Robert W. Ressler, Brandeis University
What’s the issue?
A mission statement is like a window into the soul of an organization. For nonprofits, the need to communicate with many diverse stakeholders creates an incentive to craft a mission statement that is reflective of an organization’s purpose, values, and on-the-ground activities. That makes a nonprofit’s mission statement a uniquely reliable source of information regarding actual organizational characteristics, especially ones that are not elsewhere easily categorized in a single activity code on a tax form, such as organizational identity, target audience, or theory of change. In addition, nonprofits must provide a mission statement to the IRS. But until recently, the technology simply didn’t exist to easily access and process nonprofit mission statements (for example, high-speed computers running text processing software on digitized tax forms).
My recently published NVSQ article with Brad Fulton and Pamela Paxton, titled Activity and Identity: Uncovering Multiple Institutional Logics in the Nonprofit Sector, takes advantage of technological developments to illustrate how mission statements can be used to categorize nonprofits on new, dynamic, dimensions. Specifically, we estimate how many religious organizations span the nonprofit sector in fields like education and health care. When we started the project five years ago, we chose a dictionary approach, or using a list of specific terms selected by content experts, to process mission statements. But recently, more complex and resource intensive classification methods have been developed using machine learning. So as our article has approached publication, I’ve had to spend a little time thinking about the relative benefits of a dictionary approach to a machine learning one. I’m sharing those takeaways here.
What are the benefits of using a text-based dictionary approach to classify nonprofit organizations based on mission statement?
A text-based dictionary approach offers several attractive characteristics to individuals attempting to classify nonprofits beyond their activity code.
Transparency: The words and phrases used in the dictionary are clear and visible, providing greater transparency into the classification process which allows for it to be easily replicated, reviewed, and evaluated by others.
Reliability: A dictionary approach ensures reliability in the classification process. Since dictionaries use clearly defined lists of words or phrases to classify organizations, their appropriateness to a given scenario can be easily assessed.
Ease of use: A dictionary approach is relatively simple, easy to implement, and does not require significant resources. Well-crafted dictionaries can be used for many practical purposes with little specialized skill, computational power, or complicated software.
Machine learning classification methods offer their own advantages, such as flexibility and accuracy. Algorithms can refine their classification systems as they adapt and learn from new data, potentially making them more accurate when classifying complex concepts over time. This advantage, however, is considerably outweighed by the amount of time and effort it takes to train the algorithm, and the extra attention required when applying the algorithm to a context for which it was not initially designed.
With relative ease, however, people can use a dictionary approach to analyze the mission statements of organizations to gain a better understanding of the landscape of particular issue areas. Classifying nonprofits based on broad categories such as women, children, or cultural communities can similarly help identify gaps in services.
A dictionary approach to the classification of nonprofit mission statements could also facilitate funding allocation based off broad criteria. By categorizing nonprofits by mission statement, foundations can identify organizations that may already be working in issue areas of interest. Similarly, practitioners can identify organizations with complementary missions, find new collaborators, and work together to achieve shared goals.
Nonprofits are a crucial part of society and provide a complex range of programs dedicated to supporting vulnerable populations, addressing social issues, and promoting public welfare. Categorizing and understanding these organizations, however, need not be an insurmountable challenge for those with limited resources. A dictionary approach has several characteristics that make it a useful tool for meaningfully classifying nonprofits based off their mission statements, translating ephemeral organizational qualities into useful data. We hope the use of such a method can help promote nonprofit work on the ground, and a deeper understanding of variation in nonprofit mission and identity.
Click here to read the full article: Ressler, R. W., Fulton, B. R., & Paxton, P. (2023). Activity and Identity: Uncovering Multiple Institutional Logics in the Nonprofit Sector. Nonprofit and Voluntary Sector Quarterly. https://doi.org/10.1177/08997640231164375