Data Analysis Framework: Defined

A simple-to-use tool that helps legal services organizations use data to identify efficient ways to offer high quality and effective services. The tool includes:

  • 5 high level and 118 detailed data questions about eligible people and their legal needs
  • Instructions on how to perform various types of analyses
  • Simple analyses for those just getting started
  • Complex analyses options for data-savvy organizations
  • Recommendations about data sources
  • Example analyses from legal aid programs nationwide

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Things to think about

Watch for Data Patterns

  • The data analyses you undertake are going to reveal patterns in your data.
  • Watch for patterns that are unexpected and explore further when you find them.
Data Pattern Example 1: Amish County Anomaly Data Pattern Example 2: Senior Trends
Spatial analysis of intake data from one legal aid uncovered a large portion of the service area from which no intakes originated over a 5-year period. At first, staff were surprised and concerned, but then they realized that the area in question is Amish country, from which intakes are not expected. One legal aid became concerned about a drop in both the number of intakes and cases opened for seniors. Analysis of census data revealed that both the number and proportion of seniors in poverty in the service area had likewise decreased indicating that the intake and case data were mirroring need among this population. While the staff concern was ameliorated, the legal aid remained committed to assisting seniors and decided to continue its significant outreach efforts to this population.

Run every data finding by staff

  • A statistical analysis of legal aid data will never, by itself, provide the whole picture.  
  • Involve staff in the process early on by asking for their assistance interpreting findings because they know the clients best. 
  • Staff input will guide your analysis in the right direction.
Staff Input Example 1: Pre-Consolidation Lingering Habits
One legal aid noticed that a larger than expected proportion of its domestic violence cases were originating in one of the counties in its service area. The staff wondered if that indicated a concentration of domestic violence in that county, which would require an increased regional focus on DV issues.  But organizational leaders explained that the county in question used to be its own independent organization focused almost exclusively on DV issues.  Leadership suggested that the community surrounding that office likely still thinks it has that primary focus. The legal aid decided to conduct increased outreach in that county about all the other services available.

Dealing with difficult data

  • Uncollected data: Sometimes you do not or cannot collect data about something that is important to your clients.
Uncollected Data Example 1: Formerly Incarcerated Uncollected Data Example 2: Disabled
One legal aid was curious about its work with formerly incarcerated individuals, but for policy reasons, it decided not to ask clients about previous incarceration.  That legal aid is limited to external data sources with information about the re-entry population. One legal aid wanted to analyze data about the disabled clients it serves, but it wasn’t collecting disability information consistently.  The legal aid decided to use proxy information as the best alternative.  The proxy variables they used were: 1) Income = Disability Financial Assistance or SSI, and/or 2) Legal Problems = SSDI, SSI, Mental Health, or Physically disabled rights.
  • Incompatible data formats: Sometimes your internal data and the external data are in different formats or measurements, which makes it difficult to compare.
Incompatible Data Example 1: Domestic Violence Incompatible Data Example 2: Veteran Status
One legal aid wanted to compare internal data about its domestic violence clients with external data about domestic violence victims in the state. It gathered information internally about DV clients, but the only external DV data available from the state courts did not include poverty information. Thus, those external data lacking poverty information could not be compared to the internal data, which is limited by levels of poverty. Still, trends in both sets of internal and external data could be considered together for deeper understanding about DV victims. One legal aid’s veteran field was a drop down question labeled “Veteran in Household?” with answer options of “Yes”, “No”, and “N/A”. The Veteran Status Census field refers only to an individual, not members of a household, making a comparison of internal and external data problematic.  The legal aid is considering having two fields: one to capture client veteran status (for comparison to Census veteran data), and a separate field to capture the veteran status of household members.

Data Integrity

  • Analysis of erroneous or incomplete data is counterproductive as a gauge of client service effectiveness and efficiency.  
  • Before starting to analyze your data, it’s important to assess the quality and reliability of the data. 
  • Audit your data and identify the variables about which you wish you knew more, but that consistently show missing data; erroneous data; or data that are hard to interpret when you run a report analyzing the variable. 
  • Clean up the data issues you uncover and develop standard data entry procedures to ensure data integrity moving forward.
  • See Perry, Rachel, “Data Integrity: The Untapped Treasure of Legal Services Data,” MIE Journal, Summer 2014, pp. 22-26.

Factors that can skew your data

  • Case Acceptance Guidelines/Organizational Priorities: Limited resources mean that legal aids are not able to help everyone in need so they are forced to prioritize their service offerings, usually via case acceptance guidelines. That prioritizing means that legal aid case data will be skewed in favor of the prioritized legal issues and/or client groups. 
  • Not a Random Population: The clients who come to legal aid are not a random sample of an area’s poverty population. Their decision to come to legal aid may be influenced by location and/or transportation options, a legal aid’s reputation and/or visibility, economic or even popular trends, etc. 
    • Example 1: Spike in clients bringing foreclosure cases to legal aids as a result of the housing market crash.
    • Example 2: An NFL player abuses his wife and it is caught on camera. There is public outcry and legal aids see a spike in clients with domestic violence issues.
  • Even Imperfect Analyses are Useful: You can still analyze skewed data and get a deeper understanding of your clients and their legal needs.  Randomization and zero skew are not required for non-academic analyses.  Just make sure to understand these factors and how they influence your data.