1
COMPLETE GUIDE FOR DATA TRIANGULATION MODEL ||GRADED A+ ||
DATAILED ANSWERS|| REVIEWED.
What process you do you engage in when reviewing data?
When reviewing student data, I follow a structured approach that aligns with the data
triangulation model presented in the webinar. My process includes:
1. Collection of Multiple Data Points: Following Deming's principle that "data are not
taken for museum purposes; they are taken as a basis for doing something," I gather
various assessment data including screening, diagnostic, formative, and summative
assessments to create a comprehensive picture of student performance.
2. Data Organization and Analysis: I organize data by achievement levels and
standards coverage, looking for patterns across different reporting categories. I pay
particular attention to:
o Current vs. expected performance levels
o Performance across different domains/standards
o Progress over time using longitudinal data
o Consistency of performance across different assessment types
3. Root Cause Analysis: Similar to the "Data Decision Tree" presented in the webinar, I
drill down to identify specific skill gaps, examining:
o Whether the issue is with word recognition or comprehension
o Specific phonics, fluency, or vocabulary challenges
o Whether the issue appears across multiple assessments or is isolated
4. Strengths and Growth Opportunities Identification: I categorize findings into:
o "Strengths to Build Upon" - areas where students demonstrate proficiency
o "Growth Opportunities" - areas needing targeted instruction
COMPLETE GUIDE FOR DATA TRIANGULATION MODEL ||GRADED A+ ||
DATAILED ANSWERS|| REVIEWED.
What process you do you engage in when reviewing data?
When reviewing student data, I follow a structured approach that aligns with the data
triangulation model presented in the webinar. My process includes:
1. Collection of Multiple Data Points: Following Deming's principle that "data are not
taken for museum purposes; they are taken as a basis for doing something," I gather
various assessment data including screening, diagnostic, formative, and summative
assessments to create a comprehensive picture of student performance.
2. Data Organization and Analysis: I organize data by achievement levels and
standards coverage, looking for patterns across different reporting categories. I pay
particular attention to:
o Current vs. expected performance levels
o Performance across different domains/standards
o Progress over time using longitudinal data
o Consistency of performance across different assessment types
3. Root Cause Analysis: Similar to the "Data Decision Tree" presented in the webinar, I
drill down to identify specific skill gaps, examining:
o Whether the issue is with word recognition or comprehension
o Specific phonics, fluency, or vocabulary challenges
o Whether the issue appears across multiple assessments or is isolated
4. Strengths and Growth Opportunities Identification: I categorize findings into:
o "Strengths to Build Upon" - areas where students demonstrate proficiency
o "Growth Opportunities" - areas needing targeted instruction
