Case Study Writing Strategies

This is a tale of the two approaches I took to writing up case study research based on fieldwork and qualitative coding.

When I started writing up my dissertation case studies, I really had no idea how to do it. I’d read plenty of case studies but never tried to emulate them. I did, however, have a handy-dandy theoretical framework that needed to be worked into the findings.

I had three cases to report and more than enough data. Multiple case studies are typically used for comparative purposes, meaning that not only does this research design require writing up the individual cases, but also a cross-case comparison. I ended up writing four chapters to cover all of that material, with about 184 pages for the three cases and around 50 pages for the cross-case comparison.

I started off by writing up the case that had the most data – might as well get the big one out of the way, right? I wish I’d taken the reverse approach so that I would have saved some work when I found that my first try at writing up a case fell flat!

Method 1: Theoretical Framework Laundry List

I was told to be thorough in my dissertation writing. That may have been a mistake on my advisor’s part, as the final document was over 400 pages long, but I was determined to be as methodical and thorough as I could.

I started off by structuring my case description by the theoretical framework that I had developed. I went through every code in my framework and pulled out illustrative quotes that I organized under each heading, and then wrote up what I found for each concept in the framework. Even with rich and interesting empirical data to draw upon, however, it was deadly dull. It turned into a horrific laundry list in which readers became lost, much like one of those freaky hedge mazes you see in horror movies. It was ponderous and really soporific.

Repeating that two more times for the cases? No way. It was extremely slow and laborious writing, jerky and discordant, and there was no way I could meet my writing deadlines with that strategy. Fortunately, my writing group set me straight and offered suggestions of alternative structures. I listened, as one should when others are kind enough to read through drafts of heavy academic material and give thoughtful comment thereupon. Then I started over.

Method 2: Semi-Structured Thematic Template

I started over by cutting the chapter into strips and then physically coding and rearranging them into themes. Suddenly, there was a story and a flow to the material!

The first draft of the case study, cut into shreds and reassembled into a new structure.

It was done in a day. I remembered (just in time) to mark each strip of paper with the page number from which the material originated so that I could find it in the digital document to cut and paste. The process of cutting, pasting, and smoothing over transitions took another couple of days. I had every theoretical concept covered, and the material took on a much more palatable and interesting shape.

As I wrote the next two cases up, I started again with quotes, retrieving them systematically and writing up notes on the insights gleaned from them. Next, I organized them thematically rather than by conceptual framework constructs. It was easy to write the material that connected the quotes into a (mostly) coherent story, and much more interesting as the writing process generated more insights. I actually had fun with a lot of that writing!

I structured each case study chapter to start with sections providing the history and organizational setting of the case, an overview of the technologies and participation processes, and then continued from there with the thematic sections. At the end of each chapter, I included a summary with the main themes from each case and linked the highlights back to the research questions and constructs therein.

The overly-structured approach to writing a case study was painful and frustrating, but going with my intuition (while remaining steadfastly systematic) produced better results much faster. It also reduced repetition from linking concepts together and made those relationships much clearer. I expect every researcher will have to figure out an individual writing strategy, but it’s valuable to remember that the first approach may not be the best, and taking a different tack does not mean throwing out all the work you’ve already done.

The strategy for constructing the case comparison chapter, however, was a different matter entirely and a story for another day.

Outcomes for and Benefits to Participants

Conference on Public Participation in Scientific Research, Day 1 Session 3, 8/4/2012


Building Evaluation Capacity for PPSR
Tina Philips

Focus on evaluation and why it’s needed. Running Nestwatch piqued interest in evaluation, happens in many contexts – pretty much every sector does it.

Many reasons to evaluate – why not do it? Much to gain from better understanding impacts. What evaluation is not: an audit; assessment; survey – biggest misconception, key to process of evaluation but not the whole thing; research – goals, audience, end products are very different. End goal of evaluation is improving something. Evaluators are not dementors!

Evaluation is systematic collection of data to determine strengths and weaknesses of programs, policy, products, so as to improve their overall effectiveness. Involves planning, implementation, and reporting out – similar to scientific research methods and does use similar methodologies. Takes into account stakeholders, all of them. Because stakeholders and contexts are unique, every evaluation is different.

When to evaluate? Many times – Front-end, formative, summative. Questions about what is evaluated? Individual outcomes – cognitive, affective and behavioral. Also programmatic and community-level, but focus here is individuals. Reason to look at this is participants are people, not technicians or laborers, they come to interact and do something meaningful. We owe it to them to let them know what they’ll get out of participating, and evaluation is needed to understand.

Challenging work – main reason it doesn’t happen is time and money constraints, and many PPSR leaders are interdisciplinary – not trained in evaluation. This is the reason for the development of the DEVISE toolkit to help non-evaluators conduct quality evaluations.

For evaluation and design, really important to know goals, outcomes and indicators. Goals are broad, outcomes are more specific, and indicators are the evidence of outcomes. Common pitfalls: wishy-washy outcomes, not aligning outcomes with activities, expecting too much of project, expecting learning through osmosis, not providing support for learning – including behavior change.

Intro to basic DEVISE framework: behavior & stewardship; skills of science inquiry; knowledge of the nature of science; motivation; efficacy; interest in science & the environment. Work in progress, but toolkit is going to address these domains & constructs. Shouldn’t try to evaluate them all, choose and align to the project itself.

Take aways: evaluation is doable, can improve your program, improve chances for sustainability, lead to best practices, and demonstrate impact as a field.

Understanding the Connection Between Participant Motivation and Program Outcomes for Effective Program Design
Kris Stepenuk

Started working with water quality monitoring as a kid, family activity based on concern for kids’ health. Outcomes were identifying hotspots along river for contamination, which it did. Now she coordinates the program, looking to understand motivations, outcomes based on literature, and what we don’t know. Challenge: become researchers of the discipline.

Presented motivations for her project; social outcomes are important parts of motivation. In general, motivations tend to be altruistic and/or related to personal learning.

Indian Country 101: Tribal Communities as Partners in Environmental Restoration
Chris Shelley

If you want to do PPSR on tribal lands, you need to understand the needs and context. In the Columbia River Basin, salmon is critical – 30% of calories in diet, 300 lbs/person/year, they consider themselves salmon people and they take care of the fish. But salmon are in crisis, and so are the communities – what makes them who they are is disappearing.

Was part of the “Salmon Corps” which has 7 site locations and is part of AmeriCorps – map of 4 reservations and their ceded land that was given up in treaties of 1855. Treaty tribes didn’t cede all lands, also retained rights for fishing. Salmon Corps did restoration – fencing pastures to keep cattle out of streams. They also got college credit for work, so it wasn’t just labor but also education.

The Corps members embraced hip-hop culture, wearing jeans halfway down their ass, but doing restoration work helped re-connect them with their culture. Did a culture camp where they learned how to do traditional tribal skills, and they did a lot of cool stuff that were important services: wolf introduction, native plantings, restored habitats, assisted people during flash floods, etc.

But again, didn’t give up fishing rights despite ceding lands, so they have the right to co-manage the salmon resources. They get to do cool stuff off-reservation to help manage salmon, which sometimes butts up against what scientists think is right due to a cultural gap for what is appropriate in science. There was no salmon in the Umatilla River because of land usage, but they restored it back to a natural salmon spawning stream. Great quotes from participants about the meaningfulness of this work: “I know I need an education, but I also want to help the environment and help my people.” – Jeanine Jim-Bluehorse

Most people live near a reservation for which tribes still retain some rights off-reservation due to interpretations of treaties by Supreme Court. Still have access to resources like water, so they have the right to manage those resources – so how does this intersect with PPSR? Hope there are things you want to know about the traditional lands of indigenous people and you’ll collaborate with them to help them manage their resources and help you learn things about the resources that you couldn’t know otherwise. Believes salmon crisis cannot be involved without tribal partners being central. Their input will upset some scientists because it’s based in traditional knowledge, not Aristotelian. It will be hard to reconcile, but it’s still worth doing.

Working with these groups will be frustrating to outsiders but incredibly mutually beneficial. Wherever you have cultural diversity in a stable community, you also have biodiversity – this needs to be preserved and supported.

Citizen Science: Science as if People Mattered
Raj Pandya

Very funny intro! We should look at participants as partners in science, not as people doing our science. Science developed with communities, in the context of communities, doing things that communities can live with.

Whatever you call it, PPSR demographics show under-represented groups participate less than majority groups, less affluent participants also outnumbered by affluent ones. Huge group of people are not at the table, and if you’re not at the table you’re often on the menu. Why?

Many issues of access, these are the easiest to fix. Gets harder as you go down the list – cultural barriers can be solved with time and effort. Relevance is most difficult – are the problems investigated by citizen science aligned with community priorities? If we keep on this way, we’ll continue developing “whitey” programs, no offense intended.

Student project in Louisiana Delta, called Vanishing Points, with mobile phone app where people can collect stories/images/etc for culturally, personally, economically important places, and look at what’s likely to happen to those places. Another set of projects around wild rice in the White Earth nation. Third project working on managing meningitis in the Sahel. Meningitis is epidemic in this area, every few years cases spike, lots of mortality and disability. Everyone who lives there tells you it’s a dry season problem, and when the rain arrives the problem goes away. Using this knowledge is really important for effectively distributing limited vaccine supplies.

Steps to take: Align research with community priorities – requires working in interdisciplinary teams and talking to a lot of different community members. Plan for co-management – something is going to go wrong at some point, and you need a plan for trying to deal with that. Incorporate multiple kinds of knowledge – Chris already covered this, just need to harken back to that sense of humility and make space for other knowledge to be relevant and important to the project. Communicate: often has to happen in really small settings, constant work day after day in community settings. It’s really all about engaging the community at every step of the process, deciding what counts as data, what data means, how and when data will be collected, what data is appropriate to share, and working with communities to apply that data to their needs.

By paying even more attention to doing science with people, citizen science can provide a model for making science more relevant and useful.

PPSR’s Contributions to Science

Conference on Public Participation in Scientific Research, Day 1 Session 2 – 8/4/2012


To Use or Not To Use: Is That the Data?
Terry Root

Examples of PPSR biogeography – huge collections with enormous monetary value. Can go back to the 1800s to understand egg laying for phenology changes. Everything was fine with older data – 800K eggs have been digitized, but newer data is problematic because eggs couldn’t be collected. So now nest record cards need to be digitized. These data were used to save peregrine falcon and brown pelican from DDT risks.

Starting in Victorian times, people were interested in learning more about what they saw but no field guides available – so Arm & Hammer distributed species cards in their baking soda! Has evolved into Christmas Bird Count through a circuitous route.

Once data were computerized, they started to find data could be used very well to answer RQs. Birdwatchers knew more about irruptive phenomena than scientists, probably caused by climate. Was then able to look at distribution and abundance from CBC data and find out the range change for species.

Many other data sets go way back, priceless info saved by hiding under a mattress! Many of the historical data are in private and museum collections. Big growth in large long-term datasets that are badly needed to address big questions. We are now poised on the edge of a huge explosion of data, but what does misidentification mean for data quality? We used to throw away these data, but now we can use smartphones with cameras and social networks to get info about many things from large number of people into scientifically useable datasets. iNaturalist a great example of how this is working really well – already seeing exponential growth and going viral soon. 3 yo can use it and get excited about finding out what a species is.

The Role of “Citizen Science” in Weather and Climate Research
Noel Doesken

Early traditions of weather observation started in US by Ben Franklin and Thomas Jefferson. They communicated to try to understand what was going on, but didn’t have spatial and temporal context. Smithsonian project from 1849 introduced new technologies, telegraph to share weather observations.

Analysis and interpretation of volunteer data more difficult than recruiting volunteers, getting as many as 500K data points/year, which was when standardization became a big issue. Took 12 years to report on the data, so volunteers had to be very patient.

Colorado state weather service started establishing state-based weather observing networks in the late 1880s with only $2K. Within 10 years there was a solid reporting network, led to nationwide “Cooperative Network” that continues today. First purposes were simple – climate resources of the country, particularly what crops could be grown where and when, also equally important to predict extreme weather.

In bigger picture, most of the data are very skewed geographically, but a very impressive foundation. Many applications, both scientific and practical, such as climate and health – “a stinking big deal.” Have learned from historical data that there are weather cycles that is helping model weather. Dustbowl and depression increased interest in weather and climate, advanced use of volunteer data. Majority of drought monitoring is from citizen science.

Naturally this leads to understanding climate change – CoCoRaHS is keeping this going. We need more rain gauges and finer granularity of placement.

Foldit and Games for Scientific Discovery
Seth Cooper

Many people playing video games, what if we could direct that to solving problems for science? Combining human and computational power, as well as a way to motivate people to engage and solve problems they didn’t think they could contribute to.

One area where there’s lots of potential is biochemistry – proteins and protein folding. Very important part of life. Two ways to look at it, sequences and 3D structures. Hard to solve folding problems algorithmically. Foldit lets gamers use the 3D visualizations and both human and computational tools to solve problems. Have had 250K people play the game, over 100 protein structure puzzles.

Scoring and leaderboards help promote competition which motivates gamers. Technical structures are complex, but robust for solving difficult puzzles. Constantly releasing new features and bug fixes, and giving players feedback. Worldwide community participating, multiple languages. Players have produced very exciting results, protein related to AIDS virus in monkeys, algorithms failed but players succeeded in 3 weeks!

Ended up implementing scripting structure for “recipes” so players could reuse functions – player algorithm independently discovered scientist algorithms, perform better! Made trophy for early winning player, keeps it on his desk. Also co-creating structures, now making interface tools for scientists – when tool is fun and easy for everyone, also useful for scientists.

The Many Benefits of PPSR
Linda Silka

CBPR – community based participatory research – no research on us without us. Academic research may not be the right way to address problems.

Working with tribal groups on emerald ash borer in Maine – not many ash trees but they are critical to tribal traditions and economic opportunities. CBPR is growing just like citizen science – there are organizations, journals, and grants for training and cross-disciplinary support.

CBPR successes – adding rigor to data collection, need to merge professional and local knowledge to solve problems. Example from tribal lands in Nevada about nuclear contamination – researcher vector model didn’t take into community food sources. Other examples of ways that community knowledge is strengthening scientific outcomes, e.g. incinerators and air quality household health studies – very concerned about children but had concerns, wanted dialogue with researchers. Similar outcomes in emerald ash borer studies, nutritional studies.

Linking knowledge to action by bringing in local stakeholders; federal research agencies/foundations reviewing proposals differently to promote broader impacts. Using research cycle as tool to understand issues that emerge at each stage. Many questions remain about assumptions and unknowns.

Lots of resources at

Looking Back, Moving Forward in PPSR

Conference on Public Participation in Scientific Research, Day 1 Session 1 – 8/4/2012


PPSR: How We Got Here and Where We Go Now
Abe Miller-Rushing

Exciting to bring wide range of disciplines together and develop a more global perspective. Take-aways: PPSR is not new, has always been important to science, and is growing and innovating very quickly.

Models for PPSR – taxonomy of projects by degree of involvement of participants, contributory, collaborative, and co-created.

How we got here: Science began as amateur research with Plato and Aristotle, often by rich people who had money and time. Some of the most important science has relied on public participation, e.g. Linneas. Professionalization of science has marginalized public participation and that’s where we are today.

Nonetheless, PPSR has continued, just not always labeled as such or recognized as broadly as it should be. Originally it was often specimens, but now it’s usually observational data. It’s also used to solve local problems, and that’s an important role. Big data sets are also being generated through citizen science, some of these are the most important for their field, for example NOAA’s weather data that is being used to understand climate change.

Recent developments: huge improvements in tech, communication, data storage, analysis & best practices, this kind of revolution is not without precedent. Another big advance is with explicit participant-focused outcomes, which is still fairly new.

We need data over wide time periods and geographic ranges to achieve many of our scientific goals moving forward. Things like fine-scale weather observations through CoCoRaHS which is really important for decision-making; looking at changes in phenology to understand climate change and losses in biodiversity, e.g. findings from looking at Thoreau’s data through time with current citizen science data.

Many applications: images and sound analysis; real-time data for near-term predications; collection and transcription of historical records; health and environmental justice.

Huge growth in PPSR recently, e.g. with ISI on peer-reviewed publications – exponential growth in last 6 years. More to come as cross-disciplinary dialogue, collaboration & innovation develop. Now we see a need to formalize and support the field and practitioners. Still getting push-back at NPS for making management decisions based on PPSR data.

Where do you want PPSR to go? What does the field need? What do you need in your role in PPSR? What should an organization for PPSR do? Poster session opportunities to post your responses to important questions. Will be using this feedback in closing session discussion – please participate and help us act on these recommendations from the community.

Q: Issues – recognition of citizen science and use of the language in publications – what is this doing to help legitimize PPSR.

Grand Challenges and Big Data: Implications for PPSR
Bill Michener

Challenges we face, scientifically and technologically, focusing on data issues as that’s where the rubber hits the road from a science perspective. Many issues we are concerned about, primarily related to climate change, clean energy, and so on. We’re in a new age where we’re hitting some tipping points and likely to see very abrupt changes that will have significant impact on future and quality of life on earth.

Many tools being used for data-intensive science, but data management is one of the challenges standing in the way of results – we need to speed up time to results and reduce time on mundane tasks like data management. Another key challenge is expanding participation.

Major concern – where are the data? We need to be able to integrate data to address major scientific challenges. This leads to the long-tail distribution of data problem with many data orphans. Jim Gray – “Most of the bytes are at the high end, but most of the datasets are at the low end.” Brings up an important question – we’re all familiar with the research life cycle, but how do we link it to the data life cycle?

Solutions: DataONE is addressing some key issues, e.g. data preservation. Intro to

One of the main science data management/analysis tools currently in use is Excel [shifting toward Google Docs]. D1 is developing tools for R, which is one of the second-most popular tools for analysis, working with many partners.

Issue 2: Data discovery – not easily found with traditional search tools. Major project of D1 has been ONEMercury for searching across datasets.

Issue 3: Tools for innovation and discovery. We’re in the 4th paradigm of research, focus on data-intensive research that requires new tools, techniques, and ways of doing research. Another way the investigator toolkit fits in to address this question. Examples include DMPTool, data management planning tool – helps get grants funded for agencies requiring data management plans, but should be a consideration for PPSR projects. Supports 12+ templates required by different agencies with walk-through series of steps to address required points for data management plans. At the most, all you need to do with this after going through wizard is change font.

Upcoming tool: DataUp to check Excel spreadsheets for best practices, create metadata and connect to ONEShare, one of the D1 repositories – all for free.

Finally, need for tools for exploration, visualization, and analysis. Example needed data layers from several sources to address research question, one only found through word of mouth, had to develop new modeling tools and work with new tool (VisTrails) to develop visualization.

People and Participation: Educational and Community Components of PPSR Projects
Heidi Ballard

Came to PPSR as HS teacher, then worked in community-based forest management, and then working with science education at UC Davis.

Need to look at PPSR across different practices. Many disciplines and goals represented here: biochem, ecology, astronomy, nat rsc mgt, and public health. Also have: psych, sci & enviro ed, social justice & community development, sociology, anthropology.

Names for PPSR categories are called different names by other scholars, this is explored in recent Ecology & Society article. Need to think about more than degree of participation and what part of scientific process. Other important aspects related to quality – Whose interests are being served, and to what end? Who makes decisions? Who has the power?

This leads into discussions of democratizing science – role of PPSR is improved science understanding for everyone, which results in better research. Examples include LiMPETS monitoring 600 miles of CA’s National Marine Sanctuaries, students taking it very seriously because it will be used for science. Additional examples related to rice growing and health impacts.

Looking at social and educational outcomes of PPSR – individual, programmatic, & community level. Often PPSR focuses on programmatic level – audience reach, engagement, program strengths/weaknesses, etc. Current work focusing on individual learning outcomes, and community-level outcomes are exciting area to develop – social capital, community capacity, economic impacts, trust between public, scientists & managers.

Her main question is: if we think about intertwining of social & ecological systems, many stakeholders involved, can we improve their resilience?