Using Data to Persuade

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Using Data to Persuade
Preparing Oklahomans to Succeed in the
Workplace, in Education, and in Life.
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Why is using data to persuade relevant to grant
writing?
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Age of Accountability

• Data collection, analysis and reporting mandated
  by the federal government.
• States required to set standards, assessment
  tools, and collect reports.
• School districts must carry out testing, and
  report disaggregated data.
• All schools accountable for progress of all
  children on high stakes tests.

Karen Greenwood Henke, Using Data Effectively in
Decision Making, Nimble Press, 2007.
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Moving Beyond the Mandates

• The current environment is an opportunity to
• Use data to transform programs to courses,
  teaching, learning, and administration.
• Inform decisions about everything from classroom
  instruction to professional development.
• Provide a rationale for decisions that parents,
  teachers, taxpayers, students and grant reviewers
  can understand.

Karen Greenwood Henke, Using Data Effectively in
Decision Making, Nimble Press, 2007.
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Data-Driven Decision Making

• Process of making choices based on appropriate
  analysis of relevant information.

Data can influence change
Karen Greenwood Henke, Using Data Effectively in
Decision Making, Nimble Press, 2007.
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10 Reasons to Bring Data Into Decision Making

• 1. Assess the current and future needs of
  students.
• 2. Decide what to change.
• 3. Determine if goals are being met.
• 4. Engage in continuous school improvement.
• 5. Identify root causes of problems.
• 6. Align instruction to standards.
• 7. Provide personalized instruction.
• 8. Track professional development.
• 9. Meet accountability provisions.
• 10. Keep constituents informed about progress.

Karen Greenwood Henke, Using Data Effectively in
Decision Making, Nimble Press, 2007.
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Many Ways to Use Data

• As pieces of information (as individual facts)
• As sets of information (as groups of facts)
• Over time (as trends)
• In levels (institutions, departments and
  programs)
• In relationships (program in relationship to the
  institution, relationships among counterparts,
  and contrasting pieces of information)
• Creatively (providing perspectives and
  proactively)

David C. Smith, Using Data Effectively, AACTE
Annual Meeting, 2005
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To Persuade, Consider

• The kinds of data.
• The uses and interaction among pieces of data.
• The uses and interaction among sets of data.

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Why use comparisons?

• The importance of proportions.
• Percent of faculty or students, budget, dollars
  generated.
• To establish the context for a piece or set of
  information.
• Student evaluation of teaching or performance
  within the school/district or compared to other
  programs.
• Student placement, earnings, licensure.

David C. Smith, Using Data Effectively, AACTE
Annual Meeting, 2005
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Enrollment Data

• Hale North Technology Center
• Information Technology Enrollment
• Fall 2004 3,050 FTE
• What does this tell us? (precious little)

Modified Example David C. Smith, Using Data
Effectively, AACTE Annual Meeting, 2005
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Enrollment Data continued

• Hale North Technology Center
• Information Technology Enrollment
• Fall 2004
• Secondary 1,450 FTE Students
• Postsecondary 1,600 FTE Students
• Now what do we know? (just a little more)

Modified Example David C. Smith, Using Data
Effectively, AACTE Annual Meeting, 2005
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Enrollment Data continued

• Hale North Technology Center
• Information Technology Enrollment Fall 2004
• Secondary 1,450 FTE Students
• Postsecondary 1,600 FTE Students
• Total 3,050 FTE Students
• Institution Enrollment
• Secondary 17,006 FTE Students
• Postsecondary 4,500 FTE Students
• Total 31,506 FTE Students
• (Enrollment within the context of the
  institution tells us much more than simply the
  enrollment in the unit.)

Modified Example David C. Smith, Using Data
Effectively, AACTE Annual Meeting, 2005
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Enrollment Data Now what do we know?

• That the HNTC IT program has 8.5 of the
  secondary students
• That the HNTC IT program has 35.6 of the
  postsecondary students
• That the HNTC IT has 9.6 of the total students
• (Now we are beginning to know something about
  the size of the program within the context of the
  institution and can begin to infer something
  about the prominence of the program within the
  institution.)

Modified Example David C. Smith, Using Data
Effectively, AACTE Annual Meeting, 2005
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Group Exercise
Karen Greenwood Henke, Using Data Effectively in
Decision Making, Nimble Press, 2007.
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Looking at Examples

• Note the kinds of data used.
• Note the manner in which these data are
  presented.
• Note the nature of the conclusions and
  comparisons drawn.

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Key Definitions

• High-Demand Occupation in which state, local, or
  regional labor market indicators show employment
  demand exceeds supply.
• High-Skill Occupations that require
  industry-recognized certificates, credentials,
  postsecondary training, apprenticeship or
  degrees.
• High-Wage Occupation with a statewide average
  hourly rate equal to or greater than the average
  hourly rate of all occupations as reported by the
  Oklahoma Employment Security Commission. (15.35
  per hour)

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Programs of Study Justification Examples

• Information Technology Cluster
• 18.5 projected growth by 2012 (632,000 jobs).
• Computer Software Engineers (80 openings, 4.14
  increase)
• Computer Support Specialist (270 openings, 3.01
  increase)
• What data comparisons were used?

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Program of Study Justification Examples

• Information Technology Career Cluster
• Network Computer System Administrator
• Average Annual Wage 43,580
• Median Annual Wage
• Nation 59,900
• State 51,300
• Listed as one of Oklahomas top 30 fastest-growth
  occupations, 2004-2014
• What new data were introduced to
• strengthen the rationale?

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Program of Study Justification Examples

• Architecture and Construction
• U.S. Department of Labor expects a growth slower
  than the average however, the analysis indicates
  opportunities are best for individuals with at
  least a two-year postsecondary degree.
• Finance
• U.S. Department of Labor expects growth slower
  than average.
• Manufacturing
• Both Department of Labor and Department of
  Commerce indicate slower than average growth
  rate. However, local job analysis indicates a
  higher than average demand for trained
  machinists.
• Is is a good idea to implement a Finance Cluster
  based upon the rationale/data provided?

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Program of Study Justification Examples

• Health Science
• LPN License (High Skill)
• XX WIB Emphasis (High Demand)
• Transportation Distribution, and Logistics
• 19.62 hour (High wage)
• XX WIB Emphasis (High demand)
• How could these data be strengthen?

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Critical Success Measures Examples

• Goal Increase Alliance enrollment, retention,
  and transition, for both secondary and
  postsecondary
• Factor 1 Increase Alliance Enrollment (5)
• Measurement College Alliance Enrollment
• Factor 2 Increase postsecondary/employment
  transition by 5
• Measurement Transition to postsecondary
  institutions
• What critical success factors/elements are
• missing in the measurements?

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Critical Success Measures Examples

• Goal Develop effective Cooperative Alliance (CA)
  Professional development activities.
• Factor 1 Provide sending school principals CA
  information and updates at first meeting.
• Measurement 80 of sending schools will have
  principal/representative at meeting.
• Factor 5 Provide sending schools counselors CA
  information and enrollment stats at first
  meeting.
• Measurement 80 of sending schools will have at
  least one counselor or career advisor present.
• Do these measures evaluate effectiveness?

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Critical Success Measures Examples

• Goal Promote Career Cluster pathways and
  seamless transition from secondary/postsecondary
  into high-demand, high-wage positions in
  workforce.
• Factors Increase number of students enrolled in
  Alliance courses by Career Cluster.
• Increase number of program completers.
• Improve relationships among college, tech center
  and partner schools.
• Increase awareness and participation of schools
  in the development and implementation of Career
  Clusters.
• Measurement Alliance Enrollment
• Does the measurement identified capture the
• breath and depth of of the goal?

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Data to Consider

• Career Cluster Profiles
• ODOC Workforce/Industry Regional Reports
• WIA District Profile
• Bureau of Labor Statistics
• 10th - Grade PLAN Assessment
• Regional Education Opportunities
• Salary

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Labor Market Resources

• Oklahoma Department of CareerTech (Career
  Clusters)
• www.okcareertech.org
• Oklahoma Department of Commerce
• http//www.okcommerce.gov/
• Oklahoma Employment Security Commission
• http//www.oesc.state.ok.us/
• ONet Occupational Information Resource
• www.onetcenter.org
• Oklahoma Career Information System (OKCIS)
• http//okcareertech.org/guidance/OKCIS.html
• U.S. Census
• http//www.census.gov

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Educational Statistics

• Oklahoma Office of Accountability
• http//www.schoolreportcard.org
• Oklahoma State Regents for Higher Education
• http//www.okhighered.org
• Oklahoma Department of Education
• http//www.sde.state.ok.us
• Oklahoma Department of Career and Technology
  Education
• http//www.okcareertech.org
• Alliance
• Tech Prep
• Career Clusters

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Contact

• Sheryl Hale, Ed.D.
• Oklahoma Department of Career and Technology
  Education
• Shale_at_okcareertech.org