Learning Center
Plans & pricing Sign in
Sign Out

Data Driven Decision Making


									Data Driven Decision Making

             Missouri PBS Summer
             June 28 & 29, 2006

   Provide guidelines for using data for team
   Provide guidelines for using data for on-
    going problem solving
   Apply guidelines to examples
Improving Decision Making

From      Problem                     Solution

To     Problem      Problem Solving        Solution
Key features of data systems that work

   The data are accurate and valid
   The data are very easy to collect (1 % of staff time)
   Data are presented in picture (graph) form
   Data are used for decision-making
    –   The data must be available when decisions need to be
        made (weekly?)
    –   Difference between data needs at a school building and
        data needs for a district
    –   The people who collect the data must see the information
        used for decision-making.
Why collect discipline data?

   Decision making
   Professional accountability
   Decisions made with data (information) are
    more likely to be 1) implemented and 2)
What data to collect for decision

Use what you have:
 Attendance
 Suspensions/Expulsions
 Vandalism
 Office discipline referrals/detentions
   – Measure of overall environment. Referrals are affected by 1)
      student behavior 2) staff behavior and 3) administrative
   – An under-estimate of what is really happening
   – Office referrals per day per month
When should data be collected?

   Continuously
   Data collection should be an embedded part
    of the school cycle, not something “extra”
   Data should be summarized prior to
    meetings of decision-makers
   Data will be inaccurate and irrelevant unless
    the people who collect and summarize it see
    the data used for decision making.
Organizing Data for “active decision

   Counts are good, but not always useful

   To compare across months use “average
    office discipline referrals per day per month”
Using Data for On-going Problem

   Start with the decision, not the data
   Use data in “decision layers” (Gilbert, 1978)
    –   Is there a problem? (overall rate of ODR)
    –   Localize the problem
            (location, problem behavior, students, time of day)
   Don’t drown in the data
   It’s “OK” to be doing well
   Be efficient
Interpreting Office Referral Data: Is
there a problem?

   Absolute level (depending on size of school)
    –   Middle, High Schools (1> per day per 100)
    –   Elementary Schools (1> per day per 250)

   Trends
    –   Peaks before breaks?
    –   Gradual increasing trend across year?
   Compare levels to last year
    –   Improvement?
What systems are problematic?

   Referrals by problem behavior?
    –   What problem behavior is most common?
   Referrals by location?
    –   Are there specific problem locations?
   Referrals by student?
    –   Are there many students receiving referrals or only a small
        number of students with many referrals?
   Referrals by time of day?
    –   Are there specific times when problems occur?
Designing Solutions

   If many students are making the same
    mistake it typically is the system that needs
    to change, not the students.
   Teach, monitor and reward before relying on
Application Exercise

   What is going well?
   Do you have a problem?
   Where?
   With whom?
   What other information might you want?
   Given what you know, what considerations
    would you have for possible action?
SWIS: School-Wide Information

   SWIS Readiness Checklist
   SWIS Compatibility Checklist

   Transform data into “information” that is used
    for decision making
   Present data within a process of problem
    –   Use the trouble-shooting tree logic
    –   Big Five first (how much, who, what, where, why)
   Ensure the accuracy and timeliness of data

To top