“Daily life in districts and schools requires educators to effectively navigate a sea of data: diagnostic and norm-referenced standardized assessment data, reading assessment data, state and local assessment data, in combination with other data related to instructional programs and demographic, attendance, and dropout trends. This new level of applied data use requires district and school administrators, teacher leaders, and classroom teachers to be data literate—that is able to use multiple types of assessment and other data to inform decisions that lead to higher student achievement.” (Fox, 2013). Victoria Bernhardt (2000 & 2003) also talks about the importance of intersecting multiple data categories. She says, "Not until you intersect all data categories at the school level and over time will you be able to answer questions that allow you to predict whether the actions, processes, and programs that you are operating will meet the needs of all students. By crossing all four data categories, you are taking into account who your students are, how they prefer to learn, which subgroups of students are achieving, and with which process students achieve."
According to Bernhardt (2002 & 2003), there are four kinds of data (demographic, student learning, perceptions and school processes). Demographic data provide information on enrollment, attendance, grade level, ethnicity, gender, native language, etc. Student learning data provide information in terms of standardized test results, grade point averages, standards assessments, etc. To help us understand what all stakeholders (students, parents, teachers and others) think about the learning environment, we use perceptions data. School process data define programs, instructional strategies and classroom practices that occur in a building.
Demographic Data
Demographic data are known as the characteristics of the human population. In education, demographic data include the following: numbers of students in the school, numbers of students with special needs, ethnicities of the students in the school, number of graduates, number of students who drop out of school each year and number of teachers by years of experience. These data clarify who are students and staff are. Demographic trends can assist in predicting the number of students ethnicities with which a school or district can expect to work in the future. Historically, these data can assist in determining how well the school or district has served its past and current population and identify any changes that might be needed in the future (Bernhardt, 2004).
Perception Data
The definition of “perception” leads to synonyms such as “observation,” “opinion,” “belief,” “view,” and “conviction.” According to Bernhardt (2004), “It is what teachers think, what teachers believe, and what teachers do at the level of the classroom that ultimately shapes the kind of learning that young people get.” Common approaches to understanding perceptions in schools or districts include the use of questionnaires, interviews, and focus groups.
Questionnaires can be used to collect information to describe, compare, and explain knowledge, attitudes, perceptions or behavior. They can be completed anonymously and re-administered to assess the changes in perceptions over time. If you are look for an in-depth understanding of individual teacher and/or student perceptions, interviews can be used. Focus groups (a smaller group of students or teachers) can be used to understand what the larger group is thinking or why they responded in a certain way (Bernhardt, 2004).
Student Learning Data
According to Bernhardt (2004), “A student assessment program that truly meets the needs of every student must be congruent with its purposes, uses and practices. The program can then begin to predict not only student needs, but also the approaches required to meet those needs.” Student learning has been traditionally measured by standardized tests, norm-referenced tests, criterion-referenced measures, authentic assessments, teacher-made tests, teacher-assigned grades, performance assessments and standards-based assessments. Using different student learning measures allows the school and/or the district to determine if students perform differently on one type of test versus another. Comparing different measures can also provide teachers with insight into what teaching strategies, as well as testing strategies, work best with students.
Bernhardt (2004) also states, “School personnel typically think about data analysis as being only about student achievement results. Schools must assess the same measures, grades, and students, over time, in order to use the information in a comprehensive fashion. These scores will provide an understanding about what needs to change to get different results.”
School Processes Data School processes are what produce school and classroom results. Of the four types of data, school processes are the only data schools can control. If different results are desired, processes must be changed. According to Bernhardt (2004), to change processes, teachers must be clear on what is being implemented, study the results of these processes over time, understand the relationship between the processes, results, mission/purpose, and actions, study the research on effective processes and build hypotheses and action places to achieve different results.
Intersections and Data Analysis
In order to engage in true data-driven decision making, principals must have the right mind-set (Fox, 2013). Fox discusses nine attributes of an appropriate mind-set for data driven decision making: (1) The principal believes data is vital for sound decision-making and effective problem-solving; (2) The principal understand the classroom is the critical point of impact for student learning; (3) The principal believes one of her primary responsibilities is to establish a culture of continuous improvement.; (4) The principal focuses on variables over which the school has control; (5) The principal understand that data is a means to an end, rather than end in itself; (6) The principal distinguishes between change and improvement; (7) The principal establishes a “but-free zone” for problem solving; (8) The principal understands the difference between a situation and a problem; and (9) The principal realizes “hope” is not a strategy. The two minds-set attributes that stand out as most important are the ability to distinguish between change and improvement and the ability to understand the difference between a situation and a problem.
According to Fox (2013), “schools have changed a lot—they just haven’t improved very much.” He further states that the skilled principal should on improvement rather than change. Questions such as How will adjusting the master schedule lead to improved achievement for English learners? and How will a block schedule lead to improved achievement for all students? should be posed.
When using data to make decisions and solve problems, the principal must be able to distinguish between a situation and a problem. According to Fox (2013), “a situation is something for which there is no solution, while a problem has a solution.” He uses the example of demographics to illustrate his point. Demographics are a situation rather than a problem. A school district cannot control the demographic data and therefore, demographics themselves are not a problem. The problem is being able to understand the effects of the demographics on student learning and selecting and delivering the appropriate instruction that will lead to improved student achievement.
“To make effective use of data, the principal needs both technical knowledge and skills with which to interpret data, and a mind-set that allows her to effectively apply that knowledge and those skills for the purpose of improving instruction and increasing student achievement. Without the appropriate mind-set, the principal’s knowledge and skills will serve little purpose.” (Fox, 2013).
According to Bernhardt (2004), intersection analyses allow a school or district to look closely and understand each piece of information gathered. She emphasizes the importance of staying clear on the purpose for the analysis as it is easy to keep gathering data and forget the question(s) to be answered. An intersection of different types of data can tell a school or district what all the data say about strengths, challenges and areas for improvement. The intersection of data can also help a school or a district conduct a root cause analysis (the real reason(s) that problems or challenges exist. If a root cause is not identified and understood, schools or districts could be merely treating a symptom and never get to the real reason for the results.

Bibliography

Bernhardt, V.L. (2004). Data Analysis for Continuous School Improvement. Larchmont, NY: Eye on
Education

Bernhardt, V. L., (2003). No Schools Left Behind. Educational Leadership. 60(5), 26-30.

Bernhardt, V. L. (2000). Intersections: New routes open when one type of data crosses another. Journal of Staff Development, (21) 1, 33-36.

Bernhardt, V. L., (1998, March). Invited Monograph No. 4. California Association for Supervision and Curriculum Development (CASCD).

Fox, D. (2013). The principal’s mind-set for data. Leadership. 42(3), 12-16.