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An examiniation of staff perceptions of a data driven decision making process used in a high performing title one urban elementary school

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An examiniation of staff perceptions of a data driven decision making process used in a high performing title one urban elementary school

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Content Running head: STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 1

An Examination of Staff Perceptions of a
Data Driven Decision Making Process
Used in a High Performing Title One Urban Elementary School
by
Cassandra Ziskind

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

May 2013
Copyright 2013 Cassandra Ziskind
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 2
Dedication
This dissertation is dedicated to my family members, who have supported and inspired
me throughout the entire process. You have encouraged me to reach high and achieve my
dreams. I am blessed to have such a loving family in my life. I am especially thankful to my
wonderful husband and best friend for the last 30 years. Your love and support helped me get
through it all. You were always there with a back rub and a joke to make me laugh. To my son
and daughter, you are the best children I could ever have imagined. Thank you for believing in
me. You both lift me up every day. I love you both and I hope that I have inspired you to reach
your dreams. To my mother, thank you for always believing in me and empowering me to do
my best. Thank you to the best family in the world!
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 3
Acknowledgements
I am very grateful to all of my committee members for their support, time, and
encouragement. I especially would like to thank my dissertation chair, Dr. Pedro Garcia for your
patience, guidance, and good humor throughout the dissertation process. You have been a true
inspiration, and I thank you for all that you have done to assist me through this process. I feel
truly blessed and grateful to have been able to complete the dissertation process under your
tutelage as my chair.
I would also like to thank my other committee members, Dr. Rudy Castruita, Dr. Evelyn
Mahmud, and Dr. Terrance Jakubowski. I am grateful for your flexibility and time with this
dissertation process. I know how busy you all are and I appreciate your willing support and
guidance.
Finally, thank you to the amazing faculty and support staff at the University of Southern
California. I feel honored to have gone on this amazing journey at such a prestigious university.
The amount of encouragement and guidance was invaluable throughout the process.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 4
Abstract
The national focus in education continues to center on school accountability and student results.
Districts and states are continually searching for ways to improve student performance and
learning outcomes. There are several Data Driven Decision Making models currently being used
in school districts across the country. Research continues to expand in the investigation of Data
Driven Decision Making models. This study examines how a Data Driven Decision Making
(DDDM) process is perceived at an elementary school for identifying possible problems,
analyzing problems, designing and modifying instruction, and consistently evaluating student
learning in an effort to provide knowledge in the field of education on how data is used to inform
instruction and planning.planning. This study addresses the following questions:
1. Is a DDDM process used by faculty when analyzing student data?
2. Are teachers utilizing data to affect their instruction?
3. Is professional development provided to teachers to help them in their efforts to
utilize data to guide their instruction?
Through research, this study found that the school used in this study utilized a Data
Driven Decision Making process to improve student outcomes. Schools can make important
instructional decisions with the use of data to improve teacher capacity with effective
professional development, strengthen the curriculum by strategically analyzing student data,
motivate students, and create long term goals for continued growth.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 5
Table of Contents
Dedication .....................................................................................................................................2
Acknowledgements .......................................................................................................................3
Abstract .........................................................................................................................................4
Table of Figures ............................................................................................................................8
Chapter One—Overview of the Study ..........................................................................................9
Background of the Problem ......................................................................................................9
Statement of the Problem ........................................................................................................12
Purpose of the Study ...............................................................................................................15
Research Questions .................................................................................................................16
Importance of the Study ..........................................................................................................16
Definition of Terms.................................................................................................................18
Limitations of the Study..........................................................................................................19
Organization of the Study .......................................................................................................20
Chapter Two—Literature Review ...............................................................................................21
NCLB History and Mandates ..................................................................................................22
The Impact of No Child Left Behind ......................................................................................25
Data Driven Decision Making ................................................................................................29
Building a Foundation for Data Driven Decision Making ......................................................30
Establishing a Culture of Data Use and Continuous Improvement ........................................31
Investing in an Information Management System ..................................................................32
Selecting the Right Data .........................................................................................................34
Building School Capacity for Data Driven Decision Making ................................................37
Analyzing and Acting on Data to Improve Performance ........................................................39
Summary of Literature Review ...............................................................................................40
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 6
Chapter Three—Methodology ....................................................................................................42
Research Methods ...................................................................................................................43
Sample and Population ...........................................................................................................43
Data Collection Procedures .....................................................................................................45
Data Analysis ..........................................................................................................................48
Summary .................................................................................................................................48
Chapter Four—Results................................................................................................................49
Purpose of the Study ...............................................................................................................49
Research Questions .................................................................................................................50
Sample and Population ...........................................................................................................50
Data Collection Procedures .....................................................................................................52
Analysis of Findings ...............................................................................................................54
Research Questions .................................................................................................................55
Summary .................................................................................................................................72
Significant Findings and Trends .............................................................................................73
Chapter Five—Summary Conclusions and Implications ............................................................75
Connections to PriorResearch .................................................................................................76
Limitations of the Study..........................................................................................................78
Implications for Practice .........................................................................................................79
Future Research ......................................................................................................................81
Conclusion ..............................................................................................................................82
References ...................................................................................................................................84
Appendix A—Principal Assent Form .........................................................................................88
Appendix B—Principal Interview Protocol ................................................................................89
Appendix C—Teacher Assent Form ...........................................................................................91
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 7
Appendix D—Teacher Interview Protocol .................................................................................92
Appendix E—Staff Questionnaire ..............................................................................................94

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 8
Table of Figures
Figure 1—Survey results of whether a DDDM is used at my school. .......................................... 56
Figure 2—Survey results of whether different types of data used at my school. ......................... 57
Figure 3— Survey results of whether instructional gains have been made at my school. ........... 59
Figure 4—survey results of whether data is shared with students. ............................................... 60
Figure 5—Survey results on the existence of a strong DDDM culture. ....................................... 61
Figure 6—Survey results on whether actions are taken after data analysis. ................................. 62
Figure 7—Response to the question: Data is used to inform instruction. .................................... 62
Figure 8—Survey results on whether teachers understand how to use DDDM data.................... 63
Figure 9—Whether student performance benchmarks have been established. ............................ 64
Figure 10—Survey results on monitoring student progress. ........................................................ 65
Figure 11—Survey results regarding the value of teacher opinion vs. data. ................................ 66
Figure 12—Survey results on whether staff has time to review data. .......................................... 67
Figure 13—Survey results on whether staff has time to analyze data. ......................................... 68
Figure 14—Whether teachers are supported through professional development. ........................ 69
Table 1—Frequency Table for All Survey Responses ................................................................. 70

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 9
Chapter One —Overview of the Study
Background of the Problem
The No Child Left Behind Act of 2001 (NCLB) propelled educational accountability into
the forefront of American society. Educational accountability refers to the practice of holding
educational systems responsible for the quality of their products—students’ knowledge, skills,
and behaviors (Kirby & Stretcher, 2004). The No Child Left Behind Law is a reauthorization of
the Elementary and Secondary Education Act of 1965 (ESEA) which was intended to reform
education and improve accountability in our nation’s educational systems. NCLB puts an
emphasis on student performance results from standardized assessments. Therefore, as a
component of accountability, the use of data in schools is argued to enhance the quality of
instruction and school based decision making (Diamond & Cooper, 2007).
Despite over 10 years of reform, achievement gaps among different groups of students
continue to persist at the same time pressures andmonetaryy sanctions for failure are
increasinginr schools. The achievement gap in America can be analyzed and delineated by race
and socioeconomic status. When the United States Army over a century ago began to use
intelligence tests to assess recruits, “The results showed that white recruits outscored their black
peers by substantial margins,” (Paige & Witty, 2010, p. 10). These results were seen as
validating the concept that whites were superior to minorities—quite willingly ignoring the
institutional racism that had existed since the United States was founded. We know from history
that our educational system in America was not really created to provide everyone with an equal
and appropriate education. Black Americans and other minorities have been subjected to poor
educational opportunities from the beginnings of our country.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 10
In 1954 the Brown vs. Board of Education decision was viewed as a way to repair the
damage that was caused by segregation. Fifty six years after the Brown decision the gap still
exists today. Asian and White students perform significantly higher than Latino and Black
students on standardized assessments across the country. A key directive of NCLB was the
implementation of mandates that make all schools and districts accountable for educating all
students. Former Secretary of Education, Margaret Spelling, made the following statement in
2001, “For the first time ever, we are looking at ourselves in the mirror and holding ourselves
accountable for the education of every child. That means all children, no matter their race or
income level or zip code,” (USDE, 2005, . .1).
The No Child Left Behind Act is a long and complex document. However, the major
components and logic of its accountability system are simple and easy to comprehend. NCLB
outlines several mandates that states and school districts must follow to receive federal education
funds. These mandates include the following three accountability components: goals,
assessments, and consequences.
NCLB goals are concrete statements of desired student performance. Each state must
have content standards for students in kindergarten through twelfth grade (California Department
of Education, 2003). Districts, schools, and teachers use the content standards to guide
instruction. Expectations are shared with all stakeholders. Schools and districts are held
accountable for annual progress of students. Schools must have high expectations for all
students. Past prejudices and discriminations which were rooted in low expectations for minority
and poor students, are no longer taken into consideration under the No Child left Behind Act.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 11
Assessments are used to measure student success with learning and mastery of the
content standards. Districts, schools, and teachers are held accountable under No Child Left
Behind. Each year student progress is measured using the standardized assessment results—and
proficiency percentages are calculated for each school and school district.
Student achievement goals increase each school year along with incentives for schools
and districts based on student achievement (Kirby & Stecher, 2004). NCLB requires all students
to be proficient in the areas of reading and mathematics by the year 2014, based on results from
state adopted tests (California Department of Education, 2003). Districts and schools must make
progress towards this goal by reaching specific targets for each year. Schools that do not meet
state academic standards for two years in a row are identified as, “in need of improvement.”
Schools that do not meet state standards for three years, must offer free tutoring or other
academic services to eligible low income students.
An incredible amount of pressure and external scrutiny has been placed on districts,
schools, and teachers as a result of NCLB. Sanctions may be placed on schools and districts that
cannot demonstrate that their students are making adequate yearly progress towards reaching the
goal of all students being proficient by the year 2014. President Obama’s administration is
currently offering states flexibility around this demanding and controversial goal. The flexibility
or waiver has conditions attached. Many states have rejected the waiver— or have voiced
serious concerns about the idea. Under the waiver, states are given an opportunity to write new
academic goals that are “ambitious but achievable,” create new evaluation systems for teachers
and administrators based on student test scores, and plan for improving low performing schools
(Cavanagh, 2012).
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 12
Statement of the Problem
Schools and districts have more data available to them as a resource under NCLB than
ever before. They are aware of the achievement gaps and the increasing sanctions if student
performance and achievement does not improve. Schools and districts are also aware of the fact
that they need to improve. Having more data available is not enough. The question is how to
effectively to use it to improve student achievement. The availability of data is very promising
and creates the possibility of real improvement in student learning. “Using data to guide action
is the most powerful lever we have to improve schools; and yet, despite the increasing quantity
now available, data is woefully underutilized as a force of change,” (Love, 200. , p.5).
The state standardized assessments provide valuable data to districts and schools about
student performance and progress each year. These assessments are used in most states today for
accountability purposes but not as instruments for helping teachers improve their instruction.
Teachers do not receive the data until the school year has ended—by which time their students
have moved on to the next grade level. The state standardized assessments are usually very
general and are not detailed enough to assist teachers in targeting the specific instructional needs
of individual students.
The type of assessment that is suited to help teachers to improve instruction is called a
formative assessment. James Stigler defines formative assessment as a continuous process
during the course of teaching and learning to provide teachers and students with information and
data that will help them reach learning goals and close achievement gaps (Heritage, 2010).
Black, Harrison, Lee, and William (1998) concluded in their research that, “student learning
gains triggered by formative assessment were amongst the largest ever reported for educational
interventions, with the largest gains being realized by low achievers,” (1998b, p. 141).
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 13
Examples of formative assessments include quizzes, tests, writing assignments, and other
assessments that teachers give in their classroom. These types of assignments generally have a
direct relation to what has been taught in the classroom and to the instructional learning goals of
the class. Formative assessment results are immediate and can be used effectively along with a
Data Driven Decision Making (DDDM) process to assist teachers in making instructional
modifications.
Although the importance of data use for improving student learning has been clearly
outlined in NCLB and established as an effective practice to close student achievement gaps,
very few teachers receive professional development and training on designing assessments and
analyzing data from them. Also, the policies in NCLB do not provide clear expectations for
teachers or administrators for data use. A study by Stiggins (2006) found that fewer than half of
the states in America require competence in assessment design or analysis for teacher licensure.
Formative assessments that are not academic can also assist teachers in making important
instructional decisions. These nonacademic assessments include attendance data, behavior, or
discipline data, health records, and other data that allow teachers to form a large range of
knowledge about how a student is progressing—and other factors that may contribute to a
student succeeding.
Six DDDM key strategies of schools who successfully utilized data to close achievement
gaps and improve student learning were identified in a study conducted by Amanda Datnow,
Vicki Park, and Priscilla Wohlstetter (2007). The six DDDM strategies outlined in Achieving
with Data are as follows:
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 14
Build a Foundation for Data Driven Decision Making: Measurable goals must be
established for the school, classroom, and student levels. All stakeholders must come to
consensus around these measurable goals. Stakeholders include students, parents, teachers, and
administrators at a school.
Establish a Culture of Data Driven Decision Making: Administrators must foster a
culture where data based decision making is used on a consistent basis and accountability
measures are in place around norms and expectations for data use.
Invest in an Information Management System: There are many data management
systems available today. Organizing data in an assessable manner for teacher usage is critical to
success. Many school districts have district-wide data management systems in place for all
schools to utilize.
Select the Right Data: Schools must select data that is relevant to the standards being
taught in classrooms. The data selected should be a meaningful resource to teachers. Teachers
should be able to identify what they taught well and what they need to improve on for students to
achieve.
Build School Capacity for Data Driven Decision Making: Schools should provide
professional development for teachers and administrators on how to use and analyze data
effectively. Schools must also provide teachers with time to collaborate using data. Districts
should share data system wide to connect teachers and improve instruction.
Analyze and Act on Data Driven Decision Making: Teachers and administrators must
act on data after it has been analyzed. Feedback on student progress and performance must be
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 15
immediate. Areas of instructional weakness should be identified and structures put into place to
improve student learning and build teacher capacity.
Purpose of the Study
There are many Data Driven Decision Making (DDDM) models available to educators
today. Most of the research on data driven decision making in schools is focused on how data is
analyzed and gathered and not on how it is used to inform instruction. There is limited research
on how districts, schools, and teachers build a foundation for data based decision making,
establish a data based decision making culture, organize data using data systems, build school
capacity for using data, and most critical- what is done after data has been analyzed.
When teachers and administrators at the school level become knowledgeable about the
importance of data and its use to improve student learning, they can review their practice,
identify weaknesses, and make plans for improvement (Earl & Katz, 2006). Unfortunately,
many Title 1 schools and Non-Title 1 schools in large urban school districts lack the capacity to
analyze data and use it to drive instruction. The decisions that are made after data has been
analyzed are critical to school success and improved outcomes. The achievement gap is
widened, and valuable time is lost when schools do not act quickly after data analysis.
Decisions about instruction, class environment, curriculum, and student can be made with
data to plan and build an infrastructure that effectively supports students and builds teacher
capacity and pedagogy. This study examines how a Data Driven Decision Making (DDDM)
process is perceived in an elementary school. The DDDM process is useful for identifying and
analyzing problems, designing and modifying instruction, and evaluating student learning.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 16
Research Questions
Current research on data use has been very limited to data information that focuses on
summative assessments for accountability purposes. The purpose of this study is to examine
staff perceptions of the DDDM process used at a Title 1 urban elementary school to close the
achievement gaps and improve learning. Essentially this study attempts to address the following
questions:
1. Is a Data Driven Decision Making Process (DDDM) process used by teachers and
administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
3. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
These research questions will be examined through the use of administrator/ teacher
interviews and administrator/teacher surveys at a Southern California elementary school located
in a large urban school district. The research questions will be addressed throughout this study
in detail.
Importance of the Study
The No Child Left Behind Act of 2001 set a strict level of accountability for states by
requiring them to follow a regimen of annual testing, in grades three through eight, and by
sanctioning schools and districts that fail to make adequate yearly progress. Under this act,
schools and districts must utilize student achievement results to identify areas with lack of
improvement improvement, to implement appropriate interventions and/or modify instruction to
effect growth and progress on a yearly basis. More data is being used in education with the
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 17
increase in accountability. Unfortunately, many educators have little or no experience in using
data systemically to guide their instructional decisions (Learning Point Associates, 2004).
Many schools and districts are using data to investigate achievement gaps in student
learning. Most have developed annual goals, but the goals have in most cases not been created
through the continuous and careful study of student performance data. There is a limited amount
of research on the effective use of data to inform instruction and improve student learning in
schools. Most of the current research on the use of data in schools focuses on how data is
analyzed rather than how it is used to inform teaching practice. This study attempts to fill in a
gap in the research by examining how data is being used at a high performing Title 1 elementary
school to inform and support teacher practice. This study looks beyond the analysis component
of data use, and into the how instruction is modified after data analysis to meet the needs of all
students so as to close achievement gaps in learning. The essential purpose of this study is to
examine instructional modifications as a result of teacher analysis of student data.
This study examines foundational information on the different types of data currently
available to educators, effective strategies for analyzing and understanding data, and methods for
determining how to use data for instructional planning and goal creation. Ultimately, this study
provides schools and districts with effective ways data is currently being used in the hope that
educators will understand the importance of using data analtsis to inform instruction. Perhaps
the successful data strategies outlined in this study will encourage school and district educational
leaders to create data rich environments and increase school staff capacity to use data to improve
student achievement and learning.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 18
Definition of Terms
Achievement Gap: the disparity in academic performance among groups of students.
This gap usually shows up among success measures like standardized achievement tests, dropout
rates, course selection, college enrollment, and college completion. The term achievement gap is
most often used to describe the academic performance gap between Black and Hispanic students
as compared to higher achieving Asian and White students. In California, Black and Hispanic
students are over- represented among the lowest scoring students on the California standardized
assessment test and under-represented among the highest scoring students.
Adequate Yearly Progress (AYP): a measurement by which schools, districts, and
states are held accountable for student achievement performance under Title I of the No Child
Left Behind Act of 2001. It measures progress towards the goal of 100% of students proficient
in English Language Arts and Mathematics by 2013-2014.
Academic Performance Index (API): is a single number, from 200 to 1000, which
reflects the performance level of a school, Local Education Agency, or a subgroup, based on the
results of statewide testing. The API was defined by the California department of education in
2011. The API is calculated by converting a student’s standardized assessment score, across
multiple content areas, into points on the API scale. These points are averaged across all
students and all tests to create the API score.
Assessment: the process of analyzing interpreting information
Data: information that is factual that can be organized and used to make decisions after
it has been analyzed. Schools and districts can utilize data to evaluate their effectiveness around
student achievement.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 19
Data Driven Decision Making: decisions that are made based on data and not solely on
opinion. Utilizing demographic, student performance, perceptual, and school process data to
inform decisions related to the school (Bernhardt, 2004).
High Achieving School: a school that has met its AYP and has an API at or above 800.
Progress Monitoring: a process where teachers continuously monitor how a student is
progressing and responding to instruction and intervention. Individual student data and
classroom data is analyzed to determine if a student or students are progressing at an adequate
rate to meet specific learning targets and master grade level standards.
Title 1 School: a school that receives federal funds to support effective, research based
educational strategies that close the achievement gap between high and low performing students
and enables students to meechallenging stateng academic standards.
Limitations of the Study
One limitation of this study is the relatively small sample group size. One elementary
school was chosen based on its status as both a High Performing school and a Title 1 school.
The school was chosen based on its use of data to make decisions and improve student
achievement. The design of the investigation case study limits the transferability of results to
other school sites. Factors specific to the school in this study might make the application of the
findings in this study to other school sites difficult. The school factors can include teacher
retention rates, student demographics, administrator retention rates, funding, and support
systems. Additional factors include administrators and faculty, data analysis, and data systems.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 20
The short duration of this study is an additional limitation. This study was conducted
using a survey instrument and interviews which provide a “snapshot” of how data is used to
inform teacher practice. The practice of using data to inform instruction is an ongoing process.
Steps for effectively utilizing data include: collecting and analyzing data, developing or
modifying instruction based on data, and evaluating student data over a period of time to see if
student learning gaps are closing. The short duration of this study did not reflect how the school
used data to inform instruction over an extended period of time. The data may not reflect a
representative sample of the teacher population at the school because not all teachers participated
in this study. Participation was on a voluntary basis only. The reliability and validity of the
interview and survey instruments used in this study can be considered a limitation because they
were created solely for use in this case study and are not research based instruments.
Organization of the Study
This study was conducted at an elementary school located in an urban California school
district. A qualitative and quantitative mixed methods research design was used in this study.
Research questions were examined through the use of teacher and administrator interviews,
classroom observations, and data meeting observations. The study was conducted over a six
week period.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 21
Chapter Two —Literature Review
The No Child Left Behind Act of 2001 policies and regulations have increased public
awareness of student data and school performance; the effectiveness of districts and schools is
measured by performance indicators based on student results from standardized achievement
tests. With the advent of the federal No Child Left behind (NCLB) Act, the push for increased
accountability and improved student achievement in public schools has never been greater
(Datnow, Park, & Wohlstetter, 2007). Educators are adopting Data Driven Decision Models
(DDDM) as a central focus to monitor student progress towards meeting performance targets.
There is an enormous need to understand how DDDM impacts schools and students as we move
towards the 2014 deadline set forth in the NCLB legislation that mandates for all students to be
proficient by the deadline.
The use of data is a very important practice for educators to use to adjust their practices
so as improve student performance. Data can shed light on existing areas of strength and
weakness and guide improvement strategies in a systemic and strategic manner (Dembosky,
Pane, Barney, & Christina, 2005). Schools that rely on opinions from teachers and
administrators without using data, risk increasing performance gaps and more failing students.
Without analyzing and discussing data, schools are neither likely to identify and solve the
problems that need attention, nor to identify appropriate interventions to solve those problems,
nor to know how they are progressing toward achievement of their goals. Data are the fuel of
reform (Killion & Bellamy, 2000).
Recently, because of the increased focus on school accountability and effectiveness, more
research has been conducted on the use of Data Driven Decision Making and its impact on
student learning and school improvement. The goal of the following literature review is to
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 22
investigate current research on the use of data by school sites and the impact using data has on
teachers and students. The following topics will be discussed in this literature review:
 NCLB Mandates
 The Impact of NCLB
 Data Drive Decision Making Skills
 Challenges Affecting the Use of Data
 Data Based Decision Making Professional Development
 The Impact of Data Driven Decision Making on Instruction
NCLB History and Mandates
The status of public education became a national concern with the release of the report A
Nation at Risk in 1983 by the National Commission on Excellence in Education. The
commission was formed to “review and synthesize the data and scholarly literature on the quality
of learning and teaching in the nation’s schools, colleges, and universities, both public and
private, with special concern for the educational experience of teenage youth,” (U. S.
Department of Education, 1983). The academic performances of American students were
compared to students in other countries in this document. The report stated:
Part of what is at risk is the promise first made on this continent: All, regardless of
race or class or economic status, are entitled to a fair chance and to the tools for
developing their individual powers of mind and spirit to the utmost. This promise
means that all children by virtue of their own efforts, competently guided, can hope
to attain the mature and informed judgment needed to secure gainful employment,
and to manage their own lives, thereby serving not only their own interests but also
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 23
the progress of society itself (National Commission on Excellence in Education
1983b, p. 141).
Several specific risk indicators of risk are listed in the report (U.S. Department of
Education, 1983c) such as the following:
 Approximately 13 percent of all 17 year olds in the United States can be considered
functionally illiterate. Among minority youth the functionally illiterate may have run
as high as 40 percent.
 College Board’s Scholastic Aptitude Tests (SAT) scores consistently decline in
verbal, mathematics, physics, and English subjects.
 Close to 40 percent of 17 year olds cannot draw inferences from written material;
only one fifth can write a persuasive essay; and only one third can solve a
mathematics problem requiring several steps.
 Remedial mathematics courses in public 4 year colleges increased by 72 percent and
now constitute one quarter of all mathematics courses taught in those institutions.
The recommendations made by the National Commission on Excellence in Education in
A Nation at Risk, 1983 report promised educational reform through the statement, “the
best effort and performance from all students, whether they are gifted or less able, affluent or
disadvantaged, whether destined for college, farm, or industry” (U. S. Department of Education,
1983e, 1983x, pgs. 241-287).
The educational system in the United States continues to struggle with the risk indicators
listed in the “A Nation at Risk (1983)” report almost thirty years later. Achievement gaps still
exist among minority students and students of poverty with White and Asian students. President
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 24
George W. Bush signed the No Child Left Behind Act into law on January 8, 2002 because the
achievement gaps were not being closed. The act was proposed by George W. Bush almost
immediately after he took on the office of President of the United States in 2001. The No Child
Left Behind Act, also known as NCLB, is a reauthorization of the Elementary and Secondary
Education Act of 1965—considered the most far reaching federal legislation affecting education
that has ever been passed by Congress.
Both the Elementary and the Secondary Education Act (ESEA 1965) and the No Child
Left Behind Act (NCLB 2001) put an emphasis on a quality education for all students. Both acts
also establish rigorous standards and accountability. The ESEA was initially authorized for a
five year period ending in 1970 but, the government has reauthorized the act every five years
since its authorization. President George W. Bush proposed the name No Child Left Behind for
the current reauthorization.
NCLB is a landmark education act that supports standards based education reform that is
based on the foundation that establishing measurable goals and setting high rigorous standards
will improve student learning outcomes in education. In doing so, it has had a significant impact
on elementary and secondary educational programs (Simpson et al., 2004). There are four
overarching principles in NCLB that are related to student achievement:
1. School and district increased accountability for student performance, achievement
results, and employment of highly qualified teachers.
2. Increased flexibility for parents of students enrolled in Title 1 schools that fail to meet
annual achievement targets.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 25
3. Rewards for schools meeting or exceeding annual achievement targets and sanctions
for schools that do not meet annual achievement targets.
4. An increase in federal funding to promote the President’s Reading First Initiative
(Odland, 2006).
In other words, the purpose of NCLB 2001 was to fulfill the promise of a fair, equal, and
adequate education for all students regardless of race, gender, or economic status. It was
developed from the recommendations listed in the A Nation At Risk Report released in 1983.
The Impact of No Child Left Behind
NCLB supporters believe that the increased accountability under the act motivates
teachers and schools to make improvements on standardized test scores to avoid decreases in
funding and other sanctions. The State of California began implementing school reform policies
before NCLB was in place. The Public Schools Accountability Act (PSAA) was approved by
the state in 1999, predating NCLB 2001. This act mandated statewide standardized testing,
rewards and sanctions based on student scores, and state standards for learning at every grade
level. Issues of equality and opportunities to learn for all students were addressed in the act.
Schoolsschools were not just responsible for school wide targets, but for the first time were
required to meet targets for racial, ethnic, and socioeconomic subgroups of students (Woody et
al., 2006). Attention was given to students who historically performed lower on standardized
tests. Standardized testing data made it clear that many groups of students were receiving a less
than adequate education. Increased accountability required educators to become responsible for
all students and close achievement gaps. There is evidence that state level accountability
systems have improved outcomes for all students (Skrla, Scheurich, Johnson, & Koschoreck,
2004).
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 26
Opponents of the No Child Left Behind Law of 2001 believe that the punishments and
sanctions have hurt the schools and have not contributed to improvements in education. A main
criticism asserts that NCLB reduces quality effective instruction and student learning by causing
states to lower achievement goals and incentivescentivises teachers to “teach to the test.” Critics
of NCunderfundedthat the grossly underfunded policy negatively adds burdens to the unequal
and inadequately funded urban schools and requires these schools to meet high test score targets
that disproportionately penalize them for their failures (Darling-Hammond, 2004). Many
educational leaders argue that the NCLB law has harmed schools, districts, states, teachers, and
students even though the intentions were good and just (Darling-Hammond, 2004; Woody et al.,
2006).
Sanctions increase yearly for schools that fail to meet their state mathematics and
language arts Adequate Yearly Progress (AYP) targets for any of their subgroups or their school-
wide Academic Performance Index (API) targets. A school’s failing status is publicly identified
when AYP targets are not met for the first time. Schools are labeled Program Improvement (PI)
after the second year of not meeting AYP targets. More and more schools are falling under this
category because of the increase in Annual Measurable Objectives (AMOs) in the areas of
mathematics and language arts.
Each year, the requirements for the number of students percent proficient increases until
the goal of 100 percent students proficient by the year 2014 is met. Schools in their third year of
Program Improvement (PI) status are required to meet the same requirements as year two. That
means using 10 percent of all Title 1 funds for professional development, and notifying parents
of their opportunity to transfer to a Non-Program Improvement school. Schools in year three
status are also required to provide free intervention services for enrolled students. Schools that
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 27
get to year four and year five Program Improvement (PI) status can be subjected to corrective
actions by the state—like reconstitution of staff or restructuring as a charter school (Abernathy,
2007).
The overemphasis on testing and reporting for accountability purposes has led many
educators to utilize specific practices, such as narrowing instruction to a subset of skills that will
lead to higher test scores. Approximately 71 percent of schools in the United States have
realigned resources from areas like history, arts, and music, to mathematics and language arts—
which are more heavily weighted on standardized tests (Jennings & Rentner, 2006). This has
been seen to be at the expense of sound effective instructional practices by critics of NCLB.
Teachers are encouraged by administrators to use certain practices that lead to overly
improved student scores or behave in particular ways, knowing the positive and negative
outcomes that may transpire as a result of high or low test scores (Koretz, 2002). Many
educational leaders have questioned the effectiveness of the education students are receiving at
low performing schools with immense achievements in an educational environment where all
schools have placed an increased emphasis on basic skill drills and less emphasis on teaching
content depth and breadth. The issue of teacher evaluations being tied to student test scores has
led many teachers to focus on test taking strategies in the areas of mathematics and language arts
rather than on effective instruction in all curricular areas. They know that their careers can be
jeopardized because of the results from a single test. The current low hiring rate and lack of
educational funding only increases the moral dilemma faced by teachers as regards providing
effective instruction and broad content to students.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 28
A supportive claim for NCLB asserts that systematic testing provides data to be used by
schools and districts to identify which students are making achievement gains—through effective
teaching—and which students are not making gains, so that interventions can be made to
improve outcomes for all students—while reducing the achievement gap for disadvantaged and
disabled students (Simpson et al., 2004).
Although there are many opponents of the NCLD Act of 2001, there is no denying the
fact that NCLB has helped to raise the level of quality in education; specifically for minority
students. Standards based accountability requiring educators to measure and quantify student
achievement has moved educators in the right direction. But there is an assumption in NCLB
that administrators and teachers will be able to effectively use data just because it is mandated in
the act. The capacity to utilize data varies between school systems. Many schools struggle with
professional development training for teachers and administrators that is required for schools to
meet the accountability requirements outlined in NCLB. These public schools have struggled to
develop the capacity to meet the demands of high stakes accountability policies (Elmore, 2002).
There is an abundance of data available to schools. School staffs must learn to
understand and use the valuable data that is available to them. Most schools have less funding,
resources, and personnel due to the current budget crisis that our nation and state have fallen
under. Being knowledgeable about data can enable schools to use their resources wisely both to
meet the needs of their students and close achievement gaps. Research has shown that schools
that use Data Driven Decision Making in their daily practices are able to increase student
outcomes, especially for students who previously have been low performing (Leahy et al., 2005).
Examining how data is successfully utilized by teachers to adjust their practices and increase
student achievement is critical to school reform.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 29
Data Driven Decision Making
There are several different Data Driven Decision Making models available to educators
that emphasize various components that are essential to the process. Many of the models have
common themes, such as building a data culture, developing a plan, implementing a plan,
providing professional development for staff, and analyzing and acting on data to improve
student performance. All of the themes are centered on the importance of the use of data for
continuous school improvement.
Unfortunately, two valuable steps that can be found in most Data Driven Decision
Making processes are infrequently missing from the literature. These two valuable steps are
critical for continuous improvement. One step is the evaluation process also known as progress
monitoring, where schools stop and evaluate the impact that their modified instruction and
interventions are having on student achievement. If teachers do not stop to evaluate how
students are responding to their instruction, intervention achievement gaps can grow and students
can fall further behind in learning. The second step that is not regularly mentioned in the
literature for Data Driven Decision Making (DDDM) is the step where educators adjust
instructional and intervention strategies that were put into place after data analysis and
evaluation. This is a critical step for students to improve because if strategies are not working,
continuing them will not lead to an improvement in student learning.
Schools have to continually examine data for teachers and administrators to reflect and
focus on their practices and efforts towards improving student learning. Involvement of all staff
members in the analysis of data was a critical component of the school improvement process
(Schmoker, 1999). The six key strategies outlined by Datnow, Park, and Wohlstetter (2007) will
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 30
be used in this literature review to discuss different key components that are essential to the Data
Driven Decision Making process.
The six key strategies of performance driven school systems that utilize a DDDM are as follows:
1. Building a foundation for data driven decision making
2. Establishing a culture of data use and continuous improvement
3. Investing in an information management system
4. Selecting the right data
5. Building school capacity for data driven decision making
6. Analyzing and acting on data to improve performance
Building a Foundation for Data Driven Decision Making
Data Driven Decision Making is not a practice that can happen in isolation at a school
site. There must be an agreed upon goal for all staff members at a school to come to consensus
on the importance of using data to improve student learning and set measurable goals for
learning. Goals can be set at the state, district, school level, grade level, classroom level, and
individual student level for continuous progress monitoring. According to Datnow et al., (2007)
schools cannot effectively utilize data without setting student achievement goals to reach a
specific outcome. Having measurable goals allows teachers to intentionally focus on planning
their instruction to meet the specific needs of each individual student in their classroom. Most
schools utilize goals that are set by their district and are rooted in NCLB targets, specifically
Adequate Yearly Progress targets and Academic Performance Index (API) targets.
Goals are crucial for teacher collaboration meetings and professional development
planning. Goals allow schools to identify problems at an individual student level, classroom,
level, grade level, and a school-wide level. Teachers and administrators can effectively analyze
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 31
data with a set goal in place, implement a plan for improvement, and evaluate their process for
continuous improvement. According to Wade (2001), once data has been systematically
collected and analyzed, the data will provide educators with their only real evidence of the
success or failure of their educational programs. Data can only reveal evidence of success or
failure if a goal or benchmark of success has been established. Teachers and administrators must
have an indication of what constitutes success and identify the variables that are required for
success.
Establishing a Culture of Data Use and Continuous Improvement
Creating a DDDM culture that collaborates on a regular basis with using data to affect
instructional practices is essential to continuous school improvement. Teachers and
administrators must begin the collaborative process when using data with the belief that all
students are capable of learning and that the goals established by the school are attainable and
realistic for students to achieve. School educators that collaborated on analyzing student
achievement data demonstrated high levels of ownership of both the DDDM process and of the
data itself (Lachat, 2005). It is very important for teachers not to view data as a source of
negative information. A positive data culture has to be established where schools do not blame
students for poor performance or penalize teachers for poor student performance. Teachers and
administrators need to experience the positive benefits of utilizing data for them and their
students (Datnow et al., 2007).
Norms and expectations for using data need to be established and agreed upon by all
teachers and administrators at a school site for a data culture to be maintained (Datnow et al.,
2007). Administrators are responsible for modeling positive behavior with the use of data and
building trust. Teachers will not see the benefits associated with using data if they feel
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 32
threatened or embarrassed by the results. Data results and scores cannot be seen as results that
are used to label and categorize teachers and students. Teachers should view data as a valuable
resource that allows them to understand how their instruction is impacting student learning and a
resource that identifies areas of strength and weakness. Through collaborative analysis of data,
teachers are able to share best practices and strategies for improving student achievement.
“Knowledge creation occurs in the process of social interaction about information (Light,
Wexler, & Heinze, 2005, p. 2).
According to Schmoker (2003), schools accomplish more together than in isolation,
regular collective dialogue sustains commitment and feeds purpose, evidence promotes buy-in,
and teachers learn best from other teachers. Collaborative analysis of data can empower schools
and create a sense of urgency the facultyamong faculty when teachers are aware of their
successes. A sense of community is also established when teachers share successful practices
and meet challenges. Data allows teachers to refine their instructional practices through
collaboration using a DDDM.
Investing in an Information Management System
The No Child Left Behind Act does not detail the expectations of data use for teachers or
administrators at the school site to impact student achievement. Accountability at the federal and
state levels require schools to access, analyze, disaggregate, and use student data to inform their
effectiveness towards improving student achievement. This can be a very complex process for
many schools when many in this current economic environment lack the equipment, personnel,
and resources necessary to meet the data accountability requirements. There are many
challenges with implementing DDDM at school sites even when data is in abundance. Datnoww
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 33
et al. (2007) state that just merely having data does not ensure that data driven decision making
will take place.
The sanctions that are attached to student achievement data makes it imperative for
teachers and administrators to understand how to organize data in a comprehensible and user
friendly format. Teachers will not utilize data that they cannot understand. Most schools, like
the one examined in this study, use an electronic student information system to organize data in a
comprehensible manner. There are a large number of electronic student information systems
available. These data analysis and storage tools vary in their capacity and features. Many of the
available tools today permit educators to store, access, and analyze data effectively (Wayman,
Stringfield, & Yakimowski, 2004). These tools also provide teachers with data that is current
and relevant so that they can quickly modify their instruction and intervention to meet the needs
of individual students—which is a foundational DDDM practice.
Most data analysis tools offer reporting capabilities that are aligned to NCLB. Data is
organized by common descriptors such as race, gender, language classification, economic status,
and disability. Valuable time can be saved by using tools to organize student data. Educators
can identify trends and patterns that occur over time using these tools. Multiple variables can be
analyzed along with academics over time—including attendance, behavior, and transiency.
School program data can also be input into these systems so that all decisions are referenced by
using data. Systems and processes can be evaluated for impact on student learning so that
resources can be better utilized to increase student achievement.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 34
Selecting the Right Data
Schools cannot rely on a single type of assessment data. Using multiple forms of data is
a key strategy in the data analysis stage of DDDM. According to Bernhardt (1998, p. 13),
“Because learning does not take place in isolation, or only at school, multiple measures must be
considered and used to understand the multifaceted world of school.” (Bernhardt, 1998, p. 13)
The collection of data has to be a planned and purposeful process. The first step in selecting the
right data is to begin with the right questions and aligning assessments to answer these questions.
Herman and Gribbons (2001) identified three basic questions that are foundational to data
analysis. The three questions are as follows:
1. How are we doing?
2. Are we serving all students well?
3. What are our strengths and weaknesses?
Assessments must be aligned to goals that are standards based for student performance
data to identify whether a school is succeeding in developing student skills and learning.
Organized and detailed data will allow the school to determine if all students are succeeding.
Assessments have to be alwithned to standards based instruction for a school and teachers to
identify areas of strength and weaknesses. Alignment is the alliance between the teacher
expectations and the assessment tools used to measure whether or not students are meeting
expectations (Herman, Webb, & Zuniga, 2003). Assessments must be based on the instruction
happening in the classrooms for accurate and reliable results that lead to decisions that impact
student learning.
There are many types of assessment tools available to schools today that provide valuable
data and meet accountability requirements. Common assessment tools used by many schools are
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 35
high stakes standardized assessments, benchmark assessments, and formative assessments. The
different types of assessments vary in length and frequencies. Schools use the different
assessments at different times to inform their effectiveness towards meeting set goals. Schools
are able to adjust instruction to meet the academic needs of students from results gathered from
different assessments.
High stakes standardized assessments are commonly used in accountability systems
today. Judgments are made about the effectiveness of districts, schools, and teachers based on
this single assessment administered towards the end of each school year. These assessments can
provide general information about student achievement, but the frequency of the test limits the
amount of feedback that teachers need to inform their instruction and develop interventions for
students. These tools are not effective instruments for school improvement based on the lack of
real time needed to provide guidance to teachers for curriculum or pedagogic adjustment (Baker,
2001).
The vast amount of time between when a high stakes summative assessment is
administered and when the results are available to teachers, narrows the effectiveness of the
results. Students have moved onto the next grade and teachers have new students by the time
results are available. The large amount of content on the test also limits the value of the results
to teachers. The test is so large that other forms of data are needed to yield results that reflect
student learning and provide guidance on strategies necessary for improvement. Many
educational policy researchers advocate for multiple forms of assessments in addition to annual
standards based assessments to inform instructional decisions (Baker et al., 2002).
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 36
Benchmark assessments provide data more frequently when compared to high stakes
standardized assessments. This type of assessment is mandated district wide and given typically
four to six times a year. The data gathered from this type of assessment provides teachers,
principals, and district teams with more detailed and timely information. They usually contain
less content and are related to a set number of learning standards.
A study conducted by Snipes, Doolittle, & Herligh (2002) highlighted the potential
benefits for teachers and principals when using benchmark assessments. Benchmark
assessments allow schools and districts to evaluate their allocation of resources to meet the needs
of students. Patterns and trends can be identified based on the data gathered from these
assessments to inform instruction in a timely manner. Data system tools can organize
benchmark assessment data and present it in a comprehensible manner for teachers, principals,
district, and sometimes parents to utilize.
Formative assessment data is often valued over other types of assessments by researchers
and educators. Black et al. (2004) underscore the importance of formative assessment when
compared to other types of assessments as a source of information for teaching and learning.
Data from formative assessment is both timely and relevant for impacting instruction. It is
standards based and related to instruction that is currently being taught in a classroom.
Although formative assessment tools are usually included in curriculum programs
available to teachers, the use of formative assessment data is not currently a widespread practice
among educators. Even with established research that supports the use of formative assessments
for school improvement, many believe that summative standards based assessments along with
benchmark assessments provide adequate data for school reform. In their landmark meta-
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 37
analysis of formative assessment, Black and William (1998) provided strong evidence that
formative assessments can raise student performance levels. The type of data that schools gather
helps them to make informed decision that lead to continuous school improvement. When
schools and districts gather multiple forms of data, they can make important decisions on
different topics that are related to school-wide reform (Datnow et al., 2007).
Building School Capacity for Data Driven Decision Making
There is a lack of research on professional development for teachers in the areas of data
analysis and the application of data for impacting instruction and student learning. There is
however, research available on capacity building DDDM professional development for teachers
and administrators. The level of skill with using data varies at every school site. An assumption
made in NCLB is that schools that use data will make improvement (Heritage & Chen, 2005).
Many educators are not comfortable with using data. They avoid using data to make
instructional decisions and view it as time consuming and neither a benefit to themselves or their
students because of their own lack of knowledge and skill. Teachers and administrators require
professional development on the best practices for using data and monitoring student progress.
Successful schools utilize data by working hard to support and empower educators on using data
to inform instruction (Datnow et al., 2007).
Professional development must be an ongoing process in the area of data driven decision
making at school sites. Teachers must be trained in both the data management systems and by
using data reports effectively. A data culture built on asking important questions of data, and
collaboration on best practices, creates improvement. When teachers and administrators at
schools work together to analyze data and monitor student achievement, continuous use of data
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 38
to drive decision making processes became more prevalent (Heritage & Chen, 2005). Thus the
more teachers were given an opportunity to analyze data as a collaborative team, the more they
realized the benefits of using data on a regular basis to inform decisions and make
improvements.
Professional development provided at the district level or school level must be relevant to
the work of teachers in their classrooms. There must be a connection to systems that they use,
their goals, and their student population. Data must be available that meets the needs of every
school and district as a whole in professional development. Using real world problems when
using data helps teachers to make connections and build relationships. Datnow et al., (2007)
described key points for building staff capacity with data driven decision making:
 Provide ongoing support when necessary for staff.
 Make a commitment in professional development for staff.
 Be prepared to support the different levels of skills teachers have with using data.
 Provide time for teacher collaboration that is specifically for data driven decision
making.
 Discuss data across all grade levels at the school to identify school-wide strengths and
weaknesses.
According to Schmoker (2003), teachers can easily be taught how to conduct analyses
that would provide them with the information they need to improve their teaching and student
achievement. Investing in professional development for staff is a wise investment for schools.
Building the capacity of staff on effective practices saves valuable time that can be used to
improve processes and close achievement gaps in student learning. DDDM professional
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 39
development empowers teachers and administrators with the skills and strategies that are
necessary to monitor effective practices that improve student achievement and learning.
Analyzing and Acting on Data to Improve Performance
NCLB does not mandate ways teachers should use data analyses to make modifications
in instruction and intervention for students. Although the actions that are taken after analysis of
student data are critical to school improvement, there is research on this aspect of the DDDM
process. Data analysis protocols can vary within a district and even within a school. Schools
that have been successful using the DDDM process develop a school-wide data protocol that
provides staff with a common framework and language for uthe data data. Datnow et al. (2007)
suggest that schools develop school-wide standard protocol templates for data discussions and
decision making that focus on trends in the following:
Strengths Weaknesses
Grade level trends Ethnic trends
Gender trends Language subgroup trends
These discussions must be followed by making plans. Grade level teams and/or school
level teams can collaborate on best practices to close achievement gaps. Teachers should bring
relevant data and student work samples to these discussions. Examining actual student work
samples allows teachers to reflect on their own practices and plan for future instruction based on
how students respond to instruction. William and Kirst (2006) identified four best practices that
are related to the DDDM process. These practices have been included in other research studies
and are associated by many educators with improvements in Academic Performance Index (API)
scores. The four practices are as follows:
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 40
1. Prioritize student achievement data.
2. Use a standards based curriculum.
3. Analyze student assessments.
4. Provide additional assistance to teachers as needed.
Using and analyzing student data must be a priority for schools to implement reform and
improvement. Time must be allocated and used wisely and productively with a focus around
different kinds of student assessments for teachers and administrators to evaluate the
effectiveness of instruction and guide improvements.
Summary of Literature Review
The importance of accountability measures and use of data to inform educational
practices and improve schools has gained national attention. There is more pressure on states,
districts, and schools to show improvements in student achievement more than ever before in our
nation’s history. Data Driven Decision Making (DDDM) has been accepted by many
educational institutions as an effective strategy that can be used by schools to make continuous
improvements. Unfortunately, the current accountability measures provided in NCLB (2001) do
not regulate the process schools must use to implement a data decision making process. DDDM
processes vary between districts and schools.
Although data is readily available at most schools, the skill level of teachers and the
quality of professional development to build staff capacity also varies between schools and
districts. Many states, districts, and schools are developing processes for making data driven
decisions as they go through the processes. Systems may not be in place for effective use but are
being created on the spot because schools and districts are determined to develop DDDM
processes, design professional development, analyze data, evaluate processes, and implement a
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 41
data based approach. Research indicates that many administrators and teachers are not prepared
to accomplish the task of utilizing data to make decisions for school improvement.
The lack of regulation in NCLB and in the literature on the use of data for decision
making at schools by teachers and administrators might be a contributing factor to the lack of
skill and knowledge on the effective use of data at school sites. Specifically, in the areas of data
analysis and actions that are taken after analysis. The examination of these two areas in this
study is important for school reform knowledge and may contribute to growth at schools that
might benefit from successful practices and processes used by the school observed in this study.
There was an abundance of research conducted on using data to inform and improve
practicesint schools in 2003 when the act was first implemented. There is a limited amount of
current research and literature to assist districts and schools in this important area.
Accountability measures continue to increase but achievement gaps continue to grow. Schools
and districts need more assistance for providing effective Data Driven Decision Making for
student growth and achievement.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 42
Chapter Three —Methodology
The purpose of this mixed methods study is to investigate the perceptions of teachers and
administrators on the use of a Data Driven Decision Making (DDDM) process to improve
student instruction. According to Schmoker (2003), most school districts that use a DDDM
process, can ensure continuous improvement by simply creating annual goals and creating a
focused plan for improving student assessment scores. This case study was conducted at a high
performing Title 1 elementary school in a large urban K-12 school district.
The focus of the study is on the analysis and evaluation practices used in a high
performing Title 1 elementary school. Schools meeting the accountability measurement
benchmarks for Academic Performance Index (API) under NCLB (2001) are considered high
performing and schools not meeting API are considered low performing.
This chapter focuses on the methodology that was used to conduct this study. Teachers
and administrators at George Washington Carver Elementary in the Stevens Unified School
District were surveyed and interviewed to provide information on how data is used and perceived
at the school. This study essentially attemptsattemptS to address the following guiding research
questions:
1. Is a Data Driven Decision Making (DDDM) process used by teachers and
administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
1. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 43
Research Methods
This case study is a mixed methods research study that utilizes quantitative and
qualitative research design methods. A case study design was ideal for this study because it
allowed for an in-depth and profuse examination of the DDDM process used at George
Washington Carver Elementary. Qualitative research design methods were also used to provide
insight and understanding into the Data Driven Decision Making (DDDM) process used in one
school. Teachers and administrators were interviewed on the way data is used to inform
instruction at the school site. According to Merriam (1998), the case study design offers a means
of studying complex social units that consist of multiple variables of importance in
understanding a phenomenon.
Sample and Population
Multiple measures were used to find an elementary school that met the characteristics of
this case study and had an administrator and teachers who were willing to participate. Title 1
High Performing Elementary schools were researched using the California Department of
Education website. Schools that met these two characteristics and were located in a southern
California Urban school district were identified. District and site administrators were contacted
by phone about the possibility of participating in this study. The necessary characteristics for the
case study school were articulated to the district and school site administrators. These
characteristics include the following:
1. The school must be located in and urban southern California school district with a
diverse student population.
2. The school must be both high performing and Title 1.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 44
3. The school must have a teaching population that is utilizing student data on a
consistent basis to inform their instructional decisions.
4. The school must have ten to twenty teachers out of the total teaching population that
are willing to participate in this case study.
George Washington Carver Elementary was selected for this case study because it met all
of the four qualifications necessary for the study. Initial discussions with the school principal
determined that the school used data on a consistent basis to inform their instructional decisions.
Verbal and written approval was provided by the school principal before the study began.
Verbal and written approval was also provided by the school district and each teacher that
participated in this case study.
George Washington Carver Elementary is a K-5 elementary school that is located in a
large urban school district in southern California. The school Ethnic demographics are 4%
African American, 3% Asian, 6% Filipino, 84% Hispanic, and 2% White. Other demographics
as provided by the California Department of Education website showed that one hundred percent
of the student population received reduced or free lunch services. 55% of the student population
are English learners, 12% are students with disabilities, and 21% are reclassified as fluent
English proficient (RFEP). There were seven hundred and forty one students enrolled in the
school at the time of this study.
One hundred percent of the teaching staff at George Washington Carver Elementary is
fully credentialed. The teaching staff is composed of many veteran teachers with years of
experience ranging from 10 to 30 years. Most teachers had only taught at the school and had no
full-time teaching experience at another school site. The principal of the school has several years
of experience as an educator. She had twenty one years of classroom teaching experience and
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 45
ten years as a principal. She has served on several national and state education advisory boards
and councils.
The school utilizes data to make all instructional decisions on a consistent basis (every 1-
2 weeks) and has shown consistent growth over several years as determined by the state
Adequate Yearly Progress (AYP) and Academic Performance Index (API) indicators that are
based on the results of the state standardized assessment test. The school has met AYP for all
subgroups and has increased in API over the last three years. The API for the 2010-2011 school-
year was 862. This was an increase of 51 percent points from the 2009-2010 school-year from a
Base API of 811 as reported on the California Department of Education website. The 2008-2009
shows an API Base of 781, which the school increased by thirty percentage points the following
year. The principal and staff at the school attribute the growth in API to the school-wide use of
student data to improve instructional practices and inform instructional decisions.
Data Collection Procedures
Qualitative data were utilized in this case study through interviews with volunteer
teachers from the staff and the principal. The Interview Guide Approach as outlined in Patton
(2002) was used in this study. General questions and topic areas were outlined by the primary
investigator before the interview process began for participants but the exact wording and order
of the questions asked during the interviews were open and depended on each participant. This
allowed for focused, systematic, and comprehensive responses from participants.
Four types of questions from Patton (2002) were asked in the interviews:
 Experience and/or behavior,
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 46
 Opinions and/or values;
 Knowledge
 Background and/or demographic
Descriptions of the four question types are as follows:
1. Experience and/or behavior types—questions that ask participants about their
current and past behaviors and experiences.
2. Opinion and/or values types—questions that ask participants about their
personal opinions and values.
3. Knowledge types—questions that ask participants about fact based
information.
4. Background and/or demographic types—questions that ask participants about
their demographic information like age, education, job, etc.
Quantitative research methods were also utilized in this research case study to provide an
in depth examination of statistical data. Quantitative data provided numerical statistics from a
survey instrument that allowed the findings from this study to be unbiased and generalized
towards a larger population. Teachers and administrators were surveyed on their perceptions of
the Data Driven Decision Making (DDDM) process used at George Washington Carver
Elementary. Both descriptive and analytic statistics were used in this study. All survey and
interview instruments were created to answer the three research questions that were examined in
this case study.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 47
Four teachers and the school principal were interviewed for this study. The duration of
each interview was approximately 30 minutes in time. The participants were asked thirty-five
questions. The interviews were recorded after gaining written and verbal permission from each
participant. Notes were also kept from each interview. The notes and recordings from the
interviews were transcribed. Teacher volunteers were recruited at a staff meeting where a
description of the study was presented. A sign-up sheet was circulated at the meeting and
interested teachers were asked to sign. Twenty teachers from the staff were recruited to
participate in a written survey at the same staff meeting. The duration of the survey was
approximately fifteen to twenty minutes for each teacher to complete. The interviews took place
on the school campus at a time that was convenient for each participant. The teacher volunteers
were provided a survey containing thirty eight questions based on the three research questions
and focused on the purpose of the study:
1. Is a Data Driven Decision Making Process (DDDM) process used by teachers and
administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
3. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
The survey instrument was administered to each individual participant and returned to the
primary investigator at the school site after a staff meeting. The interview protocol for teachers
and principal can be found in Appendices A and B. Survey Protocol for teachers and the
principal can be found in Appendices C and D.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 48
Data Analysis
The analysis of the data generated from this case study began with the examination of the
transcribed interviews from the principal and volunteer teachers. Data spreadsheets and graphs
were created using Microsoft Word and Microsoft Excel programs. Themes and patterns were
examined from the interview data based on each interview question. The teacher data was
compared to the principal data to examine similarities and differences in the findings. According
to Creswell (2005), there is no single way to analyze qualitative data. He suggests that it is an
eclectic process that you can only try to make sense of (Creswell, 2005). The survey data was
also examined to determine overall themes and patterns. Graphs and data spreadsheets were
created using Microsoft Excel and Microsoft Word programs based on each survey question.
Principal and teacher survey data was then examined to determine similarities and differences.
Summary
The methods of study were detailed in this chapter. The research methods used in this
case study are qualitative and quantitative. The sample population of the school consists of the
principal and 20 teachers from George Washington Carver Elementary. This school met all of
the characteristics necessary for this case study. The main characteristics were high performing
and Title 1 School with consistent use of the DDDM process at the school. The school also
showed academic growth over a period of several years. Survey and interview instruments that
were based on the research questions were used in this study to collect data. The data was
collected and structured using Microsoft Word and Microsoft Excel programs. The data was
then analyzed to determine patterns, themes, similarities, and differences in the findings. These
patterns, themes, similarities, and differences will be reported in the following chapter.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 49
Chapter Four —Results
Purpose of the Study
Schools today have the option of using different Data Driven Decision Making (DDDM)
models that are available to educators today. The research is very limited on how districts,
schools, and teachers build a foundation for data based decision making, establish a data based
decision making culture, organize data using data systems, build school capacity for using data,
and most critically, what is done after the data has been analyzed.
Many Title 1 schools and non-Title 1 schools in large urban school districts lack the
capacity to analyze data and use it to drive instruction. When teachers and administrators at the
school level become knowledgeable about the importance of data and its use to improve student
learning, they can review their practice, identify weaknesses, and make plans for improvement
(Earl & Katz, 2006). The important decisions that are made after data has been analyzed are
critical to success. When schools do not effectively analyze data in a timely manner, valuable
time is lost in student intervention and learning opportunity.
Critical decisions about curriculum, class environment, instruction, and the student can be
made with data to plan and build an infrastructure that effectively supports all students and
builds teacher capacity and pedagogy. This study investigates how teachers perceive a DDDM
process at an elementary school when identifying possible problems, analyzing problems,
designing and modifying instruction, and consistently evaluating student learning in in effort to
provide knowledge to the field of education on how data is perceived and utilized to inform
instruction and planning.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 50
Research Questions
Current research on data use has been limited to data that focuses on summative
assessments for accountability purposes. The purpose of this study is to examine staff
perceptions of the Data Driven Decision Making (DDDM) process used at a Title 1 urban
elementary school to close the achievement gaps and improve learning. Essentially this study
attempts to address the following questions:
1. Is a Data Driven Decision Making Process (DDDM) process used by teachers and
administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
3. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
Sample and Population
Multiple measures were used to find an elementary school that met the characteristics of
this case study and had an administrator and teachers who were willing to participate. Title 1
High Performing Elementary schools were researched using the California Department of
Education website. Schools that met these two characteristics and were located in a southern
California Urban school district were identified. District and site administrators were contacted
by phone about the possibility of participating in this study. The necessary characteristics for the
case study school were articulated to the district and school site administrators. These
characteristics include the following:
1. The school must be located in an urban southern California school district with a
diverse student population.
2. The school must be both high performing and Title 1.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 51
3. The school must have a teaching population that utilizing student data on a consistent
basis to inform their instructional decisions.
4. The school must have ten to twenty teachers out of the total teaching population that
are willing to participate in this case study.
George Washington Carver Elementary was selected for this case study because it met all
of the four qualifications necessary for the study. Initial discussions with the school principal
determined that the school used data on a consistent basis to inform their instructional decisions.
Verbal and written approval was provided by the school principal before the study began.
Verbal and written approval was also provided by the school district and each teacher that
participated in this case study.
George Washington Carver Elementary is a K-5 elementary school that is located in a
large urban school district in southern California. The school Ethnic demographics are 4%
African American, 3% Asian, 6% Filipino, 84% Hispanic, and 2% White. Other demographics
as provided by the California Department of Education website showed that one hundred percent
of the student population received reduced or free lunch services. 55% of the student population
are English Learners, 12% Students with Disabilities, and 21% are Reclassified as Fluent English
Proficient (RFEP). There were seven hundred and forty one students enrolled in the school at
the time of this study.
One hundred percent of the teaching staff at George Washington Carver Elementary is
fully credentialed. The teaching staff is composed of many veteran teachers with years of
experience ranging from 10 to 30 years. Most teachers had only taught at the school and had no
full-time teaching experience at another school site. The principal of the school has several years
of experience as an educator. She had twenty one years of classroom teaching experience and
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 52
ten years as a principal. She has served on several national and state education advisory boards
and councils.
The school utilizes data to make all instructional decisions on a consistent basis (every 1-
2 weeks) and has shown consistent growth over several years as determined by the state
Adequate Yearly Progress (AYP) and Academic Performance Index (API) indicators that are
based on the results of the state standardized assessment test. The school has met AYP for all
subgroups and has increased in API over the last three years. The API for the 2010-2011 school-
year was 862. This was an increase of 51 percent points from the 2009-2010 school-year from a
Base API of 811 as reported on the California Department of Education website. The 2008-2009
shows an API Base of 781, which the school increased by thirty percentage points the following
year. The principal and staff at the school attribute the growth in API to the school-wide use of
student data to improve instructional practices and inform instructional decisions.
Data Collection Procedures
Qualitative data were utilized in this case study through interviews with volunteer
teachers from the staff and the principal. The Interview Guide Approach as outlined in Patton
(2002) was used in this study. General questions and topic areas were outlined by the primary
investigator before the interview process began for participants but, the exact wording and order
of the questions asked during the interviews were open and depended on each participant. This
allowed for focused, systematic, and comprehensive responses from participants.
Four different types of questions from the six outlined in Patton (2002) were asked in the
interviews; Experience/behavior, opinion/values; knowledge; and background/demographic
questions were asked in this study. Descriptions of the four question types are as follows:
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 53
 Experience/behavior- are types of questions that ask participants about their
current and past behaviors and experiences.
 Opinion/values- are types of questions that ask participants about their personal
opinions and values.
 Knowledge-are types of questions that ask participants about fact based
information.
 Background/demographic- are questions that ask participants about their
demographic information like age, education, job, etc.
Quantitative research methods were utilized in this research case study to provide an in
depth examination of statistical data. Quantitative data provided numerical statistics from a
survey instrument that allowed the findings from this study to be unbiased and generalized
towards a larger population. Teachers and administrators were surveyed on their perceptions of
the Data Driven Decision Making (DDDM) process used at George Washington Carver
Elementary. Both descriptive and analytic statistics were used in this study. All survey and
interview instruments were created to answer the three research questions that were examined in
this case study.
Four teachers and the school principal were interviewed for this study. The duration of
each individual interview was approximately 30 minutes in time. The interviews were taped
with written and verbal permission from each participant. The participants were asked a series of
thirty-five questions. Notes were also kept from each interview. The notes and recordings from
the interviews were transcribed. Teacher volunteers were recruited at a staff meeting where a
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 54
description of the study was presented. A sign-up sheet was circulated at the meeting and all
interested teachers were asked to sign. Twenty teachers from the staff were recruited to
participate in a written survey at the same staff meeting. The duration of the survey was
approximately fifteen to twenty minutes for each individual teacher to complete. The interviews
took place on the school campus at a time that was convenient for each individual participant.
The teacher volunteers were provided a survey containing seventeen questions that were based
on the three research questions and focused on the purpose of the study:
1. Is a Data Driven Decision Making Process (DDDM) process used by teachers
and administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
3. Is professional development provided to support teachers in their efforts to
effectively utilize data to inform their instruction?
The survey instrument was administered to each individual participant and returned to the
primary investigator at the school site after a staff meeting. Interview Protocol for teachers and
the principal can be found in Appendices A and B. Survey Protocol for teachers and the
principal can be found in Appendices C and D.
Analysis of Findings
This chapter provides an analysis of the data from this study on the staff perceptions of a
Data Driven Decision Making (DDDM) Process used at a High Performing Title One Urban
Elementary School. Many interesting findings about the school staff perceptions on the use of
data and their applications to instruction were found and are examined in this study.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 55
The survey and interview questions are compiled into sections in this chapter for analysis
purposes. The question types were randomly distributed throughout the survey and interview
questions. Each question included a 5-point Likert Scale: “Strongly Agree,” “Agree,” “Neutral,”
“Disagree,” and “Strongly Disagree.” For purposes of analysis, the responses “Strongly Agree”
and “Agree” were considered together as agree and “Strongly Disagree” and “Disagree” were
considered together as disagree. The response “Neutral” was included as a choice for
participants who did not have experience with the particular behavior asked in the question.
The sample size (N=20) represented more than 67% of the staff at George Washington
Carver Elementary school. The sample size was determined by voluntary participation of all
participants. All 30 teachers were given an opportunity to participate in the study but 20 agreed
to volunteer for the study. The small population sample size and the short term duration of this
study are recognized by the researcher as study limitations. The researcher would not
recommend that the results of this study be used to generalize teacher’s perceptions about a Data
Driven Decision Making (DDDM) process at an elementary school. Further study would be
necessary to provide reliability and validity of the results found in this study.
The findings of this study will be presented under three main categories that attempt to
answer the research questions in this study that are listed below:
Research Questions
1. Is a Data Driven Decision Making (DDDM) process used by teachers and administrators
when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 56
3. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
Research Question #1: “Is a Data Driven Decision Making (DDDM) process used by
teachers and administrators when analyzing student data?
When participating teachers in this study were asked if a Data Driven Decision Making
(DDDM) process was used at their school by both teachers and administrators when analyzing
student data, all participants agreed. 19 chose “Strongly Agree” to the question and 2 chose
“Agree.” The principal strongly agreed with this question as well and is included in the results.
Figure 1 shows the positive responses to the question.

Figure 1—Survey results of whether a DDDM is used at my school.

Figure 2 shows a positive agreement among participants when asked the question-
Different types of data are used at my school. 17 chose “Strongly Agree” and 4 chose “Agree.”
0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
A Data Driven Decision Making
process is used at my school.
A Data Driven Decision
Making process is used at
my school.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 57
Teacher #1 gave the following response in an interview when asked if a Data Driven Decision
Making process is used at the school:
We use both formal and informal assessments. The data is analyzed at the least every six
weeks. We sit down with the principal and we go over the data results. Then we look at how we
can use it. What the student is lacking and what skills are needed are outlined. We look at the
next unit and see exactly if the skills fit in and what intervention we can do during independent
student work time.

Figure 2—Survey results of whether different types of data used at my school.

Although the school has made significant gains over the last few years as outlined earlier
in this study, there were fewer “Strongly Agree” responses chosen for this question as compared
to the first two. Figure #3 shows that 16 participants chose “Strongly Agree” and 5 chose
“Agree” to the question- Instructional gains have been made at my school using a Data Driven
Decision making process. The participants who were interviewed were all in strong agreement
0
2
4
6
8
10
12
14
16
18
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Different types of data are used at
my school.
Different types of data are
used at my school.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 58
about the importance of the use of data and the role played in the gains made at the school. The
interview responses to the following related question (How instrumental has DDM been to
improve your school’s performance and your practice?) are as follows:
Principal: As I have said before, there is no other way. It is that simple. Data must be
used to inform our practice.
Teacher #1: The use of data has been the most instrumental thing that we have done to
improve as a school. Nothing has been more important.
Teacher #2: The use of data has been huge. How can anyone not use it as a tool?
Anything that we can get that will help us improve our work and our students is a good thing. I
don’t know, yeah, I cannot imagine if we did not have it. The way they have it broken down for
us to use is so helpful. It would take forever if we were to try to put something like that together.
Teacher #3: It has been 100 percent effective. All credit goes to using data. It changed
our practice.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 59
Teacher #4: It’s been pretty massive. I would say a huge influence.

Figure 3— Survey results of whether instructional gains have been made at my school.

Most teachers and the school administrator strongly agreed that data is shared with
students at the school as illustrated in Figure 4. Many viewed this process as one of the most
important factors in the Data Driven Decision making (DDDM) process that is used at the school
site.
Administrator: Our students know how they scored on the CST. Our students know what
standards they need to work on. We believe in telling them what the goal is so that they can do
what they can to help us get there. We all have goals including weight loss, football games, etc.
Sometimes people keep goals a secret. But with children they need to know the goals.
Teacher #3: We have what we call a Data Chat with students. We discuss where they are
at and what their weaknesses are. We use to just do it for CST but now we do it for everything.
We discuss what their weaknesses are and what they need to work on. We do it every six weeks
0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Instructional gains have been made
with using a Data Decision Making
Process at my school
Instructional gains have
been made with using a
Data Decision Making
Process at my school
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 60
to see if they are meeting their goals. I don’t know if all grade levels do it. We pull assessments
before each grading period and we sit down with them and go over what they need.

Figure 4—Survey results of whether data is shared with students.

According to Datnow et al. 2007, the establishment of a culture of data is a critical
component of a systems effort for improvement. Building a culture that values the consistent use
of data on a regular basis is an essential for student success. Figure 5 shows that all participants
agreed that a strong Data Driven Decision making culture exists at the school. Research has
established that creating and maintaining a culture of data use remains an ongoing process.
0
2
4
6
8
10
12
14
16
18
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Data is shared with students
Student data is shared at
my school
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 61

Figure 5—Survey results on the existence of a strong DDDM culture.

Research Question #2: Are teachers utilizing data to inform their instruction?
Participants agreed that both questions were centeredon theedataa being used to inform
instruction at the school. Figure 6 depicts the response to the question: Actions are taken
school-wide after data has been analyzed to improve instruction.

0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
My school has a strong Data Driven
Decision Making culture
My school has a strong
Data Driven Decision
Making culture
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 62
Figure 6 shows a response of 16 strongly agree, 4 agree, and 1 neutral.

Figure 6—Survey results on whether actions are taken after data analysis.
Figure 7 had a higher positive rate of 14 strongly agree and 7 agree. According to the
administrator and teachers that were interviewed, data is used on an ongoing basis to inform
instruction.

Figure 7—Response to the question: Data is used to inform instruction.
0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Actions are taken school-wide after
data has been analyzed to improve
instruction
Actions are taken school-
wide after data has been
analyzed to improve
instruction
0
2
4
6
8
10
12
14
16
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Data is used to inform instruction
Data is used to inform
instruction
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 63
Teacher #2: We look at data as a grade level team and make decisions together. We
come back later to discuss if it worked and if we should try something different. We assess all
the time to validate data. Over time a picture is built that tells the truth.
Participants agreed that teachers understood how to effectively use data at their school
site as shown in Figure 8. Teachers sited trainings, professional developments, school
leadership, and Program Improvement status as motivators for teachers to become proficient
with the use of data.

Figure 8—Survey results on whether teachers understand how to use DDDM data.
All participants agreed that benchmarks for student performance are established at the
school. Some teachers cited the California Standardized Test (CST) as a benchmark that is used.
The principal and other teachers stated that each grade level establishes benchmarks over time-
depending on the class and grade level.

0
2
4
6
8
10
12
14
16
18
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Teachers understand how to
effectively use data at my school
Teachers understand how
to effectively use data at
my school
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 64
Figure 9 shows the positive response to the question about the establishment of
benchmarks for student performance. There were 18 strongly agree responses and 3 agree
responses as shown in Figure 9.

Figure 9—Whether student performance benchmarks have been established.
The monitoring of student progress is a critical component of any Data Driven Decision
Making (DDDM) process. Student progress needs to be monitored in order for teachers and
administrators to evaluate if instruction and interventions are successfully meeting the needs of
individual students. If student progress is not monitored, schools risk the chance of students who
are not succeeding falling further behind. When student progress is effectively monitored,
teachers and administrators have the ability to further modify instruction and interventions to
meet the needs of students for academic success. Students should be monitored on a regular
basis to ensure that achievement gaps are closing and student targets are being reached at an
adequate rate.
Figure 10 illustrates that all participants agreed that student progress is monitored on a
regular basis at the school. The school uses both teacher created assessments and district
0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
My grade level has established
benchmarks for student performance
My grade level has
established benchmarks
for student performance
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 65
periodic assessments that are given three times a year to progress monitor students. Teacher
created assessment are provided to students on a weekly basis or every two weeks depending on
the teachers informal assessment of students in their classrooms. Students who are not
succeeding are assessed more frequently than students who are meeting benchmarks.
Teachers meet on a regular basis to discuss progress monitoring as a grade level. During
grade level and staff meetings, teachers have an opportunity to discuss best practices and share
assessments that are used for progress monitoring. Teachers review data provided from progress
monitoring assessments to make important decisions about instruction and interventions that are
needed at the school. Grade level teams make a decision on the use of a strategy that is provided
to students receiving intervention on a concept or curricular area. Grade level teams apply the
strategy then reconvene to discuss successes and next steps when necessary. Progress
monitoring is a continuous practice and essential to a successful instructional program.

Figure 10—Survey results on monitoring student progress.
Figure 11 illustrates the responses to the question: My opinion is more important than
data when examining student performance. This question received the most diversity in
0
5
10
15
20
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
I monitor student progress on a
regular basis
I monitor student
progress on a regular
basis
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 66
responses. Two strongly agreed, two agreed, twelve were neutral, and five disagreed. Teachers
often believe that their opinion is greater than data when analyzing data.

Figure 11—Survey results regarding the value of teacher opinion vs. data.

Research Question #3: What type of professional development is provided to support
teachers in their efforts to effectively utilize data to inform their instruction? Participants agreed
that staff meets on a regular basis to both review and analyze data. Establishing time for teachers
and administrators to meet regularly to review and analyze data is critical to the Data Driven
Decision Making (DDDM) process. Teachers and administrators should have an established
protocol when reviewing and analyzing data.
Principal: Teachers meet weekly in grade level meetings and in staff meetings. I would
say we look at data every time. It is not always about numbers. We tie things to data. I learned
to state more clearly the connections. We look at quantitative data one way and qualitative data
another way. You know another thing we do is diagnostic data. We really try to get down to the
bottom of what is the problem. That may be a combination of multiple sources of data. I would
0
2
4
6
8
10
12
14
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
My opinion is more important than
data when examining student
performance
My opinion is more
important than data when
examining student
performance
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 67
say that one point of data is not the end. Data tells you what you need to look for. Figure 12 and
Figure 13 illustrate the responses to both questions: Staff has time to meet and review data
regularly and Staff meets to analyze data regularly.

Figure 12—Survey results on whether staff has time to review data.
The second thing is that you really do need multiple sources of data. Think of a child
who cannot read well are they an English language learner or are they not an English language
learner? If they are making significant progress then what are the problem areas. I really have a
problem when people take a look at one piece of data and think they know what the problem is.
You need multiple sources. I am never surprised by my data.
0
2
4
6
8
10
12
14
16
18
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Staff has time to meet and review
data regularly
Staff has time to meet
and review data regularly
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 68

Figure 13—Survey results on whether staff has time to analyze data.
Professional development is a key component for building teacher and administrative
capacity in the effective use of data. Figure 14 shows that 11 participants agreed with the
question that asked if teachers are supported through professional development at the school site.
Seven participants agreed with the question and three were neutral. Many participants stated that
when the school first began to focus on using data to inform instruction, professional
development was provided to staff on a regular ongoing basis over a period of two years. The
amount of professional development centered on the use of data has been limited over the last
two years. Some participants believed that the limited amount of professional development for
the use of data has been limited because the staff is composed of veteran teachers who received
the initial training.
Teacher # 4: In years past when we were coming out of Program Improvement and then
becoming a Title 1 school we talked about data every week. We went to Response to Instruction
training that was provided by the district and we held several meetings at our school. Now that
we have been successful and have more experience, we have more freedom.
0
2
4
6
8
10
12
14
16
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Staff has time to meet and analyze
data regularly
Staff has time to meet
and analyze data regularly
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 69
Teacher #1: We had a lot of professional development in the past. We have not had any
on the use of data in the last 3-5 years because there are no new people and we are all
comfortable with using data.
Teacher # 2: We use to train on using data like more so when I first started. A few years
ago we were doing it all of the time then it started trickling down and now it is more like a grade
level thing. So, yeah, we don’t really do too much anymore as a group with the data. It is now
like guys you know what you are doing so we now collaborate as a grade level using data.

Figure 14—Whether teachers are supported through professional development.
Table 1 below shows the total frequency of responses from the survey questionnaire.
Survey responses in the first research question category-(Is a Data Driven Decision Making
(DDDM) process used by teachers when analyzing student data?) were positive, out of a total of
10 questions in the category with a possible 200 points for each item there were 182 strongly
agree responses and 18 neutral. Survey responses to the second research question (Are teachers
utilizing data to inform instruction?) had a more dispersed response pattern. There were 231
strongly agree, 29 agree, 15 neutral, and 5 disagree responses out of a possible 280 points for
0
2
4
6
8
10
12
Strongly
Agree
Agree Neutral Disagree Strongly
Disagree
Teachers are supported through
professional development at my
school with using data effectively
Teachers are supported
through professional
development at my
school with using data
effectively
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 70
each item in this category of questions. Finally, question number three had a total of 12
questions with a possible 240 points for each response in this category. There were 201 strongly
agree, 35 agree, and 4 neutral responses in this question category.
Table 1—Frequency Table for All Survey Responses
Category by Research
Question
Total
Questions
in
Category

Strongly
Agree

Agree

Neutral

Disagree

Strongly
Disagree
1. Is a DDDM process
used by teachers when
analyzing student data?
10 182 18
2. Are teachers utilizing
data to inform
instruction?
14 231 29 15 5

3. Is professional
development provided
to support teachers in
their efforts to
effectively utilize data
to inform instruction?
12 201 35 4

The findings in this study were overwhelmingly positive and showed that teachers and
administrators at the school used in this study believe that a Data Driven Decision Making
(DDDM) process is being used at the school. The responses were 85% strongly agree. This
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 71
percentage was calculated using the following figures: 182+ 231+ 201=614 strongly agree
totals. 614 divided by total possible scores of 720 equals 85%). The agree response was 11%
based on 18 + 29+ 35=82 then dividing 82 by total score of 720 to get 0.11. The neutral total of
2%was based on the responses of 15 +4= 19. Then divide 19 by the total score 0f 720 to get .02.
Finally, the disagree total percentage of .7% is based on the total neutral response of 5 for all
categories to get 5 divided by 720 equals 0.007. The discrepancy between research question
numbers one and three from that of question number two can be related to the fact that
perceiving that a Data Driven Decision Making (DDDM) process is used at your school is
different from being the actual process being used on a regular basis.
The principal and teachers at the school were not aware of the school district policy on
the effective use of data when asked in an interview. The school established its own policy for
the use of data. The school principal said, “Schools that become more accountable themselves
are more accountable than any external accountability measure.” This accountability has to be a
team goal.ntability has to be a team goal. Everyone must feel accountable and believe in the
goal of school improvement. Building a team vision is not an easy goal. The principal took a
strong leadership when establishing this culture. She stated in the interview:
It took a lot of training and modeling in the beginning but I made it very clear to my staff
that there was no other way. This was going to be our path. Some teachers left because it is not
what they wanted to do. I am glad that they were honest and left. We became a Program
Improvement (PI) school in 2005 and that is where we began to make changes. We knew we
had to do things differently. We wanted different results and we were willing to do things
differently to improve. It took about three years to get to the place we are now where the use of
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 72
data is a habit for informing instruction. We had to get there as a team. We argued it out and
learned together.
Many schools use data in our current culture of accountability in education due to the No
Child Left Behind Act and other accountability measures. Building a school culture centered on
using a Data Driven Decision Making (DDDM) process to inform instruction could be seen as
the most valuable act of a successful principal establishing a culture of change and improvement.
The principal and teachers that were interviewed all stated that it too a long time to establish a
Data Driven Decision Making culture at the school. Strong leadership and teachers with strong
accountability for improvement are necessary in a system centered on change and improvement.
Summary
This chapter presented data generated from both a survey and interviews with teachers
and an administrator at George Washington Carver Elementary.. The sample size of this study
(N=20) represented more than 67% of the staff at George Washington Carver Elementary school.
The sample size was determined by voluntary participation of all participants. All teachers were
given an opportunity to participate in the study but 20 out of 30 agreed to volunteer for the study.
The small population sample size and the short term duration of this study are recognized by the
researcher as study limitations. The researcher would not recommend that the results of this case
study be used to generalize staff perceptions about a Data Driven Decision Making (DDDM)
process at an elementary school. Further study would be necessary to provide reliability and
validity of the results found in this study.
This study revealed that the participating staff at the elementary school used in this study
are using a Data Driven Decision Making (DDDM) process at the school site and perceive it as
being instrumental to the improvements and increases in student achievement that have occurred
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 73
over the last five years. These improvements include increases in the school’s Academic
Performance Indicators (API) rate, Adequate Yearly Progress (AYP) rate, and removal from
Program Improvement (PI) status. The school now has an API over 800 and is no longer a
program improvement school. Many participants credit the change in school culture to an
increased focus on the use of student data to inform instruction.
Significant Findings and Trends
The following key components were deemed important by the staff and perceived to be
available at the school. These components were communicated to the researcher through the
interview process and the survey questions used in this study.
 Strong leadership vision
 Professional development provided for teachers and staff
 Shared responsibility by all staff
 Collaborative processes among teachers and administrators
 Continued use of the Data Driven Decision Making (DDDM) process
 Process modeling
 Some staff who did not share the vision, left the school
 A culture of improvement
 Empowered teachers
 Time provided for teachers to collaborate using data
The responses for all categories based on each research question were 88% strongly agree,
18% agree, 2% neutral, and .7% disagree. These statistics may be a result of the fact that having a
Data Driven Decision Making (DDDM) process at the school and valuing it does not necessarily
mean it is used on a regular basis. The findings in this chapter will be discussed and examined further
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 74
in the next chapter. The final chapter, Chapter 5 will include a summary of the findings, limitations of
the study, implications for practice, possible future research that is needed as a result of the findings of
this study, and a conclusion.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 75
Chapter Five —Summary Conclusions and Implications
Recent Federal and state accountability policies focused on increasing student academic
achievement in public schools with an increase in school accountability. The No Child Left Behind
Act 2001, holds states, districts and school accountable student achievement and performance as
measured by annual assessment results from a state standardized assessment. Educators are utilizing
data for school improvement because school effectiveness is being measured by performance
indicators for the state annual standardized assessment. Schools are able to learn more about the
effectiveness of their practices and identify areas of strength and weakness in order to make
improvements.
Schools and districts have a large amount of data available to them in today’s performance
driven educational environment, but many do not have a clear understanding of how to effectively
build a Data Driven Decision Making (DDDM) culture at the school or how to effectively use data to
make instructional decisions to increase student achievement. The purpose of this study was to
examine the perceptions of staff at one school site about the use of a Data Driven Decision Making
(DDDM) process and its benefit towards student outcomes.
Teachers and an administrator were interviewed for this study about their perceptions of the
Data Driven Decision Making (DDDM) process that is used at the school. A survey was also
completed by teachers and an administrator at the school site used in this study. The survey and
interview questions were developed to answer the three research questions that guided this case study.
The research questions are as follows:
1. Is a Data Driven Decision Making Process (DDDM) process used by teachers and
administrators when analyzing student data?
2. Are teachers utilizing data to inform their instruction?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 76
3. Is professional development provided to support teachers in their efforts to effectively
utilize data to inform their instruction?
Connections to PriorResearch
There were many findings generated after the analysis of data from this study about the staff
perceptions of the use of a Data Driven Decision Making (DDDM) process at George Washington
Carver Elementary school. Datnow et al., (2007) found five characteristics present at school that
successfully use data to inform instruction. These five characteristics are as follows:
1. Build a foundation for the DDDM process by setting goals. by setting goals.
2. Invest in a data management system.
3. Select the right data.
4. Provide ongoing professional development.
5. Analyze and act on data.
One finding showed that strong leadership was perceived to be a factor in building a
successful school culture that embraced the use of data. According to Datnow et al (2007), it is
essential for a leader to establish a shared goal and clearly communicate these goals to all
stakeholders. The administrator in this study had encouraged staff to leave the school who did not
share the vision of using data to make instructional decisions. Staff left who did not share the belief
that a DDDM process was necessary for change and improvement. She provided time for staff to
train on the use of data, analyze data, and collaborate as a team using data to develop best practices for
student achievement and success. She modeled the use of data by holding “data chats” with each
teacher individually where they looked at class data; discuss interventions for students not succeeding,
and next steps that were necessary for moving forward.
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 77
A second finding was the provision of constructive professional development on the use of
data for staff. Datnow et al. (2007), suggest that providing professional development to staff to build
their capacity with the use of data is essential for staff to develop knowledge that is necessary to
improve practice. All participants that were interviewed and surveyed for this study agreed that
professional development had been ongoing and continuous for the first two years that the school
decided to use a DDDM process to make instructional decisions. Many teachers did state that the
professional developments were no longer geared towards developing capacity with the effective use
of data.
Some teachers along with the principal viewed this as a matter of trust. The principal and
some staff members felt that they had received adequate training on the use of data and they felt very
comfortable using the data management system purchased by the district to organize school wide and
district level data. Some teachers felt that they needed additional training with the use of the district
data management system because it had changed and improved over time adding new features that
they had not received training on in previous years.
A third finding was the important role of changing the school culture from a system that relied
on teacher opinion and not necessarily data to make instructional decisions. The principal sets the
tone early on and sent the message to all stakeholders the important role that data would play in
making decisions at the school. She was strongly influenced by the performance indicators set in
place by the state to meet the No Child Left Behind accountability markers. The school had an initial
goal of getting out of Program Improvement status. This was a motivating goal that allowed the staff
to come together as a team to make necessary changes towards improving the instruction at the
school. The staff continues to use data to make instructional decisions because they have been
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 78
successful with making huge gains in their Academic Performance Indicator (API) as measured by
student results on the yearly standardized assessment.
These findings illustrate the success that George Washington Carver Elementary has made in
their use of data as a decision making tool to improve instruction, culture, and practice at the school.
Teachers and administrators have been able to consistently improve student results on the state
standardized assessment each school year for the past five years. The school has been able to leave
Program Improvement (PI) status and become a High Performing Title I elementary school. The
school has been out of Program Improvement (PI) for the last five years, not including the required
two years of holding before leaving PI status. George Washington Carver is a model school on the
use of data for its large urban district.
Limitations of the Study
A limitation of this case study is the small sample group size. This investigation case study
design limits the transferability of results to other school sites. Factors that are specific to the school
used in this study might make the application of the findings in this study to other school sites
difficult. The school’s factors can include teacher retention rates, staff professional development,
district support, student demographics, administrator retention rates, funding, and support systems.
Additional factors include administrator and teacher faculty knowledge with analyzing data and using
data systems.
The short duration of this study can be considered an additional limitation. This study was
conducted using a survey instrument and interviews which provide a relatively small “snapshot” of
how data is used at the school site to inform teacher practice and improve student learning. The
practice of using data to inform instruction is an ongoing process. Steps for effectively utilizing data
include: collecting and analyzing data, then developing or modifying instruction based on data, and
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 79
finally, evaluating student data over a period of time to see if student learning gaps are closing. The
short duration of this study did not reflect how the school used data to inform instruction over an
extended period of time.
The data may not reflect a representative sample of the teacher population at the school
because not all teachers participated in this study. Participation was on a voluntary basis only. There
was only one administrator participating in the study because there was only one administrator
employed at the school full time. A contrastive analysis was not made between teacher data and
administrative data because only one administrator participated in the study. The administrator survey
results were included in the graphs along with teachers because the study focused on staff perceptions
as a whole and not separate grouped based on grade level or other descriptors.
The reliability and validity of the interview and survey instruments used in this study can be
considered a limitation because they were created solely for use in this case study and not research
based instruments. The instruments could produce data that were not initially intended in this study.
The instruments used in this study could also produce different results in another school setting even
when used to answer the same research questions.
Implications for Practice
Based on the findings in this study and prior research findings on the use of data, the following
implications for the use of data should be considered by a school district and a school site
administrator to develop a positive perception of the use of data, and an effective practice for the use
of data by staff..
 Establishing a regular time period each week for teachers to collaborate around data
and develop best practices motivates teachers and allows them to reflect, learn, and
share their knowledge (Walsh, 2003; Oberman et al., 2005; Datnow et al., 2007).
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 80
Teachers are able to improve their knowledge and build their capacity as educators
when provided time to reflect on their practices for the benefit of student academic
success.
 The current lack of financial resources in districts requires schools to utilize their
current resources and current personnel. Teachers have to be trained and empowered
to use data in their classrooms. Many schools no longer have the funding to employ
instructional coaches and data coordinators. Teachers and administrators at school
sites need to be knowledgeable about how to access data using district data
management systems when they are available or other online free data management
systems. Teachers and administrators also need to understand what kind of data to
use, how to effectively analyze data, and what actions to take when making
instructional decisions using data to improve practice.
 The responses for each research question category were 88% strongly agree, 18%
agree, 2% neutral, and .7% disagree. These statistics may be a result of the fact that
having a Data Driven Decision Making (DDDM) process at the school and valuing it
does not necessarily mean it is used on a regular basis. Administrators and districts
must create a data driven culture and monitor their use to ensure that fidelity and
motivation do not deteriorate over time.
 Using data to inform instructional decisions is a collaborative process that should be
valued by all staff at a school site. Data information analysis and gathering is not a
top down process. Teachers need to understand and make sense of the data so that
they can effectively modify their instruction and develop successful intervention
strategies to improve student academic achievement. Teachers and administrators
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 81
need to be able to do the following when using a Data Driven Decision making
(DDDM) process:
1. Identify a student or class learning gap
2. Analyze data and identify variables that may be causing the problem
3. Create an intervention or modify instruction to close learning gaps.
4. Evaluate if interventions and or modifications in instruction are successful.
Student performance must be monitored throughout the Data Driven Decision Making
(DDDM) process so that alternative modifications or interventions can be implemented in a timely
manner if students are not succeeding with the current interventions.
Future Research
This case study was conducted by conducting a survey and interviews at a single elementary
school. It would be beneficial for future research to conduct the survey and interviews at several
schools and view the data for trends and patterns over a longer period of time than what was used in
this study. It would be interesting to examine if school size, student demographics, and a school’s
socioeconomic status generate significant findings when collecting data from several schools.
The No Child Left Behind Act 2001, does not detail how data should be used at school sites
by administrators, teachers, and district staff. The school site used in this case study developed its
own procedures for the use of data. These procedures varied from grade level to grade level at the
school. Future research on effective Data Driven Decision Making (DDDM) process steps that focus
on analyzing data and not just identifying student gaps and problems would provide consistency and
clarity for schools that strive to effectively implement a successful data driven process. This future
research should also focus on developing actions after data has been analyzed. The research should
not make suggestions or prescribe actions because each classroom is different and strategies should be
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 82
developed based on the individual needs of students. The research should outline for teachers how to
develop necessary actions after data has been analyzed so that they can create targeted strategic
intervention supports for students.
Lastly, future research should focus on analyzing the different types of professional
developments that are available for staffs that are using a data Driven Decision Making (DDDM)
process at their school. Important information could provide valuable insight on using data to inform
instruction, create staff buy-in with using data, building staff capacity by using data, and developing a
school wide culture that values the use of data in a collaborative model. In this particular study,
professional development was provided to staff at the school site over a two year period on a regular
basis. The professional development was presented by district staff and was eventually conducted by
school personnel. Future research would provide schools with resources for attaining research based
best professional developments that have been successful in several schools.
Conclusion
This study examined the staff perceptions of a Data Driven Decision Making (DDDM)
process used at a high performing Title 1 elementary school located in a large urban school district.
Research has shown that schools that implement a data based focus on student learning and
achievement are able to successfully identify weaknesses and strengths for continuous improvement.
The recent focus on school accountability has put education and student performance in the national
spotlight. School data is available for public review and is often displayed in the news.
Pressure has been put onto school, districts, teachers and administrators to show that they are
making progress and improving student learning. Potential sanctions are placed on schools that do not
meet performance indicators based on student scores from standardized state assessments. Many
schools and districts are struggling to meet the performance indicators and make adequate yearly
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 83
progress. Data Driven Decision Making has been shown as a valuable process that can assist schools
with monitoring student progress and accessing practices for growth and improvement. Further
research is necessary to provide clarity on best researched based professional developments that are
available to schools and effective steps for schools to take when analyzing data and developing actions
for interventions. The staff at George Washington Carver Elementary has taken the steps to build
their capacity with the use of data and make significant gains in student learning and school
improvement. The result of their hard work and dedication is this staff is empowered through the use
of data to make important decisions about instruction and learning to the ultimate benefit of their
students.

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 84
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STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 88
Appendix A —Principal Assent Form

Dear Madame/Sir,
My name is Cassandra Ziskind, and I am a doctoral candidate in the Rossier School of
Education at University of Southern California. I am conducting a research study as part of my
dissertation, focusing on An Examination of Staff Perceptions of a Data Driven Decision making
Process used at a Title 1 High Performing Urban Elementary School and you are invited to
participate in the study. If you agree, you are invited to participate in an interview and/or survey.
The survey will last approximately 15 minutes and the interview will last approximately
20 minutes. The surveys and interviews will take place at the school site at a time and place to
be determined by you. The interview will be audio-taped by the principal investigator on a
voluntary basis. All participants have the option not to be recorded for the interview. The
identity of all participants will remain confidential before and after the study.
Participation in this study is voluntary. Your identity as a participant will remain
confidential at all times during and after the study.
If you have questions or would like to participate, please contact me at ziskind@usc.edu.
Thank you for participating in my survey.

Cassandra Ziskind
University of Southern California

STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 89
Appendix B —Principal Interview Protocol

1. Please describe the Data Driven Decision Making process used at your school.
2. What type of data is used at your school?
3. How is data collected at your school site?
4. How often do you receive data?
5. How are benchmarks and expected levels of student performance decided by staff?
6. How are problems identified using data?
7. How often do teachers meet to review data?
8. What are the expectations of your district for how data should be utilized at your school?
9. What are your expectations for support as a teacher for using data?
10. Please describe how data is analyzed at your school?
11. How is the analysis validated?
12. Can you give examples of actions that have been taken after data has been analyzed?
13. Is data shared with students? Example?
14. How would you describe the effective use of data by a teacher?
15. How do you monitor student progress using data?
16. What types of professional development is offered to teachers to build their capacity by
using data?
17. What type of support has been provided to you to build your capacity by using data?
18. Describe an effective professional development for staff for using the data?
19. What do you feel is needed for you to become more proficient with the use of data?
20. Can you describe how data is used at your school to inform instructional programs and
decisions?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 90
21. What data is most used at your school and in what capacity?
22. Is school data reviewed at staff meetings?
23. Is student data shared among teachers?
24. How comfortable are you with using data?
25. What are your background experiences with data?
26. What are your background experiences with professional development for Data Driven
Decision Making (DDDM)?
27. How are teacher meetings organized?
28. How data used at teacher meetings?
29. What are your expectations for teacher meetings?
30. What additional steps do you feel are necessary for your school with the Data Driven
Decision Making (DDDM) Process at your school?
31. What data collection documents do you use at your school site? Other resources?
32. How comfortable are you with sharing your class data at grade level meetings with your
grade level group?
33. What steps were taken to build a Data Driven Decision Making (DDDM) culture at your
school?
34. How much time was given to building the culture if it exists?
35. How instrumental has DDDM been to improving your school’s performance and your
practice?
36. Is there any additional information I should be aware of?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 91
Appendix C —Teacher Assent Form

Dear Madame/Sir,
My name is Cassandra Ziskind, and I am a doctoral candidate in the Rossier School of
Education at University of Southern California. I am conducting a research study as part of my
dissertation, focusing on An Examination of Staff Perceptions of a Data Driven Decision making
Process used at a Title 1 High Performing Urban Elementary School and you are invited to
participate in the study. If you agree, you are invited to participate in an interview and/or survey.
The survey will last approximately 15 minutes and the interview will last approximately
20 minutes. The surveys and interviews will take place at the school site at a time and place to
be determined by you. The interview will be audio-taped by the principal investigator on a
voluntary basis. All participants have the option not to be recorded for the interview. The
identity of all participants will remain confidential before and after the study.
Participation in this study is voluntary. Your identity as a participant will remain
confidential at all times during and after the study.
If you have questions or would like to participate, please contact me at ziskind@usc.edu.
Thank you for participating in my survey.

Cassandra Ziskind
University of Southern California
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 92
Appendix D —Teacher Interview Protocol

1. Please describe the Data Driven Decision Making process used at your school.
2. What type of data is used at your school?
3. How is data collected at your school site?
4. How often do you receive data?
5. How are benchmarks and expected levels of student performance decided by staff?
6. How are problems identified using data?
7. How often do teachers meet to review data?
8. What are the expectations of your district for how data should be utilized at your school?
9. What are your expectations for support as a teacher for using the data?
10. Please describe how data is analyzed at your school?
11. How is the analysis validated?
12. Can you give examples of actions that have been taken after data has been analyzed?
13. Is data shared with students? Example?
14. How would you describe the effective use of data by a teacher?
15. How do you monitor student progress using data?
16. What types of professional development is offered to teachers to build their capacity with
using data? What type of support has been provided to you to build your capacity with
using data?
17. Describe an effective professional development for staff for using data?
18. What do you feel is needed for you to become more proficient with the use of data?
19. Can you describe how data is used at your school to inform instructional programs and
decisions?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 93
20. What data is most used at your school and in what capacity?
21. Is school data reviewed at staff meetings?
22. Is student data shared among teachers?
23. How comfortable are you with using data?
24. What are your background experiences with data?
25. What are your background experiences with professional development for Data Driven
Decision Making (DDDM)?
26. How are teacher meetings organized?
27. How data used at teacher meetings?
28. What are your expectations for teacher meetings?
29. What additional steps do you feel are necessary for your school with the Data Driven
Decision Making (DDDM) Process at your school?
30. What data collection documents do you use at your school site? Other resources?
31. How comfortable are you with sharing your class data at grade level meetings with your
grade level group?
32. What steps were taken to build a Data Driven Decision Making (DDDM) culture at your
school?
33. How much time was given to building the culture if it exists?
34. How instrumental has DDDM been to improving your school’s performance and your
practice?
35. Is there any additional information I should be aware of?
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 94
Appendix E —Staff Questionnaire
Please circle the response below that best describes your beliefs for each question.
1. A data Driven Decision Making process is used at my school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
2. Different types of data are used at my school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
3. Staff has time to meet and review data regularly.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
4. Staff has time to meet and analyze data regularly.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
5. Actions are taken school-wide after data has been analyzed to improve instruction.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
6. Data is used to inform instruction.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
7. Instructional gains have been made by using a Data Decision Making Process at my
school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
8. My grade level has established benchmarks for student performance.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
9. Student data is shared at my school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
10. Teachers understand how to effectively use data at my school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
STAFF PERCEPTIONS OF DATA DRIVEN DECISION MAKING 95
11. Teachers are supported through professional development at my school with using data
effectively.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
12. I monitor student progress on a regular basis.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
13. I use data to inform my instruction.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
14. My opinion is more important than data when examining student performance.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
15. I am comfortable with using data at my school.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
16. Data is regularly reviewed at staff meetings.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)
17. My school has a strong Data Driven Decision Making culture.
(Strongly Agree) (Agree) (Neutral) (Disagree) (Strongly Disagree)

Abstract (if available)

Abstract The national focus in education continues to center on school accountability and student results. Districts and states are continually searching for ways to improve student performance and learning outcomes. There are several Data Driven Decision Making models currently being used in school districts across the country. Research continues to expand in the investigation of Data Driven Decision Making models. This study examines how a Data Driven Decision Making (DDDM) process is perceived at an elementary school for identifying possible problems, analyzing problems, designing and modifying instruction, and consistently evaluating student learning in an effort to provide knowledge in the field of education on how data is used to inform instruction and planning. This study addresses the following questions: 1. Is a DDDM process used by faculty when analyzing student data? 2. Are teachers utilizing data to affect their instruction? 3. Is professional development provided to teachers to help them in their efforts to utilize data to guide their instruction? ❧ Through research, this study found that the school used in this study utilized a Data Driven Decision Making process to improve student outcomes. Schools can make important instructional decisions with the use of data to improve teacher capacity with effective professional development, strengthen the curriculum by strategically analyzing student data, motivate students, and create long term goals for continued growth.

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Asset Metadata

Creator Ziskind, Cassandra F. (author)

Core Title An examiniation of staff perceptions of a data driven decision making process used in a high performing title one urban elementary school

School Rossier School of Education

Degree Doctor of Education

Degree Program Education

Publication Date 04/02/2013

Defense Date 11/27/2012

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

Tag data driven decision making,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor García, Pedro Enrique (committee chair), Castruita, Rudy Max (committee member)

Creator Email cziskind@sbcglobal.net,ziskind@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-229238

Unique identifier UC11292724

Identifier usctheses-c3-229238 (legacy record id)

Legacy Identifier etd-ZiskindCas-1502.pdf

Dmrecord 229238

Document Type Dissertation

Rights Ziskind, Cassandra F.

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