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TIPS & TRAPS: A LAYMAN’S GUIDE TO USING SHELTER DATA FOR “HOMELESSNESS” RESEARCH

TIPS & TRAPS: A LAYMAN’S GUIDE TO USING SHELTER DATA FOR “HOMELESSNESS” RESEARCH. Canadian Conference on Homelessness Toronto, May 2005. Harvey Low City of Toronto Social Policy & Research Unit. Purpose of this Presentation. 1) Homelessness research & Toronto’s S helter data

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TIPS & TRAPS: A LAYMAN’S GUIDE TO USING SHELTER DATA FOR “HOMELESSNESS” RESEARCH

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  1. TIPS & TRAPS: A LAYMAN’S GUIDE TO USING SHELTER DATAFOR “HOMELESSNESS” RESEARCH Canadian Conference on Homelessness Toronto, May 2005 Harvey Low City of Toronto Social Policy & Research Unit

  2. Purpose of this Presentation 1) Homelessness research & Toronto’s Shelter data 2) How research helps address homelessness 3) The “demography” of homelessness 4) Goal:a“TIPS & TRAPS” guide for others exploring the use of shelter data for similar purposes

  3. Who are we?

  4. Social Development & Administration • Strategic policy & research arm • Assists with data & analysis • Works w/ other stakeholders (ex. From Streets to Homes) • Shelter Support & Housing, Hostel Services • Service planning & delivery arm • 10 directly-operated shelters • 58 purchase of service

  5. 1) Homelessness Research & Toronto’s Shelter Data • Early Challenges: • lack of data • inconsistent data collection • service & administrative data only • TIP: Foster relationships with “all” shelter providers. • TIP: Develop core set of information. • TIP: Establish data standards.

  6. Collection Challenges: • no consistency in collection (historical consistency) • no process for collection • errors during data capture • recognizing different types of Hostels • TIP: Use a common form (the “PINKS”). • TIP: Establish consistent and uniform times of collection. • TIP: Develop codes to differentiate hostel type, and avoid estimates & adjustments.

  7. Data Preparation Challenges: • long data “time lag” (time from collection to actual reporting) • errors during inputting • Privacy Challenges: • ensuring good data without compromising identity • TIP: Document all data assumptions & limitations. • TRAP: Using internal staff for data entry. • TIP: Use external data entry (minimize keypunching error). • TIP: Establish a unique identifiers.

  8. Toronto’s Core Shelter Data • Semi-Unique ID (initials + birthdate + gender) • Hostel & Hostel Type (derived) • Demographics: Age, Gender, Accompanying Spouse • Family Type (derived) • Number of Dependants • Residence 1 Year Ago • Reason for Service • Admission & Exit Date • Length of Stay (derived) • Reason for Disposition • TRAP: Collecting TOO MUCH DATA!

  9. 2) How Research helps address Homelessness • Reporting Challenges: • too technical (audience not kept in mind)! • data not maximized for planning uses • data not put to use for the public good • TRAP: Reporting on statistical methods – and not outputs! • TIP: Use data for BOTH internal and external purposes.

  10. Making the Data/Research RELEVANT • Policy Support: • Mapping & Toronto’s Shelter By-Law • Hadley Inquest • Internal Planning: • Next Steps Project • Reporting / Indicators: • Housing & Homelessness Report Cards • Vital Signs • Federation of Canadian Municipalities QOL System • TIP: USE! USE! USE!

  11. Making the Data/Research RELEVANT – cont’d • Positive Collaboration • St. Michael’s Hospital (street deaths) • Status of Women Canada “Young Women & Homelessness” • TRAP: Doing research for research sake! Not making research relevant to the community.

  12. 3) The “Demography” of Homelessness * Excludes provincial assaulted women’s shelters.

  13. * Excludes provincial assaulted women’s shelters.

  14. 4) A Users Guide – TIPS & TRAPS • TIP: Foster relationships with “all” shelter providers. • TIP: Develop core set of information. • TIP: Establish data standards. • TIP: Establish consistent and uniform times of collection. • TIP: Use a common form (the “PINKS”). • TIP: Develop codes to differentiate hostel type, and avoid estimates & adjustments. • TIP: Establish a unique identifier.

  15. 4) A Users Guide – TIPS & TRAPS - cont’d • TIP: Use external data entry (minimize keypunching error). • TIP: Document all data assumptions & limitations. • TIP: Use data for BOTH internal and external purposes. • TIP: USE! USE! USE! • TRAP: Using internal staff for data entry. • TRAP: Collecting TOO MUCH DATA! • TRAP: Reporting on statistical methods – and not outputs! • TRAP: Doing research for research sake! Not making research relevant to the community.

  16. For more information contact: Harvey Low City of Toronto Social Development & Administration Division Social Policy Analysis & Research Unit 416-392-8660 hlow@toronto.ca toronto.ca/demographics

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