By Jewlya Lynn, CEO, Spark Policy Institute; Sarah Stachowiak, CEO, ORS Impact
It’s easy for evaluators to sometimes get tied up in the technical terms around our work, leaving lay people unclear on what some of our decisions and choices mean. Without care, we can also risk being opaque about what a particular design can and can’t do. With this blog, we want to untangle what we think our design will tell us, and what it won’t do.
With this research study, ORS Impact and Spark Policy Institute are seeking to understand the degree to which the collective impact approach contributed meaningfully to observed positive changes in people’s’ lives (or, in some cases, species or ecosystems). In other words, when and under what conditions did collective impact make a difference where we’re seeing positive changes, or are there other explanations or more significant contributors to identified changes? While we’ll learn a lot more than just that, at its heart, that’s what this study will do.
Our primary approach to understand the core question around contribution and causal relationships will be to use process tracing. Process tracing provides a rigorous and structured way to identify and explore competing explanations for why change happens and to determine the necessity and sufficiency of different kinds of evidence to support different explanations that we’ll find through our data collection efforts.
To implement the process tracing, we will dig deeply into data around successful changes—a population change or set of changes plausibly linked to the CI efforts—within six sites. We’ll explore these changes and their contributing factors with data from existing documents, interviews with site informants, focus groups with engaged individuals, and a participatory process to review and engage in sense-making with stakeholders around the ways in which we understand change to have happened. We’ll try and untangle the links between implementation of the collective impact approach and early outcomes, the links between early outcomes and systems changes, and the links between systems changes and ultimate impacts.
Figure: Diagram of “Process” for Tracing
Note: Future blogs will provide more information on the different rubrics we’ve developed and are using.
Using a process tracing approach also means that we’ll explicitly explore alternate hypotheses for why change happened—was there another more impactful initiative? Was there a federal funding stream that supported important related work? Was there state policy that paved the way that was unconnected to stakeholders’ work? Would these changes have occurred whether collective impact was around or not?
Additionally, we’ll look at two sites where we would expect to see change but don’t, with the expectation that these sites can help us understand if the patterns we’re seeing at successful sites are absent or showing up differently, findings that would help give us more confidence that the patterns we’re seeing are meaningful.
Process tracing as our approach does mean that our unit of analysis—the sphere within which we will be exploring change and causal relationships—is going to be approximately eight sites. While we hope to find sites where a cluster of impact outcomes result from a specific set of activities (or “process”), we are choosing to go deeply in a few sites with an approach that will provide rigor around how we develop and confirm our understanding of the relationships between activities and changes. And because we are looking across diverse sites, working on varied issue areas (e.g., food systems, education, environmental issues, etc.) and at different scales (e.g., cities, multiple counties, entire states), identifying patterns across diverse contexts will increase our confidence around what collective impact conditions, principles and other contextual factors are most related to these successes.
With more data around if and when we find causal relationships, we will also go back to our data set of 22 sites that we are also engaging with early to see if we can, likewise, find similar patterns to those found through the process tracings. For these sites, we’ll use data we will have collected on their fidelity to collective impact, efforts around equity, successes with different types of systems changes, and types of ultimate impacts. Are we seeing similar patterns around the necessity of fidelity to certain conditions? Are we seeing similar patterns in the relationship between certain types of systems changes and impacts?
Despite the strengths we believe this study has, it will not be the end-all-be-all, final say on the efficacy of collective impact. All studies have limitations, and we want to be clear about those as well. Given time and resources, we can’t conduct in-depth evaluations of the full range of efforts and activities any given collective impact site is undertaking. Our unit of analysis isn’t a full site; it won’t take in the full complexity of the history of the initiative, or the full array of activities and efforts. For example, it’s likely that a site that we engage with around a particular success has also experienced areas with no discernable progress. We also are not comparing collective impact to other change models. That doesn’t make the exploration of causality around successful changes less meaningful, but it does mean that we’ll understand contribution to specific changes well rather than understanding and judging the success of collective impact at a community-level or comparing collective impact to other models of driving systemic change.
We do believe that this study will fill a gap in the growing body of research, evaluation and evidence around collective impact by deeply understanding contribution in particular cases and by looking at a diverse and varied set of cases. The social sector will benefit from continued interrogation of collective impact using many methods, units of analysis and approaches. In the end, the more we learn, the better we can make meaningful progress on the gnarly issues that face vulnerable places and populations.