1 / 29

Getting to Outcomes: Next Steps

Getting to Outcomes: Next Steps. Doug Tanner Youth Catalytics 978-544-2067 dtanner@youthcatalytics.com. Workshop Objectives. Know how a data management project can help: Improve program design Demonstrate effectiveness Highlight the best work being done Compete for funding, and

yitta
Télécharger la présentation

Getting to Outcomes: Next Steps

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Getting to Outcomes: Next Steps

  2. Doug TannerYouth Catalytics978-544-2067dtanner@youthcatalytics.com

  3. Workshop Objectives Know how a data management project can help: • Improve program design • Demonstrate effectiveness • Highlight the best work being done • Compete for funding, and • Mobilize public support.

  4. Workshop Objectives Learn about: • Identifying expected outcomes, and • defining objectives and incremental indicators of success consistent with your mission.

  5. Workshop Objectives • Understand the elements and stages of a data management planning process • Be familiar with common barriers and costs associated with data management

  6. Workshop Objectives • Learn how quality data can influence and inform the strategic planning process. • Explore options for tracking and using data efficiently at reasonable cost.

  7. Examples of Data Compilation: • City of Pittsfield Neighborhood evaluation • Combining & analyzing data from multiple sources • DIAL/SELF (Greenfield, MA) Transitional Living Program housing outcomes • Sorting and interpreting data from a single collection source (Lets go to visit source tables in Excel then come back to PowerPoint to review graphs)

  8. Building permits over 20K by Pittsfield neighborhoods:

  9. Intake by age range

  10. Issues at intake by age

  11. Housing Outcome Data

  12. Project Planning 101: • Bring key people together at each stage of planning process • Administration, program directors and supervisor participation is critical in early stages, but direct care staff can be helpful too (ask questions!)

  13. Project Planning 101: • Initial planning stages require a deep understanding of the resources (funding, technology/equipment, and staff time) required to plan, implement and maintain a data management project/data driven culture.

  14. Project Planning 101: • It may be worthwhile to invest in a consultant or devote substantial administrative time to produce useful estimates of the time and cost involved in implementing and maintaining a data driven culture

  15. Planning Guidelines • Understand the purpose of your project - what will this data do for your organization? • Identify data priorities • Plan to start small and efficiently – you can grow as you learn and achieve - look for the intersection of what data you can easily obtain and what you would want to know in an ideal world! (go to flip chart )

  16. Staff Commitment & Support • As you move into more detailed planning, direct care staff input becomes extremely important. • Involve staff in a formal way and carefully assess what support they will need to succeed! • Design formal systems for Training, Support and Accountability

  17. Gather the Information You Need to Get Started • Reports/data you already need for funders • Identify information for internal evaluation and improvement (even if it isn’t currently required by funders) • Develop a functional draft of outcomes, objectives and indicators (your dataset) prior to shopping for a database or building a data collection system

  18. ImProve OutcomesSM A Brief Summary

  19. ImProve OutcomesSM Language • Objectives = desired participant changes or achievements • Indicators = measurable events • Outcomes = level of achievement

  20. ImProve OutcomesSM& Logic Models Basic Logic Model Inputs resources Outputs actions Outcomes achievements ImProve OutcomesSM Model Objectives expectations Outcomes achievements Inputs resources Outputs actions *Identify tracking method Indicators events

  21. ImProve OutcomesSM is… • Extension of logic models • Based on incremental change • Means of prioritizing information • Method of categorizing information

  22. Bloom’s Taxonomy Levels of Learning Mastery • Knowledge/Comprehension • (learn about it) • Application • (use it, try it out) • Synthesis • (integrate with other knowledge)

  23. Indicators Should be SMART • Specific • Measurable • Achievable • Relevant • Timely

  24. Indicators are Activity or Behavior Based (observable) • Use active verbs to describe indicators • Look for achievement opportunities at levels that are relevant to the services, time frame or intervention level of your program • Indicators reflect participant capacity for positive change and choices that indicate forward movement

  25. Database Options • Web/Cloud Based • Require reliable, high speed internet connection(s) • Each user has own license – can access from anywhere • Easy to monitor data entry • Evaluate capability and cost of compilation, sorting and reporting • Carefully evaluate ownership of data and “worst-case scenarios” (e.g., you or the provider go out of business?) • PC-Based • You own software and data that is on your computer • Speed depends on speed of machine • May require additional software to run the database • Can be difficult to synchronize data from multiple sources. • Ease of data retrieval depends a lot on initial design and software used.

  26. Tools you can use – now! • 1. Surveys: • Useful to capture information from participants • You have to ask the right question(s). That takes planning and some experimentation to gather aggregateable data. • Results can be compiled in Excel – but consider using Survey Monkey where you can get reports and export to excel. • 2. Microsoft Access: • Good for demographic data and tracking objectives and indicator completion – data that changes or needs to be cross-referenced. • Inexpensive, but requires expertise to develop functional applications • Easy to retrieve data through queries

  27. More Tools you can use – now! 3. Daily Logs (paper or software) • Most useful if data is aggregated and entered into a database or spreadsheet regularly (daily, weekly or monthly) • Like surveys, the right questions have to be asked to get useful, accessible information • With proper planning, could be used to track a variety of participant achievements. 4. Exit interviews! • Build some of the questions to have aggregateable answers (e.g., multiple choice, name at least one xxx, etc.)

  28. Implementation Questions? Please Contact: Doug Tanner dtanner@youthcatalytics.org 978-544-2067

  29. Training Questions? Please Contact: Cindy Carraway-Wilson cwilson@youthcatalytics.org 203-561-6099

More Related