Data Analysis and School
Improvement
Targeted Improvement Plan Development
Activities
TAIS Framework
Background and Information
Welcome and Introductions
Sr. Education Specialist
713.744.6596
Richard.blair@esc4.net
Introductions
Welcome
Tools for Today
• Results: The Key to Continuous School
Improvement, 2nd Ed.; Schmoker, 1999.
• Leading School Change; Whitaker, 2010.
• Campus Data
• 2014-15 Targeted Improvement Plan
Data Analysis and School
Improvement
Data Analysis
• Important part of the Texas Accountability
Intervention System (TAIS) Continuous
Improvement Process
– Not a one-time activity
– Not ONLY an end-of-year activity
• Looking for patterns and trends
• Important for decision-making on plans
Types of Data
• Qualitative –
– characteristics,
attributes, properties,
qualities, etc. of a thing
is described
– It is data in language,
not numbers.
– Categorical data
– High validity
• Quantitative –
– Numerically counted or
expressed is collected
– Easily put into tables,
charts, and graphs
– Often deals with scale
measurements
– Lacks description and
explanation
Qualitative Data Example
• Freshman Class
–
–
–
–
Friendly demeanors
Civic minded
Environmentalists
Positive school spirit
Quantitative Data Example
• Freshman Class –
– 672 students
– 394 girls, 278 boys
– 68% are on the honor
roll
– 150 student accelerated
in mathematics
Qualitative and Quantitative
At your table, please spend a few minutes
answering the following questions. We will
share out answers in about 10 minutes.
1. What quantitative data do we have?
2. What qualitative data do we have?
3. What data that we do not currently have would
be helpful in developing our plans?
4. Do we have enough data to do our work?
Why do We Analyze Data?
The purpose of analyzing data is to
obtain usable and useful information.
The analysis, irrespective of whether the
data is qualitative or quantitative, may:
Why Do We Analyze Data?
• Describe and summarize data
• Identify relationships between/among
variables
• Compare variables
• Identify the difference between variables
• Forecast outcomes
Common Data Myths
• Complex analysis and
big words impress
people.
• Analysis comes at the
end after all data are
collected.
• Most appreciate
practical and
understandable
analyses.
• We must think about
the analysis before
starting so that we have
the data we want to
analyze.
Common Data Myths
• Quantitative analysis is
the most accurate type
of data analysis.
• Data have their own
meaning.
• Quality of analysis
process is what matters.
• Data must be
interpreted. Numbers
do not speak for
themselves.
Identify Limitations
Interpreting the Data
Understanding the Data
Organizing the Data
Organizing the Data
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All forms/questionnaires in one place.
Check for completeness and accuracy.
Remove incomplete data points
Record your decisions
Enter data
– By hand
– By computer
Understanding the data
•
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•
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What do you want to know?
How will you use the data?
Compare/Contrast data?
Does data exist?
– Worst case: develop plan and data cannot be
obtained
• Is data relevant?
Interpreting the Data
• Numbers are not an end unto themselves
– Must be interpreted by someone
– Meaning given by people
• Attaching meaning to the data
– What does it mean?
• 55 students passed the test
• 25% met the standard
• 4 coded other
Interpreting the Data
• Fair and careful
judgments
– Often can be interpreted
in different ways
– Articulate how
interpreted
• Not done in isolation
– Meetings
– Groups
– Input
Interpretation of Data
• What did you learn?
– Ah-has?
– New?
– Expected?
• Surprises?
• Further study needed?
Identify Limitations
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•
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Be explicit
Be prepared to discuss
Be honest
Do not make claims without real analysis and
design
• Make sure you have adequate information to
make claims
What gets measured gets done.
-Peters 1987
Teams get results.
-Katzenbach and Smith 1993
But what does “pretty well”
mean?
-Byham 1992
Teamwork
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Data Analysis is often done by ONE person
Data Analysis should be conducted by a TEAM
Collaboration is important
Different viewpoints are important in
addressing needs
• All members of team must participate
Meetings Table Talk
• What is a major cause of
"unproductive, unrewarding
meetings" (p. 15)?
• How well do your data meetings
match the structure presented on
p. 119-120?
Effective Goals
• SMART
– Specific
– Measurable
– Attainable
– Realistic
– Time-Bound
Effective Goals
• Few in number
– Remember this is for a TARGETED IMPROVEMENT
PLAN
– Address the areas identified in the data analysis
• Allows for collaboration and collegiality in
meetings
– Failure can result in ineffective meetings
Measurable Goals
“Far too many teams casually accept goals
that are neither demanding, precise,
realistic, nor actually held in common. . . .
Teamwork alone never makes a team.”
(p. 23)
- Katzenbach and Smith
Specific Goals
• Convey a message
directly to teachers that
they are capable of
improvement
• Provide a basis for
rational decision
making
• Enable teachers to
gauge their success
Targeted Improvement Plan Activity
• In your school teams, look over your targeted
improvement plan.
• Look for your goals.
• Identify the goals which are specific and
measurable.
• Do these lead teachers to know exactly what
to do in their classrooms to affect change in
the goal area?
• Rewrite one goal to be more specific.
Data Driven Instruction
Quality Data
Jigsaw
• Assigned Tables will review the appropriate
section of the CSF document.
• Discuss the relevance of the section.
• Report out to the group.
Data Analysis and School
Improvement
Reducing Threats
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Elimination of Staff
High Stakes Prematurely Implemented
Collective analysis
Pay for performance
Teacher autonomy in data choice
Create successes
TELPAS
STAAR – Alternate 2
STAAR-A
•
STAAR
Preliminary Data
• Pearson Reports have a TON of data
• Reports on all students tested
– Important when looking at individual students
– Important in comparing with accountability data
• Provides an “Early Indicator” of success or
improvement needed
• Data files available as well as paper reports
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Reports available on TAMS
Data files available on TAMS as well
Updated files
Available to upload to other data systems to
assist in data analysis
Common Data Systems in Region 4
• eduphoria!
• DMAC
• On Data Suite
What to Look for?
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Trends
High/Low performance
Comparison of groups
Teacher/classroom level data
Student level data
Meaning of Data
• Remember, data only has meaning that we
give it
– Important to be honest about data
– Important to be honest about trends
– Important to be honest about specific
teachers/students
– Important to determine the relationship of data to
school performance
Reminders
• Look for a variety of data points
• Be sure to develop goals based on your data
information
• Make sure you are able to analyze interim
data as you go through your year
• Determine what data you need to determine
effectiveness of interventions
Look at your Reports/Data
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•
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Take time to analyze your data.
Look for trends, highs, lows, etc.
Determine areas for improvement
Identify a goal
– Write a SMART goal which will help teachers
understand the role they have in the success of
the goal
– Determine data needed to know goal is met
Look at your TIP
• Determine if you made progress
• What interventions worked?
– Why?
– Continue?
– Implement in other area?
• What didn’t work?
– Why?
– What to do next?
TAIS Process
• One component of the continuous
improvement process
• Occurs frequently during the year
• Continue process before school starts
• Begin year with updated plan
Questions?