How to Perform a Collaborative Data Analysis

Tests are always changing, as new contractors are hired and new tests are added. Therefore, having the skills to analyze a range of data reports is important.

Features of Data Reports

  • All states collect and publicly report summative test results.
    • By federal law these results are used for school (and district and state) accountability purposes
    • Examples of federal law: NCLB (No Child Left Behind Act) and IDEA (Individuals with Disabilities Education Act)
  • Achievement data are disaggregated by student demographic and/or socio-economic status (SES).
    • Examples of this include: White, Hispanic, African American, English Language Learners (ELL), Students with Disabilities, Poverty.
    • If a subgroup consists of a very small number of students the data for that group are not reported because that risks identifying individual students
  • Data are reported for each subject and grade tested.
  • Reports often contain results for an entire school and comparison results for similar schools.
  • Reports may include past performance and trend analysis.
    • Often the reports compare students in the same grade in prior years. Keep in mind that a comparison of one year’s grade 3 results with another year’s grade 3 results, by definition, means a comparison of two different groups of students.  You cannot use these data to show individual growth or progress over time.
  • Reports may include contextual data (e.g., rates on dropout, attendance, teacher attributes, and student demographics).

These data don’t tell you much (if anything) about how you should be changing your instruction. 

States can follow and report progress of individual students from one grade to the next using a “longitudinal” or “cohort” analysis, which is a prerequisite for
“value-added analysis”.  Some companies can manipulate/reconstruct the summative testing data to create reports for teachers that indicate past performances of individual students. 

States and district often produce reports on additional information about schools beyond state-wide test results.  These can include: local assessment results and contextual data such as rates and statistics (e.g. on drop-outs, graduation, promotion, teacher attributes, student demographics).  Every school has a school report card that provides additional information as well as a summary of much of the information just described. 

Data analysis is most effective when a group of people with a variety of experiences and perspectives looks at the data together

Data analysis:

  • Consists of looking for patterns, themes, associations, and interrelationships among data
  • Means organizing and finding meaning in the data collected
  • Involves presenting data in ways that allow for interpretation

The purpose of data analysis is to:

  • Describe or summarize data clearly
  • Search for consistent patterns or themes among data
  • Enable us to answer our questions and/or support (or refute) our hypotheses

Levels of data analysis:

  • Individual
  • Class
  • School
  • District
  • State

Data analysis can be done by an individual or in groups.  When the process is collaborative, more voices, experiences, and perspectives help create a richer understanding of the data and of the problem to be identified and resolved.

Guiding assumptions for Collaborative Data Analysis

  • Data are the focus of our conversations; opinions or individual experiences are not.
    • We must focus on the data—not on our opinions. This means that we must point to the evidence to support our statements.
  • We use process skills and communication to exchange viewpoints, build shared understanding, and create meaning from the data.
    • Effective communication and the use of “process skills” help with the exchange of ideas and the creation of meaning from the data
  • Teachers’ knowledge about their practice is an essential component of data analysis.
    • The knowledge that teachers bring about their practice is an important component of the data analysis.

Steps for Collaborative Data Analysis

  • Presenting the School Profile:
    • Provides a shared understanding of the school
    • Ensures that everyone involved in the data analysis understands the context of the school.
  • Exploring and Discovering the Summative and Contextual Data:
    • Affords opportunities for data inquiry without rushing to conclusions and problem-solving
    • It allows us to wonder about what the data are saying.
  • Organizing and Integrating:
    • This is the point in the process where we shift our focus to generating hypotheses about the data mean
    • Offers opportunities to develop causation theories
    • Includes “triangulation,” (see below) or looking at three or more data types that are independent/not connected to each other
    • “Triangulation” increases the confidence we have in our theories because we’re bringing in information from multiple data sources that relate to an issue we are trying to address
  • Goal-setting and Action Planning:
    • Focuses/prioritizes action that are most important and how to get it done
    • Assigns individual and collective accountability for success


 Triangulation means using data from three or more different sources to:

  • Compensate for the imperfections of data
  • Increase confidence in results when you find similar results across multiple sources
  • Raise follow-up questions when multiple sources yield different results

An example of triangulation:

Might be an analysis of school effectiveness that incorporates data from teacher surveys, parent interviews, student test results, and reports about students’ after-school activities. 
Must have at least three data points from different assessments: summative, formative, and contextual. 

 ORID describes the process that groups will be using to analyze the data.

  • O = Objective Level
    • Examine the data and identify factual information (what do you see?)
    • A school profile addresses the school location, the type of students and teachers in the schools, the curriculum, parent involvement, etc. When working with a group, it is important to establish common information and to get everyone on the same page with respect to the school before moving into the data analysis process.
    • The facilitator should monitor the discussion to ensure that only clarifying questions about the data are being asked at this time – not “why” or “because” questions
      • “What” questions are helpful:
        • Example: What types of activities are parents involved in, besides orientation?
      • “Because” questions jump to conclusions too soon:
        • Example: Are fewer parents involved during the school year because…
  • R = Reflective Level
    • Begin making connections among the data
    • Encourage free flow of ideas and imagination – brainstorm
      • What surprised you?
      • What encouraged you?
      • What discouraged you?
    • It is important that the data analysis process continues to build on what you learned from the objective level. The continuing analysis should relate back to what you learned, but should take it a step or level deeper into what you are seeing in the data
  • I = Interpretive Level
    • Identify patterns and determine their meaning or significance (what does the data tell us about our school and what priorities does it help us uncover?)
      • What does the data tell us?
      • What new insights do you have?
      • What good news is there for us to celebrate?
      • What doesn’t it tell us?
      • What else might we need to know?
      • What areas of needs seem to arise?

Based on your answers to these questions, identify at least one major priority for the school.

  • This level will help summarize what we know about the school based on the data. Be sure to include the school’s strong and weak points.  Data analysis is typically couched in the idea of improvement.  This can result in our analysis focusing heavily on the school’s weaknesses or problem areas; however, weaknesses are often best addressed with a consideration of the school’s strength in mind.
  • An example of a priority is: “to improve math achievement among English Language Learners”
  • D = Decisional Level
    • Participants will be synthesizing what they have learned about their school and will be proposing next steps for an action plan
      • What are our proposed next steps?
      • What decisions can we make?
      • What is our action plan for moving forward to address our priority?
    • The Decisional Level is based on the cumulative data, which should have been specified at the Interpretive Level.
    • It is now ok to use the word “because”.
      • For example, using the same example as before, “math achievement is lower among ELLs because math teachers may lack specific strategies for teaching content to ELLs”
    • An action step related to the previous example might include “meet with math department to see if this is the case” or “get specialized training to math teachers to focus on providing them with professional development in ELL instructional strategies”.

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