After exploring data related to your CCMR problem of practice, you will craft a question that will drive your cycle of inquiry. Ideally, the question(s) you generate will be grounded in evidence, will be related to actionable issues (i.e., issues that are within the district’s control and can be improved over time), will be connected to the district’s strategic plan(s) or board goals, and will significantly improve your students' preparation for postsecondary opportunities. After you collect data related to that question, the next task is to analyze that data. It is important to note that these steps may be cyclical rather than linear in practice. During the data analysis process, teams will often discover that they have questions that will require additional data collection.
Protocols can be very helpful for teams as they begin to analyze the data. A protocol for examining data used by the Network for College Success incorporates several steps that are common across many data analysis protocols (additional examples are listed in the related resources section below):
Team members review the data, look for patterns, and describe what they notice. During this noticing stage, it is important to stick to statements of fact that can be observed in the data.
Team members ask questions as they collectively make sense of the data (wondering stage). These questions may lead to potential interpretations of the data, explore limitations of the data, or suggest that the team needs to collect additional data.
Team members discuss implications of the data, brainstorm potential strategies to test out, and outline next steps that emerged from the conversation. Next steps should be actionable items that educators can address or influence directly.
It can be difficult for educators to remain focused on the facts when looking at data. To address this challenge, the Data Wise team uses a ladder of inference graphic to encourage data users to remain on the lower rungs of the ladder while exploring the data in order to avoid quickly jumping to conclusions that might be based on opinions rather than facts. An important habit that is emphasized in the Data Wise process is a relentless focus on evidence. View a Data Wise video below that illustrates the noticing and wondering steps described above. As you watch, reference the data file that the Data Wise team discussed during the video.
After the initial exploration of the data, your team might want to dig digger to delve into the root causes for your CCMR problem of practice. Two protocols that could be particularly helpful with root cause analysis are:
FIve whys for inquiry protocol, developed by the School Reform Initiative. You might use this protocol together with related guidance that includes a template and an everyday example.
Fishbone protocol, developed by the High Tech High Graduate School of Education. A continuous improvement toolkit, created by the Regional Education Laboratory Northeast and Islands, contains additional tools and information related to fishbone diagrams (pp. II-14 - II-24) that might be helpful for your team.
In the video below, view an example of a school that used a fishbone diagram to identify root causes. Reference the completed diagram they created and use the template to create your own diagram.
Data for equity protocol developed by the High Tech High Graduate School of Education
The School Reform Initiative (SRI) website houses protocols that can be used for many purposes. Other SRI protocols to consider for data analysis include: