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

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 Purpose of this Presentation

Who are we?

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

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

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: Use a common form (the “PINKS”). TIP: Establish consistent and uniform times of collection. TIP: Establish consistent and uniform times of collection. TIP: Develop codes to differentiate hostel type, and avoid estimates & adjustments. TIP: Develop codes to differentiate hostel type, and avoid estimates & adjustments.

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: Use external data entry (minimize keypunching error). TIP: Use external data entry (minimize keypunching error).  TRAP: Using internal staff for data entry. TIP: Document all data assumptions & limitations. TIP: Document all data assumptions & limitations. TIP: Establish a unique identifiers. TIP: Establish a unique identifiers.

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 Toronto’s Core Shelter Data  TRAP: Collecting TOO MUCH DATA!

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

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 Making the Data/Research RELEVANT TIP: USE! USE! USE! TIP: USE! USE! USE!

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

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

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

4) A Users Guide – TIPS & TRAPS - cont’d TIP: Use external data entry (minimize keypunching error). TIP: Use external data entry (minimize keypunching error). TIP: Document all data assumptions & limitations. TIP: Document all data assumptions & limitations. TIP: Use data for BOTH internal and external purposes. TIP: Use data for BOTH internal and external purposes. TIP: USE! USE! USE! 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.

For more information contact: Harvey Low City of Toronto Social Development & Administration Division Social Policy Analysis & Research Unit toronto.ca/demographics