The word “unintended consequences” gets tossed around a lot in development and social sector work–often as a glib euphemism for “bad stuff we didn’t expect” or a “better call qual” moment.
However, many “unintended consequences” are indeed anticipate-able, especially with grounding in theory (+theory of change), the literature, and the context. It is the job of the program design and research teams to consider ways in which a program could lead to undesirable outcomes (those not consistent with social impact, progress) and to mitigate them (in the program) and capture them (in the research). It is not necessarily helpful to give all ‘bad’ outcomes the same label, especially an ‘oopsie-daisy, we didn’t mean for that to happen!’ label!
To walk quickly through my post-it:
NW quadrant: these are things that are ‘good’ and often the targets of the program or foreseeable good knock-on effects. These are often measured, celebrated.
NE quadrant: these may tie-in with positive deviance. It can be useful to capture these outcomes and, so, even in the context of quant/closed-ended research, may want to have an open-ended question that asks if an experience or intervention brought about any other things that the respondent deems ‘good.’ Too often, these outcomes are captured and celebrated idiosyncratically and anecdotally when they could have been captured systematically with a little thought.
SW quadrant: too often things that get the ‘unintended consequences’ label fall in this box, even though they are often quite foreseeable. They should be programmatically measured and captured as part of the monitoring, measurement, decision-making plan.
SE quadtrant: Of course, some ‘bad’ things will happen that are not foreseeable even with a reasonable amount of thought and hindsight will always be 20/20 while foresight is not. Similar to the NE quadrant, it is easy enough to include one open-ended question about things that were challenging or bad. It may be the work of deeper, richer qualitative work to investigate this more if the issues are surfaced but one open-ended question on that matter will go a long way to more systematically understanding problems and harms.
I will be speaking more about research ethics in the coming month (and beyond!). In light of this, I am documenting some formative ethical experiences I have had and that, to some extent, still puzzle me. Perhaps befitting someone initially trained in anthropology and grounded theory, these examples stem from individual interactions I had ‘on the ground’–moments when research participants or research staff directly critiqued me.*
I am glad they did.
Of course, these are only the ones who spoke up. (What if we asked more consistently?)
1. Ghandruk, Nepal
My first research experience was spending ~3 months in Nepal, including an extended stay in Ghandruk in the Annapurna range, not long after its ACAP office had been blown up by Maoists. There were many ‘firsts’ for me on that trip during undergrad: first time in an LMIC, first homestay, first research project, first primary data collection, first fieldnotes, first altitude sickness, first time working with an interpreter, first goat sacrifice… All around an influential time for me.
An unexpected breakfast, Ghandruk, Nepal
As with many first research projects (and we can definitely discuss some additional ethics around this!), the biggest impact was probably solely on me, not the local community in which I was working and not the world. It will likely come as no surprise that I did not solve the question of where, when, and why people seek care for what would be diagnosed as mental illness, and specifically depression, according to the DSM. Nor did I unravel how illness narratives might shape responsiveness to pharmaceutical treatment.
Nevertheless, I did learn some things! Two snapshots, in particular, have stayed with me from that time (besides the fact that my host mom said I should only come back and visit once I was married…).
1a. This is self-answering.
About three interviews into my project, the interpreter with whom I was working turned to me and suggested I was not making good use of people’s time because I was simply asking the same questions over and over (my first prepared interview guide!). Indeed, he thought I was quite daft and not ‘getting it.’
Some questions he deemed ‘self-answering’ and not worth asking at all (for example, was it hard to pay for health care in this village). Others, he felt that once we had heard from one or two people in the village, we had more or less settled the matter and there was no need to take anyone else’s time. While I ultimately prevailed that there was merit in asking the same questions over and over–and that at a minimum it was what was expected of me by my professor–the point still sticks with me, about how others might view not just the time researchers ask of individuals but also the collective time taken from a community and how they understand researchers’ strange ways.
1b. What will you do for me?
Probably the ‘epiphanic moment’ for me to want to pursue applied public health rather than medical anthropology** happened after interviewing a woman who entered my field notes as Old Woman #3 and later received a pseudonym. My questions–some closed-ended, some open-ended–were about illness etiology and health-seeking behavior. I asked her to list her ailments, to describe how she got it / it got her, what she had done and care she had sought, and whether it was challenging to pay for it.
As we closed the interview, and I believe I provided a university-logoed pen, she asked, now that she had listed all her problems, how I would help her with them. I, of course, had no answer.
This gets at a point raised in several places including forthcoming work from Alex Avdeenko: It’s weird for strangers to come and ask you about all your problems and, then, really weird for them to not offer to do something about it after you’ve provided the list. Or, in a bizarre non-sequitur, to give you a pen. It is no wonder that people may often believe that such question-asking is (or should be) a needs assessment that will ultimately be attached to succor.
2. Tamale, Ghana
I spent a year in Ghana as part of my thesis research, both working with IPA and doing my own work on the side.
2a. Ask me at the end and I will tell you if I agree.
While piloting for work on malaria medication completion in Ghana (and funneling my salary back into my thesis research, follow-up interviews***, and fabric), I accompanied data collectors. Plenty of times, the informed consent and other processes don’t quite go as was written on paper–you said interviews would be conducted in private but actually the respondent wanted someone else there with them. Challenges in keeping to the letter and/or the spirit of the Belmont principles have started getting more ink in the past decade (such as, though very much not limited to, here, here, here)–a trend that should continue and not only situations of randomization.
During one particular interview, an older gentleman said something that struck me. We went through the informed consent process and then he said, ‘but I don’t really know what you are asking. First ask me all of your questions and then I will tell you if I consent.’ From an IRB perspective, this is, of course, backwards, as you have to get consent before you get to your questions, on the assumption that the upfront information you provide is sufficient.
But from the perspective of someone less familiar with research in general and certainly the particulars of your research, it may not be. No matter how well-crafted your consent statement. Consent could be reimagined, potentially, to start by getting us to the point of beginning the interview, to have more scripted reminders throughout that people can skip or stop, and to have an ultimate ‘consent,’ at which point they could request to have their data trashed now that they know everything we planned to ask. If we started to take a view that research participants should learn something about research in the process, rather than just taking CYA measures, it might lead us to reconsider how the informed consent process (possibly reimagined) can be truly informative (and ultimately understood). A corollary is that we might wish to investigate what is of interest to potential research participants, so that we not only say what we are obligated to say more clearly (again, stay tuned for work from Alex!) but, also, to say what will be interesting and useful to those deciding to join.
2b. Honestly, imagine.
Sometimes the lessons on what is locally ‘ok’ don’t come from research participants but rather from research staff. This one has more to do with social norms and relationships but it was an element of positionality and potential offensiveness that I had not considered before. Specifically, our questionnaire included items on working memory, in which participants were giving increasingly long strings of numbers to remember and say back. I know it was a little nerve-wracking, as my thesis advisor/PI Prof Dr. Günther decided to give me a quiz!
Nevertheless, I did not expect it to be a major issue during data collection. I was wrong. However, when we did a feedback survey among the enumeration team at the end of data collection about what went well and what did not, it turns out that asking this question was mortifying to them. While it had not trickled up to me during data collection (my fault), the sheer horror and impropriety of asking someone older than you to remember and recite numbers came through very clearly in the open-ended responses. Imagine, they said, asking this to an uncle. Honestly, imagine.
This may not strictly be a ‘research ethics’ issue but it does hint at the fact that ‘we’ may not always understand what will be sensitive, uncomfortable, or even unacceptable in specific contexts. ‘Sensitive’ (and to whom) is one of the terms in research ethics (like coercion, undue burden, vulnerability, and a few others) that are important enough to be used a lot but so vague as to let you more or less define them as you will–that is, not actually terribly useful beyond bureaucratic CYA.
2c. You can’t send that, it’s not fair.
‘We’ do not have consensus on what constitutes appropriate compensation for research participation, veering away from coercion but also providing fair benefits, accounting for the risks imposed by the research. There has been a more robust conversation in medical and pharmaceutical research, such as dialogue on ‘fair benefits’ work by Emanuel, Wendler et al. across several papers and essays. Norms across different types of research and situations vary. The interpretations of non-monetary offerings by research participants may not be what was intended.
Into this space, and on a very low-budget project, we decided we would offer education/information about malaria as compensation for people’s time and also to make sure they better understood our research. This included seeding information about how antimalarial resistance developed when you don’t finish your course of medicine (as part of communication experiment that fell out of my thesis), including using these fabulous illustrations from Liz Venable, which we narrated out loud.
In addition, at the very end of the whole study, we planned (and ultimately did) send an SMS urging people to make use of bednets as well as to use as ACT and complete the course when ill with malaria. Again, the idea was that providing information might not only be useful but reflect some degree of compensation for time spent in our study.
The data collection team was NOT into it. First, they pointed out, almost certainly accurately, that people already knew about bednets. This contrasted with the reason you need to finish all your malaria medication, which might be genuinely new info. More interesting ethically, however, was how upset they were that we would tell people do something but not help them do it. In their view, it was very uncool to say you should use a bednet but not actually provide one.
Our longitudinal, semi-structured work has involved, give or take, monthly conversations with a small number of respondents, who tell us about how they are doing and challenges they are facing. Each month we asked a set of similar questions (how they are doing, general sentiment in the settlement and the market, etc.) as well as a focused set of questions on a topic (education one month, health another, detailed transfer-spending plans in a third, say). We have two qualitative interviewers on this project (temporarily paused) and they have done a fabulous job building rapport with our respondents.
While data collection was active, Rico was diligent about reviewing incoming notes and transcripts in more-or-less real-time, which allowed him to give regularly feedback on interview technique and additional probe ideas to our team (blog post forthcoming on this remote management of qualitative data collection by less-experienced interviewers). He was also doing the regular management check-ins with our interviewers. He was the first to note, then, that Stephen and Christian were reporting that sometimes the respondents were calling them at unscheduled time just to chat. In a few cases, the fieldnotes and transcripts also revealed that the respondents felt they had no one else to talk to, that they had suicidal ideation, etc.
At base, this reveals the importance of having a resource-referral list (for which you have checked that the resources are truly operational) as part of standard practice. Working with a population that has recently and collectively experienced trauma makes this all the more important, even if you are not asking ‘sensitive’ questions. This can be really challenging when, for example, you are working in a country that does not host a national suicide hotline–and certainly not one in all the languages relevant to refugees. Nevertheless, it is critical.
This experience, born of extended engagement, also points at challenges, well-known in anthropology and increasingly recognized in other fields (with/out acknowledgement), when the line between ‘data collector’ and ‘confident’ become blurred.
3b. You’ve got a man in Africa.
This final point raises a question about who ‘confidentiality’ is for and who wants it. In some cases, of course, it is absolutely essential to maintain strict confidentiality, with literal life/death implications. But that is not always how it may be seen on the ground. Take Martin, pictured below. I sat in on an interview with him, led by the wonderful Stephen, who had just started as a qualitative interviewer on this project after serving as an enumerator at baseline. I know Stephen went through the process of providing study information carefully and sought a clear statement of consent. I know how much time walking around the settlement Rico and I spent reviewing the informed consent script with our team, making sure it came across as conversational and not a monologue.
Following consent, first interview guide for these opening talks in a series of longitudinal data collection focused on some history of arriving the settlement, whether people knew or got along with their neighbors, what people thought of the GiveDirectly lottery to determine order of transfer receipt. Following all of this, Martin had Stephen translate a request to me. Namely, that he wanted people to know his story and that I should spread it far and wide. That I should go back to the US and let people know that “I had a man in Africa” who needed help. He insisted on me taking the picture below, to share along with his story. Which, now I have.
*This might be contrasted with, say, ex-post getting interested in ethics after your study is critiqued.
**Though another would come later when a mentor let me know that the medical anthropologist with whom I was considering pursuing a PhD would try to sleep with me and would make things quite unpleasant for me if I did not. That, too, was a decisive moment–and a different kind of ethics.
This post represents joint work with the fabulous Mike Frick. It has been a long time coming–to my horror, I see that we first drafted this at end-December of 2010. It’s gone through a lot of iterations, trying to fit in this journal (for example, this working/submitted version here) or that rather than just getting our ideas out in the world. It’s never been a ‘perfect fit.’
However, I find that I return to some of these ideas time and again, so want something out there. There are places in the manuscript that still had comments, in which Mike and I both acknowledge we wanted to return to the text and see if could push a little deeper, be a little clearer. Nevertheless, sharing out.
Key reasons for results-sharing and shared sense-making
Key takeaways & recommendations for action
In this paper, we argue for recognizing and including research participants as important stakeholders interpreting and learning from evidence generated through social science research. Nigerian social commentator Onye Nkuzi asked, “If you are doing ‘ground-breaking social science research on Africa’ that Africans cannot access, what, ultimately, is the point?” (Nkuzi, 2011). We agree. “The point” matters.
Researchers often do not share research outputs and results to surveyed and studied communities and individuals, limiting access and engagement. (While we focus on study participants in this paper, many of our points have equal applicability to a range of involved stakeholders, including research field staff, street-level implementers, local (LMIC) ethics boards, and local media.)
This oversight does not always reflect the purposeful disregard of participants. There are several constraints to sharing results and co-interpreting with participants. Some of these may bind in ‘our’ (global health and development research) current operating set-up. Budgets and time may bind. Recognition of what kinds of participant-engagement activities can fit an on-going or longitudinal research program may constrain sharing. Creativity about meaningful modes of sharing may bind, given the limited precedent on which to draw (itself a product of the current lack of budget for such activities). And consideration of the benefits and drawbacks of sharing collected information with those from/with whom it was collected may be constrained.
Whatever the reasons, we contend that these constraints have negative implications for both study participants and researchers. It limits access and engagement in research. It elides the important role research participants can (and should) play in interpreting and learning from evidence generated through social science research.
We discuss the ethical and practical reasons for sharing results with study participants in social science and development research post-data collection. We propose that this practice has the potential to: (1) improve the credibility and validity of research findings; (2) provide fair benefits through knowledge enhancement and empowerment; and (3) promote long-term relationships, trust and collaboration between researchers and participants.
To develop the evidence on this, we call for more trials, errors, and research into the modalities and effects of sharing results with participant-communities to articulate the possibilities, benefits, and challenges. We close by examining the challenges of results sharing and discuss the roles different stakeholders—from investigators to funders to ethics boards to communities themselves—can play in this activity.
*We acknowledge that in the years since our first draft, there have been a few additional calls for similar action, though not necessarily embedded in the framework we propose and not all pushing beyond ‘results sharing’ into shared sense-making. To give credit where it is due (and you can let us know if we overlook anything major): a call for sharing results is made in this F2P2 post, ODI provides an example of results sharing (though we are not convinced of the usefulness of that particular mode), and the call for structured ethics appendices has a placeholder for results-sharing.
An ethical framework for results-sharing
We find it useful to develop an understanding why results-sharing and shared-sensemaking are important, rather than just stating it is so. Although ethics in the social sciences merit a broader outlook than the biomedical gaze, clinical trials still provide a good place to start for social scientists thinking on ethical engagement (Alderman, Das, and Rao 2013). A particularly active area of debate centers on clinical trials that lead to the development of a tangible and useful product, such as a novel drug or vaccine (Bhutta 2002).
The Council for International Organizations of Medical Sciences (CIOMS) guidelines require sponsoring agencies to “agree in advance…that any product developed through such research will be made reasonably available to the inhabitants of the host community” (CIOMS 2002). These and similar guidelines apply to tangible products of research. They may not perfectly map to social science research, which may not generate a new product or service but rather knowledge to inform future policy, programming, and research (Bhutta 2002). While social science research often does not impose the same kinds of risks on participants as testing vaccines or drug therapies, this does not attenuate ethical obligations to share research results. Relative to biomedical research, the opportunities for shared sensemaking, about social constructs and processes, are heightened in social science work.
Even in biomedical research, the final output of research may be knowledge—individually and/or collectively meaningful—rather than a marketable product or technology. For example, in genomic research, where the research product often is knowledge (i.e., the results of genetic testing), a key concern is sharing this information with specific participants (Dressler et al. 2012; Kristman and Kreiger 2008; Simon et al. 2012). Since the field’s start, its researchers have considered rapid and open release of genomic data as critical. For example, in the U.S., the National Human Genome Research Institute requires its grantees to release genetic sequences into open-access databases maintained by the National Institutes of Health (NIH) and other institutes (Lowrance and Collins 2007). While this system of data sharing enables scientific research, it does not connect individual study participants to relevant and medically actionable results. A review of consent forms from genomic studies found that participants are usually told genetic analysis will occur “without any explanation of with whom the resulting data will be shared” (McGuire and Gibbs 2006). An exception is research conducted among indigenous and aboriginal communities, who often have the right to access their genomic data and exercise some determination over its future use (Jacobs et al. 2010; Schuklenk and Kleinsmidt 2006; Taniguchi, Taualii, and Maddock 2012).
Against this backdrop, several thought-leaders in biomedical ethics—looking beyond products such as vaccines but still referring to clinical trials—have called for researchers to (1) meaningfully present research purposes and findings to study participants and (2) involve these participants in further results interpretation and manuscript preparation (Weijer and Emanuel 2000). These components are two in a larger ethical framework proposed by Ezekiel Emanuel and colleagues to govern clinical research in lower- and middle-income countries (LMICs). In order to promote the social value of such research, the architects of this framework suggest that research should be structured as a collaborative partnership between researchers and communities (Emanuel et al. 2004); sharing results is a component of this. This framework holds that consent forms, de-identification of data, locked files (physical and virtual), and other tools of confidentiality build the cornerstone of ethical research—protecting individuals—but remain only the starting point for research that produces fair benefits and social value (Benatar 2002). That is, that is ethical.
A recent review of the literature indicates that participants in biomedical and clinical research want “aggregate and clinically significant individual study results communicated to them,” or at least want the opportunity to choose to access these results (Shalowitz and Miller 2008). In a famous example, Richard Doll and Bradford Hill presented their findings on smoking and lung cancer to participants in the British Physicians Health Study. In a report on their findings, Doll and Hill write: “We thought it necessary, in view of the nature of our results, to lay these preliminary data before the survivors of the 40,000 men and women who made them possible” (DOLL and HILL 1954). Doll and Hill informed participants of the results through the British Medical Journal, an established information source for physicians such as those taking part in the study. The presentation of results to study participants in LMICs requires further creativity and effort given information channels and barriers in these settings.
At present, Institutional Review Boards / Ethical Review Committees and some grant-making agencies that support research in LMICs make researchers delineate how they will share their results. Yet these bodies give limited guidance or mandate on which information will be shared with whom, when, through what means, and with what aims. Scant research systematically assesses the desire of LMIC study participants to learn more about the research in which they take part, including the results. From our anecdotal experience, participant-communities and individuals would like to know the results of research and to share in benefits that may stem from them. This is an area for further, more systematic exploration—particularly in relation to two stages of the research process: (1) data validation and analysis; and (2) results dissemination and communication. This exploration is the scope of this paper; here, we make the case for why it is needed.
Sharing results during data validation and analysis: reciprocity, transparency, and validity
Dialogue and action in the social sciences may pave further the way for discussions about including research participants in validating and analyzing results prior to dissemination. These include conversations about limiting extractive research (spearheaded by Robert Chambers), about increasing research transparency, and about enhancing research validity.
Often, research is ‘extractive.’ That is, outsiders gather raw information to take away for processing and construction into valuable and meaningful (knowledge) products (Chambers 2007). Chambers has helped shape an appreciation for a participatory approach to development and research as the antithesis of extractive approaches, a dichotomy echoed by Leslie Groves (Groves 2015). But much of the current work on participation focuses on involving stakeholders in the generation and collection of data (such as mapping geographic space and power relationships as understood locally, as well as his newer focus on participatory numbers and statistics). This may be empowering in some measure but it does not necessarily achieve the true opposite of extraction, which is to insert or reciprocally create something of value. In our estimation, a more productive alternative to “extractive” (Groves 2015 – paper, not blog) research — which could still occur even if individuals or communities under study actively participate — is research that is reciprocal.
We encourage researchers to take this step beyond participatory data collection by calling for those providing raw data to play a role in the construction of knowledge products – that is, in the interpretation of results that considers multiple perspectives and that builds partnerships with study participants. This, we believe, goes some way to limit the extractive nature of research by attending to fair benefits and social value, and by producing interpretations and recommendations that accord with the experiences of participant-communities. This notion fits within the ‘feedback typology’ provided by Groves, who points out that beneficiary feedback can be elicited during four stages of program evaluation (an activity related to but distinct from research): (1) evaluation design; (2) data collection; (3) validation (sensemaking) and analysis; and (4) dissemination and communication (Groves 2015). Our interest in this paper is particularly on the latter two elements as a key frontier for ethical research.
Creating reciprocal exchange around research results will require increasing the transparency of research outcomes. Recently, research transparency has received increasing focus in the social sciences, particularly with regard to research design and data analysis (Miguel et al. 2014; Ioannidis et al. 2014). Popular measures to increase the transparency of research include pre-analysis plans to prevent later data-mining and specification-searching and open data and journal access to allow new or replicated analyses (for one example, see (King and Alvarez 2014)). These efforts are usually directed toward transparency vis-à-vis other researchers, as the assumed end-users of research. They only tangentially address transparency from the perspective of research participants themselves. Even broader conversations about transparency in research for the benefit of the public at large do not always acknowledge that a subset of the public may have special claim to receive research results, given their role as research participants.
In addition to increasing transparency, researchers may strengthen the quality and internal validity of their work by engaging study participants during data analysis and interpretation (Lincoln and Guba 1986; Schwandt, Lincoln, and Guba 2007). Often, researchers gain interpretive insight into their results through a chance participant comment (e.g. (Thornton 2008). Member-checking (also called shared sensemaking) offers a systematic means of verifying or co-interpreting the results with the participant-community, allowing them to also serve as analysts. Member-checking refers to a practice crystallized by Lincoln and Guba to enhance research ‘credibility’ (internal validity) by providing validity and reality checks (Lincoln and Guba 1986; Schwandt, Lincoln, and Guba 2007). Throughout the research process—as is currently more common in a qualitative research paradigm—researchers may gain insight from informal tests of initial interpretations among the participant (or a comparable) community. Researchers may reject some interpretations and consider new ones.
Lincoln and Guba also recommend “terminal, formal testing of the final case report with a representative sample of stakeholders” (Lincoln and Guba 1986). Terminal testing offers an opportunity to share research data, processes, results, and interpretations with participant-communities (Carlson 2010). Given possible concerns about presenting the raw data for auditing and clarification, we advocate sharing aggregated results and interpretations. Clear decisions on the array of information to be shared need not always be determined ex ante but can emerge over the course of the research—a flexibility that is easier to achieve in the social sciences, but also possible within biomedical research.
Taking time (and resources) to return to participants to involve them in member-checking results allows researchers to assess the internal validity of their work and ensures that participant-communities do indeed receive these results. It also acknowledges that insights arise from interactions and relationships between researchers and participants. Member-checking gives voice to participants in a way merely collecting data does not afford (Carlson 2010).
As an extension, participants may also contribute to manuscript preparation. What a research team might normally publish represents a single interpretation of the data collected. Consensus need not be reached on the interpretation of results; competing interpretations can be published, even within the same paper (Weijer and Emanuel 2000). Of course, study participants will not be involved in each manuscript decision. However, these discussions can pave the way for long-term research efforts and even the development of new intervention strategies (Emanuel et al. 2004).
Involving participants and other community stakeholders in the dissemination of research findings creates further avenues to build partnerships with participations that extend beyond data collection. The opposite of extractive research is research that is, variously, participatory, inclusive, authentic, and/or empowering, with empowerment seen as a continuum rather than a threshold (Chambers 2007; Lincoln and Guba 1986). Lincoln and Guba suggest that research authenticity stems, in part, from the extent to which it raises consciousness of a problem among participants, fosters appreciation of others’ views of an issue and its potential solutions, and, most challengingly, stimulates not only action by participants, but successful action (Lincoln and Guba 1986). Such consciousness-raising accords with the Hegelian or Freireian sense of enabling people to see the structures underlying problems facing them.
Sharing initial and final results, shared sensemaking, and communication
Participants stand to benefit most when shared information is clear and contextualized. Researchers need pertinent, creative ways to present information to participants. To do so, they may need to return to communities after data collection or during analysis. To package information, researchers should consider the literal translation of scientific terms into local languages and anticipate the challenges participants may experience in understanding the nature and implications of formal research (Appadurai 2006, building on ideas from Freire). The degree to which sharing the research purpose, design, and findings is empowering will depend on what is shared, through what modes, with whom, and with what degree of acceptance.
In the biomedical research field, many clinical trials networks have dedicated community engagement staff or community advisory boards (CABs) that play a primary role in disseminating results and communicating study findings to a broad constituency of involved stakeholders. One example is the Community Partners program, which guides community engagement work across the research networks supported by the Division of AIDS of the U.S. National Institute of Health. Community Partners produces lay summaries of trials protocols, relays information to community stakeholders during trial conduct and advises study teams on selecting appropriate and targeted dissemination strategies for releasing study findings (Kagan et al, 2012).
As a starting point for selecting modes of dissemination, social scientists should consider how laypersons and non-experts interpret scientific data, including the use of heuristics (Grotzer A., Miller B., and Lincoln A. 2012; Nickerson, Perkins, and Smith 1985; Sunstein 2002). In one example of results-sharing, at the end of a study on sexual networks and the spread of sexually transmissible infections (STIs) in East Africa, researchers held an educational session with participating women. Though the full analysis remained underway, the researchers shared de-identified sexual network images and engaged participants in a hands-on (quite literally, hand-holding) activity on the differential risk for STI transmission in serial versus concurrent sexual partnerships. This made the risks immediately interpretable to the women involved (M. Morris, personal communication, April 2009), although to our knowledge, the researchers did not conduct further research on whether this, in turn, influenced behavior within networks.
Such proactive results-sharing is the exception, even in biomedical research. The Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease (the Union) reviewed content from eight years’ worth of study protocols—clinical trials, observational studies, and record reviews—they supported. The Union found that “few researchers described how their results would be presented to study participants and their communities,” although all proposals mentioned dissemination in scientific journals (Edington et al. 2013). Members of the Union’s Ethics Advisory Board argue with force that sharing knowledge about health problems and potential solutions with communities creates opportunities for participants to “exercise their rights regarding health and other services” (Edington et al. 2013). In this way, results dissemination might be understood as an enabler of empowerment processes.
In an optimistic view of empowerment, people may be able to use research results to “gain or improve a service, assert their rights or obtain a better deal” (Chambers 2007). Participants may be able to use information shared with them to affect—directly or through collective action and advocacy—local behavior or improvements in well-being. This is, for example, part of the idea behind Vijayendra Rao’s work with the Social Observatory — and Rao notes that linking findings with action remains a challenge (World Bank 2016; Rao and Aiyer 2016).
In theory, if the information reveals that conditions or problems are different or more severe than participant-communities had understood, then study information may lead to reflection on the present situation and a new articulation of problems, which may also lead to a new framing of possible solutions. By hearing about the research process and results, respondents may think in a more critical and evidence-informed way about their communities, rather than relying on stereotype or other assumptions about their context. Understanding the research in which they took part may be enjoyable and have intrinsic value for participants and, further, lead respondents to garner a sense of control and even skill in considering their own problems in the future (Appadurai 2006; Chambers 2007; Zimmerman A. 1990).
However, from years of information-sharing campaigns in public health and beyond, we know that much lies between the provision of information and taking (collective) action (for a recent example outside of public health, see (Lieberman, Posner, and Tsai 2014). People must be ready, willing and able to translate information into action (Lesthahghe and Vanderhoeft 2001). Moreover, as in participatory approaches to development, much depends on elite capture within a community and the capacity and incentives for action within a community as a whole, which itself may be moderated by degree of heterogeneity (Mansuri and Rao 2012). Whether, when, and how these processes benefit participant-communities remains an empirical question. Evaluating the impact of sharing research results with participant-communities will enhance our understanding of the potential of the approach for which we advocate here.
Despite the limited evidence base, the development field has produced several examples of long-term research initiatives in communities that have included efforts to affect social change through knowledge-sharing and empowerment among community stakeholders. One example is the Townsend Thai Project, which has produced panel data on the spending of Thai households for over 150 consecutive months. Robert Townsend and his study team are now beginning to give research results to a randomly selected subset of households to see if sharing this information changes their financial decision-making. This approach suggests that results sharing itself can adopt an experimental, investigative form (Townsend, Sakunthasathien, and Jordan 2013).
Beyond fostering empowerment and inspiring collective action, including communities in results dissemination may also promote long-term ties with research teams and improved understandings of the place of research in social life. Emanuel et al warn that “very little can create more resentment, mistrust and a sense of exploitation than unfair distribution of the benefits of collaboration” (Emanuel et al. 2004). Particularly for researchers planning to work in a single study area with the same or similar participants over a long period, such resentment and mistrust must be avoided.
Mistrust can result when study participants feel disconnected from the research process or the conclusions it generates. Even before results are ready, information can be shared on the research process, including the importance of particular questions to the generation of results. Sharing this kind of information might respect a community’s desire to do more than offer its data to researchers while still protecting later study rounds from contamination. This may also help limit research fatigue among community members in the face of repeated research.
One of the authors (MWF) saw such fatigue among a community of intravenous drug users in an East Asian country who have participated in repeated public health surveys. These individuals have come to see research interviews as a rote exercise with little benefit to themselves, beyond the immediate, small financial incentive for participation. One participant asked, “Will this kind of research actually help people in our position? Will people in power actually listen to us?” Limiting research fatigue, and the linked tendencies to not cooperate or provide accurate or complete information, is a key benefit of continuing dialogue with participant-communities after—or between rounds of—data collection.
Challenges to results sharing
There are several significant challenges to results sharing in the social sciences. First, this effort may be costly in time or resources, especially as studies become larger or longer in geo-spatial or time scale (Alderman, Das, and Rao 2013). Often a long period elapses between a specific research study, the release of the results, and the possible changes in programs or policies aimed at improving well-being or development in the participant-community (Emanuel et al. 2004). However, experiences from clinical and social science randomized controlled trials show that results can be shared with large numbers of participants without adding undue costs. After concluding the first phase IIb efficacy trial of a new TB vaccine in over forty years, the South African TB Vaccine Initiative (SATVI) went door-to-door to inform the nearly 2,800 mothers with enrolled infants that the vaccine was not effective. Investigators at SATVI point out that this effort allowed them to “take the community’s pulse” after a negative result. Many mothers reportedly reacted to the disappointing news by asking when they could enroll their children in the next trial. They further expressed pride in their participation in the search for a new TB vaccine (M. Tameris, personal communication, October 2013).
While SATVI successfully disseminated results by going door-to-door, the method of results dissemination need not match the method of data collection. Researchers may beneficially use technology or media to share results and solicit feedback. For example, one of the authors (HEL) used text messaging to convey basic process and outcome information to those participating in a malaria medications study in Ghana that used text messaging as a key component of the intervention (Raifman et al. 2014). Creative approaches that consider results dissemination beyond village meetings or door-knocking may unlock feasible options to share research results on a wider scale without great financial burden.
Second, researchers may worry that sharing results might bias or “contaminate” studies by adding an additional intervention component (or “treatment”) that will influence the behavior or responses of participants over the course of the study. This can make it more difficult to make causal claims about the effect of the original intervention on the outcomes observed. For example, if researchers are studying or evaluating an intervention that distributes X health product, feeding back mid-term results to study communities about the impact of X on behavior or health outcomes may itself change behavior. This is a case in which sharing results may be best held until after the last round of data collection is complete or where data shared over the course of the study would need to be carefully selected as unlikely to influence outcomes.
Third, dissemination might pose special risks for vulnerable or minority groups within participant-communities and researchers will need to consider this possibility when designing subject protections. The process of preparing to share results can itself encourage researchers to thoughtfully engage with participant-communities throughout the research process. In this process, researchers will be guided by soliciting community input about what information is appropriate to share, how information could be disseminated, and who should have access. Social scientists must balance the community’s desire for certain data against the need to maintain confidentiality and anonymity. The ethical dissemination of study results will benefit greatly from meaningful engagement with community representatives early in the research process (Bhutta 2004).
In clinical trials, such decisions are often aided by community advisory boards (CABs), which facilitate effective communication between investigators and the host community on important ethical issues (Boulanger et al. 2013). Investigators studying HIV and TB have also received guidance on results dissemination in the Good Participatory Practice Guidelines for Biomedical HIV Prevention Trials (GPP-HIV) and the Good Participatory Practice Guidelines for TB Drug Trials (GPP-TB) – two guidance documents developed in conjunction with affected communities, trial sponsors, civil society, and researchers (Regimens 2012; UNAIDS 2011).
For all of the activities discussed in this paper, it may be difficult to determine who constitutes “the community” and its leaders, although these questions arise at the beginning of most studies. Indeed, approaching research with the expectation of continued engagement may help researchers think about issues of community more clearly during the early stages of research design. Early engagement can allow researchers to better understand what information the participant-community feels would be “too sensitive” to collect or to share, which may then require either a careful articulation of the benefits of this information to the participants or a reconsideration of aspects of the research design (e.g. (Boga et al. 2011)).
Conclusions and considerations
Each of these challenges can be overcome, often in ways that may strengthen research processes and outcomes. To realize this potential, however, results sharing must occupy a more central position in the planning of research projects. The following activities may help social scientists and research institutions resolves some of the above challenges and integrate results dissemination into their work:
Create community advisory structures. Development organizations that conduct social science research, especially long-term projects in a given location, could consider establishing community advisory boards (CABs) that can maintain a dialogue with communities throughout the research process and guide results dissemination and co-interpretation. Here, social scientists can receive practical guidance from their colleagues involved in HIV and TB clinical trials, where CABs have played an increasingly important role in communicating study findings and maintaining reciprocal, sustained relationships between researchers and participant communities.
Build results sharing into experimental designs. Just as many clinical trials include small studies evaluating trial operations (for example, sub-studies exploring the quality of informed consent administration or laboratory conduct), development organizations and social science research institutions should build formal examinations of community engagement and results sharing into the larger plan and timeline of research projects. These process studies, which can often be nested into larger studies at modest additional cost, may help to generate comparative data on effective modes of results dissemination and post-trial engagement with communities. Building results sharing into protocols may also open the door for IRBs and ethics boards to gain experience and familiarity in evaluating these types of activities.
Plan for results sharing from the outset. Planning for results dissemination starts with protocol development. Including a commitment to share results with participant communities in early drafts of protocols will encourage researchers to anticipate and plan for results dissemination in a more forthright, participatory manner. One recent example comes from the Tuberculosis Trials Consortium (TBTC), a research consortium housed at the U.S. Centers for Disease Control and Prevention that conducts trials evaluating shorter-course therapies for TB drug regimens. One of the authors (MWF) coordinates the TBTC’s community advisory board (called the Community Research Advisors Group), which successfully advocated for the principal investigators planning a phase III study to include language in the protocol that “overall (aggregate) study results will be shared with study participants through mechanisms and materials reviewed and approved by the TBTC Community Research Advisors Group” (TBTC Study 31 protocol team, personal communication, December 2013). This statement ensures that community representatives will play a role in results dissemination without locking the investigators into overly prescriptive statements about what specific results will be shared or how this will occur. A similar approach could be used to mainstream results dissemination into protocols for social and behavioral science studies, whether descriptive surveys or experimental investigations.
Building greater participant-community engagement through results dissemination honors the ethical obligations to provide study participants with “fair benefits” in the face of potential harms and further acknowledges the expressed desire of communities to do more than give data. Whether engagement takes the form of “member-checking” to validate data or wider dissemination of research information, sharing results offers social scientists an avenue for building the kind of collaborative partnerships that make sustained research possible. Moreover, it allows researchers to refine and reconsider their interpretation of the results and the implications of research. To achieve this, researchers will need to think beyond the minimum requirement of extant IRB protocols and explicitly address how they will share results with study participants, what kind of information will be shared, and when. Our contention is that results sharing can generate research that is more transparent, more fair, and possibly more trustworthy and more useful. It does greater justice to allowing participants access to the research in which they participate. More empirical research is required on the means and effects of sharing results with participant-communities to fully understand the benefits of this approach for participants, researchers, and the partnership between them. But, as the clinical trials literature suggests, the ethical obligation to engage communities in the results of research is already clear.
Acknowledgement and notes
This paper began as a shared interest between the authors in a research methods course co-taught by Dr. Daniel Halperin (now at the University of North Carolina at Chapel Hill Gillings School of Global Public Health) and Dr. Heather Lanthorn. Both Mike and Heather were strongly influenced by not only what they had seen during data collection but also personal experiences of participating in clinical trials and Psych-101-required studies and never hearing anything back about results. We thus have deep empathy for communities that want to hear about findings, as well as intellectual/justice commitment to shared sensemaking.
Dr. Halperin encouraged our thinking and to pursue a paper (though without specific guidance on how to get a conceptual paper out in the world). Both authors have had important interactions while working on research projects, including with Abubakari Bukari and Salifu Amadu at Innovations for Poverty Action – Ghana and investigators at the Tuberculosis Trials Consortium. Drs. Richard Cash (Harvard T.H. Chan School of Public Health and the Public Health Foundation of India), Howard White (formerly of the International Initiative for Impact Evaluation), and Marc Zimmerman (University of Michigan School of Public Health) have offered important critiques on drafts on this paper. Dr. David McKenzie (World Bank), Dr. Biju Rao (World Bank), Dr. Robert Townsend (Massachusetts Institute of Technology), and Raag Bhatia (International Initiative for Impact Evaluation) have offered encouragement and insight at different stages of the process. Of course, all mistakes are our own.
The journey of this paper has been frustrating. When we submitted to journals like Social Science & Medicine, we were told our point was obvious and well-known to anthropologists. Meanwhile, economics-led development journals thought the ideas could be interesting, not broadly relevant, and offered little guidance on where a conceptual paper might live, at least allowing us to get a timestamp on our ideas. In any case, these ideas certainly seem they are becoming more relevant now than ever.
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In this post, I share a teaching plan as I am returning after 20 (omg) years to facilitate one session of an undergrad Intro to Ethnography seminar course by the same prof who taught me. All of my first interviewing experiences, which took place in undergrad, were directly or indirectly through this professor: through a summer in Nepal (Annapurna side, in Ghandruk) during which I tried to research interpretations of and care-seeking for mental illness; through service-learning for a medical anthropology course, helping with office work and learning about patient experiences interacting with doctors about diabetes at a local free clinic; and through interviewing farmworkers and food bank employees about food insecurity among the migrant farmworker community of North Carolina. These experiences were formative and I am incredibly grateful for them, even if I don’t think I was an excellent researcher in all cases. I suspect far more practice and feedback were warranted before I undertook any of those activities. Hopefully I have gotten better in the years that have followed.
What I am sharing now
In this post, I share my draft student guide and slides and then revisit in a follow-up post with the final class prep guide and pre-work for students, slides, lecture notes, and reflections on what worked and what did not. Will be happy for anyone to pick up and use the materials at that point.
I most certainly welcome critique and ideas in advance of lecturing/facilitating!!!
DRAFT Class session prep sheet and pre-work for students (pdf)
By the end of your pre-work and our session together, you will be able to:
Objective 1: Articulate a definition of positionality and some key dimensions in your own words.
Objective 2*: Articulate a working statement of your own positionality, including how it may vary in different contexts and how you will approach it in your research project.
Objective 3: Articulate a definition of epistemic justice in your own words.
Objective 4: Highlight steps Cramer took to mitigate such injustice and state two more steps you think she could have taken.
Objective 5*: Articulate a practical working definition of active listening and a list of three things you can do to support and demonstrate this given the modality in which you will conduct your own interviews.
Objective 6*: List interview techniques, including question wording and probes, that you can use to get at the information you want during an interview—without leading.
*s indicate key goals for our seminar sesssion
(Re)read Bernard Chapter 9, Interviewing: unstructured and semi-structured
Read Cramer Chapter 2, A method of listening
Read through this learning guide and answer the key questions, including mailing in the three requested definitions. This will help you be prepared in case of cold/warm calling in seminar, to engage with the material more deeply, and allow us to rapidly move beyond definitions to invigorating seminar discussion. That is, completing this guide will allow you to learn more and avoid embarrassment.
Prep guide and pre-work
Positionality: a definition
Some working definitions of positionality:
“Positionality refers to the stance or positioning of the researcher in relation to the social and political context of the study—the community, the organization or the participant group. The position adopted by a researcher affects every phase of the research process.” (Coughlin and Brydon-Miller)
“The fact that a researcher’s social, cultural, and subject positions (and other psychological processes) affect: the questions they ask, how they frame them…; their relations with those they research in ‘the field’ or through interviews; interpretations they place on empirical evidence; access to data; institutions and outlets for research dissemination; and the likelihood they will be listened to and heard.” (Gregory et al.)
“Positionality is dynamic. Our lives are in flux and, as a result, so are our subject positions.” (Correia 2012)
Prepare and send: Drawing on these and your discussions with Dr. Folmar so far, please craft a 1-2 sentence definition of positionality that works for you, such as how you would describe it to your roommate, parents/guardians, or other, older family members. Now that autumn is upon us, you might consider how you would introduce this idea of what you are learning around the Thanksgiving or other holiday table. <Submit your definition to Folmar by…>
Positionality: In A method of listening
Prof Folmar has noted that one’s own positionality is nearly impossible to describe in full but that, for data collection, there are different ways to ‘lean into’ our own positionality to create more authentic interview and observation experiences. There are also ways to modify aspects of our appearance, behavior, and shared biography to mitigate aspects of our positionality that might otherwise diminish the quality of data we collect.
Despite the challenges in fully articulating our positionality, it is important for us to reflect deeply on it when deciding what to research and how to prepare for and conduct data collection in particular contexts. Positionality matters for the quality of data we collect. For this reason, there is value in stating aspects of stating those dimensions we think are likely to influence our work clearly, as Cramer does, so that readers of our research can take these factors into account when reading our work and determining whether our results seem credible to them. That is, why should they think you got close to ‘the truth.’ We should not strive for “a gaze from nowhere,” but, instead, to be honest about our own gaze. Some dimensions of positionality map onto Bourdieu’s discussion of capital, including cultural capital, and this can be useful additional reading and may provide an organizational principle that resonates with you.
Prepare: As you read through Cramer’s ‘A method of listening’ from her book The politics of resentment, please consider and note down your answers the following questions.
In what ways does Cramer identify herself as having an emic perspective on the people and places she investigates? (Be sure to refresh yourself on ‘emic’ and ‘etic’ if you don’t remember.)
In what ways does Cramer identify herself as having an etic perspective on the people and places she investigates?
Overall, do you think that Cramer was well-suited to conduct research in/on/with the participants and topics she did? Why/not?
Throughout the chapter, Cramer lists multiple dimensions of her positionality and how it might influence her work. Note down each dimension or characteristic she suggests and prepare to speak about one that was particularly interesting to you and why.
Throughout the chapter, Cramer suggests steps she took to adjust her positionality or how she would be perceived. Note down each adjustment she makes. Prepare to discuss either: (1) one alteration that resonated with you, which you might consider as you prepare for your own data collection or (2) if none of her alterations resonated with you, or you don’t think alterations should made, be prepared to explain why. If Bernard’s examples in ‘presentation of self’ resonate more with you, you can also speak about those.
Provide an example from popular media and culture (such as podcast, other interview format, movies, or TV shows) in which you think the questioner or presenter does a good job of being honest about his/her/their positionality and how it might influence what follows.
One thing that becomes clear from reading Cramer’s chapter is that the interview—including rapport-building and positionality-defining work—begins far before an interview starts, including how you arrive, how you introduce yourself, how you set things up before you ask your first “formal” question.
Prepare: Revisit the lists you made of Cramer’s dimensions of positionality and steps she took to play up or play down some of these dimensions.
Mark or sort the lists you made above, distinguishing between those that are particularly relevant before interviews, those that are particularly relevant during, and those that matter as you close and leave the interview.
With these lists in mind—and with reference to your intended research participants and setting you have in mind for your own research project—what is one thing you will plan to do (1) before your interviews, one thing you will plan to do (2) during your interviews, and one thing you will do as you (3) close your interviews to support more honest, authentic, and rich exchanges?
With reference to your particular research topic and intended participants, what are three key dimensions of your positionality you need to own, modify, or otherwise address?
Epistemic justice and shared sense-making
Epistemic justice: A definition
The theory of ‘epistemic justice’ has been articulated by Fricker (2007), and indicates that “we can be unfairly discriminated against in our capacity as a knower based on prejudices about the speaker, such as gender, social background, ethnicity, race, sexuality, tone of voice, accent, and so on.” We can also discriminate against others, such as our research participants, interpreters, and so on in their capacity as ‘knowers.’ We usually seek people out to interview because they are expert in something (including their own lives, jobs) and yet we may undervalue their role as ‘knowers’ as we analyze, interpret, and report findings. Issues of epistemic injustice have recently been raised and anthropology and related applied fields, such as global health (such as in Tsosai 2017 and in Bhukani & Abímbọ́lá 2021).
There are two dimensions of such injustice (drawing on Bhukani & Abímbọ́lá):
Testimonial injustice: occurs when a hearer (such as an interviewer or analyst) prejudicially ascribes lower credibility to a speaker’s (such as a respondent’s) word. This can include silencing, undervaluing, or distorting the knower’s contributions, marking them with a ‘credibility deficit.’
Hermeneutical/interpretive injustice: occurs, in part, when ‘knowers’ are not included in interpretive processes (are interpretively marginalized), such that their efforts to make sense of and share their experience of the world is not heard.
A call for epistemic justice might be summed up as, “nothing about us without us.”
Prepare and send: Drawing on these definitions, please craft a 1-2 sentence definition of epistemic justice that works for you, again with the Thanksgiving/family/friend table in mind. <Submit your definition to Folmar by…>
Epistemic justice, sense-making, and member-checking: In A method of listening
In a 1986 paper, But it is rigorous?, Lincoln and Guba articulate the idea of ‘member-checking,’ as one way of co-interpreting findings with research participants. They define this as “the process of continuous, informal testing of information by soliciting the reactions of respondents to the investigator’s reconstruction of what she/he/they have been told or otherwise found out, and the constructions [or sense-making] offered by other respondents/participants or sources. It also includes a terminal, formal testing of the final report with a representative sample of stakeholders.”
In her chapter, in what ways, if any, does Cramer pursue member-checking or, more broadly epistemic justice, in her research? What is one additional step you think she could have taken (and which you may wish to pursue in your own research)?
In his chapter, Bernard also raises examples of positionality, valuing what the ‘knower’ has to say, and co-creating answers. Reviewing the example of Bernard and Savas, what do we learn both about positionality and joint sense-making?
Getting what you came for with interviews
Active listening: a definition
“Assure your respondents that their participation is crucial and that you are truly interested in what they have to say (and you’d better mean it or you’ll never pull it off)” (Bernard).
Both Bernard and Cramer provide examples of ‘active listening,’ although they never provide a precise definition for the term. It is useful to have a working definition as well as some tactics to pursue it in the modality in which you plan to conduct your interviews.
Prepare and send: Using what Cramer and Bernard apply, as well as the internet, develop a 1-2 sentence Thanksgiving-table definition of ‘active listening.’ <Submit your definition to Folmar by…>
Active listening: In Cramer and Bernard
Like Moliere’s protagonist, Monsieur Jourdain, as you read more about actively listening to others, you may find that you and those around you have been doing it all along. You may also see how other professionals–medical, journalistic, and others–use (or fail to use) solid active listening techniques. In data collection, it is important to intentionally cultivate context-appropriate ways of demonstrating that you are really attending to what a participant is saying and feeling. you may have to adapt these for the modality you plan to use (for example, face-to-face, Zoom-to-Zoom, or over-the-phone).
Drawing examples from Cramer and Bernard, make a list of techniques think you can use to demonstrate that you are actively listening and attending to a research participant in your planned interview context/setting and modality.
Send: To practice active listening, please record a short 3-10 video of yourself demonstrating how you actively listen when Folmar is lecturing. It can be a selfie video or you can ask someone to film you. Nothing fancy! <Submit your video to Folmar by…>
Probing and encouraging deeper reflection: In A method of listening
As Bernard explains, effective probing is key to successful interviews, as that is how we pursue high-quality, rich data that isn’t just a reflection of ourselves. He provides seven probe types (separate from tactics to encourage particularly verbal and nonverbal respondents).
Make a list of the seven types of probes Bernard suggests that you can use.
Do you think there are any probe types that Bernard overlooked? If so, please add them to your list.
For each, include an example from Cramer, Bernard, or another class reading that shows the type in action. If you cannot find an example, you can note down how you think you might be able to use that probe type in your own research.
Posting, with a few amendments, something on which my pop has been working, since I am not sure where and when else he will get it posted!
In England, evidently, hereditary surnames (as distinct from patronymics and other names that changed with each generation) were widespread by the mid-1400s, after slow spread over the previous four centuries. These appear to have happened by ‘folk’ processes rather than decree or specific documented event, with people adopting place and occupation terms as names (though maybe new tax and census processes were coming into play (?), which warrants more investigation than pop has done so far). A common example is Smith for those working as blacksmiths.
The Oxford English Dictionary credits the first published use of the term lanthorn(e) to John Heywood’s “Proverbs and Epigrams” published in 1562, specifically, “Of a lanthorne and light” (p.205). Closely following its appearance in Heywood’s works, lanthorn[e] was published in Flemings 2nd edition (1587) of Holinshed’s (Chronicles) History of England, Scotland, & Ireland. This involved a description of the location and ordinances for an almshouse for poor widows, built by David Smith (Smyth), who was master embroiderer to Queen Elizabeth 1, in the ward of Baynard’s Castle in London in 1584.
At about the same time as this description of this almshouse containing a lanthorn[e], the earliest known use of the surname Lanthorn occurred. Robert Lanthorn was baptized on 14 June 1585, as recorded in the Hackney parish register for the Church of St. Augustine (now St. Paul’s Cathedral), in Greater London. The close proximity in time and place for the appearance of the term and surname could reasonably suggest a direct relation between the two.
A potential hiccup in placing the origin of the term lanthorn to 1500s is the Lanthorn Tower in the Tower of London, constructed in the 1220s and 1230s. As generally described now, the Lanthorn Tower had a lanthorn on its top to aid in navigation along the River Thames. As originally built, this tower was the private quarters for King Henry III (1216-1272). One might immediately object to the idea of the tower being topped with a lantern in the 1200s on the grounds of protection for the King; putting a bright light on top of where the King was sleeping would greatly aid enemies and assassins.
London Tower curators took up my question as to whether this tower was originally called the Lanthorn Tower. They found “most of the reliable go-to sources (the King’s Works, Buildings and Institutions of the Tower, etc.) state that the Tower was not always known by Lanthorn[e] – but none illuminate what it was previously known by.” The Tower is not named in the 1532 record of James Nedeham’s alterations to the Tower but it is so named in the 1597 Haiward and Gascoyne Plan (Fig. 1). It is likely the “Lanthorn Tower” was wisely left unnamed until it no longer served as a royal residence and after the lanthorn as a navigation aid was added. This timing is also consistent with the appearance of the term lanthorn[e] occurring in the mid-1500s.
I am currently using the In-Service Teacher Training Survey Instrument ( ITTSI ), by Evans et al., as a checklist for program details to include in a paper draft. I aim to include an appendix that runs through these details in a more succinct way, which has value for systematic review and other education programming reviews.
Since I have spent some time going from the screenshots included in the original paper to a table that I can fill in, I decided to spare teams that time in the future. You can download the table for yourself and fill it in for your specific program. Feedback and comments are welcome.
This is post was created as a commitment-device. It will support a paper (built on my thesis) I am working on. I will review existing literature (broadly defined) on the idea of country ownership. I will create a database of what I find, here.
A chronological launch pad will be the Accra Agenda for Aid Effectiveness, following on the 2005 Paris Declaration. In this 2008 update, the authors provide some–weak–parameters for country ownership, stating that it requires “countries [to] have more say over their development processes through wider participation in development policy formulation, stronger leadership on aid co-ordination and more use of country systems for aid delivery.”
One of the most compelling, quips I have seen on country ownership recently is this important reminder on Twitter: “Country ownership is not yours to ensure, and you certainly cannot confer it–countries are ultimately responsible for the health of their populations.”
I won’t be adjudicating between these two, though my sympathies are with the latter. My idea is to consider what country ownership means at multiple phases of the ‘classic’ policy process and to see what ‘the literature’ says about them. For example,, does a policy need to go through legislative processes to be owned? Etc. Some of the phases I have in mind (while recognizing these are not always linear or discrete) are:
Agenda-setting / prioritization
Design / formulation
Adoption / formalization
Evidence-gathering & decision-making to continue, modify, scale-up, or terminate
Ultimately, I will be considering what happened at each of these stages in Ghana with regard to the Affordable Medicines Facility-malaria (AMFm) adoption and implementation. Adoption was a big game of “not it!” by Ghanaian stakeholders and I will unpack what we can learn from this about ownership. Implementation, however, was a different story. I think we’d be hard-pressed to say it wasn’t Ghana-led.
Inspired by a referee report completed recently, I decided to share my thoughts on reporting qualitative results. These aren’t codified norms but they reflect my understanding of qualitative (largely interview and observation) data collection and analysis, and personal – but I think reasonable — preferences. By commenting on report writing, I am obliquely making comments on collection and analysis processes too. And I welcome you to contribute.
The caveats: I largely speak about qual in the context of public health and global development. I make no claims about the approach of historians and other scholars that work primarily with texts (though I welcome input from all scholars). I also focus mostly on interview data. So, let’s begin.
A key goal of any report–quant or qual–is to give readers the tools to understand your data and analysis, then to consider your interpretations in light of strengths and gaps in data collection and analysis. My suggestions are to this end.
Helpyour reader situate your study in time. You can do this with a timeline figure showing your data collection, the policy or phenomenon of interest, and other relevant contextual events. I shouldn’t have to doodle a timeline on scrap paper to make sense of your data and what they capture. Create an easy way for your reader to understand your timeline.
Make the basis of your research clear. Make your research questions clear. If you have hypotheses or priors, even if you aren’t formally testing or updating them, make them clear. If you were guided by a theory or framework, make that clear. If you went in tabula rasa and totally exploratory, make that clear.
Choose the right kind of data. Qualitative data comes in four main flavors: text (including interview notes & transcripts), speech, observations, and images. State the type(s) you have and why that was the appropriate type or combo given your research questions.
Statethe rationale behind your site selection and sampling clearly. Site selection and sampling are important parts of credible qualitative work – you need people to believe that you went to the right places, talked to the right people, read the right things, and observed the right things to make claims about them.
Explain and justify how you choose your study sites. It is insufficient to say they were chosen ‘purposively’. What was the purpose? And how did you apply it? How do the selected sites fit into the universe of possible sites? Why should we think that your sites are sufficient and what might have been missed? There is a certain brand of researcher who equates purposive sampling with convenience sampling. Don’t be that researcher.
Clarify how your sites relate to one another? Are they similar or different and, if so, how and why? How does this help us understand your research?
Use the above points also to explain your sample selection. Yes, theoretical or purposive sampling may be the most appropriate strategy for your sample but you need to say why and to justify the sufficiency of your sample composition and size. Ideally with reference to your research questions and hypotheses.
Describe what heterogeneous views or experiences were necessary to understand your phenomenon of interest? How well did you capture them? If there is a relatively small number of relevant stakeholders, did you speak with or see all? If not, how did you fill in the gaps?
If the sample frame is much wider (e.g., not a small number of stakeholders involved in a particular decision), situate your sample in this wider context. Are they meant to be typical? Exceptional? (See: @evlieb’s nested analysis.)
State the consent you received and the compensation you provided for all primary data collected.
Explain your sample size. Not all qualitative researchers think thematic saturation is an appropriate goal but some reference to saturation, redundancy, or other ways of showing that you have captured the relevant breadth and depth of views and experiences are important. Sometimes your sample will be partially determined by the budget. Be honest about this but still doing the work of laying out what you think can/not be learned from the sample you do have.
Explain the intention behind design choices. If you do something with intention, explain it. For example, if you segregate focus groups by men and women, explain why this was important given the context and/or topic in order to get high-quality data. Sometimes (gasp!) biological sex will not be the most important or relevant division to explore.
Discuss positionality. Especially for interview data, you need to discuss positionality of those present vis-à-vis the respondent. Who was in the room (interviewer? notetaker? more?)? What kind of benefits and drawbacks exist given interviewer characteristics in terms of helping people open up and be honest or inducing one or more forms of bias. These could be inherent qualities (sex, accent) and could be intentional (clothing and transportation choices, for example).
If positionality could have induced bias, what steps were taken to mitigate this?
State how the interviews interviews recorded. Audio? Video? Was there a notetaker there? How were observations of context and non-verbal cues incorporated into the interview notes or transcripts for analysis?
For interviews, clarify how un/semi/fully structured were your guides – & why? Why was approach right for your research set-up given the skill of the interviewer, the number of interviewers, and the research set-up If you don’t know the answer, I suggest you get your hands on Bernard’s Anthro Methods.
State the interviewers’ opinions on respondent truthfulness. How is this accounted for in the analysis?
Discuss the role of iterationbetween collection and analysis in your work. Were you able to debrief and reflect after each interview and make implied adjustments to your guides or approach? Could you follow-up with respondents?
Clarify the relation between data collectors and analysts. Is there new positionality to consider given who is doing the analysis (making meaning and deciding what is important)?
Describe your analysis process. Uploading data into qualitative software is not analysis! Deploying quotes-in-colorful-boxes (or in block text) is not analysis. Coding is not complete analysis – it means you have tagged and categorized your data to begin to make sense of it. Keep going.
You do not need to use quotes in full, offsets as big blocks, to show that you did qualitative work. A quote is a data point. If you want to show one to illustrate a larger or unique point, make it is clear that is what you are doing.
You can also use smaller segments of quotes, integrated into the text, so that you use the respondents’ words without disrupting the flow of your results narrative. A parade of quotes will rarely stick with the reader.
Help the reader understandwhat you are trying to say. Is a code or a quote reflective of what many people said or did? Just one? What analytic value is it bringing?
How do you build faith in your analysis? For example, independent coders, discussions and consensus-building? Did you engage any member with checking or validation, such as taking preliminary analysis back to the respondents to see if they thought you were on the right track?
I miss being conducting interviews and data collection myself. One does get priced out of doing the things one likes!
One way that I remain connected to the research is by conducting frequent debriefs during the data collection (and early analysis phase). Debriefing isn’t just self-serving; it improves the quality of qualitative data collected and the subsequent analysis. Collecting data of any kind, but certainly qualitative data, can be draining, mentally and emotionally. Building in debriefs can help the data collector remember and reflect rather than just getting through the interviews. And an outside pairs of eyes/ears can pick up additional details and threads for additional exploration, as well as steering away from interesting but too-tangential topics.
After a particularly insightful debrief this morning, I quickly put together some thoughts about the process and why it is so important if you are conducting qualitative work.
There are a few hallmarks of a qualitative approach research that make it important to check-in regularly during the data collection (interviews, observations, focus groups) process in order to strengthen and make more credible the information being produced.
The data collector and data analyst are often the same person or small set of people (which generally implies a higher skill level for a qual data collector than the quant counterpart).
A qualitative approach to research is an iterative endeavor — this is a key distinguishing feature from a lot of large-n, more quantitative work. In quantitative work, we usually take as fixed all the items will include in a questionnaire, which serves to enhance rigor and decrease bias. In qualitative work, we have a bit more flexibility and pursue rigor a little differently. This allows us to incorporate learnings from one day’s interview work into the next day’s interviews (or observations, or focus groups), as we continue to update our understanding. For example, we can incorporate into the interview guide a new probe that worked particularly well at eliciting a rich response; or, a new line of thinking might emerge that we want to explore with future respondents; or, we may notice a potential pattern emerging that we want to pressure-test the next day.
There are some important implications of these features.
First, it is very easy to get stuck in the weeds if you are the data collector, focused on scheduling the next interview and generally being tired after so much human interaction, rather than taking time to reflect on what you have learned. It is not good to be mired in executing-but-not-reflecting-and-and-hypothesizing-and-adapting.
Second, you need to have time built in to your schedule and budget to reflect and update your approach. This time can come between interviews during the day, as well as allotting time between days of interviewing. This protected time can be used for transcription and expanding field notes, for reviewing these notes and transcripts, and to begin early analysis (such as memoing and early coding). This helps to prevent many cases of “we should have asked that!” once we get back from ‘the field.’ By doing this as we go, it is easier to return for follow-up interviewing or at least ask the rest of our participants some additional questions. It also helps us to learn more from each interview.
To facilitate this reflection without getting stuck in your own head, it can be extremely valuable to have a debrief with someone else about what you have been learning and experiencing in the field, to start to make sense and meaning of what you have been hearing. It is often easier to commit to a call/meeting time than deep-work, memoing time.
These debriefs — sometimes called peer debriefs* — can take the form of written answers shared with the larger team or can take place on phone calls or face-to-face with another team member (who did or did not participate in the interview or observation). Technology and time zones will determine what mode is most sensible; it may change over the course of a project. (Note that debriefs are also a great way to make sure that team members not ‘on the ground’ still feel involved in the project.)
These debriefs are not substitutes for early analysis or for another member of the team reviewing transcripts and field notes to provide feedback on: points that have been over- or under-emphasized, points where responses seem vague and more probing may have been required, or points the interviewer seems to have made unnecessary or unhelpful assumptions.
Below are the set of questions i am currently asking for reflection:
What was an interesting point that arose in the interviews today? Does this suggest an additional set of questions to ask in future interviews?
What surprised you most about the interviews today?
Did anything particularly challenging or difficult or unpleasant come up during the interviews today? Should we think of ways to mitigate these challenges in the future?
Are there any questions that don’t seem to be yielding useful information? Should we update or remove these questions?
Do any patterns stand out to you in responses (early coding and categorization)? Does this suggest any hypotheses that we may want to pressure test in future interviews?
*Some people argue that peer debriefs should not be conducted by a member of the research team, in pursuit of impartiality. Ideally, both someone from the research team and a neutral person outside the team can be reviewing data as it is coming in and asking questions to strengthen the research process.