2023 Data & Evaluation Toolkit



Read This Section To: Understand the scope and framework of this toolkit.


What is data analysis and why is it relevant for legal aid organizations? 

Data analysis is a process of examining data to produce useful information. Analyzed data may shed light on trends, challenge or validate existing assumptions, inform cost saving efforts, and promote greater efficiency. Legal aid organizations use data to make better-informed decisions on all kinds of questions, from operations and fundraising to staff supervision and program design. 

Data analysis projects can help address questions such as the following: 

  • To what extent did this training impact participant knowledge of a legal topic? 
  • Which communities would benefit from a new outreach campaign?
  • Where should a new office be located?
  • Is this website user-friendly?
  • Is a new program cost effective?
  • How are caseloads distributed across attorneys?

A whole host of data sources could be used to answer the questions listed above. These sources include end-user surveys, client demographic data, program finance documentation, and participant interviews. This toolkit will go into detail on selecting appropriate data and where to go once data sources are identified. 

What does this toolkit offer to legal aid organizations? 

This toolkit provides an overview of data analysis projects, tailored to legal aid providers with field-specific examples. This toolkit will be particularly helpful to those who: 

  • Want to use data to answer a specific question but are unsure how to get started,
  • Are unfamiliar with data analysis processes and terms,
  • Have an interest in improving data literacy at their organization, and/or
  • Are interested in learning about evaluations and how they make use of data analysis. 

Beyond a basic overview of common types of data analysis, this toolkit does NOT provide in-depth information on specific data analysis methods. Although this toolkit may provide enough context for providers to undertake simple data analysis efforts, more complex data projects will likely need trained data analysts or researchers. This toolkit provides a framework and shared vocabulary for engaging in data analysis projects but does not provide technical instructions on how to conduct data analysis. 

There are many actions that legal aid providers of all sizes can take today to start improving their organization’s relationship with data. The goal of this toolkit is for readers to walk away with a better understanding of data analysis in order to make greater use of data at their organization. 

How is this toolkit organized? 

The toolkit is broken down into the following sections:

1. Overview of Data Analysis Projects: Gain familiarity with the inputs and lifecycle of a data analysis project.

2. Defining the Analysis: Walk through the questions, audiences, and logistical considerations that should be addressed at the start of a project.

3. Collecting the Data: Review different data types, sources, and collection strategies, and understand why data type matters for analysis purposes.  

4. Preparing and Analyzing the Data: Explore concepts, vocabulary, and approaches relating to preparing and analyzing data

5. Presenting and Learning from the Data: Consider strategies for presenting data and processing analysis results.

6. Building a Strong Data Culture: Review tips for developing a robust data culture and staffing for data analysis.

7. Overview of Evaluations: Learn about common use cases and examples of evaluation in legal aid.

8. Data Analysis Project Examples: Explore examples of data analysis projects on online intake systems, language access, and staff workloads.


Toolkit's Table of Contents


What is data analysis and why is it relevant for legal aid organizations?

What does this toolkit offer to legal aid organizations?

How is this toolkit organized?

1. Overview of Data Analysis Projects

What resources are needed for a data analysis project?

When should an organization engage in a project involving data analysis?

What is the lifecycle of a data analysis project?

How does equity relate to data analysis?

2. Defining the Analysis

What is the key question behind the analysis?

How and by whom will results be used?

What will be measured?

What comes next?

3. Collecting the Data

How is data categorized?

How is data collected?

How is data measured?

Why do data types and levels of measurement matter?

4. Preparing and Analyzing the Data 

What is data preparation and analysis?

How is data prepared and why does it matter?

How is data analyzed?

5. Presenting and Learning from the Data

What are different ways to present the analysis?

What are additional examples and resources for data visualization?

How should the audience approach data analysis results? 

What’s next?

6. Building a Strong Data Culture 

How can organizations improve their ongoing use of data?

How can organizations maintain high quality data?

How can organizations ensure they have adequate staffing for data analysis work?

7. Overview of Evaluations

How do evaluations fit in to data analysis work? 

What is program evaluation?

Why would an organization do a program evaluation and how do they start it? 

What are Technology Innovation Grant (TIG) evaluations?

8. Data Analysis Project Examples

Project Example 1 - Website Example

Project Example 2 - Language Access Example

Project Example 3 - Performance Review Example

9. Acknowledgements

Go to Next Page: Overview of Data Analysis Projects

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Table of Contents


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