause analysis with the intent of improving student learning and to produce equitable
outcomes. The framing process provided administrators and teachers with the capacity to
understand the importance of DDDM and its application to the school context. As a result, this
framing process persuaded other administrators and teachers of the relevance of DDDM and
motivated action and improvement, which supported a culture of continuous improvement.

9
While DDDM fosters improvements in decision-making and performance, DDDM also promotes
organizational capacity building. Marsh and Farrell (2015) developed a capacity-building
framework based on their observations of teachers in a year-long case study of six low-
performing high schools in the United States. Their analysis examined the use of DDDM to
improve organizational capacity-building and performance. Marsh and Farrell (2015) found that
capacity-building was fostered through interactions among teachers, whether through one-on-one
or group coaching, and facilitated through practices, tools, and norms to process data and to
improve decision-making. Drawing on sociocultural theory, Marsh and Farrell proposed a
theoretical framework to support the adoption of DDDM. Their framework consisted of
professional development whether through one-on-one or group interactions, practices that
facilitate adoption such as modeling, artifacts whether physical or symbolic, and context which
influences adoption and takes shape in the form of environmental, historical, and cultural factors.
DDDM provides school sites with a framework to support improvement and to develop capacity-
building, which positively influences school performance. In addition to DDDM and its benefits,
the next section addresses implementation challenges at school sites.
Implementation Challenges at the School Site
Even though DDDM fosters organizational capacity-building, decision making, and
performance, there are challenges that emerge in its implementation to improve low-performing
schools. The research demonstrated that implementation is imperfect and is influenced by
external factors. Bennett et al. (2014) examined the capacity-building skills and practices of
principals in Arizona to improve low-performing schools. Bennett et al. (2014) found that
varying levels of trust hindered promoting capacity building and shared instructional leadership,
a collaborative culture, and community building and interdependency. In fact, the use of

10
directive leadership to build capacity for sustainable improvement reinforced a lack of trust
between principals and teachers (Bennett et al., 2014). According to Bennett et al. (2014),
continuous improvement strategies that took precedence over democratic collaborative processes
and engagement failed to consider the external sociocultural factors that move a school out of an
underperformance category. The findings from Bennett et al. (2014) suggests that the
implementation of DDDM requires superintendents to have an awareness of external influences
and the effect on collaboration and engagement. The local context is also an important factor,
which may hinder or facilitate continuous improvement implementation. Marsh and Farrell
(2015) identified environmental, cultural, and historical factors that influenced the capacity-
building process, and that were grouped into categories of intrapersonal, interpersonal, structural-
organizational, and environmental. Intrapersonal factors such as prior experiences with capacity-
building and data use affected a teacher’s willingness to engage in capacity-building. A lack of
trust and credibility was also found to contribute towards a resistance to use tools, participate in
observations, and to engage in dialogue. Structural and environmental factors such as a lack of
time, training, and leadership limited the implementation of DDDM. While DDDM supports
improvement and capacity-building, the implementation process raises potential challenges that
risks any potential benefits. In addition to implementation challenges at the school site, the next
section identifies factors and characteristics associated with low-performing schools.
Factors Influencing Low-Performing Schools
Low-performing schools are not a new phenomenon. Many State and federal reforms in
education policy are attributable to efforts aimed at closing the achievement gap. This section
outlines historical factors and characteristics of low-performing schools.
Historical Factors Influencing Low-Performing Schools

11
Federal and state governments have adopted reforms throughout American history in an
effort to improve low-performing schools. According to Cusick (2014), President Lyndon
Johnson made schooling a prominent topic in his war on poverty in 1965. The logic was that
poverty persisted due to a lack of schooling (Cusick, 2014). As a result, the Congress and the
President of the United States enacted the Elementary and Secondary Education Act (ESEA) to
expand federal and State control over education through federally funded programs (Cusick,
2014). The enactment of ESEA was accompanied with measures of accountability, which
provided test data on students and an increasing focus on the role of the teacher in the classroom
(Cusick, 2014). However, educational accountability changed when ESEA was reauthorized in
2001 with the No Child Left Behind Act (NCLB) (Cusick, 2014). The NCLB introduced
accountability policies with a punitive and compliance-driven orientation, which resulted in the
firing of administrative staff in low-performing schools and the adoption of rigid school practices
(Cusick, 2014). Finnigan and Daly (2012) conducted a case study analysis of three schools under
sanction as a result of underperformance under NCLB. Their findings showed that schools under
sanctions the longest focused limited attention to technical aspects of organizational learning.
Schools under sanction were also the most isolated and experienced a negative social climate
districtwide that was viewed to inhibit the social processing of knowledge to facilitate
organizational learning (Finnigan & Daly, 2012). Another study surveyed educators at program
improvement schools and non-program improvement schools under NCLB to assess the nature
of trust and leadership perceptions of threat-rigidity, which theorizes that organizations reduce
information flow, engage in poor decision-making, and limit divergent views due to a significant
threat (Daly, 2009). The findings showed that when teachers perceived greater levels of a threat-
rigid response in program improvement schools, then perceptions of trust are significantly

12
lowered than among teachers at non-program improvement schools. While NCLB promoted a
rigid response to addressing low-performing schools, its reauthorization as the “Every Student
Succeeds Act” (ESSA) in 2017 shifted the punitive and compliance-driven orientation of
educational accountability to one focused on continuous improvement. Egalite et al. (2017)
examined ESSA and relevant documents and policy analysis to trace the shift from a centralized
education system to one with flexibility as a method to address educational inequities. Their
analysis demonstrated that ESSA defined low-performance through its requirement that states
identify the lowest five percent of schools for comprehensive support, and all high schools that
fail to graduate one-third or more of their students. Unlike NCLB, ESSA takes a decentralized
approach to governance in an effort to improve student outcomes (Egalite et al., 2017). As a
result of ESSA’s decentralized governance model, Egalite et al. (2017) argued that rigorous
enforcement of ESSA will be necessary to ensure educational inequities are not exacerbated.
Low-performing schools are defined by State and federal intervention and a culture of support
and continuous improvement. In addition to the historical factors influencing low-performing
schools, the next section addresses characteristics of low-performing schools.
Characteristics of Low-Performing Schools
Low-performing schools experience significant leadership challenges, and low teacher
quality and motivation. For example, Finnigan and Gross (2007) conducted a qualitative study
that examined teacher motivation in low-performing schools. Their findings showed that teacher
motivation declines in schools that struggle due to decreased expectations and demoralization.
Their findings also showed that teachers responded to the value they placed on professional
status and the individual goals of their students, but these responses were weakened due to
decreased morale. Decreased morale and leadership challenges are a consistent characteristic of

13
low-performing schools (Finnigan & Gross, 2007). In a survey of all elementary schools in the
Chicago Public Schools, Finnigan (2010) found significant differences in leadership and
motivation in schools under sanctions in comparison to schools that were not under sanctions due
to low-performance. Their results demonstrated that principals in low-performing schools were
less likely to exhibit key leadership behaviors associated with organizational change. Their
results also showed low expectancy among teachers at low-performing schools. Leadership
challenges at low-performing schools are important because they contribute to declining
motivation and a lack of quality teaching. In a subsequent study, Finnigan and Daly (2012)
conducted a qualitative analysis of three low-performing elementary schools to examine the
transformational leadership behaviors that improved teacher motivation and student
performance. Instead, the findings showed that teachers’ lack of trust towards their principals
contributed to a decline in motivation to focus on school improvement. This decline in
motivation manifested in the form of disengagement and demoralization. Finnigan and Daly
(2012) attributed the lack of inclusive leadership not being present to the fact that organizations
in crisis centralize instead of decentralizing operations. Low-performing schools are
characterized by motivational challenges among teachers and staff, a lack of trust, and decreased
expectations and morale. In addition to the characteristics of low-performing schools, the next
section addresses the role of school superintendents as educational leaders.
Superintendents as Educational Leaders
Superintendents are the primary stakeholder of this study because they are ultimately held
accountable for the outcomes that their school district produces. In this capacity, superintendents
serve as educational leaders and their role requires a wide breadth of skills, knowledge, and

14
capacity to influence student performance. These sections explore the role of the superintendent
and their influence on school performance.
The Role of the Superintendent
The role of superintendent requires a wide breadth of abilities to effectively serve as an
educational leader. For example, Peterson and Short (2001) examined school board presidents’
perceptions of superintendents and their abilities to be effective. Their findings showed that
superintendents’ abilities to influence favorable decision-making among the board was
contingent on superintendents’ trustworthiness, which was reflected in their perceived use of
skill and knowledge of the school district. Petersen and Short (2001) also found that
superintendents’ level of expertise and social attractiveness, which was defined as compatibility
with the board president, influenced perceptions of the superintendents’ abilities to be effective.
These attributes were found to be important for the development and maintenance of cooperative
working relationships with board members and the broader community. While influencing
decision making and outcomes are essential roles for superintendents, their instructional
leadership was also important. Peterson (2002) examined the findings of the Instructional
Leadership Personnel Survey, which surveyed principals and school board members in schools
that were recognized for strong academic leadership. This study explored the leadership role of
superintendents in the curriculum and instruction from the perspective of principals and school
board members. The results showed a statistically significant relationship between
superintendents’ instructional vision and the organizational factors that contribute to the
academic success of schools. For example, the findings showed that a superintendent with a clear
and focused instructional vision demonstrated a positive correlation with school board
involvement and community participation. Peterson (2002) suggests that the instructional

15
leadership role of superintendents is an important influence on the academic success of schools,
along with a collaborative approach that engages multiple stakeholders. Decman et al. (2018)
also identified the collaborative approach as an important ability for the role of superintendent.
Decman et al. (2018) interviewed 18 superintendents in Houston, Texas to understand their
perceptions of organizational leadership and their role in management. The themes that emerged
in the study showed that superintendents view the budget process and fiscal decision making as
important activities that require transparency and public awareness. The superintendents also
identified effective group processes and consensus-building skills to foster collaborative
conversations on topics, such as financial realities, promoting a student-first agenda, and to foster
an inclusive working relationship with school board members. A final theme that was identified
was the need for superintendents to possess the capacity to address resistance and the difficulty
that emerges when change is pursued, especially in relation to continuous improvement. The role
of superintendent includes influencing the decisions of school board members, maintaining
relationships with internal and external partners, and engaging in group processes and consensus-
building. In addition to the role of the superintendent, the next section addresses the influence of
superintendent leadership on school performance.
The Influence of Superintendent Leadership on School Performance
Superintendents have the capacity to influence school performance through their
leadership. Marzano and Waters (2009) conducted a meta-analysis that examined superintendent
leadership and student achievement. Their meta-analysis of 14 studies over the span of 35 years,
which included data from 1,210 districts, found that superintendent leadership was positively
correlated with student outcomes. This finding appears in a subsequent study. Hough (2014)
conducted a quantitative survey study to explore whether superintendent accountability

16
behaviors and agreement of accountability behaviors with central office subordinates predicted
student achievement in the school district. Hough (2014) found that superintendents who
underestimated their accountability behaviors demonstrated significantly higher academic gains
in math and reading in comparison to superintendents who overestimated their accountability
behaviors. The effect sizes of these findings were strong. Marzano and Waters (2009) and Hough
(2014) suggested two key influences of superintendent leadership. The first is that superintendent
leadership influences student performance. The second is that superintendents need to have the
self-awareness and the humility, as described by Hough (2014), to understand when their
accountability behaviors are consistent or overestimated with the requirements of their position.
Otherwise, accountability behaviors that are overestimated are limited in their influence to
improve school performance. In their study, Whitt et al. (2015) conducted a qualitative case
study of superintendents on their self-perceived efficacy and its influence on instructional
leadership in low-performing schools with large populations of students of color. The findings
from this study showed that in low-performing schools superintendents failed to link their self-
efficacy perceptions to district instructional leadership efforts. Whitt et al. (2015) also found that
superintendents failed to take responsibility for instructional effectiveness, supported persistent
racism, and sustained deficit viewpoints that guided and contributed to persistent academic
failure. The literature demonstrated that superintendents influence school performance through
their leadership capacity and self-efficacy. In addition to the influence of superintendent
leadership on school performance, the next section addresses the Clark and Estes (2008)
analytical framework.

17
The Clark and Estes Gap Analytical Conceptual Framework
Clark and Estes (2008) proposed a framework for the systematic analysis of
organizational and stakeholder performance goals, and to identify performance gaps that may
emerge in the process of working to achieve such goals. The framework consists of examining
the stakeholder knowledge, motivation, and organizational influences of performance gaps
(Clark & Estes, 2008). Krathwohl (2002) defined the knowledge and skills influences as four
types: factual, conceptual, procedural, and metacognitive knowledge. The motivational
influences are manifested through active choice, persistence, and effort (Clark & Estes, 2008;
Rueda 2011). The motivated behaviors of choice, persistence, and investment of effort are
underpinned by psychological influences such as self-efficacy and competence beliefs,
attributions and control beliefs, task value, and goal orientation (Rueda, 2011). The
organizational influences consist of work processes, material resources, and culture (Clark &
Estes, 2008).
The Clark and Estes (2008) framework is utilized to provide an understanding of
superintendents’ knowledge, motivation, and organizational needs to meet their performance
goal of improving low-performing schools. The first section provides a discussion of assumed
knowledge and skill influences in working towards the stakeholder performance goal. The
second section provides a discussion on the motivational influences related to the attainment of
the stakeholder performance goal. The third section explores the assumed organizational
influences related to the attainment of the stakeholder performance goal. Each assumed
stakeholder influence was examined through the methodology section discussed in Chapter 3.

18
Superintendents’ Knowledge, Motivation, and Organizational Influences
This literature review examined the knowledge and motivation influences required for
superintendents to implement DDDM with the goal of improving low-performing schools. The
organizational global goal that guides this literature review is derived from the State’s system of
support, which is to ensure that 100% of school districts reclassify low-performing schools or
student subgroups within four years of identification. The system of support is the result of a new
public school accountability system enacted due to State and federal reforms. The new
accountability orientation shifts from a top down, transactional system to a systematic approach
that emphasizes continuous improvement, tailors to local needs, and that works in tandem with
the school community.
Knowledge and Skill-Related Influences
The performance goal to implement DDDM relies on the knowledge of superintendents,
otherwise a lack of applicable knowledge creates a risk of a performance gap. According to
Clark and Estes (2008), performance is the result of three key influences: knowledge, motivation,
and organizational factors. This section examines the literature related to knowledge and skills
gaps that influence the performance goal. The knowledge dimension focuses on determining
whether individuals know how, when, what, why, where, and who when working to achieve
performance goals (Clark & Estes, 2008). Krathwohl (2002) synthesized the knowledge
dimension into four categories. Factual knowledge consists of terminology, details, and elements
(Krathwohl, 2002). Conceptual knowledge requires an understanding of relationships between
basic facts and structures, theories, models, principles, generalizations, classifications, and
categories (Krathwohl, 2002). Procedural knowledge emphasizes an understanding of the process
for completing a task, using skills, techniques, or methods of inquiry (Krathwohl, 2002).

19
Metacognitive knowledge consists of strategic knowledge and the capacity to know when and
why to do a task, which also requires self-awareness and awareness of cognitive processes
(Krathwohl, 2002). The next sections explore procedural and metacognitive knowledge
influences in relation to superintendents’ capacity to implement DDDM to improve low-
performing schools.
Skills Related to Effectively Implementing Data Driven Decision Making
The procedural knowledge to effectively implement DDDM is the first knowledge
influence explored in relation to superintendents achieving their respective performance goal.
The core framework of DDDM is the integration of data and analysis as a process to foster
continuous improvement (Park et al., 2013). This integration requires understanding two key
components of continuous improvements. The first is that DDDM is integrated into continuous
improvement (Lewis, 2015). The second consists of understanding that local knowledge informs
program development and improvement (Jimerson, 2016; Lewis, 2015; Marsh & Farrell, 2015;
Park et al., 2013). Local knowledge is a key distinguishing element because the improvement
science paradigm integrates programming with local knowledge building systems (Lewis, 2015).
This requires superintendents to maintain an understanding of the local school context, DDDM
integration, continuous improvement science, and its implications. The classification of
procedural knowledge is applicable to this category because implementing DDDM requires
superintendents to translate their basic understanding of DDDM, its foundations, and
terminology to the local school context.
Reflecting on Capacity to Implement Data Driven Decision Making
The metacognitive knowledge of superintendents to reflect on their capacity to
implement DDDM to improve low-performing schools is the second knowledge influence that is

20
explored. The implementation of DDDM in the school setting is imperfect. Park et al. (2013)
demonstrated that DDDM requires an iterative process to produce change. Park et al. (2013)
found that educational leaders used framing as a tactic to promote DDDM. Education leaders
supported the DDDM paradigm through framing to encourage adoption of this continuous
improvement strategy. The implementation of DDDM requires the metacognitive capacity to
understand the challenges that may emerge, especially in relation to the school context where the
strategy is applied. As a result, this knowledge influence requires superintendents to know when
and how to integrate DDDM and to exercise an awareness of the cognitive processes that may
create challenges for implementation. Park et al. (2013) illustrated the importance of the
metacognitive capacity to make adjustments to the implementation and execution of DDDM.
The classification of the metacognitive knowledge type applies to this category since
superintendents undertake a process to identify, outline, and implement strategies that are
interwoven with DDDM to improve low-performing schools.
Table 2 categorizes the knowledge influences discussed thus far by knowledge type,
explains the assessments methods, and lists the organizational mission, organizational global
goal, and stakeholder performance goal.
Table 2
Knowledge Influence, Knowledge Types, and Knowledge Assessment
Organizational Mission
The State will provide a world-class education for all students, from early childhood to
adulthood.
Organizational Global Goal
The goal of this State is to ensure that 100% of school districts reclassify low-performing
schools or student subgroups within four years of identification.
Stakeholder Goal
By January 2020, superintendents will implement data-driven decision-making to improve
low-performing schools.

21
Knowledge Influence Knowledge Type Knowledge Influence
Assessment
Superintendents must know how to
effectively implement data driven
decision-making to improve low-
performing schools.

Procedural
Interview questions that ask
superintendents to
describe/discuss their
understanding of data driven
decision making and their
experiences with
implementation, and survey
statements that assess procedural
understanding.
Superintendents must know how to
reflect on their capacity to adapt
data driven decision making
strategies to the local context and
to overcome challenges.

Metacognitive
Interview questions that ask
superintendents to identify their
strengths and any challenges in
the process of implementing data
driven decision making, and
survey statements that reflect the
metacognitive dimension.

Motivation Influences
The motivation of superintendents is the second influence to be examined in relation to
the performance goal, since knowledge alone is not sufficient to achieve desired performance.
This section examines the literature related to motivation that is assumed to influence the
superintendents’ capacity to implement the DDDM performance goal. According to Clark and
Estes (2008), active choice, persistence, and mental effort indicate motivation and influences
performance. Active choice is the initial indicator that signals a decision to engage in a particular
task, while persistence demonstrates an individual’s level of commitment (Rueda, 2011). These
motivational indicators manifest in the form of motivation-related beliefs. For example, Rueda
(2011) identified self-efficacy, goal orientation theory, and task value as motivational beliefs.
While this list is not exhaustive, it provides a framework to understand superintendents’ capacity
to implement DDDM. The next sections explore self-efficacy and task value in relation to the
implementation of DDDM to improve low-performing schools.

22
Superintendents’ Self-Efficacy to Implement Data Driven Decision Making
The first motivational influence related to superintendents achieving their stakeholder
goal that was explored was self-efficacy. Bandura (2005) developed self-efficacy from social
cognitive theory. Self-efficacy consists of an individual’s self-judgement of their ability to
perform specific tasks (Bandura, 2005). According to Bandura, the sources of self-efficacy
beliefs are developed from mastery experience, vicarious experience, social persuasions, and
physiological responses (Bandura, 2005). In practice, an individual with high levels of self-
efficacy demonstrates persistence to achieve a specific task despite distractions that may
influence active choice or mental effort (Rueda, 2011). This section examines the influence of
self-efficacy on the motivation of superintendents to implement DDDM.
Self-efficacy influences whether superintendents persist in the application of DDDM. In a
study of five California superintendents, Petersen (1999) found that the role of superintendent
was essential in the formation of an academic oriented vision and its execution within the school
district. In fact, Petersen (1999) developed a model to explicate the relationship of the
superintendent to the rest of the organization, which identifies the following four key behaviors:
Superintendent vision, assessment and evaluation, organizational adaption, and organizational
structure. Performing these four behaviors requires superintendents to be a master of their
organization, understand the vicarious experiences of their subordinates and Board Members,
and to shape social persuasions. In a subsequent study, Peterson and Short (2001) conducted an
empirical study that surveyed 131 School Board Presidents’ perception of the superintendent.
Petersen and Short (2001) found that favorable action on a superintendent’s recommendation
was correlated with perceptions of trustworthiness, expertise, and perceived compatibility with
the Board President. A subsequent survey, which also included school principals, found that an

23
ongoing positive working relationship and shared decision-making was essential to the
instructional leadership of superintendents (Peterson, 2002). Superintendents who exhibit high
self-efficacy demonstrate the ability to shape the policy decisions of their elected school boards
and to create a vision that integrates DDDM and the necessary changes in organizational
structure and adaptation. The classification of self-efficacy is applicable to this category because
the mastery, vicarious experiences, and social persuasions of superintendents shape their ability
to be instructional leaders and to implement DDDM.
Superintendents’ Value for Data Driven Decision Making
The second motivational influence related to superintendents achieving their stakeholder
goal is task value. While self-efficacy focuses on self-perceptions of ability, task value refers to
the importance that is attached to a task (Rueda, 2011). Rueda (2011) wrote that task value
encompasses four separate dimensions that determine the overall value attached to a task, which
include: attainment value, intrinsic value, utility value, and cost value. First, attainment value
refers to the value associated with doing a task well (Rueda, 2011). Second, intrinsic value refers
to the enjoyment or interest of doing the specific task (Rueda, 2011). Third, utility value refers to
the usefulness of the activity associated with achieving a future goal (Rueda, 2011). Finally, cost
value refers to the cost of doing the task in relation to time, effort, or other dimensions (Rueda,
2011). The foundational motivational principle of task value is that an individual who assigns a
high value to a task is more likely to choose, engage, and persist in the task (Rueda, 2011). This
section examines the value that superintendents attach to implementing DDDM to improve low-
performing schools.
Task value influences whether superintendents will effectively implement DDDM.
Tichnor-Wagner et al. (2017) conducted a comparative case study of two large urban school

24
districts that implemented PDSA to improve the outcomes of high school students, and examined
the perceptions in the development, adaptation, and implementation of PDSA. Their findings
revealed that the school sites that were implementing PDSA saw its value and perceived it to
build on their past experiences. However, they also found challenges and contradictions
associated with the implementation of PDSA. They found that practitioners saw PDSA as
disconnected from their daily work at school, a lack of ownership over PDSA processes, and
insufficient time and expertise to dedicate for PDSA. In a similar study, Marsh and Farrell
(2015) conducted a year-long comparative study of interventions designed to build the capacity
of teachers to use data and improve teaching. Marsh and Farrell (2015) found that a compliance
driven orientation towards data or a lack of time, training, and leadership inhibited capacity
building. These studies demonstrated that the implementation of DDDM was influenced by its
value and was inhibited by educational leadership. The classification of task value is applicable
to this category because the value associated with continuous improvement influences its
implementation.
Table 3 categorizes the motivational influences discussed thus far by motivation type,
explains the assessments methods, and lists the organizational mission, organizational global
goal, and stakeholder performance goal.
Table 3
Motivational Influences and Motivational Influence Assessments
Organizational Mission
The State will provide a world-class education for all students, from early childhood to adulthood.
Organizational Global Goal
The goal of this State is to ensure that 100% of school districts reclassify low-performing schools
or student subgroups within four years of identification.
Stakeholder Goal

25

Organizational Influences
In addition to knowledge and motivation influences, there are organizational factors that
affect the ability of superintendents to effectively implement continuous improvement at low-
performing schools. Clark and Estes (2008) identified organizational factors as gaps that
manifest in work processes, material resources, and value chains and value streams. According to
Clark and Estes (2008), these gaps hamper organizational performance because they are
inefficient and ineffective. A work process that is a major contributor to these gaps is
organizational culture, since it influences worker identity, what is valued, and how the work is
completed (Clark & Estes, 2008). Organizational culture is understood through cultural models
and cultural settings (Rueda, 2011). Cultural models consist of shared beliefs, values, and views
about how the world works or should work (Rueda, 2011). Cultural settings account for the
visible aspects of culture, such as the norms, customs, rituals, and behaviors of daily life (Rueda,
2011). These elements of organizational culture are shaped by individuals and groups, and
operate through a reciprocal relationship that is dynamic and interactional (Rueda, 2011). This
literature review uses these concepts to explicate the organizational gaps confronting
superintendents as they work to implement DDDM. The next sections discuss the cultural
By January 2020, superintendents will implement data driven decision-making to improve low-
performing schools.
Assumed Motivation Influences Motivational Influence Assessment
Self-Efficacy – Superintendents need to be
confident in their ability to effectively implement
data driven decision making strategies to improve
low-performing schools.
Interview questions that ask superintendents
about their self-efficacy to implement data
driven decision making, and survey
statements that assess self-efficacy.
Task Value – Superintendents need to see
implementation of data driven decision making as
a priority.
Interview questions that assess whether
superintendents value the implementation of
data driven decision making, and survey
statements that assess whether data driven
decision making is a priority.

26
models and cultural settings that influence the capacity of superintendents to implement DDDM
to improve low-performing schools.
Organizational Culture That Supports Data Driven Decision Making
Superintendents are subject to multiple layers of accountability and fiduciary
responsibilities that emphasize compliance rather than a culture that integrates DDDM. Trujillo
et al. (2013) conducted a case study analysis of an urban school district in California under high-
stakes accountability. Trujillo et al. (2013) found that the superintendent and the board members
organized their goal and decision-making almost exclusively around the accountability measures,
and facilitated a decision process that was restrictive and that excluded local participation and
input. Trujillo et al. (2013) demonstrated that accountability influences the superintendent’s
approach to organizational culture. Conflicting accountability requirements frames the
organizational culture of superintendents, and distinguishes how the world works and how it
should work in the field of education. Firestone and Shipps (2005) argued that educational
leaders are faced with the problem of navigating multiple and conflicting accountability
requirements that are external and internal to school district operations. The classification of
cultural model is applied to this category because it frames the organizational environment where
DDDM is implemented.
Professional Development to Implement Data Driven Decision-Making
DDDM in the school context remains a relatively new approach, which is why it requires
professional development to sustain its implementation. In a case study of four urban school
systems, Wohlstetter et al. (2008) examined the creation of data driven decision making systems.
Their findings demonstrated that the adoption of continuous improvement strategies requires
factors such as autonomy for school site decision-making, structuring collaboration, providing

27
professional development and training, and establishing a common language and a culture of
data use. The integration of DDDM is a new skill that is expanding the role of superintendents,
which already consists of supervision of schools, the hire and dismissal of personnel, consensus-
building, and negotiation (Hillard & Newsom, 2013). A case study conducted by Marsh et al.
(2017) reinforces the need for professional development. Their findings showed that educational
leaders demonstrated strong support for a holistic accountability system that was adaptable to the
local context and that emphasized capacity-building over punishment and sanctions. However,
district administrators reported that limited capacity created obstacles in the implementation of a
holistic accountability system. One factor that explained the effect of limited capacity on
implementation was school district leadership. District administrators reported low-levels of
deep learning in the early stages of adoption, despite engaging in useful technical problem
solving. As a result, district administrators expressed a desire to engage in deeper learning that
expanded beyond the adoption of best practices and into inquiry-oriented organizational learning.
The purpose of professional development is to assist superintendents in their capacity to integrate
DDDM to school district practices and operations. The capacity of superintendents is important
because their leadership determines organizational structures and context to foster the integration
of DDDM and its execution (Mac Iver et al. 2019). Mac Iver et al. (2019) conducted a case study
on the role of the central office in improving college readiness for historically underserved
students. Their analysis found that a commitment from leadership was the first step towards
productive and an effective central office role to improve student outcomes. The reason was
because leadership commitment determines the structure and context of capacity-building
activities and ensures execution. The classification of cultural setting is applied to this category
because professional development frames the organizational practices that fosters DDDM.

28
Table 4 categorizes the organizational influences discussed thus far by organizational
type, explains the assessment methods, and lists the organizational mission, organizational global
goal, and stakeholder performance goal.
Table 4
Summary of Assumed Organizational Influences and Organizational Assessments
Organizational Mission
The State will provide a world-class education for all students, from early childhood to
adulthood.
Organizational Global Goal
The goal of this State is to ensure that 100% of school districts reclassify low-performing
schools or student subgroups within four years of identification.
Stakeholder Goal
By January 2020, superintendents will implement continuous improvement cycles at low-
performing schools.
Assumed Organizational Influences Organization Influence Assessment
Cultural Model Influence 1: The State
Department of Education needs to promote a
culture that supports the implementation of
data driven decision making in school
districts.
Interview questions that ask superintendents
about their experiences interacting with the
State Department of Education and its
culture towards data driven decision making,
and survey statements that assess culture
climate from the State.
Cultural Setting Influence 1: The State
Department of Education needs to provide
professional development to support
implementation of data driven decision
making.
Interview questions that ask about the
professional development opportunities that
have been provided, if any, by the State to
support implementation of data driven
decision making, and survey statements that
assess professional development support
opportunities from the State.

Conceptual Framework
According to Maxwell (2013), the conceptual framework consists of concepts,
assumptions, expectations, beliefs, and theories that inform the research and its design. The
purpose of the conceptual framework is to function as a tentative theory that guides the
investigation and that outlines the model of the research problem (Maxwell, 2013). This study
used the Clark and Estes (2008) analytical framework as the conceptual framework that guided

29
this gap analysis. The Clark and Estes (2008) framework examines the knowledge, motivation,
and organizational influences associated with the implementation of the stakeholder goal. This
section elaborates on the conceptual framework.
This study asserts that the knowledge and motivation of superintendents, along with the
statewide organizational setting, influence the implementation of DDDM to improve low-
performing schools. Since superintendents are ultimately held accountable for the actions of their
school districts, they were selected as the primary stakeholders for examination in this study.
Firestone and Shipps (2005) argued that educational leaders face multiple and conflicting
accountability requirements in their capacity as organizational leaders. Efforts to improve low-
performing schools are implemented within this context, which is why the organizations of focus
are the school districts of superintendents. The stakeholder performance goal to implement
DDDM to improve low-performing high schools was selected due to recent State and federal
reforms (ESSA, 2015). Achieving this goal depends on the capacity of superintendents to
implement this initiative within their organizational culture and context. As a result, this study
selected procedural and metacognitive knowledge influences, and self-efficacy and task-value
motivation influences as key components of the conceptual framework. The organizational
influences of the conceptual framework consist of cultural settings such as sustained professional
development to implement DDDM, and a cultural model that focuses on continuous
improvement over an emphasis on compliance.
While the conceptual framework presents each of the influences in isolation, these
influences interact with each other to influence the stakeholder goal. For example, Park et al.
(2013) demonstrated that a key factor in the implementation of DDDM is knowledge of the local
context and the ability to apply DDDM within the local context. This requires the metacognitive

30
knowledge to adapt a strategy to a context that is dynamic. However, any shortcomings that arise
as a result of the local context are supported by interacting with the cultural setting, since
professional development influences the capacity to implement DDDM (Marsh et al., 2017).
This interaction between the metacognitive knowledge and professional development contribute
to achieving the performance goal, since the metacognitive knowledge to apply DDDM is not
sufficient to address shortcomings in the implementation. A second interaction that contributes to
the performance goal is task value and a cultural model that integrates DDDM. The literature
demonstrated that DDDM is generally accepted among superintendents, administration leaders,
principals, and teachers as a strategy to address low-performing schools (Marsh et al., 2017).
However, acceptance is not sufficient to ensure effective implementation of continuous
improvement. Marsh et al. (2017) found that meetings designed to support DDDM
implementation in a school district failed to provide content, agendas, and consistent meeting
facilitators even though DDDM was generally accepted. Task-value is a key organizational
motivator but the cultural model of continuous improvement influences execution and the
effective use of resources and time to implement DDDM (Marsh & Farrell, 2015; Marsh et al.,
2017; Tichnor-Wagner et al., 2017). This interaction between task value and integration of
DDDM over compliance contributes to achieving the performance goal. These two interactions
are important factors in achieving the performance goal.

31
Figure 1

Conceptual framework

School District Superintendents
• Knowledge (procedural) –
superintendents must know how to
effectively implement data driven
decision making to improve low-
performing schools.
• Knowledge (metacognitive) –
superintendents must know how to
reflect on their capacity to adapt data
driven decision making to the local
context and overcome challenges.
• Motivation (self-efficacy) –
superintendents need to be confident
in their ability to effectively
implement data driven decision
making.
• Motivation (task value) –
superintendents need to see
implementation of data driven
decision making as a priority.
State Department of Education
• Cultural Settings:
Professional development
– The State needs to
provide professional
development to
superintendents to support
adoption of data driven
decision making.
• Cultural Model: Data
driven culture – The State
needs to promote a culture
that supports the
implementation of data
driven decision making in
school districts.
Stakeholder Goal
By January 2020,
superintendents will implement
data driven decision making to
improve low-performing schools
within four years.

32
Figure 1 is a visual representation of the conceptual framework. The blue circle
represents the organizational influences, which consists of one cultural setting and one cultural
model. The organization of focus is the State Department of Education (SDE), which influences
the organizational context where superintendents operate. The cultural setting consists of
professional development to support the implementation of data driven decision making. The
cultural model consists of promoting a culture that supports the implementation of data driven
decision making. These organizational influences contribute to superintendents’ capacity, which
is why an arrow is connected to the green circle that represents superintendents. The green circle
captures the knowledge and motivational influences of the stakeholder, or for the purposes of
this analysis, the superintendent. Finally, the superintendent influences the stakeholder
performance goal, which is why the blue arrow extends down to the yellow box. The yellow box
captures the stakeholder performance goal, which is to implement continuous improvement
cycles at schools identified as low-performing by January 2020.
Conclusion
This literature review summarizes the broad themes on the topic of DDDM
implementation to improve low-performing schools. The literature demonstrated that DDDM
allows for the improvement of organizational capacity, decision-making, and performance.
However, the literature also demonstrated potential challenges in the implementation of DDDM
in school settings. Superintendents serve as organizational and instructional leaders, while often
navigating multiple and conflicting accountability systems to improve school performance. The
literature demonstrated that superintendents influence the implementation of DDDM within this
context. This chapter summarized a number of knowledge, motivation, and organizational

33
influences that are assumed to affect a superintendent’s ability to implement DDDM to improve
low-performing schools. The following chapter presents the methodology section for this study.

34
Chapter Three: Methods
This study focused on the knowledge and motivation of superintendents to implement
data driven decision making (DDDM) and the organizational influences associated with
implementation. This chapter on methodology outlines the survey and interview sampling
strategy and approach. This chapter also discusses factors associated with validity and reliability,
credibility and trustworthiness, and ethics. The chapter ends with a discussion on the data
collection methods and analysis techniques used in this study. The following research questions
guided this study:
1. What are superintendents’ knowledge and motivation related to implementing data driven
decision making to improve low-performing schools?
2. What is the interaction between organizational culture and context and superintendent
knowledge and motivation to implement data driven decision making to improve low-
performing schools?
3. What are the recommended knowledge and skills, motivation, and organizational
solutions?
Participating Stakeholders
The stakeholder population of focus for this study were superintendents. In this Western
State, there were approximately 438 superintendents who operated a school district with schools
that were identified as low-performing.
6
The focus of this study was the implementation of
DDDM to improve low-performing schools. As a result, the criterion that guided the selection of
the stakeholder population was whether the school district operated a school that was identified
as low-performing. While ESSA and state laws do not specifically require the use of DDDM,

6
Source is the State Department of Education’s correspondence to superintendents.

35
both reforms promote a continuous improvement system rather than a compliance driven system
to improve low-performing schools (ESSA, 2015). Within this system, superintendents are
required to improve low-performing schools and are encouraged to use DDDM, since the State’s
system identifies low-performing schools based on data and metrics. There are two key factors
that result in a designation of low-performance. The first factor is determined based on the
graduation rate of the high schools, which identifies low-performance as any high school with a
graduation rate of 67% or less (ESSA, 2015). The second factor is associated with performance
on the school data dashboard, which is the State’s multiple measures accountability system
(ESSA, 2015). This study utilized a convergent mixed method design with identical concurrent
criteria to collect both quantitative and qualitative data (Creswell & Creswell, 2018; Johnson &
Christensen, 2015). The following sections outline the sampling criteria, recruitment strategy,
and rationale.
Survey Sampling Criterion and Rationale
Superintendents of Low-Performing Schools
The State released a list of schools that were identified as low-performing. The State
applies the criteria for low-performance and generates a list for each school district. This study
sampled superintendents with schools that were identified as low-performing. In this State, there
were 438 superintendents who operated a school district with schools identified as low-
performing.
7
The selection of low-performing schools as a criterion informed the analysis of
superintendents’ knowledge, motivation, and organizational influences to implement DDDM.
Survey Sampling (Recruitment) Strategy and Rationale

7
Source is the State Department of Education’s correspondence to superintendents.

36
The survey sampling strategy was non-random, since it consisted of a census approach to
survey all superintendents who operated low-performing schools in the Western State (Johnson
& Christensen, 2015). All 438 superintendents who operated a school district with low-
performing schools were emailed an invitation to participate in the survey. The survey assessed
dimensions of knowledge, motivation, and organizational influences to implement DDDM.
Interview Sampling Criterion and Rationale
School Districts With 15,000 Students or More. This study utilized a purposeful
sampling strategy to select superintendents for an interview. The criterion that guided this
analysis was whether the superintendent operated a school district with 15,000 students or more.
According to Johnson and Christensen (2015), qualitative interviews provide an opportunity to
gather in-depth information and to understand the inner world of the stakeholder. A school
district with large student populations tend to operate more schools. As a result, such school
districts may operate more schools that are identified as low-performing. For this reason, this
criterion was used to select superintendents for an interview.
Interview Sampling (Recruitment) Strategy and Rationale
The goal of this study was to explore the knowledge, motivation, and organizational
influences of superintendents associated with the implementation of DDDM to improve low-
performing schools. This study utilized a purposeful sampling approach to recruit interview
candidates consistent with the criterion (Maxwell, 2013). Superintendents who completed the
survey had the option to provide their information to be contacted for a potential interview. The
total interview sample size for this study was nine superintendents who operated a school district
with low-performing schools. Johnson and Christensen (2015) argued that a general rule,
especially for qualitative sampling, is to reach a point of saturation where more or new

37
information does not emerge as more data is collected. A key strategy was to interview
superintendents in the urban settings, which account for a larger proportion of low-performing
schools.
8

Validity and Reliability
Validity and reliability are important elements to ensure accuracy and consistency of the
quantitative components in this study. Validity refers to whether the quantitative instrument is
measuring its intendent target (Robinson & Firth Leonard, 2019). For example, a survey
statement may not actually measure what it was articulating if the respondent interprets the
statement in a different way. Reliability refers to whether the quantitative instrument was
consistent each time it was used (Robinson & Firth Leonard, 2019). This study created its own
survey instrument to measure knowledge, motivation, and organizational influences in relation to
the research questions. The content validity and reliability of the survey instrument were
addressed through the use of two strategies. The first strategy conducted an expert review of the
survey instrument (Robinson & Firth Leonard, 2019). The purpose of the expert review was to
ensure that survey questions and statements are appropriately understood by the respondents
(Robinson & Firth Leonard, 2019). This strategy strengthens the content validity of the survey
items by ensuring clarity.
The second strategy was to pilot test the survey instrument. Pilot testing was a pretest
with the purpose of improving the overall survey process, identifying problematic questions, and
determining the needs for survey instruction (Robinson & Firth Leonard, 2019). The pretest was
conducted by recruiting a small but meaningful group of practitioners, in a similar fashion to the
expert review strategy. The combination of these strategies strengthened the content validity and

8
Source is the State Department of Education’s fingertip facts.

38
reliability of the survey instrument. These strategies also helped establish confidence in the
survey instrument, since they provided an opportunity to correct any inconsistencies or problems
that emerged when conducting the survey.
Credibility and Trustworthiness
Merriam and Tisdell (2016) highlighted that the researcher is the interpreter or translator
of data in qualitative research. As a result, the researcher was responsible for ensuring the
credibility and trustworthiness of the data. This study used three strategies to increase the
credibility and trustworthiness of the qualitative data collection process and analysis. The first
strategy was to ensure rich data collection. According to Maxwell (2013), a quality of rich data
in interviews consists of verbatim transcripts of the interviews. This study utilized a rich data
strategy in the interview phase of the study by recording in-person interviews to ensure verbatim
transcripts. The second strategy conducted peer examination and review. The purpose of peer
examination was to assess whether the findings were plausible based on a review of the raw data
(Merriam & Tisdell, 2016). This study was being conducted to satisfy the requirements of a
doctoral program, which included a peer review process as part of the dissertation committee and
the dissertation defense process. The third strategy was respondent validation. Respondent
validation consists of the systematic solicitation of feedback from the research subject on the
data and conclusion (Maxwell, 2013). This approach allows the researcher to identify
misinterpretation, and any potential biases or misunderstandings. In this study, respondent
validation consisted of a review of a key finding and the relevant section of the interview
transcript. The respondent had an opportunity to review the finding and how it was informed by
the interview. This review occurred following the data analysis stage of the study. The researcher
emailed the respondent the finding and the relevant portion of the interview transcript. The

39
respondent was provided with one week to review and provide feedback on the accuracy of the
findings and interpretation of the interview transcript. Any feedback from the respondent was
incorporated into the finding. These strategies were included in the data collection and analysis
process to increase the overall credibility and trustworthiness of the study.
Ethics
As a qualitative researcher, I have several ethical responsibilities which are identified in
this section and that contribute to the design of the study. The research methodology for this
study was framed within the ethical responsibilities of the researcher. Merriam and Tisdell
(2016) argued that the ethics of the investigator affects the validity and reliability of a study.
According to Glesne (2011), five key ethical principles are applied when involving human
participants in research. The five ethical principles are the following: (1) sufficient information
must be available for the participant to make informed decisions; (2) the participant must be able
to withdraw from the study without a penalty; (3) all risks to the participant are eliminated; (4)
the benefits of the study must outweigh risks; and (5) experiments are only to be conducted by
qualified investigators. These ethical principles are used to frame the research methodology of
this study and to protect research participants. I also submitted my study to the University of
Southern California Institutional Review Board (IRB) and followed their rules and guidelines to
protect participants.
Superintendents were the human participants in this study. All participants were informed
that participation in this study was voluntary and that they may withdraw without penalty at any
point in time. Withdrawal without penalty was important for this study, since superintendents
have demanding schedules. The information sheet about this study, which was subject to IRB
approval and was incorporated into the beginning of the survey, was provided to participants

40
prior to an interview. The identity of the participants remains confidential. I asked for permission
to audio record the interviews, which were stored and secured on my password protected laptop.
Any recordings and interview materials were destroyed after the conclusion of this study. The
findings were also reported in the aggregate and anecdotes or quotes are not traceable to any one
individual. The purpose of these processes was to protect research participants.
My relationship with the organization and the context that I studied was also important to
consider in relation to the research participants. The purpose of a qualitative study was to explore
another person’s perspective through interviews and questions (Patton, 2002). As a result, the
quality of information that was obtained during interviews was dependent on the interviewer and
their relationship with the organization (Patton, 2002). Since I studied superintendents, I
interacted with multiple organizations and not a single organization. Superintendents lead school
districts. In my professional capacity, I work for a school district and at the direction of the
superintendent. I am an advocate for a large school district. For this reason, the research
participants were not in a subordinate role where they may be susceptible to potential harm, such
as coercion or pressure to participate. I also clarified on the consent form that participation in this
study allows me to fulfill requirements of an education doctorate program. This clarified any
potential confusion about my dual roles as a professional and a researcher.
My experience attending low-performing schools, my ethnicity as a Latino male, my low
socioeconomic status as a child, and my professional role as an advocate for a school district are
possible sources of bias. This background may contribute to assumptions about the motivation of
my research and my data collection, analysis, and reporting activities. In terms of my
professional capacity, the work I do is not directly related to the implementation of DDDM,
since my responsibilities are related to legislative advocacy. As a result, I examined the

41
implementation of DDDM from a different perspective that is not related to my professional
responsibilities. In relation to my identity and personal experiences, my goal was to establish
rapport and neutrality during the interview and research process. Patton (2002) wrote that the
responsibility of the interviewer was to establish rapport and neutrality, so that the research
participant feels that they can share their responses without engendering the favor or disfavor of
the researcher. Rapport and neutrality were important because, according to Patton (2002), an
effective interview consist of a two-way communication that is conversational and that solicits
quality responses. By collecting quality interview responses for analysis and reporting, I
minimized the impact of any potential biases. This study also reported the methodology for
collecting data and analysis for the purposes of transparency and to facilitate the reader’s own
interpretation.
Quantitative Data Collection and Instrumentation
Quantitative data collection was obtained through the use of a survey to explore
motivational and organizational influences of superintendents to implement DDDM. The
purpose of the survey was to quantify the strength of the knowledge, motivation, and
organizational influences of superintendents, and its relationship to the implementation of
DDDM. As a result, the survey measures the extent of agreement with statements pertaining to
knowledge, motivation, and organizational influences. According to Robinson and Firth Leonard
(2019), survey instruments capture attributes, behaviors, and thoughts about attitudes, beliefs,
feelings, awareness, and opinions. These aspects of survey instrumentation make it a fit to
explore the motivational and organizational influences of superintendents. The purpose was to
identify which knowledge, motivation, and organizational influences of superintendents affect
the implementation of DDDM.

42
Surveys
Survey Instrument
The survey contained five descriptive statements and 26 declarative statements related to
knowledge, motivation, and organizational influences presented in the conceptual framework and
demographic information related to the respondent. The survey also included a section with
background information about the study, and the terms related to consent. The declarative
statements followed a four-point scale that included the following anchors: strongly disagree,
disagree, agree, and strongly agree (Robinson & Firth Leonard, 2019). The declarative
statements covered knowledge, motivation, and organizational influences with two sub-
constructs for each influence. The knowledge influences covered the procedural and
metacognitive knowledge of superintendents. The motivational influences covered task value
and self-efficacy among superintendents. The organizational influences covered cultural model
and cultural setting sub-constructs. The agree-disagree scale measured the extent of agreement
by superintendents on statements related to the motivational and organizational influences on the
implementation of DDDM (Robinson & Firth Leonard, 2019). These scales consisted of an
ordinal measure, where response options were ranked in order of agreement (Salkind, 2017).
Additionally, the consistency in response options minimized cognitive overload and facilitated
survey completion (Robinson & Firth Leonard, 2019). The purpose of this survey instrument was
to explore superintendents’ perceptions of their own knowledge, motivation, and organizational
influences in the implementation of DDDM. The survey was emailed to superintendents with
low-performing schools as determined by the selection criteria.
Survey Procedures

43
The survey was administered using Qualtrics, which was a software specifically designed
to conduct surveys. The response rate was monitored through the use of the software, and weekly
reminders were sent on a timely basis to survey recipients. The survey stayed open for three
weeks, which meant that respondents received three reminders during that timeframe. Survey
participants were also informed of the number of questions and to expect to spend no more than
10 minutes on the survey. The survey was administered by emailing the link to the online survey
portal, where participants were able to review the purpose of the study and statements related to
confidentiality. The researcher administered the survey upon approval from the University of
Southern California Institutional Review Board. The survey was administered first because even
though participation in the survey was confidential, participants had an option to express interest
in also participating in the interview phase of the study. The surveys were administered online to
facilitate completion at a time that is convenient for superintendents. Again, due to their busy
schedules, an online platform for the survey allowed the flexibility to complete the survey at a
convenient time.
Qualitative Data Collection and Instrumentation
The qualitative data collection and instrumentation process provided an opportunity to
explore the knowledge influence of superintendents related to the implementation of DDDM to
improve low-performing schools. According to Merriam and Tisdell (2016), interviews are
useful when observation of behaviors, feelings, or interpretation of the surrounding context are
not possible. The interview approach provided an opportunity to explore how superintendents
observed and interpreted their own knowledge of DDDM implementation, since observations
were not within the scope of this study. Specifically, qualitative responses provided an in-depth
exploration of the knowledge of superintendents (Merriam & Tisdell, 2016). The interview

44
approach also informed the research questions with an understanding of the knowledge capacity
of superintendents.
Interviews
Interview Protocol
The interview protocol consisted of a semi-structured interview format to explore the
knowledge, motivation, and organizational influences of the conceptual framework (Merriam &
Tisdell, 2016). The semi-structured approach allowed the interview to flow as a conversation and
to understand the unique ways the participant may interpret the world (Merriam & Tisdell,
2016). The knowledge influences explored whether superintendents demonstrated components of
the procedural and metacognitive knowledge to implement DDDM. The knowledge influences
consisted of two sub-constructs, which were factual and metacognitive knowledge. The
questions asked superintendents to define the basic concepts of DDDM, and to identify their
strengths and the challenges they experienced in its implementation. For example, how a
superintendent responds to the question related to their strengths illustrated their metacognitive
capacity to implement DDDM. The motivation influence consisted of questions that covered task
value and self-efficacy constructs. The purpose was to explore the motivation of superintendents
in their capacity to implement DDDM. The organizational influence consisted of the cultural
model and cultural setting sub-constructs, which assessed the influence of organizational culture
and resources on the implementation of DDDM. In total, the semi-structure interview covered 11
questions.
Interview Procedures
Once survey data collection was completed, interviews with willing participants began.
Superintendents had an opportunity to identify themselves to participate in an interview through

45
the survey process. A separate link was provided so that the superintendent who would like to
participate in an interview can do so by replying on a separate access portal, which maintained
the survey findings confidential. The recruiting of interview participants consisted of purposeful
sampling to ensure the selection of superintendents were consistent with the selection criteria
(Maxwell, 2013). In addition to the survey process, the researcher also identified superintendents
as potential interview candidates. The aim was to conduct eight to 12 one-on-one interviews with
each lasting between 45 to 60 minutes with an approximate total interview time of up to nine
hours. Interviews were recorded and transcribed to facilitate data analysis. Finally, since data
collection occurred during COVID-19, interviews were conducted through the phone or using
tele-conference software.
Data Analysis
The purpose of data analysis is to derive meaning from the data that was collected
(Merriam & Tisdell, 2016). This process consisted of calculating frequencies and looking for
meaningful correlations in quantitative data and finding and creating themes and categories in
qualitative data. The process of data analysis was complex, but it creates a process that allows
the researcher to move into findings and results to contribute new knowledge and understanding
(Merriam & Tisdell, 2016). For this study, the researcher utilized both quantitative and
qualitative data analysis. The results and findings provided insights into the research questions of
this study.
The survey was administered through the software Qualtrics and the data analyzed with
the report functions provided by the software package. Frequencies and percentages were
calculated for each of the declarative statements in the survey. The survey responses provided

46
information about the participating superintendents’ capacity to implement DDDM in the context
of their knowledge, motivation, and organizational influences.
Upon completion of an interview, the researcher transcribed each interview. Analytic
codes were created after all interviews were transcribed. The purpose of the analytic codes was
to categorize themes that emerged during the analysis of transcribed interviews. The analytic
codes were developed to be consistent with the conceptual framework. The researcher also
compared and contrasted the qualitative themes with the quantitative responses, and used survey
responses to contextualize themes that emerged.

47
Chapter Four: Results and Findings
This chapter provides the results and findings of this study, which examined the
knowledge and motivation of superintendents to implement data driven decision making
(DDDM) and the organizational influences. This study utilized a convergent mixed
methodological approach. The results and findings included survey data and interviews with
superintendents. Results are a reference to quantitative survey responses. Findings are a
reference to qualitative interview responses. This chapter organized results and findings by first
providing a description of participating stakeholders for each methodological approach. The
chapter then turns to answering each research question by providing the results and findings for
each influence. The following research questions guided this study:
1. What are superintendents’ knowledge and motivation related to implementing data driven
decision making to improve low-performing schools?
2. What is the interaction between organization culture and context and superintendent
knowledge and motivation to implement data driven decision making to improve low-
performing schools?
3. What are the recommended knowledge and skills, motivation, and organizational
solutions?
Participating Stakeholders
The quantitative phase of the study consisted of a survey that was emailed to a list of 438
superintendents with identified low-performing schools. The list was produced in 2019 and is
maintained by the State Department of Education. The survey had a completion rate of 14.6%.
The survey was available for completion over the course of three weeks with reminders emailed
one per week. Survey responses were provided on a four-point agree-disagree Likert scale

48
without a midpoint. Nearly all (96.88%) of participating stakeholders reported their position as
superintendent as opposed to a deputy or assistant superintendent (1.56%), which means that
nearly all (96.88%) also reported their organization as a school district. More than half (about
55%) of participating stakeholders reported being in their position “more than 3 years.” Finally,
about 80% of participating stakeholders reported having “less than 10 schools” that were
identified as low-performing. Table 5 provides descriptive responses to questions about position,
organization type, years in position, and low-performing schools.
Table 5
Survey Responses to Descriptive Questions (N = 64)
Survey Question or Statement
Participant Survey Responses
n %
Position – Please select the title that best fits your position.
Superintendent 62 96.88
Deputy or Assistant Superintendent 1 1.56
Education Management Professional 0 0
Principal 1 1.56
Organization – Please select the best description of your
organization.

County Office of Education 1 1.56
School District 62 96.88
Charter School 1 1.56
Other 0 0
Years in Position – How long have you worked in your current
position?

Less than one year 11 17.19
1 to 2 years 8 12.50
2 to 3 years 10 15.63
More than 3 years 35 54.69
Low-Performing Schools – How many schools within your school
district have been identified as low-performing according to State
and federal laws?

Less than 10 schools 51 79.69
11 to 25 schools 10 15.63
25 to 50 schools 3 4.69
Over 51 schools 0 0

49
The qualitative phase of the study consisted of purposeful sampling. Participating
stakeholders in the survey had an opportunity to identify themselves for a follow-up interview.
The researcher purposefully recruited superintendents to participate in the qualitative phase of
the study. A total of nine superintendents were recruited to participate in interviews. A
pseudonym is used for each participant and student enrollment numbers are turned into ordinal
measures to protect confidentiality. The gender make-up of the sample consisted of five males
(55.56%) and four females (44.44%) from a total sample of nine individuals. Table 6 provides
descriptive information regarding each interview participant.
Table 6
Interview Participants by Position, Student Enrollment, and Low-Performing Schools (N = 9)
Pseudonym Position Student Enrollment Number
Number of Low-
Performing Schools
Participant 1 Superintendent 20,000 – 25,000 13
Participant 2 Superintendent 15,000 – 20,000 4
Participant 3 Superintendent 40,000 – 50,000 34
Participant 4 Superintendent 20,000 – 25,000 2
Participant 5 Superintendent More than 50,000 40
Participant 6 Superintendent 20,000 – 25,000 9
Participant 7 Superintendent 25,000 – 30,000 3
Participant 8 Assistant Superintendent 20,000 – 25,000 13
Participant 9 Superintendent 15,000 – 20,000 7
Note. Student enrollment numbers and the number of low-performing schools were collected
from the State Department of Education.

Knowledge Results and Findings
As one of the foci, this study examined superintendents’ knowledge influences to explore
the implementation of DDDM to improve low-performing schools. Among the three dimensions,
Clark and Estes (2008) identified the knowledge dimension as an essential component for
stakeholder performance. Krathwohl (2002) synthesized the knowledge dimension into the
following four types: factual knowledge, procedural knowledge, conceptual knowledge, and
metacognitive knowledge. This study explored superintendents’ procedural knowledge and

50
metacognitive knowledge as key influences in the implementation of DDDM to improve low-
performing schools. The knowledge results and findings demonstrated a gap among participants
in the domain of procedural knowledge, and affirmed metacognitive knowledge as an existing
asset among superintendents. A gap was established when there was evidence of insufficient data
to support superintendents’ use of an influence, for instance when responses demonstrated less
than 60% agreement, or when a comparison of survey results and interview findings revealed
inconsistent or contradictory responses. In contrast, an asset was established when there was
sufficient support, such as 60% agreement or above, and when responses were consistent when
compared with survey results and interview findings. These criteria were applied to all survey
results and interview findings. The following sections elaborate on these results and findings,
and answers the first research question related to the knowledge influence of superintendents to
implement DDDM to improve low-performing schools.
Superintendents’ Procedural Knowledge Gap for DDDM Implementation
This study found that procedural knowledge was a gap for superintendents. Procedural
knowledge frames the first knowledge influence that was explored, which was defined as
knowing how to effectively implement DDDM to improve low-performing schools. When
superintendents were asked in the survey whether they have a clear understanding of how to
apply DDDM to improve low-performing schools, nearly all (96.83%), responded with an
“agree” or “strongly agree” answer. Appendix C presents a table with all the survey responses.
However, the interview findings demonstrated a procedural knowledge gap for superintendents.
Even though the survey results suggest that superintendents have a high understanding related to
the application of DDDM, there was insufficient data in the interview findings to support the
procedural knowledge influence and its use by superintendents. Instead, the interview findings

51
suggested that superintendents may possess conceptual knowledge rather than procedural
knowledge to implement DDDM to improve low-performing schools. According to Krathwohl
(2002), an understanding of basic facts and structures, theories, models, principles,
generalizations, classifications, and categories and their relationships frames conceptual
knowledge. This is a consideration for future research and is further discussed in the next
chapter.
Procedural knowledge as a gap was primarily evident in the first interview question.
During interviews, superintendents were first asked: “How do you define data driven decision
making?” A common theme in responses to this question was the identification of data concepts
rather than procedural steps in the definition of DDDM. For example, Participant 2 discussed
framing as a concept to explain student progress, Participant 4 identified the use of triangulation
to assess and gather data, and Participant 7 cited various types of data they use such as surveys,
test results, and observations. In these responses, participants identified theories, concepts, and
classifications of data that influence DDDM implementation. Follow-up questions asked
superintendents about their experiences in implementing DDDM and how they went about
supporting its implementation. A consistent finding that emerged in those responses was a gap
related to procedural knowledge. In contrast, procedural knowledge of DDDM focuses on the
process rather than the concepts. According to Haecker et al. (2017), DDDM is a process where
data and analysis informs local decision making and practice. The interview responses showed
wide variation when articulating a definition for DDDM.
However, this is not to suggest that none of the participants demonstrated procedural
knowledge. Participant 3 provided the following response when asked for a DDDM definition:

52
You use data effectively to better understand a problem first and then to identify
eventually what is, you know, the root cause, or a causal system analysis. That then
informs the change that you're going to introduce to test whether or not you've solved the
problem that the data set, you use to help you identify. Then, eventually, you're refining
the data gathering to decide whether or not the problem that you were seeking to resolve.
Participant 3 provided a definition of DDDM that frames a process for making decisions. The
response first identified the purpose of the DDDM, which is to understand the problem, and then
turned to providing a process to solve the problem. Participant 3 and Participant 6 identified this
process as “improvement science.” In the field of education, “improvement science” is used to
apply rigorous disciplined inquiry to effectively implement educational interventions that
improve practice (Bryk et al., 2015). This process adopts a plan-do-study-act cycle that
incorporates data, analysis, and decision making into an iterative process of inquiry, but that also
relies on networked communities to accelerate learning (Bryk et al., 2015). The procedural
knowledge influence allows superintendents to outline a process that is intentional and that uses
data and analyses to make decisions.
Superintendents’ Possess Robust Strategic Knowledge to Adapt DDDM to Local Context
This study found that superintendents demonstrated strategic knowledge in the process of
adapting DDDM to the local context. Metacognitive knowledge was the second knowledge
influence that was explored, which was defined by the use of strategic knowledge and
demonstrated by knowing how and why to do a specific task (Krathwohl, 2002). When
participants were asked in the survey whether they dedicate time to reflect on how to best use
data to inform their decision making, nearly all (96.88%), responded with an “agree” or
“strongly disagree” answer. A follow-up question in the survey asked participants whether they

53
reflected on how to best use DDDM to improve low-performing schools, and nearly all (96.83%)
responded with an “agree” or “disagree” answer. Appendix C presents a table with all the survey
responses. These results were also consistent in the interview findings, which demonstrated how
superintendents used strategic knowledge to implement DDDM to the local context and to
overcome any potential challenges. For example, Participant 9 demonstrated how their reflection
to implement DDDM identified potential challenges and ways to address them:
The teachers have been a little harder because they didn’t have data in this district. They
literally skipped the first phase of the standardized test movement, which is not
necessarily a bad thing, but by skipping it, they didn’t understand the use of data. So, we
actually brought someone in and our teachers use data significantly better, but not all of
them have bought in because not all of them believe in it.
Participant 9 demonstrated an understanding of the challenges of implementing DDDM in their
local context. This strategic understanding was grounded with the intention to improve data use
and implementation, which is a process that requires reflection.
The effective implementation of DDDM relies on the capacity of superintendents to
navigate the local context. As a result, the metacognitive knowledge influence requires reflection
as part of using strategic knowledge and to demonstrate a self-awareness to guide DDDM
implementation. Participants demonstrated these aspects of metacognitive knowledge through
their use of framing and professional learning communities to encourage local adoption,
protocols as a way to guide local decision making, and an awareness of advantages and
limitations of data use. For example, Participant 3 identified a common challenge when it came
to data use, which was that sometimes one was confronted with too much information.
Participant 3 described this as a situation when you are “data rich but analysis poor.” In response

54
to such a challenge, participants utilized framing to strategically prioritize data that was relevant
for the purposes of decision making. Participant 2, for instance, described the use of “headwinds
and tailwinds.” Students with tailwinds have “no headwinds, no issues, no challenges, no special
ed, no ESL, no foster youth, no free and reduced lunch.” According to Participant 2, students
with tailwinds have a lot of support and individuals who push them to succeed, while students
with headwinds have challenges that make it harder for them to learn and that push them down.
This process of framing was used to implement DDDM and for participants to describe their
decisions.
The professional learning communities also facilitated this process, since participants
used professional learning communities to share information and to accelerate learning and
implementation. Participant 1, Participant 2, Participant 4, and Participant 8 highlighted this
practice. Participant 2 noted how the professional learning communities provided an opportunity
to foster learning among teachers because “there is a team that has been trained” and that was
where “they started to really, really understand.” In this way, the professional learning
communities promoted the use of data to make better decisions about classroom instruction. The
strategies that participants used to implement DDDM was not only limited to framing or
professional learning communities. Participant 3 described the use of protocols as a way to
establish a process of inquiry, which adopted the plan-do-study-act cycle of inquiry, to foster
decision making among school principals. Participant 3 stated that “we put together these
protocols, so that in the end we are able to assess whether those data sets led to scaling of things
you’ve introduced.” The purpose of these protocols was to provide a scientific process of inquiry
to guide local decision making at each school site. Participants also demonstrated an awareness
of the advantages and limitations of data use, which was essential to strategic knowledge and

55
overcoming implementation challenges. For example, Participant 1 described how teacher
assessments provided a better understanding of student progress, since they were developed by
teachers and applied more frequently. Participant 1 said, “I tend to rely more on assessments that
are created by teachers that are given frequently so that we can modify the instruction as we go.”
These assessments provided a better understanding of student progress and whether to adjust
curriculum, in contrast, “if you rely on state assessments” then data is only provided “once per
year.” According to the participants, this awareness allows them to assess the best course of
action and to overcome potential challenges, which often emerged as a disbelief or resistance to
data as noted by Participant 7, Participant 8, and Participant 9. Through these strategies,
participants used their metacognitive knowledge influence to implement DDDM in the local
context and to overcome potential challenges.
Motivation Results and Findings
Motivation was the second influence examined in this gap analysis study. According to
Clark and Estes (2008), motivation influences whether individuals engage with tasks to achieve a
desired goal. Active choice, persistence, and mental effort are motivation-related behaviors
(Clark & Estes, 2008). The motivated behaviors are brought about by motivation-related beliefs,
such as self-efficacy, goal orientation theory, task value, and attribution theory (Rueda, 2011).
This study explored self-efficacy and task value as key influences in the implementation of
DDDM to improve low-performing schools. The motivation results and findings established that
superintendents had high self-efficacy and task value for DDDM implementation to improve
low-performing schools. The same criteria from knowledge influences were applied to the
motivation influences to determine a gap or asset. Evidence of insufficient data to support
superintendents’ use of an influence established a gap, such as when responses demonstrated less

56
than 60% agreement, or when a comparison of survey results and interview findings revealed
inconsistent or contradictory responses. An asset was established when there was sufficient
support, such as 60% agreement or above, and when responses were consistent when compared
with survey results and interview findings. The following sections elaborate on these results and
findings.
Superintendents’ High Levels of Self-Efficacy Related to Implementing DDDM
The data demonstrated that superintendents possessed high levels of self-efficacy to
support the implementation of DDDM. According to Bandura (2005), self-efficacy is an
individual’s self-judgment about their ability to perform a task, which is developed from mastery
experiences, vicarious experience, social persuasions, and psychological responses. An
individual with a high level of self-efficacy is persistent in their ability to achieve a task despite
any distractions that may influence their motivation (Rueda, 2011). Participants in the survey
reported high levels of self-efficacy in response to self-judgement statements about their ability
to perform DDDM-related tasks. For example, when participants were asked if they were
confident about their interpretations when they examine data reports, nearly all (98.39%),
responded with an “agree” or “strongly agree.” In a subsequent statement, nearly all (96.72%)
participants responded with an “agree” or “strongly agree” when asked if they were confident in
what action steps to take next when drawing conclusions using data. In a third self-evaluation
statement, nearly all (93.34%) participants reported with an “agree” or “strongly agree” response
that they prioritized DDDM even when confronted with pressure from external stakeholders not
to release data. These responses demonstrated high levels of self-efficacy among participants.
Their high levels of self-efficacy are likely to positively impact their motivation to implement

57
DDDM to improve low-performing schools. Table 7 provides survey responses to self-efficacy
related statements.
Table 7
Survey Responses to Self-Efficacy Related Statements
Survey Question or Statement
Participant Survey Responses
n %
When I examine data reports, I am confident that my interpretations
are accurate.

Strongly Disagree 0 0
Disagree 1 1.59
Agree 45 71.43
Strongly Agree 17 26.98
Once I draw conclusions using data, I am confident in deciding what
action steps to take next.

Strongly Disagree 0 0
Disagree 2 3.28
Agree 42 68.85
Strongly Agree 17 27.87
I prioritize data driven decision making even when I am confronted
with resistance from external stakeholders to not release data.

Strongly Disagree 1 1.67
Disagree 3 5
Agree 34 56.67
Strongly Agree 22 36.67

While the survey findings demonstrated that participants were confident in their ability to
use DDDM, the interview results also demonstrated that high levels of self-efficacy were
reflected in the participants’ view of their role as leaders. Specifically, each participant stated a
leadership approach and the source of influence that framed their self-efficacy, and to
successfully navigate any challenges that they encountered. For example, Participant 3 stated,
“With our strategy, we immediately called out our principal supervisors as the units of change,”
since Participant 3 had come from a background of “already having done a lot of work using
data.” The purpose of this strategy with “principal supervisors was adopting that mindset” to
implement DDDM and to support the “theory of action” where principals were the “units of

58
change for this to successfully penetrate the system.” In this example, Participant 3’s background
and ability inform the leadership approach that was selected to coach principals to be agents of
change. Participant 6 more directly summarized their leadership approach as a coach and to
model as a leader. Participant 6 stated:
You got to show people this is how we should use it. For example, I remember that I
taught all my business folks and you’re talking about custodial supervisors, grounds
people, about 30 something managers on the business side how to make data driven
decisions using the improvement science method. They were pretty excited about it. I had
them actually create what’s called… the PDSA cycles to tackle a problem.
Participant 4 also identified modeling behavior, since the role of superintendent means that
“people are always watching” and so as “superintendent, we model what we want others to
aspire to also do.” As a result, one of the first tasks Participant 4 undertook as a new
superintendent was a listening tour to demonstrate their capacity to listen, read, and learn. High
self-efficacy framed the majority of participants’ interview results, which demonstrated a belief
in their ability to succeed and a commitment to overcome challenging tasks. Participant 5
identified proactively working to avoid a teachers’ strike by using data to convince teachers to
join efforts to improve student outcomes, or in other words, by building “data converts.” For
instance, Participant 5 stated, “I try to lead this district by building converts and people who want
to come alongside and the only way I can build converts is with good solid data that I can share.”
The commitment to overcome challenging tasks was also reflected on by Participant 1 who
identified their cabinet of advisors as a source for solving challenging tasks, “If there’s a
challenge when they come to cabinet, it is going to get solved.” Their high level of self-efficacy

59
appears to support superintendents in their ability to implement DDDM and overcome any
challenges that may emerge.
Superintendents’ High Degree of Value to DDDM Implementation
This study found that superintendents prioritized the implementation of DDDM to
improve low-performing schools. According to Rueda (2011), the more that an individual values
an activity, then the more they choose, engage, and persist in the activity. The survey findings
demonstrated that participants attached high levels of priority to DDDM implementation. For
example, when participants were asked whether they believed that data driven decision making
was an effective method to improve low-performing schools, nearly 60% responded with
“strongly agree.” Participants also demonstrated that DDDM was a priority in their
organizations. When asked whether they made implementing data driven decisions a priority in
their organizations, nearly all (95.08%), responded with an “agree” or “strongly agree.” For
“agree” or “strongly agree” responses, this survey statement also asked participants to indicate
how they made DDDM a priority in their organizations. A summary of their responses identified
the following themes: to conduct regular updates on progress; share data points with stakeholders
prior to a decision; use data during planning meetings with Cabinet, school principals, and
curriculum development; frame weekly meetings with school principals on student progress;
assess progress toward desired goals and action items; and to train staff on data use. These
themes are also illustrated in the follow-up survey statements that were asked, and in the
interview findings in the next section. For example, the survey included statements related to
motivating others, providing professional development, and including community stakeholders.
Table 8 provides survey responses to these task value related statements.

60
Table 8
Survey Responses to Task Value Related Statements
Survey Question or Statement
Participant Survey Responses
n %
I believe that data driven decision making is an effective method to
improve low-performing schools.

Strongly Disagree 1 1.64
Disagree 2 4.92
Agree 21 34.43
Strongly Agree 36 59.04
I make implementing data driven decisions a priority in my
organization.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 31 50.82
Strongly Agree 27 44.26
I motivate others in my organization to implement data driven
decision making.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 35 57.38
Strongly Agree 23 37.30
I provide professional development to improve use of data driven
decision making for principals at low-performing schools.

Strongly Disagree 2 3.33
Disagree 5 8.33
Agree 29 48.33
Strongly Agree 24 40.00
I include community stakeholders as participants in the
implementation of data driven decision making to improve low-
performing schools.

Strongly Disagree 1 1.67
Disagree 8 13.33
Agree 38 63.33
Strongly Agree 13 21.67
I explain my decisions using data.
Strongly Disagree 1 1.64
Disagree 1 1.64
Agree 33 54.10
Strongly Agree 26 42.62
I include student outcome data in district-wide reports.
Strongly Disagree 1 1.64
Disagree 0 0.00
Agree 27 44.26
Strongly Agree 33 54.10
I meet consistently with my leadership team to analyze trends in
student achievement.

Strongly Disagree 1 1.64

61
Disagree 5 8.20
Agree 30 49.18
Strongly Agree 25 40.98
I train staff to use data driven decision making.
Strongly Disagree 1 1.64
Disagree 6 9.84
Agree 34 55.74
Strongly Agree 20 32.79

The interview findings provided examples that illustrated the value that participants
attached to DDDM implementation. Participants attached value to DDDM implementation by
motivating others, providing professional development, and sharing data. The purpose of these
activities was to cultivate a culture that prioritized DDDM implementation. For example,
Participant 2 articulated in relation to DDDM:
You constantly keep it at the forefront of every decision… So that’s why we cultivate a
culture through a lot of training of number one, our leadership team. Everybody uses the
same common language. Everybody understands what do we mean by data driven
decision making.
Participant 2 suggested that the process of training supported the leadership team to use common
language and through that, attach value and meaning to DDDM implementation. The training
was a way to communicate to others in their organization the value of DDDM and its role in their
responsibilities. The survey findings also confirmed this result with 88% of participants
responding with an “agree” or “strongly agree” when asked if they provided professional
development to improve the use of DDDM. Participant 4, Participant 6, and Participant 8 also
described how they promoted and motivated DDDM use through training and coaching.
Participant 4 mentioned investing in professional development conferences and trips that
emphasized DDDM. Participant 6 stated that they use the training as a way to set the expectation
that “we’re going to make the best of our ability to make data driven decisions.” In contrast,

62
Participant 3 described the value of DDDM as a way to identify inequities through data and to
“disrupt the aspects of the system design producing those outcomes.” However, value was also
attached to DDDM through celebrations of notable improvements. Participant 1 described the
value of DDDM through a belief that “the one key element that makes it successful is taking
time to celebrate.” This process of celebration could manifest in various ways, but Participant 1
utilized the board meetings as an opportunity for school principals to communicate and to
celebrate their achievements. Superintendents set the expectations within their organizations,
which is why attaching value to DDDM implementation influences the improvement of low-
performing schools.
Organizational Results and Findings
This study explored organizational influences related to the implementation of DDDM to
improve low-performing schools. Organizational influences are the third component of gap
analysis, which identifies influences that manifest in work processes, material resources, and
value chains and value streams (Clark & Estes, 2008). Gallimore and Goldenberg (2001)
synthesized these organizational influences into cultural models and cultural settings. Cultural
models are the shared beliefs, values, and views about how the organization works or should
work (Gallimore & Goldenberg, 2001). Cultural settings are the visible components of culture,
such as norms, customs, rituals, or behaviors (Gallimore & Goldenberg, 2001). This study
identified the State Department of Education (SDE) as the primary organization that influences
superintendents. This is a key distinction in this gap analysis as described in the conceptual
framework. However, this study also considers the board of education as an organizational
influence, since superintendents also have a fiduciary responsibility to respond to the policies set
by their respective Boards of Education. The same criteria were applied to determine a gap or

63
asset. A gap was demonstrated when there was evidence of insufficient data to support
superintendents’ use of an influence, for instance when responses demonstrated less than 60%
agreement, or when a comparison of survey results and interview findings revealed inconsistent
or contradictory responses. An asset was established when there was sufficient support, such as
60% agreement or above, and when responses were consistent when compared with survey
results and interview findings. The following sections describe the findings and results related to
the organizational influences, and answers the second research question that examines the
interaction between organizational culture and context and superintendent knowledge and
motivation to implement DDDM to improve low-performing schools.
Overall Agreement That a Culture of DDDM Exists
This study found that superintendents generally agreed that a culture of DDDM exists
within the State Department of Education. According to Rueda (2011), culture is dynamic, and it
is created and recreated by individuals during their daily experiences. The notion of culture is
relevant for an SDE, where superintendents operate with autonomy and independence despite
accountability requirements. When participants were asked in the survey whether the SDE
promoted a culture that supported the implementation of data driven decision making, about 64%
responded with an “agree” or “strongly agree.” Participants also agreed when asked whether
making data driven decisions improved their ability to satisfy state and federal accountability
requirements to improve low-performing schools, about 88% responded with an “agree” or
“strongly agree.” The responses indicate that an organizational culture exists that supports the
implementation of DDDM, and suggests that this culture does not limit the ability of
superintendents to satisfy state and federal accountability requirements. This was also evident
when participants were asked whether their respective board of education promoted a culture that

64
supported the implementation of data driven decision making, nearly all (93.33%) responded
with an “agree” or “strongly agree.” The participants also agreed that their board of education
delegated to them the authority to implement a data driven decision making system to improve
low-performing schools, nearly all (95.08%) responded with an “agree” or “strongly agree.”
These responses suggest that the interaction between organizational culture and context does not
limit superintendent knowledge and motivation to implement DDDM to improve low-performing
schools. Table 9 provides survey responses related to organizational model statements. The next
section describes interview results that support these findings.
Table 9
Survey Responses Related to Organizational Model Statements
Survey Question or Statement
Participant Survey Responses
n %
The State Department of Education promotes a culture that supports
the implementation of data driven decision making.

Strongly Disagree 6 10.17
Disagree 15 25.24
Agree 33 55.93
Strongly Agree 5 8.47
Making data driven decisions improves my ability to satisfy state
and federal accountability requirements to improve low-performing
schools.

Strongly Disagree 3 5.00
Disagree 4 6.67
Agree 30 50.00
Strongly Agree 23 38.33
My board of education promotes a culture that supports the
implementation of data driven decision making.

Strongly Disagree 0 0.00
Disagree 4 6.67
Agree 42 70.00
Strongly Agree 14 23.33
My board of education delegates to me the authority to implement
data driven decision making to improve low-performing schools.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 28 45.90
Strongly Agree 30 49.18

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The implementation of DDDM operates in an organizational culture and context that
supports the interaction between superintendent knowledge and motivation. Participants agreed
that the SDE made sufficient progress in adopting recent reforms to support a culture of DDDM.
For example, Participant 6 described the progress as follows:
I’ll tell you that I’ve been around since the CST days and it really wasn’t a growth
mindset. It was like, ‘you got to get to 800. You got to beat the test,’ or whatever. And
then when it changed to the dashboard it became the idea was the growth mindset.
Participant 3 described this change as an alignment to “continuous improvement.” Participant 4
identified the State’s data dashboard as an example of an organizational culture that supports
DDDM. According to Participant 4, the State’s “dashboard is their version of ensuring data
driven decision making.” Participant 1 described the State’s approach as positive and “more
realistic and recognizes that each district is unique.” This view was reflected in relation to a
superintendent’s board of education, which was also a source of influence for organizational
culture and context. Participant 2 articulated that the board of education “understands what
systems are in place in order to just evaluate and monitor student progress.” Participant 7
identified the board of education’s role as “from a school board’s perspective, communicating
clearly that all means all, that every student population must be college, career and life ready.”
Through this process, the board of education influenced an organizational culture and context
that supported DDDM implementation to improve low-performing schools. The results
demonstrated that participants believed that organizational culture and context positively
interacted with promoting DDDM implementation. The next section describes the interaction
when organizational culture and context does not support superintendent knowledge and
motivation.

66
Professional Development Viewed as Essential to DDDM Implementation but Results and
Findings are Mixed on State Support
This study found that superintendents viewed professional development as essential to
DDDM implementation, but the results are mixed when asked whether the State provided
support for professional development. The notion of cultural settings defines the social context
and the visible aspects of organizational culture (Rueda, 2011). Professional development and
resources constitute the cultural setting in a context of DDDM implementation to improve low-
performing schools. When participants were asked whether the SDE provided professional
development to improve implementation of data driven decision making, participants were split
with half (50%) responding “agree” or “strongly agree” and the other half (50%) responding
“disagree” or “strongly disagree.” When participants were asked whether the SDE provided
adequate resources to implement data driven decision making, about 53% of participants
responded with a “disagree” or “strongly disagree.” Similarly, about 53% of participants
responded with a “disagree” or “strongly disagree,” when asked whether the SDE facilitated a
professional learning group among education leaders to learn about the implementation of data
driven decision making. In contrast, nearly all (93.44%) of participants responded with an
“agree” or “strongly agree” when asked whether their respective board of education supported
their professional development to improve implementation of data driven decision making.
Additionally, about 80% of participants responded with an “agree” or “strongly agree,” when
asked whether their board of education provided adequate resources to implement data driven
decision making. These responses suggest that the interaction between organizational settings
and context and superintendent knowledge and motivation is supported by their board of
education but not the SDE. Table 10 provides survey responses related to organizational setting

67
statements. The next section describes interview findings and suggests factors that may be
contributing to the mixed findings.
Table 10
Survey Responses Related to Organizational Setting Statements
Survey Question or Statement
Participant Survey Responses
n %
The State Department of Education provides professional
development to improve implementation of data driven decision
making.

Strongly Disagree 2 3.33
Disagree 28 46.67
Agree 29 48.33
Strongly Agree 1 1.67
My board of education supports my professional development to
improve implementation of data driven decision making.

Strongly Disagree 0 0.00
Disagree 4 6.56
Agree 42 68.85
Strongly Agree 15 24.59
The State Department of Education provides adequate resources to
implement data driven decision making.

Strongly Disagree 2 3.39
Disagree 29 49.15
Agree 28 47.46
Strongly Agree 0 0.00
My board of education provides adequate resources to implement
data driven decision making.

Strongly Disagree 0 0.00
Disagree 12 20.34
Agree 34 57.63
Strongly Agree 13 22.03
The State Department of Education facilitates a professional
learning group among education leaders to learn about the
implementation of data driven decision making.

Strongly Disagree 3 5.00
Disagree 29 48.33
Agree 28 46.67
Strongly Agree 0 0.00

Even though participants agreed that a culture that supports DDDM exists, the interview
findings suggest the State is not providing the necessary resources to sustain this culture. This
was also reflected in the survey findings, which showed mixed results in response to statements

68
related to professional development and the allocation of resources. These mixed results and
findings are likely explained by the view of participants on the State’s primary role to support a
DDDM culture. Participants articulated that the primary role of the State was to provide funding,
guidance, and data to support DDDM. For example, Participant 6 articulated the expectation of
the State as one focused on “leaders like the Superintendent, as an example, they should be
providing the leadership out there. I expect them to lead by example and provide guidance and
unfortunately a lot of times they don’t.” Participant 1 characterized the State’s role as a
“communication type of relationship with support and understanding,” but “the biggest support
from the State has been funding.” The only participant who confirmed the State’s use of
professional development articulated the following experience:
Yeah, there’s professional development probably more like a meeting, more so than
professional development. But within the meeting, they offer different sessions on use, on
generation, on writing the LCAP, on the stakeholder meetings, on all the different pieces.
How to present the data to stakeholders, how to look at data.
This interaction suggests that superintendent knowledge and motivation is a key driver in the
implementation of DDDM, rather than organizational culture and context from the SDE.
Additionally, the local context may offer better opportunities to support the interaction between
organizational culture and superintendent knowledge and motivation. The survey results
demonstrated support and adequate resources from the respective boards of education for
superintendents. For example, Participant 2 articulated that the board of education “wants to
review student achievement data from multiple sources and meets to ask the right questions.”
While every superintendent may not reflect this experience, the local context provides an

69
opportunity to shape the interaction between organizational culture and superintendent
knowledge and motivation.
Summary of Results and Findings
Chapter four answered the research questions using the survey results and interview
findings for each influence. The findings and results demonstrated a gap in the procedural
knowledge of superintendents to implement DDDM. Superintendents also confirmed the use of
metacognitive knowledge as an influence in the implementation of DDDM to improve low-
performing schools. The results and findings also demonstrated the self-efficacy and task value
as motivational influences that guided superintendents in their implementation of DDDM.
Finally, the findings and results demonstrated a complex interaction between organizational
culture and context and superintendent knowledge and motivation influences. Superintendents
agreed that an organizational culture exists to promote DDDM. However, this culture does not
provide sufficient professional development or resources to sustain DDDM implementation. As a
result, superintendents are best positioned when relying on their local organizational culture and
context to support the implementation of DDDM to improve low-performing schools. Table 11
provides a summary of the assumed influences, a description, and their status as a gap or asset in
the findings and results of this study.
Table 11
Summary of Assumed Influence, Description, and Status as Gap or Asset
Assumed Influence

Assumed Influence Description

Gap or Asset?
Procedural
(Knowledge)
Superintendents must know how to effectively
implement data driven decision-making strategies
to improve low-performing schools.
Gap
Metacognitive
(Knowledge)
Superintendents must know how to reflect on
their capacity to adapt data driven decision
Asset

70
making strategies to the local context and to
overcome challenges.
Task Value
(Motivation)
Superintendents need to see implementing data
driven decision making as a priority.
Asset
Self-Efficacy
(Motivation)
Superintendents need to be confident in their
ability to effectively implement data driven
decision making strategies.
Asset
Cultural Model
(Organizational)
The State Department of Education needs to
promote a culture that supports the
implementation of data driven decision making in
school districts.
Asset
Cultural Setting
(Organizational)
The State Department of Education needs to
provide professional development to
superintendents to support adoption of data
driven decision making.
Gap

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Chapter 5: Recommendations
The findings and results of this study identified key influences that are subject to
performance gaps among superintendents, along with influences that should be sustained in the
implementation of DDDM to improve low-performing schools. This chapter provides
recommendations for practice to address knowledge, motivation, and organizational influences.
These recommendations are interwoven into a professional development training program. This
training program is designed to last as a full-day retreat for superintendents to acquire the
knowledge, skills, attitude, confidence, and commitment to implement DDDM. The training
program is also evaluated according to the new world Kirkpatrick model. The new world
Kirkpatrick model consists of four levels that integrates training and evaluation. Level four
begins with the identification of targeted outcomes, leading indicators, and results (Kirkpatrick &
Kirkpatrick, 2016). Level three focuses on the extent to which participants adopted what they
learned in the training (Kirkpatrick & Kirkpatrick, 2016). Level two determines the extent to
which participants adopted knowledge, skills, and attitudes based on their participation in the
training (Kirkpatrick & Kirkpatrick, 2016). Level one surveys participants for their reaction to
the training (Kirkpatrick & Kirkpatrick, 2016).
Recommendations for Practice to Address KMO Influences
This section outlines the recommendations for practice to address gaps and sustain
knowledge, motivation, and organizational influences. Each influence reviews a theory that
informs its specific recommendation.
Knowledge Recommendations
This study examined two knowledge influences in relation to the implementation of data
driven decision making among school district superintendents to improve low-performing

72
schools. The knowledge influences consist of procedural knowledge and metacognitive
knowledge to implement data driven decision making. A gap was identified among
superintendents for the procedural knowledge influence. While a gap was not identified for the
metacognitive knowledge influence, this knowledge influence was prioritized to demonstrate
how it can be sustained. The framework that guides the discussion for these knowledge
influences was derived from Krathwohl (2002), who defined procedural knowledge as an
understanding of the process and skills of a task. Krathwohl (2002) defined metacognitive
knowledge according to self-awareness and awareness of cognitive tasks required to accomplish
a task. Table 12 provides a summary of the knowledge influences and recommendations.
Table 12
Summary of Knowledge Influences and Recommendations
Knowledge Influence

Principle and Citation Context-Specific
Recommendation
Superintendents must know
how to effectively
implement data driven
decision-making strategies
to improve low-performing
schools. (P)
How individuals organize
knowledge influences how
they learn and apply what
they know (Schraw &
McCrudden, 2006).
Provide a definition of data
driven decision making and
examples to sustain
understanding in the form of a
job aid.
Superintendents must know
how to reflect on their
capacity to adapt data
driven decision making.
(M)
Learning and motivation are
enhanced when learners set
goals, monitor their
performance and evaluate
their progress towards
achieving their goals.
(Ambrose et al. 2010; Mayer,
2011)
Provide best practice strategies
and practices through a job aid
to promote performance
improvement and progress
toward achieving goals.

Increasing Superintendents’ Skills Related to Data Driven Decision Making (DDDM)
The results and findings of this study indicated a gap in the procedural knowledge of
superintendents to implement DDDM to improve low-performing schools. A recommendation

73
rooted in information processing theory has been selected to close this procedural knowledge
gap. Schraw and McCrudden (2006) found that how individuals organize knowledge contributes
to how they learn and how they apply what they know. This principle suggests that the
organization of knowledge among individuals supports their learning and capacity to apply and
implement that knowledge. As a result, the recommendation is to provide superintendents with
basic knowledge that defines DDDM to support their capacity to learn and apply this knowledge
to improve low-performing schools. This consists of providing a formal definition of DDDM
along with examples that apply this knowledge to sustain understanding in the form of a job
aide.
A knowledge base to learn and apply DDDM is the first step to improve low-performing
schools. A distinguishing characteristic of DDDM is the integration of the local school context
with school programming and instruction to build knowledge systems (Lewis, 2015). The
understanding of the local school context informs program development and improvement
(Jimerson, 2016; Lewis, 2015; Marsh & Farrell, 2015; Park et al., 2013). Superintendents
organize knowledge in this context and apply what they know about DDDM to improve low-
performing schools. Since the implementation of DDDM is applied to a local context, the
evidence suggests that sustaining knowledge of DDDM contributes towards closing this
procedural knowledge gap. As a result, a job aide that provides a DDDM definition and
examples that apply this knowledge is recommended to increase and sustain superintendents’
knowledge of DDDM and its application to the local school context.
Supporting and Sustaining Reflection to Improve Implementation of Data Driven Decision
Making (DDDM)

74
The results and findings of this study found that nearly all superintendents indicated that
they engaged in reflection and strategic use of knowledge to improve DDDM implementation
and low-performing schools. A recommendation rooted in metacognition theory was selected to
sustain this metacognitive knowledge. According to metacognition theory, learning and
motivation improves when individuals set goals, monitor their performance, and evaluate
progress toward achieving goals (Ambrose et al., 2012; Meyer, 2011). This principle suggests
that individuals who take the time to reflect on their performance improve their capacity to
succeed in a task. The recommendation then is to provide superintendents with guidance on
reflection practices to improve implementation of DDDM and monitor their performance
through goal setting and evaluation. This consists of providing best practice strategies through a
job aid that promotes performance improvement in the implementation of DDDM.
The process of reflection provides an opportunity to learn from past experiences and to
improve implementation of DDDM to improve low-performing schools. Park et al. (2013)
illustrated this example through the process of using framing by education leaders to promote
and support DDDM. The framing process resulted in language and practices that encouraged
teachers to adopt DDDM as an improvement strategy (Park et al., 2013). However, Park et al.
(2013) also demonstrated that this process required iterations to produce the desired change from
educational leaders, further reinforcing that reflection influences the implementation of DDDM.
The evidence suggests that supporting the reflection process positively contributes to the
implementation of DDDM and closes this metacognitive knowledge gap. For this reason, a job
aid that provides best practice strategies and practices is recommended to promote performance
improvement and progress towards achieving desired goals.
Motivation Recommendations

75
This study examined two motivation influences related to the implementation of data
driven decision making among school district superintendents to improve low-performing
schools. The motivation influences consisted of self-efficacy and task value. While gaps were
not identified, both motivation influences are prioritized to identify efforts to sustain the
motivation influences. The framework that guides the discussion was derived from Rueda
(2011), who defined self-efficacy as an individual’s own judgements about their capabilities to
attain desired levels of performance. Additionally, Rueda (2011) defined task value as the
importance that an individual attaches to completion of a task. Table 13 provides a summary of
the motivation influences and a recommendation where a gap exists.
Table 13
Summary of Motivation Influences and Recommendations
Motivation Influence

Principle and
Citation
Context-Specific Recommendation
Superintendents need to see
implementing data driven
decision making as a priority.
(Task value)
Higher
expectations for
success and
perceptions of
confidence can
positively
influence learning
and
motivation
(Eccles, 2006)

Set high expectations for the
implementation of DDDM by
establishing practices and using
documents that promote DDDM to
structure organizational decisions and
guiding vision documents.
Superintendents need to be
confident in their ability to
effectively implement data
driven decision making
strategies to improve low-
performing schools. (Self-
efficacy)
High self-efficacy
can positively
influence
motivation
(Pajares, 2006).
Adopt behavioral models to support
high self-efficacy.

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Sustaining a High Degree of Value for Data Driven Decision Making (DDDM)
Implementation
The results and findings of this study found that superintendents demonstrated a high
degree of value for DDDM implementation to improve low-performing schools. A
recommendation from expectancy value theory was selected to sustain this motivational
influence. According to expectancy value theory, learning and motivation are positively
influenced by high expectations for success and perceptions of confidence (Eccles, 2006). This
principle suggests that superintendents who set high expectations for success and perceptions of
confidence will positively influence their learning and motivation. As a result, this study
recommends that the Superintendent of Public Instruction, who leads the State Department of
Education (SDE), promote a high value for DDDM implementation among superintendents.
While superintendents demonstrated a high degree of value for DDDM implementation in this
study, sustaining this task value influence requires the SDE to promote its use as superintendents
strive to improve low-performing schools. Examples that set a high value for DDDM
implementation include, but are not limited to, training on data use, using data to guide planning
meetings, and setting a school district vision statement for DDDM implementation.
Superintendents who set a high degree of value for DDDM implementation will
positively influence implementation, which is why the SDE needs to sustain and promote this
influence. In Park et al. (2013), for example, administrators established an iterative process of
framing to facilitate root cause analysis to improve student outcomes. Tichnor-Wagner et al.
(2017) found that school sites that were effectively implementing a DDDM strategy saw its value
and learned by building on past experiences. In comparison, Marsh and Farrell (2015) found that
factors such as a lack of leadership or training, insufficient time, and an orientation towards

77
compliance inhibited implementation of DDDM. The evidence suggests that setting a high
degree of value for DDDM supports its implementation. Therefore, this study recommends that
the SDE promote a high value for DDDM implementation among superintendents with low-
performing schools.
Sustaining a High Self-Efficacy to Implement Data Driven Decision Making (DDDM)
The results and findings of this study found that superintendents demonstrated high self-
efficacy to implement DDDM. A recommendation from self-efficacy theory was selected to
sustain this motivational influence. According to self-efficacy theory, high self-efficacy is a
positive influence on motivation (Pajares, 2006). This principle suggests that superintendents
who demonstrate high self-efficacy are motivated to implement DDDM. As a result, this study
recommends that the Superintendent of Public Instruction, who leads the SDE, support
superintendents by encouraging the development of their self-efficacy through learning about
leadership models and approaches. The findings of this study demonstrated that superintendents
used their personal stories of adversity, their responsibility as a leader, and their role model
status in the community as sources of high self-efficacy.
The role of superintendent requires establishing an academic oriented vision, which is
established through four key behaviors that include outlining a vision, assessment and evaluation
practices, organizational adaptation, and organizational structure (Petersen, 1999). These
superintendent behaviors are important because they influence self-efficacy, and the capacity of
a superintendent to proceed with organizational activities. Petersen (2002) found that school
boards take favorable action on a superintendent’s recommendation when they exhibit
trustworthiness, expertise, and demonstrated perceived compatibility with the Board President. A
superintendent’s high self-efficacy influences their capacity to shape the policy decisions of their

78
elected school boards. The evidence suggests that sustaining high self-efficacy influences the
motivation to implement DDDM. As a result, this study recommends that the SDE support
superintendents by providing training on leadership models to sustain levels of high self-
efficacy.
Organization-Related Recommendations
This study examined two organizational influences related to the implementation of data
driven decision making among superintendents to improve low-performing schools. The
organizational influences consisted of a cultural model on whether the State Department of
Education (SDE) sustains a culture that supports the implementation of data driven decision
making, and a cultural setting related to whether the SDE provides professional development to
sustain the implementation of data driven decision making. The findings and results validated the
cultural model influence, while a gap was identified for the cultural setting influence. The
framework that guides the discussion was derived from Rueda (2011) and Gallimore and
Goldenberg (2001). Cultural models consist of shared mental schema about how the world works
or should work (Gallimore & Goldenberg, 2001; Rueda, 2011). In contrast, cultural settings are
visible aspects of the cultural model, such as routines and meeting spaces (Rueda, 2011;
Gallimore & Goldenberg, 2001). Table 14 provides a summary of organization influences and
recommendations.
Table 14
Summary of Organization Influences and Recommendations
Organization Influence

Principle and
Citation

Context-Specific Recommendation
The State Department of
Education needs to promote a
culture that supports the
People are more
productive
Articulate a plan that superintendents
adopt using multiple measures of
student progress to set benchmarks,

79
implementation of data driven
decision making in school
districts. (Cultural model)
when goal setting
and
benchmarking is
essential to
evaluating
progress and
driving
organizational
performance in
accountability.
Different types of
benchmarking
contribute data to
improve
organizational
performance
(Bogue &
Hall, 2003).
and to evaluate program improvement
and organizational performance.
The State Department of
Education needs to provide
professional development to
superintendents to support
adoption of data driven decision
making. (Cultural setting)
Organizational
effectiveness
increases when
leaders are
knowledgeable
about and are
consistently
learning about
themselves and
their business
(e.g.,
learning). Staying
current (teachers
and
administrators)
with the field’s
research and
practice are
correlated
with increased
student learning
outcomes
(Waters et al.,
2003).
Develop an ongoing professional
development improvement plan
established by the State Department of
Education for superintendents and for
superintendents to do the same for
their staff.

80
Sustaining a Culture That Promotes DDDM With Student Progress and Measurement
Benchmarks
Approximately 64% of superintendents reported that they “agree” or “strongly agree”
that the SDE promotes a culture that supports the implementation of DDDM. While this finding
demonstrated progress, the SDE needs to sustain this culture to increase agreement among
superintendents. A recommendation rooted in accountability theory has been selected to sustain
this organizational finding. Bogue and Hall (2003) and Marsch (2012) found that data from
different types of benchmarking improve organizational performance. Their research
demonstrated that goal setting and benchmarking positively influenced the evaluation of progress
and organizational performance in an accountability environment. These findings suggest that
sustaining a culture that supports DDDM implementation is enhanced when the SDE sets goals
and benchmarks. The recommendation is for the SDE to promote a culture that supports the
implementation of DDDM through a plan that sets student progress goals and measures for the
adoption of superintendents. For example, the SDE may provide a data dashboard that measures
student achievement and identifies areas for growth.
Clark and Estes (2008) stated that a feature of the organizational change process is the
measurement of progress and assessment of results that are connected to the achievement of the
vision and organizational goals. Bogus and Hall (2003) and Marsch (2012) elaborated on this
principle through their finding that goal setting and benchmarking improves organizational
performance. In the case of superintendents, increases in organizational performance improves
student achievement and is supported by a culture that promotes DDDM implementation.
Peterson et al. (2009) surveyed 350 superintendents in California to examine their perceptions of
the factors that complicate their capacity to succeed. Peterson et al. (2009) found that

81
superintendents identified three main factors that inhibit their capacity to succeed, which include:
inadequate financing of schools, State and federal mandates, and too many insignificant
demands. In contrast, Trujillo et al. (2013) analyzed urban school districts in California under
high-stakes accountability and found that superintendents and board members organized their
goals and decision-making almost exclusively around accountability measures. Superintendents
are subject to multiple accountability influences that shape their perceptions, capacity to succeed,
and their interpretation of organizational culture, especially in relation to low-performing
schools. As such, these findings suggest that organizational performance for superintendents is
tied to their perceptions and approach to accountability, which is why a culture that promotes the
implementation of DDDM to improve low-performing schools is essential.
Providing Ongoing Professional Development to Sustain Implementation of DDDM
Approximately 50% of superintendents reported that they “disagree” or “strongly
disagree” when asked to respond to the statement related to whether the SDE provides
professional development to improve implementation of DDDM. The missed opportunity to
provided professional development contributes toward inconsistent performance in the
implementation of DDDM. A recommendation rooted in leadership theory has been selected to
close this organizational gap. Waters et al. (2003) found that staying updated with research and
practice in the field is correlated with increased student learning. Their findings confirmed that
organizational effectiveness increases when leaders continue to learn about themselves and the
work that they do. This suggests that superintendents need a professional development program
that supports implementation of DDDM and that sustains their learning. The recommendation is
for the SDE to develop an ongoing professional development improvement plan for
superintendents to improve implementation of DDDM and to model for their staff. For example,

82
a workshop on the latest DDDM research to improve low-performing schools to sustain ongoing
professional development of superintendents.
Clark and Estes (2008) noted that processes for organizational change also require
adequate knowledge, skills, and motivational support. Waters et al. (2003) affirmed this
conclusion through their finding. For superintendents, staying updated with research and practice
in the field improves their capacity to increase student learning within their organizations.
Wohlstetter et al. (2008) studied the implementation of DDDM for the first time in school sites
and found that its adoption requires autonomy for school site decision-making, structuring
collaboration, providing professional development and training, and establishing a common
language and a culture of data use. Marsh et al. (2017) conducted a case study on the
establishment of a new holistic accountability system that emphasized adaptability to the local
context and capacity building over punishment and sanctions. In their findings, school district
administrators reported low-levels of deep learning in the early stages of adoption, and expressed
a desire to engage in deeper learning into inquiry-oriented organizational learning. These
findings suggest that DDDM remains a relatively new approach in the field, which requires
ongoing professional development for superintendents to sustain implementation of DDDM.
Integrated Implementation and Evaluation Plan
This study uses Kirkpatrick and Kirkpatrick’s (2016) new world Kirkpatrick model to
design an integrated and evaluation plan. The new world Kirkpatrick model consists of four
levels of training and evaluation (Kirkpatrick & Kirkpatrick, 2016). The new world Kirkpatrick
model begins with level four, which identifies targeted outcomes, leading indicators, and results
(Kirkpatrick & Kirkpatrick, 2016). Level three focuses on the behavior of participants and the
extent to which they adopt what they learned in training (Kirkpatrick & Kirkpatrick, 2016).

83
Level two focuses on learning and the extent to which participants adopted knowledge, skills,
and attitude based on their participation in the training (Kirkpatrick & Kirkpatrick, 2016).
Finally, level one focuses on assessing the reaction of participants to the training (Kirkpatrick &
Kirkpatrick, 2016).
Organizational Purpose, Need and Expectations
The organization’s mission is defined by a State that expects to provide a world-class
education for all students, from early childhood to adulthood. As a result, the organization’s goal
is to ensure that 100% of school districts reclassify low-performing schools or student subgroups
within four years. Through this goal, the State works toward ensuring that all students receive a
world-class education and to address a persistent problem confronting the organization. This
problem consists of low-performing schools that contribute to inequities in student opportunities
and outcomes and that widen the educational achievement gap.
The stakeholder group’s goal is to implement DDDM to improve low-performing schools
by January 2020. This goal was selected because the superintendents’ actions influence whether
school districts have the capacity to implement DDDM to reclassify low-performing schools.
The reclassification of low-performing schools contributes to the organization’s mission and goal
through the improvement of schools and better educational opportunities and outcomes for
students.
The expectations of the desired outcomes of the recommendations consists of an increase
in the capacity of superintendents to sustain the implementation of DDDM to improve low-
performing schools. The recommendations provide an understanding of the knowledge,
motivation, and organizational influences that enhances the ability to implement DDDM. These
recommendations provide both superintendents who do not have the experience of using DDDM

84
and those who do with the information necessary to improve their performance. As a result, the
expectation is that superintendents will enhance their capacity to implement DDDM, and as a
result, improve low-performing schools through their reclassification.
Level 4: Results and Leading Indicators
Level four focuses on the extent to which targeted outcomes are achieved as a result of
the training, support, and accountability (Kirkpatrick & Kirkpatrick, 2016). Kirkpatrick and
Kirkpatrick (2016) measure short-term outcomes as leading indicators to provide an update on
progress toward the desired performance goal. This study identified two short-term outcomes
and measurements that indicate whether superintendents are achieving the desired outcomes. The
short-term outcomes and their measurements are divided into external and internal short-term
outcomes. The external outcome consists of improvements in student outcomes as measured by
the metrics that result in a school being categorized as low performing, such as a graduation rate
below 67%. The internal short-term outcome consists of formative assessments to ensure
progress toward performance goal. This outcome is measured based on transcript audits, if is a
low-performing school, or based on formative assessment and monitoring of chronic absences
and suspensions. Table 15 provides outcomes, metrics, and methods for external and internal
outcomes.
Table 15
Outcomes, Metrics, and Methods for External and Internal Outcomes
Outcome Metric(s) Method(s)
External Outcomes
Improvements in
student outcomes.
1.a. Graduation rate improvements to
increase above 67%. (For low-
performing high schools.)

1.b. Improvements in the ratings of the
multiple measures system, which
1.a. Annual report on the
graduation rate for the
high school.

1.b. Annual report on
year-over-year

85
consist of chronic absenteeism,
suspension rate, English learner
progress, English language arts, and
mathematics

improvements in each
area.
Internal Outcomes
Formative
assessments to ensure
progress toward
performance goal.
1.a. Percentage of transcript audits of
high school students’ progress to satisfy
graduation requirements.

1.b. Results of formative assessments in
math, English language arts, and for
English learners.

1.c. Rates of chronic absences and
suspensions and reasons contributing to
these outcomes.
1.a. Conduct transcript
audit twice per year at the
beginning of each
semester.

1.b. Conduct formative
assessments twice per year
ahead of statewide
assessment.

1.c. Conduct monthly
audits of reasons that are
contributing to chronic
absences and suspensions.

Level 3: Behavior
Critical Behaviors
Level three measures the extent to which participants change their critical behaviors as a
result of what they learned during the training (Kirkpatrick & Kirkpatrick, 2016). This study
identifies four critical behaviors to improve performance among superintendents. First,
superintendents adopt and reinforce a process of disciplined inquiry to support DDDM
implementation. Second, superintendents reflect on their capacity and experiences implementing
DDDM to improve low-performing schools. Third, superintendents demonstrate high
expectations in the implementation of DDDM. Finally, superintendents adopt a professional
development improvement plan to support their learning and deeper inquiry into DDDM. Table
16 outlines these critical behaviors, metrics, methods, and timing for evaluation.

86
Table 16
Critical Behaviors, Metrics, Methods, and Timing for Evaluation
Critical Behavior Metric(s) Method(s) Timing
1. Superintendents adopt
and reinforce an inquiry
process of analysis to
support DDDM
Number of meetings
that references inquiry
process of analysis
Monthly tally reporting
meetings and number of
meetings that support this
critical behavior
Every
month
2. Superintendents
reflect on their capacity
and experience
implementing DDDM to
improve low-performing
schools
Time set aside in
schedule for reflection
Superintendent’s schedule is
used to monitor time that is
being set aside for this
purpose
Every
month
3. Superintendents
demonstrate high
expectations to
implement and support
DDDM
List of DDDM
practices that support
implementation and its
adoption, such as
trainings and visionary
documents
Survey of teachers and
principals to assess
perceptions of high
expectations related to
DDDM
Twice
per year
4. Superintendents adopt
a professional
development
improvement plan to
support learning and
deeper inquiry into
DDDM
Number of hours spent
completing professional
development
Time spent in professional
association meetings or
trainings on DDDM, and
tailored professional
development that supports
gap areas in DDDM
implementation
Every
month

Required Drivers
The required drivers advance the achievement of superintendents’ critical behaviors.
These required drivers fall into four dimensions, which are defined as drivers that are
reinforcing, encouraging, rewarding, and monitoring. Table 17 provides the required drivers to
support superintendents’ critical behaviors. The motivation influences that are necessary to drive
the achievement of superintendents’ outcomes consists of task value and self-efficacy. The
organizational influences that are necessary to drive superintendents consist of sustaining a
culture that supports the implementation of DDDM and providing professional development.

87
Table 17
Required Drivers to Support Critical Behaviors
Method(s) Timing
Critical
Behaviors
Supported
1, 2, 3 Etc.
Reinforcing

The Department of Education supplies a job aid that provides
information about DDDM and its implementation.
Quarterly 1, 2, 3
The Department of Education supplies a job aid that provides
best practice strategies and practices to support reflection and
strategic use of DDDM.
Quarterly 1, 2, 3
Encouraging

Superintendents meet together to discuss and share in a
professional learning community how to set high expectations
to implement and support DDDM
Quarterly 2, 3
Rewarding

State Department of Education acknowledges the top 20 school
districts making the fastest progress in the State
Annually 3, 4
Monitoring

Superintendents adopt a professional development an
improvement plan to support learning and deeper inquiry into
DDDM and to maintain self-accountability
Quarterly 4
State Department of Education maintains a multiple measures
system of student performance to evaluate progress
improvement of low-performing schools
Annually 2, 3, 4
Superintendents’ executive assistants assist with holding and
scheduling time for the superintendent to implement and pursue
critical behaviors.
Daily 1, 2, 3, 4

Organizational Support
The organization will support the stakeholders' critical behaviors through a multiple
measures system that tracks student performance and the progress of low-performing
schools. This monitoring of organizational performance will occur on an annual basis. The
second monitoring activity that will support the superintendents’ critical behaviors is the
adoption of a professional development plan, which will support learning and deeper inquiry.
The purpose of this professional development plan is to support the implementation of DDDM

88
and to maintain self-accountability. Finally, a new supporting action to monitor progress and
implementation is the scheduling of activities that support critical behaviors and required drivers.
For this purpose, superintendents’ executive assistants were identified as a key part of the
monitoring process. These actions outline the organizational support that is necessary for
superintendents to implement DDDM to improve low-performing schools.
Level 2: Learning
Level two measures the extent to which participants acquire knowledge, skills, attitude,
confidence, and commitment as a result of their participation in a training program (Kirkpatrick
& Kirkpatrick, 2016). This section outlines the learning goals for superintendents who participate
in the training program, the program to implement these goals, and the evaluation plan for this
program.
Learning Goals
1. Apply steps to effectively implement DDDM to improve low-performing schools (P)
2. Evaluate past experiences, challenges, and the local context to implement DDDM (M)
3. Be confident in ability to implement DDDM (Self-Efficacy)
4. Value using DDDM to improve low-performing schools (Task Value)

Program
The goals listed in the previous section will be achieved with a full-day retreat with
superintendents. During the full-day retreat, superintendents will be introduced to DDDM
implementation to improve low-performing schools. The full-day retreat will consist of three
workshops. During the first workshop, superintendents will be given job aides to illustrate the
use of the knowledge influence that supports the implementation of DDDM. The second
workshop will then turn to an introduction of motivation related beliefs that support DDDM

89
implementation. The third workshop will explore how to use the State Department of
Education’s multiple measures system to set benchmarks and evaluate student progress. This
workshop will also assist superintendents in outlining an individualized professional
development improvement plan.
Evaluation of the Components of Learning

Achieving the learning goals identified above requires an evaluation of the components
of learning. Table 18 provides a list of methods and activities that will support the evaluation of
the components of learning for the program.
Table 18
Evaluation of the Components of Learning for the Program
Method(s) or Activity(ies) Timing
Declarative Knowledge “I know it.”

Multiple choice knowledge checks During and after first
workshop
Think in pairs, share examples, and share out with everyone During first
workshop
Procedural Skills “I can do it right now.”

Simulated scenarios that require procedural knowledge to solve During first
workshop
Teach back: Participants teach a group of their peers the procedures
for implementing the various concepts of DDDM
During first
workshop
Attitude “I believe this is worthwhile.”

Discussions about the value and rationale on the use of DDDM During second
workshop
Think in pairs, identify strategies that support attitude and value, and
share out
During second
workshop
Confidence “I think I can do it on the job.”

Discussions of any challenges, concerns, barriers, advantages and
disadvantages, etc.
During second
workshop
Role play to model behavior that supports confidence During second
workshop
Commitment “I will do it on the job.”

Think in pairs, identify strategies to support commitment, share out During third
workshop
Action planning: Begin individual plan to support commitment and
professional development
During and after third
workshop

90
Survey to self-report on progress After full-day retreat

Level 1: Reaction
Level one focuses on the extent to which participants find the training favorable,
engaging, and relevant (Kirkpatrick & Kirkpatrick, 2016). The goal with level one is to measure
participant reaction to confirm program quality. Table 19 provides components to measure
reactions to the program.
Table 19
Components to Measure Reactions to the Program
Method(s) or Tool(s) Timing
Engagement

Number of meaningful questions or comments During each workshop
Completion of activities During each workshop
Relevance

Pulse check in between activities During each workshop
Anonymous survey End of full-day retreat
Customer Satisfaction

Dedicated observer who monitors participant behavior and reactions During each workshop
Anonymous survey After each workshop

Evaluation Tools
This section outlines two evaluation tools to track the outcomes of the programs and
responses from participants. The two evaluation tools consist of an evaluation immediately
following the program and a subsequent evaluation that is conducted weeks after participating in
the program.
Immediately Following the Program Implementation

An evaluation will be conducted following the implementation of the full-day retreat. A
four-point survey scale, from strongly agree to strongly disagree, will be used to ask participants
a series of statements related to their level of reaction and learning. For level one, participants

91
will be asked statements that assess their engagement, relevance, and customer satisfaction. The
purpose of level one is to determine the participants’ reaction to the full-day retreat. For level
two, participants will be asked statements that assess their declarative knowledge, procedural
knowledge, attitude, confidence, and commitment. The purpose of level two is to determine
whether participants experienced learning during the full-day retreat. This evaluation will
indicate whether participants found the full-day retreat valuable. Appendix A provides the full
program implementation evaluation tool.
Delayed for a Period After the Program Implementation

A delayed evaluation will be conducted six weeks and 12 weeks following completion of
the full-day retreat. The purpose of the delayed evaluation is to determine whether participants
are applying the concepts that they gained during the full-day retreat. A four-point survey scale,
from strongly agree to strongly disagree, will be used to ask participants a series of statements
that cover level one, level two, level three, and level four. For level one, participants will be
asked whether what they learned in the retreat has been valuable to implement data driven
decision making. For level two, participants will be asked if they have been able to implement in
their school district the data driven decision making practices that they learned. For level three,
participants will be asked whether they regularly refer back to their professional development
improvement plan. Finally, participants will be asked in level four whether their low-performing
schools are improving and/or being reclassified as improving. Appendix B provides the full
program implementation delayed evaluation tool.
Data Analysis and Reporting
The data analysis and reporting will be displayed in a dashboard that provides all the results
of the evaluation and delayed evaluation following the program. The dashboard will display survey

92
results for each statement in a color format. The information is organized this way to facilitate
understanding and simplicity. Figure 2 provides an example of what the results would look like in
the dashboard.
Figure 2
Dashboard Sample Result of Immediate Survey Evaluation for the First Statement

Summary
The New World Kirkpatrick Model provides the foundation to plan, implement, and
evaluate the recommendations provided in this study. The recommendations indicated how the
SDE may optimize achieving the stakeholder goal. In contrast, the New World Kirkpatrick
Model indicates whether participants are making progress toward achieving the stakeholder goal
through planning, implementing, and evaluating. The New World Kirkpatrick Model identifies
results and leading indicators, critical behaviors and drivers, learning, and reactions. Each of
these domains maps into each level of the New World Kirkpatrick Model, and a plan is provided

93
for the implementation of each level. The purpose of this framework is to evaluate whether
participants are making progress toward achieving the stakeholder performance goals. The
recommendations of this study are integrated into the implementation and evaluation plan to
assess whether participants are displaying gaps related to knowledge and motivation influences.
Ultimately, the goal is for superintendents to possess a strong grasp of data driven decision
making and its application to improve low-performing schools.
Implications for Practice
This study illustrated the wide breath of skills and capacities that superintendents need to
utilize in their role to improve low-performing schools. Specifically, this study provided
recommendations for State Departments of Education to use in their efforts to improve DDDM
implementation in their State. These recommendations may also be used by professional
superintendent associations to supplement the training of superintendents in their efforts to
improve low-performing schools. A key finding in this study was that superintendents
demonstrated a gap in their procedural knowledge of DDDM implementation. Additional
training is needed to improve procedural knowledge and subsequently improve overall practice
and implementation of DDDM. A basic knowledge foundation is essential, especially when
superintendents face many demands and challenges, such as conflicting and contradictory
accountability systems and guidance.
Future Research
Future research should examine the factors that contribute to the procedural knowledge
gap and its implications. Superintendents enter their role from a wide range of backgrounds,
although traditionally many are promoted from teacher, to assistant principal, then principal,
district administrator, and eventually superintendent. This pipeline may not entirely support the

94
development of procedural knowledge and other relevant influences to implement DDDM. In
fact, interview findings suggested that superintendents may rely on their conceptual knowledge
rather than procedural knowledge. This may present challenges such as a lack of DDDM support,
since conceptual knowledge does not fully support an understanding of all the necessary
procedures to implement DDDM. Alternatively, superintendents may rely on conceptual
knowledge rather than procedural knowledge to support the implementation of DDDM, which
may explain the presence of a procedural knowledge gap. Future research may consider
examining whether superintendents rely on their conceptual knowledge rather than procedural
knowledge in the implementation of DDDM.
Future research should also consider the role of attributions in the context of
superintendents understanding their success or lack of success to implement DDDM. Attribution
theory is an influence under the motivational dimension (Rueda, 2011). According to Rueda
(2011), attribution theory refers to the beliefs of an individual related to the reasons of success or
failure and their capacity to control the outcome. Superintendents navigate multiple
accountability requirements to satisfy State and federal requirements, and also experience
pressure from multiple stakeholders such as teachers, parents, and community advocates. All
these factors may influence the capacity of superintendents to implement DDDM and their
perception of what contributes to their success or lack of success. Future research should
examine whether and how attributions influence the motivation of superintendents to implement
DDDM and to support them in making adaptive attributions.
Finally, future research should consider a case study of one school district that is
implementing best practices related to DDDM to improve low-performing schools. This
approach will allow for a deeper examination of the knowledge, motivation, and organizational

95
influences and their interaction as superintendents implement DDDM to improve low-
performing schools. A case study may also provide an opportunity to observe tasks and activities
related to DDDM implementation. Observational data will add additional understanding on how
superintendents operationalize DDDM implementation, and how they apply influences in that
process.
Conclusion
This study explored the knowledge, motivation, and organizational influences related to
the implementation of DDDM among superintendents to improve low-performing schools. The
key findings and results demonstrated procedural knowledge gaps among superintendents, and
an organizational influence gap related to providing professional development. These findings
are consistent with the general trajectory of DDDM implementation in the field of education.
Even though DDDM is not a new method for making decisions, the application of DDDM to
low-performing schools in the field of education is new (Lewis, 2015). DDDM implementation
remains in the early stages in the field of education. As a result, increasing knowledge of DDDM
and understanding knowledge gaps among superintendents are essential in the early stages of
implementation. This means that the State Department of Education also needs to provide
meaningful and sufficient professional development to superintendents. This effort will address
the gap identified in this study, but also support sustaining the implementation of DDDM to
improve low-performing schools. This emphasis on knowledge and organizational influences are
an important foundation for superintendents to effectively implement DDDM. The results and
findings demonstrated a high degree of value for DDDM implementation and high self-efficacy
among superintendents. In total, superintendents exhibited a high degree of motivation, but low-
levels of procedural knowledge proficiency and low expectations for State support. The long-

96
term success of DDDM implementation to improve low-performing schools will be influenced
by the capacity to address these gaps. Otherwise, the lack of increasing knowledge and State
support along with insufficient DDDM success may erode the high levels of motivation that
currently exists among superintendents. For these reasons, this study provided recommendations
to increase the knowledge of superintendents and an implementation plan to support their
professional development.

97

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Appendix A: Survey Item
Research Question/
Data Type
KMO
Construct
Survey Item
Scale of
Measurement
Potential
Analyses
Demographics –
Sample Description
NA Please select the title
that best fits your
position.
(Superintendent,
Deputy or Assistant
Superintendent,
Educational
Management
Professional, Principal)
Nominal Percentage,
Frequency,
Mode
Demographics –
Sample Description
NA Please select the best
description of your
organization. (County
office of education,
school district, charter
school, other)
Nominal Percentage,
Frequency,
Mode
Demographics –
Sample Description
NA How long have you
worked in your current
position? (less than one
year, 1 to 2 years, 2 to 3
years, more than 3
years)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
Demographics –
Sample Description
NA How many schools within
your school district have
been identified as low-
performing according to
State and federal laws?
(Less than 10 schools, 11
to 25 schools, 26 to 50
schools, Over 51 schools)
Ordinal
Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(K-P) I have a clear
understanding of how
to apply data driven
decision making to
improve school
performance. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
(K-P) I learn about data
driven decision making
by reading books,
attending conferences,
or participating in
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

103
driven decision-
making to improve
low-performing
schools?
professional learning
groups. (strongly
disagree, disagree,
agree, strongly agree)
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(K-M) I dedicate time to
reflect on how to best
use data to inform my
decision making.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(K-M) I reflect on how to best
support data driven
decision making in my
low-performing
schools. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-SE) When I examine data
reports, I am confident
that my interpretations
are accurate. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-SE) Once I draw
conclusions using data,
I am confident in
deciding what action
steps to take next.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
(M-SE) I prioritize data driven
decision making even
when I am confronted
with resistance from
external stakeholders to
not release
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

104
making to improve
low-performing
schools?
data.(strongly disagree,
disagree, agree,
strongly agree)
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I believe that data
driven decision making
is an effective method
to improve low
performing schools.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I make implementing
data driven decisions a
priority in my
organization. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I motivate others in my
organization to
implement data driven
decision making.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I provide professional
development to
improve use of data
driven decision making
for principals at low-
performing schools.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
(M-TV) I include community
stakeholders as
participants in the
implementation of data
driven decision making
to improve low-
performing schools.
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

105
low-performing
schools?
(strongly disagree,
disagree, agree,
strongly agree)
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I explain my decisions
using data. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I include student
outcome data in
district-wide reports.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I meet consistently with
my leadership team to
analyze trends in
student achievement.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What are
superintendents’
knowledge and
motivation related to
implementing data
driven decision-
making to improve
low-performing
schools?
(M-TV) I train staff to use data
driven decision making.
(strongly disagree,
disagree, agree, strongly
agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
(O-CM) The State Department
of Education promotes
a culture that supports
the implementation of
data driven decision
making. (strongly
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

106
implement data driven
decision-making to
improve low-
performing schools?
disagree, disagree,
agree, strongly agree)
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CM) My board of education
promotes a culture that
supports the
implementation of data
driven decision making.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CM) My board of education
delegates to me the
authority to implement
data driven decision
making to improve
low-performing
schools. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) My district has
articulated a clear
vision statement for
data driven decision
making. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
(O-CS) The State Department
of Education provides
professional
development to
improve
implementation of data
driven decision making.
(strongly disagree,
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

107
improve low-
performing schools?
disagree, agree,
strongly agree)
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) My board of education
supports my
professional
development to
improve
implementation of data
driven decision making.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) The State Department
of Education provides
adequate resources to
implement data driven
decision making.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) My board of education
provides adequate
resources to implement
data driven decision
making. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) The State Department
of Education facilitates
a professional learning
group among education
leaders to learn about
the implementation of
data driven decision
making. (strongly
disagree, disagree,
agree, strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range

108
What is the interaction
between
organizational culture
and context and
superintendent
knowledge and
motivation to
implement data driven
decision-making to
improve low-
performing schools?
(O-CS) Making data driven
decisions improves my
ability to satisfy state
and federal
accountability
requirements to
improve low-
performing schools.
(strongly disagree,
disagree, agree,
strongly agree)
Ordinal Percentage,
Frequency,
Mode,
Median,
Range
NA=Not Available; M-SE=Self-Efficacy; M-TV=Task Value; O-CM=Cultural Models; O-
CS=Cultural Setting

109
Appendix B: Interview Protocol

Before we begin, I want to share with you the purpose of this study, the procedures, and
provide an opportunity for any questions that you may have. The purpose of this study is to
better understand the implementation of data driven decision making to improve low-performing
schools. My intent is to identify what might be working well and where there might be areas for
growth. This study is being completed to fulfill requirements of an education doctorate program
at the University of Southern California. I am the Principal Investigator for this study. Do you
have any questions about the purpose of the study?
As a participant in this study, you should know that everything is strictly confidential.
The findings will be reported in the aggregate. In the study, I use terms such as a “Western State”
to identify the location of the study. No names will ever be used in the study. You should also
know that participation in this study is strictly voluntary. You may decide not to answer a
question. Or, you may decide not to participate in the study at any point in time without penalty.
Do you have any questions thus far?
The last part I need to cover before we begin is related to the logistics of the interview
process. I have a recorder to accurately capture what you share. The purpose of the recorder is to
assist me with focusing on our conversation rather than notetaking. Your participation is
voluntary in all aspects of this study, including data collection. May I have your permission to
record? May I have your permission to begin the interview?
Great. Thank you. We will begin by reflecting on data driven decision making.

1. How do you define data driven decision making?
a. Probe for a definition.
2. What has been your experience when implementing data driven decision making?
a. How did it go? How did the stakeholders respond? Positive/negative? What did
they focus on? What came of it? What were some of the challenges? What did they
learn from the process? Will they do it again?
b. Probe for specific examples.
3. What do you believe is the value, if any, of implementing data driven decision making to
improve low-performing schools?
a. Probe to assess whether data driven decision making is a priority.
4. How do you support the implementation of data driven decision making within your school
district or schools?
a. What practices within the school district or schools encourage data driven decision
making?
b. If not, what alternative approaches are used to improve low-performing schools?

I would also like to explore how the implementation of data driven decision making
influences the school district and school sites.

5. What makes data driven decision making successful in a school district and a school site?
a. What is the role of teachers, administrators, and school board members?
b. Who drives data driven decision making in a school district and a school site? Who
are the gate keepers? What makes it or breaks it?
c. What do resources and support look like?

110
6. How do you feel about your overall ability to effectively engage in data driven decision
making?

For the last set of questions, some are more general and others explore the State’s role in
supporting data driven decision making.

7. What do you believe is the State’s primary role, if any, in supporting data driven decision
making?
8. what degree, if at all, do you believe the State shifted its culture or not to support the
implementation of data driven decision making within school districts?
a. What is some evidence for this shift?
9. How did the State, if at all, prepare school districts for the implementation of data driven
decision making?
a. Professional development?
b. Resources?
10. What should the State provide school districts to improve and/or sustain data driven decision
making?
11. Is there a question you thought I would ask you that I did not ask?

Thank you very much for the opportunity to interview you and to have you participate in
this study. I sincerely appreciated the opportunity.

111
Appendix C: Survey Responses

Demographic Related Survey Question or Statement

Survey Question or Statement
Participant Survey Responses
n %
Position – Please select the title that best fits your position.
Superintendent 62 96.88
Deputy or Assistant Superintendent 1 1.56
Education Management Professional 0 0
Principal 1 1.56
Organization – Please select the best description of your
organization.

County Office of Education 1 1.56
School District 62 96.88
Charter School 1 1.56
Other 0 0
Years in Position – How long have you worked in your current
position?

Less than one year 11 17.19
1 to 2 years 8 12.50
2 to 3 years 10 15.63
More than 3 years 35 54.69
Low-Performing Schools – How many schools within your school
district have been identified as low-performing according to State
and federal laws?

Less than 10 schools 51 79.69
11 to 25 schools 10 15.63
25 to 50 schools 3 4.69
Over 51 schools 0 0

Knowledge Influence Related Survey Question or Statement

Survey Question or Statement
Participant Survey Responses
n %
I have a clear understanding of how to apply data driven decision
making to improve low-performing schools.

Strongly Disagree 2 3.17
Disagree 0 0.00
Agree 24 38.10
Strongly Agree 37 58.73
I learn about data driven decision making by reading books,
attending conferences, or participating in professional learning
groups.

Strongly Disagree 0 0.00
Disagree 0 0.00
Agree 36 51.74
Strongly Agree 27 42.86
I dedicate time to reflect on how to best use data to inform my
decision making.

112
Strongly Disagree 1 1.56
Disagree 1 1.56
Agree 27 42.19
Strongly Agree 35 54.69
I reflect on how to best use data driven decision making to improve
low-performing schools.

Strongly Disagree 1 1.59
Disagree 1 1.59
Agree 20 31.75
Strongly Agree 41 65.08

Motivation Influence Related Survey Question or Statement

Survey Question or Statement
Participant Survey Responses
n %
When I examine data reports, I am confident that my interpretations
are accurate.

Strongly Disagree 0 0.00
Disagree 1 1.59
Agree 45 71.43
Strongly Agree 17 26.98
Once I draw conclusions using data, I am confident in deciding what
action steps to take next.

Strongly Disagree 0 0.00
Disagree 2 3.28
Agree 42 68.85
Strongly Agree 17 27.87
I prioritize data driven decision making even when I am confronted
with resistance from external stakeholders to not release data.

Strongly Disagree 1 1.67
Disagree 3 5.00
Agree 34 56.67
Strongly Agree 22 36.67
I believe that data driven decision making is an effective method to
improve low-performing schools.

Strongly Disagree 1 1.64
Disagree 2 4.92
Agree 21 34.43
Strongly Agree 36 59.04
I make implementing data driven decisions a priority in my
organization.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 31 50.82
Strongly Agree 27 44.26
I motivate others in my organization to implement data driven
decision making.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 35 57.38

113
Strongly Agree 23 37.30
I provide professional development to improve use of data driven
decision making for principals at low-performing schools.

Strongly Disagree 2 3.33
Disagree 5 8.33
Agree 29 48.33
Strongly Agree 24 40.00
I include community stakeholders as participants in the
implementation of data driven decision making to improve low-
performing schools.

Strongly Disagree 1 1.67
Disagree 8 13.33
Agree 38 63.33
Strongly Agree 13 21.67
I explain my decisions using data.
Strongly Disagree 1 1.64
Disagree 1 1.64
Agree 33 54.10
Strongly Agree 26 42.62
I include student outcome data in district-wide reports.
Strongly Disagree 1 1.64
Disagree 0 0.00
Agree 27 44.26
Strongly Agree 33 54.10
I meet consistently with my leadership team to analyze trends in
student achievement.

Strongly Disagree 1 1.64
Disagree 5 8.20
Agree 30 49.18
Strongly Agree 25 40.98
I train staff to use data driven decision making.
Strongly Disagree 1 1.64
Disagree 6 9.84
Agree 34 55.74
Strongly Agree 20 32.79

Organization Influence Related Survey Question or Statement

Survey Question or Statement
Participant Survey Responses
n %
The State Department of Education promotes a culture that supports
the implementation of data driven decision making.

Strongly Disagree 6 10.17
Disagree 15 25.24
Agree 33 55.93
Strongly Agree 5 8.47
Making data driven decisions improves my ability to satisfy state
and federal accountability requirements to improve low-performing
schools.

Strongly Disagree 3 5.00

114
Disagree 4 6.67
Agree 30 50.00
Strongly Agree 23 38.33
My board of education promotes a culture that supports the
implementation of data driven decision making.

Strongly Disagree 0 0.00
Disagree 4 6.67
Agree 42 70.00
Strongly Agree 14 23.33
My board of education delegates to me the authority to implement
data driven decision making to improve low-performing schools.

Strongly Disagree 1 1.64
Disagree 2 3.28
Agree 28 45.90
Strongly Agree 30 49.18
My school district has articulated a clear vision statement for data
driven decision making.

Strongly Disagree 0 0.00
Disagree 15 25.42
Agree 37 62.71
Strongly Agree 7 11.86
The State Department of Education provides professional
development to improve implementation of data driven decision
making.

Strongly Disagree 2 3.33
Disagree 28 46.67
Agree 29 48.33
Strongly Agree 1 1.67
My board of education supports my professional development to
improve implementation of data driven decision making.

Strongly Disagree 0 0.00
Disagree 4 6.56
Agree 42 68.85
Strongly Agree 15 24.59
The State Department of Education provides adequate resources to
implement data driven decision making.

Strongly Disagree 2 3.39
Disagree 29 49.15
Agree 28 47.46
Strongly Agree 0 0.00
My board of education provides adequate resources to implement
data driven decision making.

Strongly Disagree 0 0.00
Disagree 12 20.34
Agree 34 57.63
Strongly Agree 13 22.03
The State Department of Education facilitates a professional
learning group among education leaders to learn about the
implementation of data driven decision making.

Strongly Disagree 3 5.00

115
Disagree 29 48.33
Agree 28 46.67
Strongly Agree 0 0.00
Making data driven decisions improves my ability to satisfy state
and federal accountability requirements to improve low-performing
schools.

Strongly Disagree 3 5.00
Disagree 4 6.67
Agree 30 50.00
Strongly Agree 23 38.33

116
Appendix D: Informed Consent and Information Sheet
University of Southern California
Rossier School of Education
3470 Trousdale Parkway
Los Angeles, CA 90089

STUDY TITLE: The Implementation of Data Driven Decision Making to Improve Student
Performance Outcomes: An Evaluation Study of Superintendents in the Western United States

PRINCIPAL INVESTIGATOR: Enrique Ruacho

FACULTY ADVISOR: Helena Seli, PhD

You are invited to participate in a research study. Your participation is voluntary. This document
explains information about this study. You should ask questions about anything that is unclear to
you.

PURPOSE
The purpose of this study is to better understand the role of data driven decision making to improve
student outcomes. Specifically, the study focuses on the implementation of data driven decision
making within school districts. We hope to learn what is working well and where there might be
areas for growth. You are invited as a possible participant because of your role as superintendent
of a school district.

PARTICIPANT INVOLVEMENT
If you decide to take part, you will be asked to participate in an online survey and/or an in-person
interview. The online survey consists of 25 questions and should take approximately 10 minutes
to complete. Almost all of the survey responses are measured along the strongly agree to strongly
disagree scale, except for demographic questions. The in-person interview consists of 12 questions
and may last between 30 to 45 minutes. You will be asked for permission to audio record the
interview to assist the researcher by focusing on the conversation rather than notetaking.
Participation in the study is strictly voluntary and all information that is shared remains
confidential. You may decide not to answer a question or to withdraw from the study without
penalty.

CONFIDENTIALITY
The members of the research team and the University of Southern California Institutional Review
Board (IRB) may access the data. The IRB reviews and monitors research studies to protect the
rights and welfare of research subjects.

Version Date: January 12, 2020 Page 1 of 2
University of Southern California
Rossier School of Education

117
3470 Trousdale Parkway
Los Angeles, CA 90089

When the results of the research are published or discussed in conferences, no identifiable
information will be used. The online survey will be conducted using Qualtrics, which is a web
based tool to collect survey responses. The survey responses will be secured in a password
protected account with Qualtrics. The survey responses will be deleted upon completion of the
study but no later than May 2021.

The in-person interview may be audio recorded. The audio recordings and transcripts will be
deleted upon completion of the study but no later than May 2021.

INVESTIGATOR CONTACT INFORMATION
If you have any questions about this study, please contact the principal investigator, Enrique
Ruacho, at eruacho@usc.edu or at 323-854-7864. You may also contact the faculty advisor,
Helena Seli, at helena.seli@rossier.usc.edu.

IRB CONTACT INFORMATION
If you have any questions about your rights as a research participant, please contact the University
of Southern California Institutional Review Board at (323) 442-0114 or email irb@usc.edu.

Version Date: January 12, 2020 Page 2 of 2

118
Appendix E: Program Implementation Evaluation Tool
Evaluation Tool: Immediately Following Program Implementation
Level 1: Reaction 4-Point Survey Scale: Strongly Disagree > Strongly Agree
Engagement I felt encouraged to participate throughout the workshops.
Relevance I found the workshops and activities relevant to my work.
Customer
satisfaction
Overall, this workshop was an effective use of my time.
Level 2: Learning

Declarative
knowledge
The content was easy for me to follow.
Procedural skills I understand the steps that are necessary for me to apply data driven
decision making.
Attitude I believe that data driven decision making is important to improve low-
performing schools.
Confidence The knowledge I gained in today’s retreat will help me do my job
better.
Commitment I would recommend this program to others.

119
Appendix F: Program Implementation Delayed Evaluation Tool
Evaluation Tool: Delayed Period After Program Implementation

4-Point Survey Scale: Strongly Disagree > Strongly Agree
L1:
Reaction
What I learned in the retreat has been very valuable to implement data driven
decision making.
L2:
Learning
I have been able to implement the data driven decision making practices that I
learned in my school district.
L3:
Behavior
I regularly refer back to my professional development improvement plan.
L4: Results My low-performing schools are improving and/or are being reclassified as
improving.

Abstract (if available)

Abstract This study explored the implementation of data driven decision making (DDDM) among superintendents to improve low-performing schools in the Western United States. Specifically, this study utilized the Clark and Estes (2008) Gap Analytic Conceptual Framework to understand superintendents’ knowledge, motivation, and organizational influences related to DDDM implementation with the ultimate goal of the state reclassifying low-performing schools within four years. A convergent mixed method design, consisting of a survey instrument and interviews with superintendents, was utilized to collect data. The survey included agree-disagree Likert-type items. This study surveyed 64 participants with a response rate of 14.6% and interviewed nine participants. The results and findings demonstrated a high degree of value among superintendents for DDDM implementation, and that superintendents used their personal stories of adversity, their responsibility as a leader, and their role model status in the community as sources of high self-efficacy. However, the results and findings also demonstrated a gap in the procedural knowledge of superintendents, and an organizational influence gap related to the State Department of Education providing professional development. Finally, this study recommended an integrated implementation and evaluation plan, designed by applying the New World Kirkpatrick Model (2016) to the performance goal of superintendents.

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Asset Metadata

Creator Ruacho, Enrique (author)

Core Title The implementation of data driven decision making to improve low-performing schools: an evaluation study of superintendents in the western United States

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Publication Date 10/16/2020

Defense Date 09/29/2020

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

Tag data driven decision making,low-performing schools,OAI-PMH Harvest,superintendents

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Seli, Helena (committee chair), Phillips, Jennifer (committee member), Roach, John (committee member)

Creator Email eruacho@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-384445

Unique identifier UC11666345

Identifier etd-RuachoEnri-9046.pdf (filename),usctheses-c89-384445 (legacy record id)

Legacy Identifier etd-RuachoEnri-9046.pdf

Dmrecord 384445

Document Type Dissertation

Rights Ruacho, Enrique

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...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA