Central office foci and principal data use: A comparative study of equity-focused practice in six districts


 
Journal website: http://epaa.asu.edu/ojs/   Manuscript received: 2/11/2020 
Facebook: /EPAAA  Revisions received: 6/10/2020 
Twitter: @epaa_aape  Accepted: 6/10/2020 
 

 

education policy analysis 
archives 
A peer-reviewed, independent,  
open access, multilingual journal  
 
 
 
 
 
  

 Arizona State University 

 

Volume 28 Number 181  December 7, 2020 ISSN 1068-2341 

 

 

Central Office Foci and Principal Data Use: A Comparative 
Study of Equity-Focused Practice in Six Districts 

 

Rachel Roegman  
University of Illinois, Urbana-Champaign 

United States  

  
Citation: Roegman, R. (2020). Central office foci and principal data use: A comparative study of 
equity-focused practice in six districts. Education Policy Analysis Archives, 28(181). 
https://doi.org/10.14507/epaa.28.5304  
 
Abstract: In this comparative study, I examine principal data use in two sets of districts. One set of 
three districts has a district-wide focus on using data to improve instruction, and the other set, also 
with three districts, has a district-wide focus on analyzing disaggregated data by student demographic 
groups. Data sources include interviews with principals in each set (18 from data-focused districts, 
and 9 from disaggregation-focused districts), and analysis focuses on ways that they talk about 
analyzing data in relation to district expectations and equity. Findings showed that each set of 
principals shared a different approach to data, either performance-focused or demographic-focused, 
suggesting the central role of the district in guiding how data are used at the building level. I 
conclude with implications for practice, policy, and research. 
Keywords: data use; principal; educational leadership; equity 
 
Focos de la oficina central y uso de datos principales: Un estudio comparativo de 
las prácticas de equidad en seis distritos 
Resumen: En este estudio comparativo, examino el uso principal de datos en dos 
conjuntos de distritos. Un grupo de tres distritos tiene un enfoque de todo el distrito en el 

http://epaa.asu.edu/ojs/
https://doi.org/10.14507/epaa.28.5304


Education Policy Analysis Archives Vol. 28, No. 181   2 

 

uso de datos para mejorar la instrucción, y el otro grupo, también con tres distritos, tiene 
un enfoque de todo el distrito en analizar datos desglosados por grupos demográficos de 
estudiantes. Las fuentes de datos incluyen entrevistas con los directores de cada grupo (18 
de distritos basados en datos y 9 de distritos basados en desglose) y el análisis se centra en 
las formas en que hablan sobre el análisis de datos en relación con las expectativas y la 
equidad del distrito. Los hallazgos mostraron que cada grupo de directores compartió un 
enfoque diferente a los datos, ya sea enfocado en el desempeño o enfocado en la 
demografía, lo que sugiere el papel central del distrito en guiar cómo se utilizan los datos a 
nivel de edificio. Concluyo con implicaciones para la práctica, la política y la investigación.  
Palabras-clave: uso de datos; principal; liderazgo educacional; capital  
 
Focos do escritório central e uso de dados principais: Um estudo comparativo da 
prática de equidade em seis distritos 
Resumo: Neste estudo comparativo, examino o uso de dados principais em dois 
conjuntos de distritos. Um conjunto de três distritos enfoca todo o distrito no uso de 
dados para melhorar a instrução, e o outro conjunto, também com três distritos, enfoca 
todo o distrito na análise de dados desagregados por grupos demográficos de alunos. As 
fontes de dados incluem entrevistas com diretores em cada conjunto (18 de distritos 
baseados em dados e 9 de distritos baseados em desagregação), e a análise se concentra nas 
maneiras como eles falam sobre a análise de dados em relação às expectativas e equidade 
do distrito. Os resultados mostraram que cada conjunto de diretores compartilhava uma 
abordagem diferente para os dados, seja com foco no desempenho ou com foco 
demográfico, sugerindo o papel central do distrito na orientação de como os dados são 
usados no nível do edifício. Concluo com implicações para a prática, política e pesquisa. 
Palavras-chave: uso de dados; diretor; Liderança educacional; equidade 
 

Central Office Foci and Principal Data Use: A Comparative Study of Equity-

Focused Practice in Six Districts 
 

Central office administration has the potential to play an influential role in the ways that 
principals enact leadership (Augustine et al., 2009; Honig, 2012; Knapp et al., 2010; Roegman, 
Perkins-Williams et al., 2018). Central office administrators, for example, may offer structured 
opportunities for professional learning based on the district’s strategic plan or mission (Darling-
Hammond et al., 2007; Duncan et al., 2011; Means et al., 2010). They also may provide clear and 
explicit expectations for principals around a range of leadership tasks and practices (Grissom & 
Harrington, 2010). However, research has found that this potential is not always fulfilled, as 
principals report learning more from their on-the-job experiences or informal conversations with 
colleagues than district-offered professional development (Davis et al., 2013; Means et al., 2010) 
When central office administrators hold expectations for principals, but provide little direct training 
in how to meet these expectations, principals are left on their own to figure out what to do 
(Hubbard et al., 2006).  

Data use is one such area of principal leadership that both has become a regular expectation 
for principals (Fusarelli, 2004; Lachat & Smith, 2005; Petersen & Young, 2004), and that also often 
does not entail clear direction in terms of how principals should actually be using data to make 
decisions (Davis et al., 2013; Grissom & Harrington, 2010). In some districts, principals may be 
expected to use data to drive instruction, to use data to analyze disciplinary actions, or to use data to 



Central Office Foci and Principal Data Use   3 

 

advance or equity, while other districts may require principals to use data as an accountability 
measure focused almost entirely on meeting state mandates (Halverson et al., 2007).  

While principals bring their own prior experience and knowledge to data use, often based on 
their previous positions (Roegman, Perkins-Williams, et al., 2018), many reported limited 
proficiency, often noting that data use was a central weakness of their preparation (Creighton, 2001; 
Duncan et al., 2011; Nelson et al., 2008). The range of principal proficiency is especially concerning 
given the current need for equity-focused data practices that enable principals to identify and address 
systemic inequities in their schools (Codding & Rothman, 1999; Lachat & Smith, 2005; Skrla et al., 
2004). It is important, however, to see principals not just as individual leaders in their buildings, but 
as individuals within specific contexts whose practice occurs within schools, communities, and 
districts (Coburn & Turner, 2011; Senge, 1997; Scott, 2001; Spillane et al., 2002). As such, principal 
practice needs to be considered within the contexts in which they work (Ishimaru & Galloway, 
2014). The role of context is especially important to consider in examining equity-focused principal 
practice, as different districts face different challenges related to providing all students with an 
equitable education (Roegman, 2017; Roegman, Samarapungavan, et al., 2018). 

Equity is defined in educational leadership in multiple ways (e.g., Artiles, 2011; Bulkley, 2013; 
Levin, 2010; Roegman & Hatch, 2015). For the purposes of this study, I draw on Gutiérrez’s (2012) 
four dimensions of equity as a way to be expansive in thinking about what equity means and how 
equity might be understood differently in different contexts by different individuals. These four 
dimensions include access (e.g., enrollment in honors classes); achievement (e.g., test scores and 
graduation rates); identity (e.g., how students see themselves as learners and how they see themselves 
within the curriculum); and power (e.g., the need for systemic social transformation). 

 

Purpose and Research Questions 
  
The purpose of this comparative study is to explore principals’ equity-focused data use 

practice in districts with two different central office emphasis on equity and data use. In one set of 
three districts, central office administrators have been focused on implementing or improving data-
based decision-practices at the building level. Two of the three districts had reached out to a 
university for support in helping their principals and teachers develop their data use skills, and the 
third was invited to join because it shared a similar focus. In the second set of three districts, central 
office administrators have been addressing systemic inequities in access and achievement, two of the 
four dimensions of equity identified by Gutiérrez (2012). Superintendents of the three districts in the 
second set were all participants in professional development related to systems-level equity-focused 
leadership. 

Research questions include: (1) What practices (if any) do principals in each set of districts 
use to analyze data through an equity lens? (2) How do central office expectations support or 
constrain different types of equity-focused data practices? It is not possible to claim causality in this 
type of study; however, if differences do exist in equity-focused data practices between the two sets 
of districts, a pattern of empirical association presents the opportunity for further study in this area. 

 

The Role of Central Office in Principal Practice 
  
 Data use is a central part of the work of both central office and site-based administrators. 
One key way that central office administrators support principals and teachers around data is 
through sharing data related to district and state assessments (Gallagher et al., 2008; Kerr et al., 2006; 
O’Day, 2002; Wayman et al., 2007; Wayman & Stringfield 2006). When central office administrators 



Education Policy Analysis Archives Vol. 28, No. 181   4 

 

share data, they may include preliminary analyses of assessment data that ideally would support site-
based educators in acting on the data (Kerr et al., 2006). Central office administrators may support 
site-based data use by providing specific time and resources for educators to engage with data, such 
as paid planning meetings or data-focused retreats (Halverson et al., 2007). Central office 
administrators can positively support principals’ and teachers’ sensemaking around data through the 
messages they send about the value of data in instruction (Honig & Venkateswaran, 2012; Massell, 
2001; Supovitz, 2006). A final way that central office administrators might support principal data use 
is through modeling—analyzing data with principals and talking through how they make sense of 
the data and use data to spur action (Jimerson, 2014). 
 Despite the many ways that central office administrators can support principal data use, 
studies have found that principals do not always see central office as a strong support for their 
regular use of data to inform instruction (Cho & Wayman, 2014; Daly & Finnigan, 2010; O’Day, 
2002). Educators have different understandings of the meaning of data and different levels of data 
literacy (Wayman et al., 2012), leading to differential professional development needs and supports. 
Principals report that learning on the job, in their buildings with their site-based colleagues, was a 
common way for them to develop their data literacy, and not from central office guidance (Grissom 
& Harrington, 2010; Jimerson, 2014; Roegman, Perkins-Williams, et al., 2018). 
 This research suggests the need to better understand how central office administrators 
influence principal data practice—especially in regards to advancing equity (Honig & 
Venkateswaran, 2012). While much of the literature on data use focuses on using data to improve 
instruction in a more general sense, there is a growing conversation about using data to both 
improve instruction and advance equity, whether administrators are focused on equity in terms of 
access to different learning opportunities, equity in terms of student outcomes such as test scores or 
graduation rates, and more broader understandings of equity in regard to students’ daily classroom 
experiences (e.g. Datnow & Park, 2018; Pollack & Zirkel, 2013). To examine principal data use from 
an equity lens, I now discuss Ishimaru and Galloway’s (2014) framework for equitable leadership 
practice as way to bring attention and intentionality to equity-focused data use.  
 

Conceptual Framework: Drivers of Equitable Leadership Practice 
   
 Ishimaru and Galloway (2014) identified three drivers of equitable leadership practice: 
framing disparities and action; construction and enactment of leadership; and inquiry culture. In 
outlining these three drivers, Ishimaru and Galloway forefront a vision of leadership as situated—
principals engage in data use practices within specific districts and policy contexts. 

In terms of framing disparities and action, they look to ways that school leaders understand 
what the problem is and how it should be addressed. Framing is a critical aspect of leadership, as 
attention to framing involves how a leader might mobilize others in their organization (Benford & 
Snow, 2000; Goffman, 1974). For example, when school leaders use language such as “achievement 
gaps” to characterize disparities in educational outcomes, they risk perpetuating deficit beliefs about 
underperforming groups and students (Darling-Hammond, 2015; Ladson-Billings, 2006; Quinn et 
al., 2019). They also risk a narrow focus on achievement data (Datnow & Park, 2018), instead of a 
more expansive assessment of students’ educational experiences and school policies and practices. 
Ishimaru and Galloway (2014) argue that exemplary equity-focused practice involves framing 
disparities in light of school policy and practice; when schools develop action plans, they must do so 
in ways that are inclusive of communities, not just school personnel. In this study, I look to ways 
that principals interpret central office messages related to data use and equity, as well as how they 
frame their own data use practices within the larger context of their work. 



Central Office Foci and Principal Data Use   5 

 

 Construction and enactment of leadership, according to Ishimaru and Galloway (2014), 
entails a consideration of how different individuals are involved in the leadership process and how 
decisions are made. In this sense, leadership is not solely the formal head of a school or district, but 
leadership is distributed throughout a school and community (Sergiovanni, 2006; Spillane & 
Diamond, 2007). Teachers, students, parents, and community members develop the capacity to 
engage meaningfully in the work of equity, and they all come together to develop a shared vision of 
where the school needs to go. School leaders hold the responsibility for creating spaces and 
opportunities for different stakeholders to meet and work together, while attending to potential 
power dynamics between and amongst stakeholder groups (Wasonga, 2009). Equity-focused leaders 
work with community members to identify areas of concern and prioritize how they will be 
addressed (DeMatthews et al., 2016). In this study, I look to ways that principals engage in data use 
practices with teachers, peers, supervisors, or other stakeholders, and whether, if at all, they work 
with these stakeholders to develop equity-focused actions in response to data analyses. 
 Finally, Ishimaru and Galloway (2014) see an inquiry culture as the third drive of equity-
focused leadership, which involves leaders creating an environment in which inquiry and continuous 
improvement are encouraged and valued. In these cultures, leaders and teachers regularly collect and 
analyze data to identify inequities, and use these data to develop plans to address them (Bensimon, 
2005; Scheurich & Skrla, 2003). Inquiry cycles are ongoing, and all members of the school 
community are engaged in this work; data may be qualitative or quantitative and are drawn from 
various sources, and leaders consider how policies and practices are supporting progress toward the 
school’s goals (Ishimaru & Galloway, 2014). School and district data inquiry is collective and social, 
as different individuals collect and interpret data from various perspectives (Coburn & Turner, 
2001). Principals can support data use at their schools through the development and management of 
organizational routines such as professional learning communities and the role of site-based coaches 
(Marsh et al., 2015; Spillane et al., 2002). In this study, I look to ways that principals see themselves 
as working within a district inquiry culture and see themselves as leading a site-based inquiry culture. 
 Ishimaru and Galloway’s (2014) framework is a logical choice to guide this study because its 
three drivers come together to serve as a foundation for equity-focused leadership. They highlight 
the importance of how leaders use equity in understanding what it means to create school cultures 
that advance equity for all students. In this study, their framework supports and strengthens my 
analysis of central office supports for equity-focused data use; their framework offers a way to 
identify where a district is in advancing equity and helps to situate principals, the focus of data 
collection for this study, within the larger context of the districts in which they work. 
 

Methods 
  
 This study is a secondary analysis of qualitative data (Heaton, 2008), comparing data that 
were collected from two different studies of educational leadership and practice during the 2015-
2016 school year. In the process of data collection and preliminary data analysis of both studies, I 
identified some differences in how principals in each set were talking about data use. I designed this 
study to explicitly and systematically compare the two data sets to explore their equity-focused data 
use practices. From my knowledge of the districts involved in each original study, I knew that each 
had different expectations and foci related to district-wide work. In this study, I examine more 
closely how principals are understanding and acting on central office expectations related to data use 
and equity.  

 



Education Policy Analysis Archives Vol. 28, No. 181   6 

 

Research Contexts 

This current study draws on data from two different studies in which I have been involved. I 
now briefly describe each study. I then discuss the district contexts for the six participating districts. 
In discussing the two studies and the six district contexts, it is clear that the districts are different in 
terms of their foci, as well as different in terms of their size, location, and demographics, amongst 
other differences.  As the purpose of this study is to explore data practices in districts with different 
central office expectations, this range of district type is beneficial.  

 

Study A 

The first study was an observational study of data literacy among secondary mathematics and 
science teachers and administrators around their implementation of data-driven decision-making in 
three different school systems. The three districts were purposefully selected for Study A because of 
their interest in data use; all three superintendents were interested in collaborating with the research 
team around supporting teacher and administrator data literacy. Two superintendents, from the 
same midwestern state, approached one of my colleagues for support in secondary teachers’ data 
use, and their early conversations led to a larger study of data use at the secondary level, including a 
survey analysis of teacher beliefs and practices. I learned of a third district that had a similar interest 
in developing teachers’ data literacy, and invited the superintendent to participate in the study.   

Study A included interviews and surveys with various stakeholders, including teachers and 
administrators. All secondary principals were invited to participate in Study A from two of the 
districts, Adeline and Bowman, and at the request of the superintendent, all principals were invited 
to participate from Cannington. In return for participating in the research, the original Study A 
research team individually shared descriptive results from the surveys and interviews with leadership 
teams from all three districts.  

Data for this study include one-on-one interviews with all 18 principals from these three 
districts who agreed to be interviewed. Interviews focused on the principals’ own data use practices, 
how they supported teachers’ data use, and their perceptions of central office expectations and 
requirements related to data use. There were also two questions related to equity: How do you 
analyze data from an equity lens? In what ways do you disaggregate data? Analysis of these data on 
their own can be found in Roegman, Samarapungavan, Maeda, and Johns (2018). Principals from 
this study are referred to as Sample A. 

 

Study B 

 The second study is a longitudinal study of professional development network for 
superintendents in a northeast state. I have been working with this network for over ten years. Over 
40 superintendents have participated in this organization over time, invited by the facilitator, a 
former superintendent, or their colleagues to join, and expected to commit to equity-focused 
practice. As part of their involvement in the network, superintendents identify and addres inequities 
in their school systems, though there is no requirement for specific definitions of equity or specific 
types of school reforms. Data collection for Study B involves annual interviews with 
superintendents and members of their leadership team who participated in various network 
activities.  

The original research team of Study B had identified a smaller number of districts within the 
network for more in-depth data collection. Thee districts were identified because their 
superintendents were long-term and active members of the network, and the superintendents were 
willing and interested in having researchers interview principals. Data collection for this subset of 



Central Office Foci and Principal Data Use   7 

 

network superintendents included annual interview data from principals and other instructional 
staff, as well as surveys of teachers. 

Data for this study include one-on-one interviews with nine principals from three districts 
whose superintendents agreed to participate in principal data collection. Interviews focused on the 
work that the superintendent was leading related to their participation in the network, principals’ 
perspective on new initiatives proposed by the superintendent, and principals’ understandings of the 
challenges their district faced related to equity. I also added questions about data use: How do you 
use data in your school? How do you analyze data from an equity lens? Principals from this study are 
referred to as Sample B. 

 

Districts and Participants 

Six different school districts participated in this study, with varying characteristics (see Table 
1). The three districts from Sample A are small to mid-sized Midwestern districts. Adeline School 
District is a small rural district with 2,200 students. The district has one primary, one intermediate, 
one middle, and one high school. The student population is over 90% white and almost 5% 
Hispanic; and about 25% of students receive free- or reduced-price lunch. The district has a history 
of high academic performance, maintaining a higher passing rate of state mandated test than the 
state average for the last eight years. The rate was about 65% in 2014-2015 (the year new standards 
were introduced). Additionally, the district has implemented evidence-based decision making across 
schools since the 1990s.  

Bowman Public Schools is a rural district with 6,700 students. It has eight elementary 
schools, two middle schools, and one high school. The student population is 75% white, 15% 
Hispanic, and 5% multiracial, with about 50% of students participating in a free or reduced lunch 
program. School academic performance has declined somewhat over the last eight years, with about 
60% of students passing the state standardized exams in 2014-2015. The district has not formulated 
a specific policy or developed a strong culture of data-based decision-making, but the 
superintendent is interested in doing so.  

Cannington School District is a mid-sized district with 11,000 students in 24 schools, 
including three middle and three high schools, and an alternative secondary school. It is 
geographically suburban and is located close to a major city. Over 99% of students qualify for free- 
or reduced-price lunch, 80% are Black, 12% are white, and 4% are multiracial. In 2014-2015, the 
year new standards were adopted, student proficiency rates ranged from about 13% (sixth grade 
math) to 60% (high school English), with overall proficiency rate of 30% of students scoring 
proficient or advanced. For the past several years, all schools use a “data team” protocol—regular 
meetings of grade-level or subject-level colleagues to engage in data analysis. 

The three districts from Study B are mid-sized districts on the east coast. Littleton Public 
Schools is situated in a city and is one of the larger districts in the state. The district has 32,000 
students in 45 schools. Over 50% of students qualify for free- or reduced-price lunch, 25% are 
Black, 15% are white, 15% are Asian, and 45% are Hispanic. Over one-third of students scored 
proficient in mathematics, and half did so in language arts in the 2015-2016 school year. This district 
had been focusing on raising the performance of Black and Latinx with attention to various data 
points (e.g. graduation, freshman retention, absences) and systems-wide professional development 
on culturally responsive pedagogy. 

Suffolk School District is a regional district, covering over 200 square miles in the same 
Northeast state. The district has 10,000 students in 6 schools. Under 10% of students qualify for 
free- or reduced-price lunch, but that number is rising. 5% are Black, 75% are white, 10% are Asian, 
and 10% are Hispanic. The district is known for overall high performance but had relatively low 
state assessment scores (35% in mathematics and 50% in language arts), which leadership attributed 



Education Policy Analysis Archives Vol. 28, No. 181   8 

 

to a high number of parents who opted their children out of taking the test. District data practices 
has focused on identifying which groups of students are not performing well, based on both federal 
subgroups (e.g. special education, poverty) and district factors, such as sending middle school. 

 
Table 1 
District Characteristics 
  

Sample 
District 
Name 

Locale 
Designation 

No. of Students 
& Schools 

Racial 
Demographics 

Free or 
Reduced 
Lunch 

Academic 
Achievement 

A 
Adeline 
School 
District 

Rural, fringe 
2,200 students 
 
4 schools 

90% White 
5% Hispanic 

25% 

61% proficient in 
Mathematics 
 
69% proficient in 
Language Arts 
 

A 
Bowman 
Public 
Schools 

City, small 
6,700 students 
 
9 schools 

75% White 
15% Hispanic 
5% multiracial 

50% 

64% proficient in 
Mathematics1 
 
68% proficient in 
Language Arts 

 

A 
Cannington 
School 
District 

Suburb, large 
11,000 students 
 
24 schools 

12% White 
80% Black 
4% multiracial 

>99% 

25% proficient in 
Mathematics 

 
55% proficient in 
Language Arts 
 

B 
Littleton 
Public 
Schools 

City, large 
32,000 students 
 
45 schools 

15% White 
45% Hispanic 
25% Black 
15% Asian 

50% 

34% proficient in 
Mathematics 
 
48% proficient in 
Language Arts 
 

B 
Suffolk 
School 
District 

Suburb, large 
10,000 students 
 
6 schools 

75% White 
10% Hispanic 
5% Black 
10% Asian 

<10% 

36% proficient in 
Mathematics1 
 
53% proficient in 
Language Arts1 

 

B 

Sheldon 
Unified 
Public 
School 
District 

Suburb, large 
9,000 students 
 
10 schools 

50% White 
15% Hispanic 
10% Black 
20% Asian 

10% 

45% proficient in 
Mathematics2 

 
63% proficient in 
Language Arts2 
 

  
Sheldon Unified Public School District is a suburban district with 9,000 students in 10 

schools. The district is over 50% white, 15% Hispanic, 20% Asian, and 10% Black, with 10% of 
students qualifying for free and reduced lunch. Similar to Suffolk, Sheldon has a high opt-out rate, 
which it sees as part of the rationale for only 45% of students proficient in math and 65% in 

                                                
1 The district had a 34% opt-out rate for standardized testing. 
2 The district had a 15% opt-out rate for standardized testing. 



Central Office Foci and Principal Data Use   9 

 

language arts. The district has been working to develop principal and teacher data skills to identify 
disproportionalities in student performance. 

Across both sets of districts, there were 18 principals from Sample A and 9 principals from 
Sample B (see Table 2). Superintendents informed their principals of both studies, and I interviewed 
all principals who were interested in being interviewed, aside from Adeline and Bowman, where only 
high school teacher were recruited.  
 
Table 2  
Participants  
 

Sample District Participant Site & Year as Principal Race and Gender 

Sample 
A 

Adeline School 
District 

Neil High school, 3rd year White male 

Timothy High school, 3rd year White male 

Bowman Public 
Schools 

Edward High school, 11th year White male 

Evan High school, 3rd year White male 

Marlies High school, 5th year White female 

Cannington 
School District 

Tanya High school, 2nd year Black female 

Mandy Elementary, 3rd year White female 

Michael Secondary, 1st year White male 

Brian Elementary, 1st year White male 

Rosa Elementary, 8th grade White female 

Melanie Elementary, 4th grade White female 

Sarah Elementary, 3rd year White female 

Arianna Elementary, 2nd year Black female 

Todd Elementary, 2nd year White male 

Darius Elementary, 1st year Black male 

Alison Elementary, 8th year White female 

Rochelle Elementary, 1st year White female 

Yvonne Middle school, 1st year White Female 

Sample 
B 

Littleton Public 
Schools 

Courtney Middle school, 5th year Black female 

Ronaldo K-8, 5th year Latino male 

Natalia High school, 4th year White female 

Suffolk School 
District 

Harvey High school, 3rd year White male 

Nick High school, 1st year White male 

Zelda High school, 14th year White female 

Sheldon Unified 
Public School 

District 

Laura Elementary, 10th year White female 

Anna Elementary, 14th year White female 

Richard Middle school, 13th year White male 

 
Data Sources  

 Data sources include semi-structured interviews of each of the principals. In two of the 
districts in Study B, Sheldon and Littleton, principals conducted the interviews together; the 
remaining interviews were all one-on-one. I used the same set of interview questions for both 
studies. Each interview was otherwise based on the specific research questions of Study A (data 
practices) or Study B (equity initiatives). The protocol from the first study included additional 
questions related to personal understandings of data, data for instructional design, and supporting 
teachers’ use of data. Study B included additional questions related to participants’ involvement in 
district-led professional learning and the district’s equity work. Follow up questions to all prompts 



Education Policy Analysis Archives Vol. 28, No. 181   10 

 

probed for equity, such as “How do you use data to make decisions about instruction/course 
placements/discipline/resource allocation?” Each semi-structured interview lasted about 35-55 
minutes and was held in the principal’s office, except for one that was held over the phone. All 
names of individuals and districts are pseudonyms. 
 Principal interviews were chosen as the focus of this study because it became clear during 
data collection that principals had different beliefs about data and equity, and they had different 
practices related to equity. Ishimaru and Galloway’s (2014) conceptual framework for equity 
leadership argues that leadership is situated; as a result, interviews with principals asked their 
perspectives on different influences on their work, including preparation, central office expectations, 
and their colleagues. This interview set offers a powerful exploration of principal practice related to 
equity-focused data use in diverse district contexts. 
 A challenge to this comparison is the potential risk of priming. While the data use question 
for principals in Sample B did not specifically name equity, it was asked within an interview where 
we had already been talking about equity. Similarly, the overall focus of interviews in Sample A was 
on data use, and principals in that study may be strong equity-focused leaders, but that did not come 
up in the interviews. This aligns with analyses that shows how discourses of equity and discourses of 
accountability are distinct and rarely overlap (e.g. Garner et al., 2017). This type of priming likely 
influenced some of the patterns that arose in the findings; however, the clear difference in findings 
suggests that priming is not the only factor in principals’ responses.  

Analysis 

 I drew on the constant comparative method to analyze the transcripts in this study, which 
had previously been analyzed individually (see Roegman, Samarapungavan, et al., 2018; Roegman et 
al., 2020). Glaser (1965) introduced this method as a way to generate “a theory which is integrated, 
consistent, plausible, close to the data, and in a form which is clear enough to be readily, if only 
partially, operationalized for testing in quantitative research” (pp. 437-438). Its goal is not reliability 
or theory testing, but to suggest properties of or hypotheses about a phenomenon (Glaser, 1965). 
Comparing data enables the researcher to create codes and categories, as well as connections and 
delineations between codes and categories (Boeije, 2002). As such, this method of analysis is ideal 
for this study, as I aim to explore principals’ equity-focused data use practice and look for patterns 
across districts with different central office foci (data use or equity initiatives). 
 The constant comparative method is also useful as I am comparing data from two different 
studies. I was already deeply involved in Study B, having served as note-taker and facilitator for 
professional development sessions with participating superintendents, members of their leadership 
teams, and some of their principals. I was in the process of interviewing principals as part of the in-
depth data collection. I had been involved in this project since 2008. In 2014, I began working with 
the research team leading Study A, and I was the lead on the administrative interviews as part of that 
study. In those interviews, when I asked the questions about equity and disaggregation, many 
principals reported that they focused on individual students and did not even think about 
disaggregating their own data. This was surprising for me, in light of the federal requirement to 
disaggregate assessment results (No Child Left Behind, 2001) and my knowledge of data practices of 
participants in the professional development sessions, and led me to ask similar questions of data 
use to the principals in Study A, to see what they would say. I assumed they would have different 
responses, and I added those questions to the interview protocol so I could have a more empirical 
grounding than just my impressions. Because I was collecting data for two studies simultaneously, 
and regularly memo-ing on my impressions after each interview, I was able to use emerging findings 
to create interview questions for each study that would inform the topic of this study, equity-focused 
data use. Comparisons between and across interviews support the internal validity of the findings 



Central Office Foci and Principal Data Use   11 

 

(Boeije, 2002), which refers to the internal validity, or trustworthiness, of the conclusions drawn 
from the data (Anfara et al., 2002). 
 While the constant comparative method is often written about, there is not one set process 
to engage in this type of analysis (Boeije, 2002). For this study, I followed Boeije’s (2002) five-step 
process, which begins with looking for comparisons within an interview and then looking at 
comparisons within a set of interviews; I used a spreadsheet to code and compare all of the data. 
Thus I began by reading each interview on its own, coding excerpts relevant to both data use 
practices and references to equity, defining both concepts as broadly as possible. I used Ishimaru 
and Galloway’s (2014) framework as a guide to create these initial codes, using participants’ own 
words as much as possible to create codes related the three drivers of equitable leadership practice.  

I then compared codes of interviews within Study A, and then did the same for interviews 
within Study B. In the third step, I compared the codes from Study A with those of Study B, looking 
for differences between the two sets of districts. In the fourth step, I compared principals from 
similar schools across studies; this is similar to the third step, except that I am looking at specific 
interviews and not as the data set as a whole. For example, I compared principals of schools that had 
a large majority of students of one race (e.g. 90% white or 90% Black), as well as schools of similar 
size and geographic region. Finally, in the fifth step, I compared groups from the fourth step with 
each other. For example, I compared codes from principals of small, rural schools with those of 
large, suburban schools. By systematically comparing interviews in different ways, as described by 
these steps, I was able to identify a number of similarities and differences within and across Study A 
and Study B. At the same time, these different levels of comparison enable me to explore whether 
some of the differences that I had initially ascribed to central office expectations might also be 
connected with geographic, size, or student demographic or performance factors.  

Through these cycles of comparison, as I looked between, within, and across data sets, I 
began to identify patterns of data practices that were relatively unique to each data set. I also 
identified similarities and differences between district types, regardless of data set.  

Dependability 

Contrasting quantitative notions of reliability—that this study could be replicated in a 
different context with different participants and achieve similar results—I draw on Lincoln and 
Guba’s (1985) notion of dependability—coherence in terms of conceptualizing the study, designing 
it, collecting data, and interpreting findings. My methodological choices are outlined in-depth, both 
the original studies as well as my use of the constant comparative method to analyze data sets from 
those studies. This enables the reader to have a thorough understanding of my methods, and the 
reader can then evaluate the coherence of these methods with the framing of the study and the 
interpretations and conclusions I have made (Shenton, 2004). 

This study is not generalizable to all school districts, in that it is not possible to argue for 
causality—that the district focus is directly responsible for the findings that I will discuss—because 
of the nature of the study design. Because the interviews were collected as part of two separate initial 
studies, there is a question of “fit” and whether data collected for one purpose could be re-used for 
a different purpose (Heaton, 2004, 2008). However, as an exploratory study, its purpose is to 
compare principal practice and consider the role of central office in their practice. Findings, below, 
do suggest a clear pattern of empirical association, creating the opportunity for future studies that 
can be designed with causality and generalizable as goals. 

 

 
 



Education Policy Analysis Archives Vol. 28, No. 181   12 

 

Findings 
  
 Patterns existed between the principals in each set of districts. Principals in Sample A 
(performance-focused) were more likely to discuss analyzing data in terms of performance indicators 
with an overall emphasis on proficiency on state standardized exams, while those from Sample B 
(demographic-focused) were more likely to discuss data in terms of student demographics with an 
overall emphasis on reducing disparities in access or outcomes (see Table 3).  
 
Table 3  
How Principals Report Analyzing Data 
 

Ways to Analyze Data 
Performance-Focused 

Principals 
(Sample A)3  

Demographic-Focused 
Principals 

(Sample B)  

By Student 
Demographics 

By race  2/18 9/9 

By gender 3/18 5/9 

By free- or reduced-price lunch 1/18 5/9 

By special education or 
language status 

6/18 9/9 

By Performance 

By teacher or grade level 11/18 7/9 

By standard 6/18 0/9 

By performance category (e.g., 
basic, proficient) 

8/18 7/9 

By individual student 9/18 4/9 

 

  
District Expectations in Both Samples 

 Principals in both samples shared different types of expectations from their superintendents 
and central office administrators about how they used data. They were in agreement that their 
superintendents and supervisors working out of central office expected them to use data, but 
between the two samples, there was a reported difference in the primary focus of the data use. 
Within each sample, differences also existed in the specifics of what “using data” meant. While the 
expectation was shared, the specific tools, practices, and processes to be used were not always clear 
to principals, especially those new to a district. 

                                                
3 One principal was not directly asked if she disaggregated data, but she did reference doing so in other parts 
of the interview. 



Central Office Foci and Principal Data Use   13 

 

Using Data to Drive Performance  

Principals from Sample A reported high expectations related to data use, in that principals 
and teachers should be using data, but not always specifics as to what they should be doing with the 
data. Timothy, from Adeline, for example, shared that central office administrators “have very high 
expectations, by all means. They want all kids to succeed, and think we should be held to a very high 
standard.” He went on to describe the different meetings related to data, data platforms the district 
used, and professional development sponsored by the district that he was expected to use. 
Cannington principal, Arianna, said that her central office supervisor just assumes that she is 
analyzing data because of “the data sheets, which we have to turn in…I guess that's a way she's 
making sure that we actually [look at data] because we do have to share out the [benchmark 
assessment] scores.” While Arianna did not report the same level of support from central office as 
Adeline principals, she, like all principals in Sample A, did report the expectation, or assumption, 
that principals use data on a regular basis in relation to student performance. 

Data use to drive performance was an expectation from central office for principals 
themselves, and for principals to support with their teachers. Several principals from Cannington, 
for example, reported that principals were supposed to complete “data sheets” and turn them in to 
their central office supervisors. They were expected to ensure that teachers worked in “data teams,” 
which was an initiative of the incoming superintendent, who was in his first year during data 
collection for this study. Evan, a high school principal from Bowman, also talked about expectations 
for teacher use. He discussed meetings that he attended with teachers focused on student 
performance on benchmark assessments. Evan reported that “the teachers will keep track of [tested 
subject areas], looking for mastery” and planning on reteaching if 80% of students did not show 
mastery.” 

Using Data to Identify and Address Disparities based on Demographics 

All of the principals in Sample B were clear about the role of the superintendent in creating 
an equity focus in their district. They all shared their understanding of the superintendent’s 
expectations that they analyze data from an equity perspective and paid attention to the performance 
and opportunities of specific groups of students. A Littleton principal, Ronaldo, for example, said 
that  

I really have to attribute it to [the superintendent’s] leadership in the district that 
really introduced the concept of equity, and us really promoting a conversation about 
it. And what it's caused me, as principal, to do is really look at those scores in a very 
finite manner, really focused manner, in looking at the performance of students 
according to the demographics. 

 
Anna, an elementary principal from Sheldon, noted that the superintendent “has really brought a lot 
of” attention to the performance of Black and Latinx students. She reported that the district “has 
always been a high performing district [and] he's brought in more of looking at where there are 
inequities.”  

Principals from Sample B shared specific expectations that central office administrators had 
for them related to demographics, equity, and data. Nick, and the other two principals from Suffolk, 
all shared ways that the superintendent and central office administrators have supported them in 
implementing new initiatives based on their equity-focused data use. He noted that “the district's 
done a good job of kind of bringing in folks to help us work on [revising curriculum], and then, 
obviously, giving us the collaborative time to get our teachers together to work on those strategies as 
well” as they prepared for more heterogonous classes, as a result of a ninth grade de-tracking 



Education Policy Analysis Archives Vol. 28, No. 181   14 

 

initiative. Beyond this, in one of the districts, each principal was expected to identity a building goal 
based on inequities in their benchmark assessment data as one of the principal’s goals for their 
formal evaluation; in another district in this set, each principal was expected to create a detailed 
“equity map,” highlighting different demographic groups’ performance on variety of metrics. 

Lack of Central Office Clarity  

Despite expectations for data use, some principals, mostly from Sample A, reported limited 
support or specifics in what this actually looked like. Timothy, an Adeline principal (Sample A) said 
that there were not specific expectations in terms of the types of analysis he should be doing 
specifically at his school. Similarly, several Cannington principals, also from Sample A, reported a 
lack of clarity of what should be happening within the data teams, and frustration that they 
submitted data sheets with no feedback or support from central office. 
 Often, principals reported learning about using data from their colleagues or on their own 
initiatives, instead of from central office directives. Zelda, a high school principal from Suffolk, 
shared how she learned about a specific practice from a supervisor who worked at a different 
building, “so I talked to my supervisor about doing some of the same things that [the other 
supervisor] is doing.” Littleton principal Courtney also shared her learning from other principals, 
and how the opportunity to be in other buildings, organized by the district, led her to learn from 
them. Sometimes, she would just ask, “Can I take a picture just really quick?" if she saw something 
interesting displayed on the walls. Sharing images of data walls was also a common practice in 
Cannington. While not all principals reported a lack of direction from central office, several noted 
where they went for help with data, which was more often their colleagues or their own research. 

Principals’ Data Practices 

Just as principals reported different expectations from their superintendents and central 
office supervisors, they also reported different ways that they used data. Principals from Sample A 
were more likely to talk about data in relation to student performance, identifying areas to focus on 
for reteaching or students for remediation. In contrast, principals from Sample B talked more about 
using data to identify disparities in students’ placement in different learning opportunities or their 
outcomes based on their demographics. 

Data Analysis by Performance 

Performance-focused principals from Sample A districts that had systems-wide efforts 
related to data use, reported regular use of data that focused on working to ensure all students 
passed the state’s standardized exams. For example, Neil, a high school principal, discussed sharing 
data with teachers with the goal of identifying areas to focus on: 
 We'll start to spin it, like everybody else does, as far as what matters. With last year's 

[state standardized assessment] we knew that reading nonfiction was our weakest 
area. That's not a surprise. It was something that we identified as one of our weaker 
areas. We've known in Algebra 1, the kids who can't solve equations or inequalities, 
the rest of it doesn't really matter. 

 
Another high school principal, Edward, shared how he and his school leadership team analyzed 
benchmark assessment results item by item, examining the mistakes that students made and 
identifying what teachers need to cover in the upcoming weeks. Then, each teacher was expected to 
identify a goal, such as, “Today my kids are at 40% proficiency on this standard or this indicator. In 
four weeks, I want my kids to be at 65%.” Together, Edward would work with teachers on 
developing plans for how to get their desired results.   



Central Office Foci and Principal Data Use   15 

 

Often, performance-focused principals analyzed data in a way to identify where support was 
most needed, such as targeting more help to one grade level over the other, or aiding specific 
teachers or subject areas. For example, Yvonne, a middle school principal, saw that the year before 
she took on the position, “we dropped a lot of points in math last year on our state assessment,” and 
she also noted that two of the teachers were “not the strongest.” As a result, she chose to focus on 
math in general as well as supporting those two teachers in particular. Working in Cannington with 
Yvonne, Darius, an elementary principal, shared how his school implemented the district’s data team 
approach: 

[It’s] mostly teacher-led…they analyze data by identifying the proficient, advanced, 
below basic students. And then we analyze each one of the strengths and weaknesses 
of those groups. And then we come up with instructional strategies where we need 
to create interventions for those groups.  
 

Like many principals in his district, Darius worked to ensure teachers were using assessment results 
to group students to provide interventions or enrichment.  

Half of the principals in this group also reported collecting and analyzing data on specific 
individual students. These principals at the elementary level had detailed visual data displays on their 
walls, which generally were grouped by teacher and had a row for each student, tracking their 
current progress, often with complex color-coded notations. Allison, for example, pointed out her 
“data wall” that had a row for each child with “the levels that they should be on, and then where 
they really are,” and the teacher’s name. She could look at the chart and see if a student was on track 
to meet grade level standards. Also in Cannington, Rochelle looked at her data printouts and quickly 
identified an area of concern in the second grade: “We had 25 children who were in basic at the first 
benchmark. So, 20 have dropped down into below basic and 5 moved up into proficient.” Todd, an 
elementary principal, had a similar concern, “looking at these individual students and why students 
are in certain areas, and they haven't made growth, or why some students are making growth?”  

Data Analysis by Student Demographics 

In contrast, principals from Sample B districts with systems-wide efforts related to 
identifying and addressing inequities reported regular analysis of data that took into account 
demographic characteristics, including race, gender, socioeconomic status, and disability or language 
status. When asked specifically about equity, principals shared a general philosophy as well as 
specific practices related to how they used data. Richard, a middle school principal, said that equity 
“sort of jumps off the page” and went on to talk about the small number of racial minorities at his 
school and how their performance on assessments and their participation in advanced coursework 
“stands out as an issue.” He focused his data analysis on identifying opportunity gaps between 
different student groups and working to close them. From a different district in Sample B, Harvey 
discussed his school’s “equity and opportunity report,” a report they run to identify “any single kid 
who has a B+ or higher in an academic level course to move them to an honors course.” Similar to 
Richard, Harvey used data to help him identity which students were ready to engage in more 
rigorous coursework, knowing that teachers in his school might not recommend a student of color 
for honors even if the student was qualified. 

For principals in these districts, equity, race, or student demographics were a regular part of 
their comments about data use. They all reported disaggregating data by race and classification status 
(special education, language learners, or both), and five of nine did so by gender and socioeconomic 
status as well. Overall, these principals understood equity-focused data use as disaggregating data by 
student demographics and then addressing the inequities that arose. These data generally included 
access data, such as enrollment in advanced coursework, as well as achievement data, such as 



Education Policy Analysis Archives Vol. 28, No. 181   16 

 

standardized assessment scores. In contrast to this common approach in Sample B, many principals 
in Sample A outright rejected the benefit of this type of analysis, such as Neil—when asked if he 
ever disaggregated data by race, gender, or any other demographic characteristic, he said that central 
office shared that data with him but “I don't pay any attention to it. I don't know if that's right or 
wrong, but I don't.” Like Neil, Sample A principals acknowledge the federally-mandated 
disaggregated reports, but few found this type of data useful nor did most disaggregate data on their 
own. Timothy, one of Neil’s colleagues in Adeline, had a similar response, almost suggesting that he 
had not thought of disaggregating data. When asked, he said, “I usually don't. Makes me think, 
because you asked me that question.” Rochelle, from Cannington, when asked if she disaggregated 
data by demographics, had a similar response: “I've never done that one. I wonder if I should.” 

One point of comparison was between principals that had similar percentages of racial 
breakdowns in terms of student demographics. For example, I looked to schools with one racial 
group was 80% or more of the population. Black students were the overwhelmingly majority at 
Cannington schools (Sample A), and Black and Latinx students were the majority at Natalia’s school, 
in Littleton (Sample B). In contrast, in the other four districts, white students were the majority in all 
district schools. Looking at the schools that were majority Black or Black and Latinx, comparisons 
shows that Cannington principals were less likely to disaggregate by race. Rochelle, a Cannington 
principal reported that doing so would be like comparing “a watermelon to a lime” due to the 
difference in number of students. In contrast, Natalia reported that she was “excited” about the 
Littleton superintendent’s attention to equity: “just knowing that the district is digging deep into 
[racially disaggregated data] and looking at success rates with ethnicity groups, looking at special 
education, it was all a win for me because it was something I am passionate about.” She appreciated 
a district leader who was advancing a vision of equity that meant “different areas and different 
schools need different things.” 

Considering the schools that had majority white populations with smaller populations of 
students of color, there was a similar difference in Sample A and Sample B responses. Principals in 
Sample A from Bowman and Adeline rarely discussed disaggregating data by student demographics 
as a desirable practice. Principal Evan said of socioeconomic differences that “we treat [low-income 
students] like everybody else” and “we're not going to sit and make policy changes” in response to 
data disaggregated by demographic. In contrast, principals from Suffolk and Sheldon in Sample B, 
all of whom had schools that were also majority white, shared specific understandings of 
disaggregation and data practices related to race in particular.  

Data Analysis to Change Beliefs  

Two principals in Sample B talked about data use in conjunction with issues of culture and 
beliefs related to students’ demographics, especially race and socioeconomic status. Ronaldo, 
principal of a K-8 school, discussed his recent reading of a book on culturally responsive teaching, 
and how it led to conversations at his school about “What are we doing, or what are we not doing to 
address the equity gaps that exist with our performance of our children?” From a different district in 
Sample B, Nick discussed teacher mindsets in response to encouraging students to take a more 
challenging course:  

you can open the barriers all you want, but if you don't change people's – if you 
don't attempt to change people's mindsets about what kids can and can't do, you're 
not going to be successful trying to raise that bar and getting open opportunities for 
kids. 
 

For these principals, data use involved identifying inequities related to demographics and then using 
these analyses to address their perception of the root causes of inequity: teachers’ mindsets and 



Central Office Foci and Principal Data Use   17 

 

beliefs. Culture and mindset did not arise in most of the interviews in Sample B, and in none of the 
interviews in Sample A; however, it is important to note that there were no direct questions related 
to teachers’ beliefs about students. 
 

Discussion and Implications 
 
Comparing these two samples of principals illustrated key differences in how they talked 

about and used data within their buildings. On the one hand, it is easy to explain this as a result of 
how they were recruited to participate in the study. Participants from Sample A were already in a 
study of data use, and they talked more about using data to inform instructional practice, which was 
their districts’ focus. In contrast, participants form Sample B were already in a study of equity 
initiatives, and they talked more about using data to identify and address disparities in data based on 
student demographics. Thus, there is a risk that the findings are merely a result of the study design. 

However, the findings also suggest a pattern of empirical association connecting central 
office data expectations and principal practice. Within each sample, a degree of variation existed, 
often related to the participant’s years of experience, but little variation appeared to exist based on 
participant’s race or gender, or geographic region—no principal spoke of the role of the state 
department of education in how they used data in relation to performance or demographics (beyond 
attention to passing state assessments). Instead, overall the principals in each sample shared very 
different ways that they used data to inform their practice, and these differences were aligned to 
their district’s data focus. This key finding aligns with Honig and Venkateswaran’s (2012) discussion 
of the role of central office in influencing principal data practices and suggests a connection between 
what central office is focusing on and what principals are focusing on in relation to data. From an 
equity perspective, considering principals within distinct organizations located within specific 
sociocultural contexts (Ishimaru & Galloway, 2014), findings from this study suggest that a system-
wide equity focus does influence the work of principals.  

Conceptualizations of Equity and Data Use  

Of note, in response to direct questions about equity and data use, as well as those related to 
disaggregating data by demographic categories, principals in Sample A overwhelmingly reported a 
strong focus on performance. Districts were focused on improving their data systems, with the goal 
of increasing proficiency on state assessments and improving student learning outcomes overall. 
Principals in these districts talked about data with an emphasis on performance that was aligned to 
the demands of the accountability movement in terms of specific proficiency levels on standardized 
assessments (Datnow et al., 2020). Their focus on the performance of individual students aligned 
with a focus on ensuring each student was prepared as best as possible for state assessments.  

This suggests an understanding of equity as meeting individual needs, which would then not 
require an expectation to disaggregate data by demographics. However, when principals 
conceptualize equity at the individual level, they are “decenter[ing] the systemic nature of 
disproportionality” (Voulgarides et al., 2017, p. 72). While disaggregating data is not the only type of 
equity-focused data analysis, it is unclear how Sample A principals, overall, are identifying disparities 
related to race, class, and other systems of oppression or considering how these disparities might 
inform instructional practice or student experiences. Though it is critical for principals to attend to 
individual students and look at data at the individual level, research suggests that focuses on students 
as individuals will not addressing existing, systemic inequities (Gooden & Dantley, 2012; Parker & 
Villalpondo, 2007). 



Education Policy Analysis Archives Vol. 28, No. 181   18 

 

Principals in Sample B reported paying attention to race, class, and special education, in 
particular, in looking at data, but rarely spoke about how data were used to inform instructional 
practice. They were more likely to use data to identify disparities, and seemed to assume that when 
teachers had these data, they would know how to respond and address disparities in their 
classrooms. Principals in these districts talked about data with explicit attention to ways that student 
demographics predicted their placement or performance, and they considered ways to alter these 
trends. 

Both Sample A and Sample B principals were attuned to access and achievement, two of 
Gutiérrez’s (2012) four dimensions of equity, but only two from Sample B reported any degree of 
attention to power or identity. While disaggregating data is one part of equity-focused data use, it is 
only one part. If district- or principal-led data practices stop at the level of disaggregation, leaders 
risk perpetuating deficit thinking that blames students of color for their own poor performance and 
suggests there is something wrong with Black and Latinx students, not with the system (Brown et al., 
2011; Kendi, 2019). A small number of principals in Sample B also discussed beginning to think 
about teacher mindsets as part of their demographic-focused practices, reporting on using data to 
create a sense of urgency for change or to show that equity-focused initiatives were working.  

Ishimaru and Galloway (2014) highlight how leaders frame inequity as a central driver for 
equity-focused leadership. When school leaders move from framing of inequities as achievement 
gaps—implicitly putting blame on minoritized students—to framing inequities as school-based 
issues—such as teacher mindset or school policy, they move along the continuum of leadership for 
equity to greater proficiency. However, when principals focus on “fixing” individual students, such 
as through providing remediation or additional supports, Ishimaru and Galloway’s framework places 
them at the very beginnings of this continuum. 

On the one hand, the data practices of both sets of principals are important for attending to 
student learning. If principals do not attend to performance, on ensuring each student makes 
progress, and attending to the accountability demands of their job, they will likely be seen as 
ineffective. In fact, when these findings were shared with the principals from Sample A, they all 
agreed that they are focused on accountability and that is an important, if not the most important, 
part of their job. However, this leaves the question of how principals identify and address systemic 
inequities if they do not examine data to identify inequitable patterns in opportunity, experience, or 
outcomes. Disaggregating data, while not the end point of equity-focused data analysis, is a 
necessary step for equity-focused leadership (Ishimaru & Galloway, 2014). Without naming the 
groups of students who are experiencing inequitable education, opportunity, and outcomes, and 
without naming the racialized dynamics that underlay educational policies and practices, inequities 
will continue (Irby & Clark, 2018). 

Implications 

These findings support the need to continue to research district-level leadership, as it plays a 
central role in shaping school-level data use (Anderson et al, 2010). In fact, studies of data use that 
include both school- and district-level participants are beneficial in increasing understandings of how 
leaders can effectively use data (Honig & Venkateswaran, 2012). While the superintendents in these 
six districts were not participants in this study, it was clear that in each sample of districts, the 
superintendent and central office focus on data use or equity influenced principals’ data practices 
related to either performance or disaggregation. Mixed methods studies are needed that can provide 
supporting evidence of the role of district-level expectations, and research is also needed in better 
understanding the mechanisms by which this process might happen. For example, it is possible that 
many of the principals were already attuned to equity prior to the district focus, or that they were 
hired because of the superintendent saw their equity focus aligned to that of the district’s. It is also 



Central Office Foci and Principal Data Use   19 

 

possible that principals in Sample A were aware of their superintendent’s interest in data around 
state assessments, and did not share their equity practice because they did not think it was what the 
researcher was looking for. These possibilities support the need for more research on how districts 
support site-based data practices and equity. A broad nationally representative survey, or a statewide 
survey of data use, administrated to principals and superintendents in the same district, and able to 
pair administrators’ results, could offer a set of generalizable findings in this area as well. More in-
depth research could better identify the degree to which principals from a range of districts engaged 
in both performance-focused and demographic-focused data practices. 
 These findings have clear implications for administrator practice at the building and district 
levels. If central office and superintendent expectations around equity and data use influence how 
principals analyze data, as this study suggests, then central office administrators need to be 
thoughtful and clear about their expectations, and they need to provide the supports necessary for 
teachers and principals to meet these expectations. As such, central office administrators need to 
reflect on their goals for data use, how inequities are made manifest in their districts, and the types 
of district-wide policies and practices that can support principals in data practices that lead to 
positive changes in their buildings. Professional learning for their principals and teachers should 
follow from district-wide goals or initiatives, and each educator should be provided with the tools, 
understandings, and time needed to engage in data in meaningful ways. 

At the district level, the direct expectation to disaggregate data, identify inequities, and 
address them was followed by all principals in Sample B. While some principals reported having 
done so prior to the superintendent’s focus, others reported that this was their first time doing so. 
For administrators to engage in the challenging work of equity-focused change, which almost always 
lead to resistance in K-12 school systems (Theoharis, 2007), they need to do so within a district 
context that supports that type of work. Mandates, in the form of central office expectations to act, 
can provide political cover for administrators in the face of backlash from stakeholder groups who 
favor the maintenance of the status quo at their sites (Welner & Oakes, 2005). Mandates matter—
not necessarily on their own as linear relationships in which policymakers dictate and practitioners 
implement—but in the sense that equity-focused leaders can use mandates to further their agendas 
when facing resistant communities.  
 These findings also carry implications for principals in both types of districts—performance-
based principals in districts focused on data-driven instruction and demographics-based principals in 
districts focused on disaggregating data—who are interested in leadership for equity. Very few of the 
principals referenced parents, community members, or students in terms of how they understand or 
use data, or in how they understand and act on inequities. However, engaging with various 
stakeholders around data is a central driver of leadership for equity (Ishimaru & Galloway, 2014). 
This is a potential area for principals from all district types to consider. Engaging with stakeholders 
can help principals better understand the challenges experienced from different parts of their 
systems and together develop a set of solutions to the problems they are facing. 
 For some principals in this study, the benefits of learning from each other were already 
apparent, often borrowing ideas across buildings. In this sense, they are already building an inquiry 
culture at the district-level, supporting their own building’s improvement and showing some signs of 
vulnerability in asking for advice or feedback. These instances were relatively thin in the overall data 
collection, and instead they present an opportunity for principal leadership. Creating an inquiry 
culture is a necessary driver of equity-focused leadership within a district (Ishimaru & Galloway, 
2014), and one that principals can do even without the direct support of central office 
administrators, as was suggested by some principals in this study. Many principals, especially from 
Sample A, already reported engaging in data analysis with teachers, though few used the language of 
inquiry. When central office expects data use, but does not provide specific information or support 



Education Policy Analysis Archives Vol. 28, No. 181   20 

 

around what that data use should look like, principals have the opportunity to work together with 
each other and within their schools to turn the potentially technical process of data use into rich, 
equity-focused inquiry. 
 Finally, findings suggest implications for policy, both at the state level and the district level. 
If superintendents and school boards develop and implement policies related to certain types of data 
use and provide principals with support in enacting those policies, they have a greater likelihood to 
influence principal practice. At the state policy level, state departments of education have flexibility 
in creating school report cards and various measures for school success. Requiring a specific set of 
data practices at the building- and district-levels sends a message to administrators about which data 
matter and how they should be analyzed. Future examination of specific state- and district-level 
policies that support equity-focused data analysis, including different states’ plans under the Every 
Student Success Act (2015), would further understandings of how different levels of policy influence 
different data practices. 

Concluding Thoughts 

 Students of color, low-income students, students who are classified or learning English as an 
additional language, all minoritized students need administrators to develop equitable policies and 
practices that lead to equitable classroom experiences and academic outcomes. Strengthening 
understandings of how district-level administrators support, and how school-based administrators 
enact, site-based data analysis from an equity perspective is a key step in this work.  
 

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About the Author 

Rachel Roegman  
University of Illinois, Urbana-Champaign 
roegman@illinois.edu  
http://orcid.org/0000-0002-4181-2502    
Rachel Roegman is an Assistant Professor of Educational Leadership in the Department of 
Education Policy, Organization and Leadership at the University of Illinois, Urbana-Champaign. 
Her research examines the development and support of equity-focused leaders. Her work has been 
influenced by her experiences as a middle school teacher in traditional and alternative schools and 
her commitment to anti-racist, equity-focused practice.  
 

 

education policy analysis archives 
Volume 28 Number 181  December 7, 2020 ISSN 1068-2341 

 

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archivos analíticos de políticas educativas 
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Editor Consultor: Gustavo E. Fischman (Arizona State University) 
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Alexandre Fernandez Vaz  

Universidade Federal de Santa 

Catarina, Brasil 

José Augusto Pacheco 

Universidade do Minho, Portugal 

Rosanna Maria Barros Sá  

Universidade do Algarve 

Portugal 

 

Regina Célia Linhares Hostins 

Universidade do Vale do Itajaí, 

 Brasil 

Jane Paiva 

Universidade do Estado do Rio de 

Janeiro, Brasil 

Maria Helena Bonilla  

Universidade Federal da Bahia  

Brasil 

 

Alfredo Macedo Gomes  

Universidade Federal de Pernambuco 

Brasil 

Paulo Alberto Santos Vieira  

Universidade do Estado de Mato 

Grosso, Brasil 

Rosa Maria Bueno Fischer  

Universidade Federal do Rio Grande 

do Sul, Brasil 

 

Jefferson Mainardes  

Universidade Estadual de Ponta 

Grossa, Brasil 

Fabiany de Cássia Tavares Silva 

Universidade Federal do Mato 

Grosso do Sul, Brasil 

Alice Casimiro Lopes  

Universidade do Estado do Rio de 

Janeiro, Brasil 

Jader Janer Moreira Lopes  

Universidade Federal Fluminense e 

Universidade Federal de Juiz de Fora, 

Brasil 

António Teodoro  

Universidade Lusófona 

Portugal 

Suzana Feldens Schwertner 

Centro Universitário Univates  

Brasil 

 

 Debora Nunes 

 Universidade Federal do Rio Grande 

do Norte, Brasil 

Lílian do Valle 

Universidade do Estado do Rio de 

Janeiro, Brasil 

Geovana Mendonça Lunardi 

Mendes Universidade do Estado de 

Santa Catarina 

 

Alda Junqueira Marin 

 Pontifícia Universidade Católica de 

São Paulo, Brasil 

Alfredo Veiga-Neto 

 Universidade Federal do Rio Grande 

do Sul, Brasil 

Flávia Miller Naethe Motta 

Universidade Federal Rural do Rio de 

Janeiro, Brasil 

Dalila Andrade Oliveira 

Universidade Federal de Minas 

Gerais, Brasil