exemplary case study meaning

An introduction to exemplar research: a definition, rationale, and conceptual issues

• PMID: 24338906
• DOI: 10.1002/cad.20045

The exemplar methodology represents a useful yet underutilized approach to studying developmental constructs. It features an approach to research whereby individuals, entities, or programs that exemplify the construct of interest in a particularly intense or highly developed manner compose the study sample. Accordingly, it reveals what the upper ends of development look like in practice. Utilizing the exemplar methodology allows researchers to glimpse not only what is but also what is possible with regard to the development of a particular characteristic. The present chapter includes a definition of the exemplar methodology, a discussion of some of key conceptual issues to consider when employing it in empirical studies, and a brief overview of the other chapters featured in this volume.

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• Biomedical Research / methods*
• Biomedical Research / standards
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• Psychology, Child / standards

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• Published: 06 May 2021

Interpersonal relationships drive successful team science: an exemplary case-based study

• Hannah B. Love   ORCID: orcid.org/0000-0003-0011-1328 1 ,
• Jennifer E. Cross   ORCID: orcid.org/0000-0002-5582-4192 2 ,
• Bailey Fosdick   ORCID: orcid.org/0000-0003-3736-2219 2 ,
• Kevin R. Crooks 2 ,
• Susan VandeWoude 2 &
• Ellen R. Fisher 3  

Humanities and Social Sciences Communications volume  8 , Article number:  106 ( 2021 ) Cite this article

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• Complex networks
• Science, technology and society

Scientists are increasingly charged with solving complex societal, health, and environmental problems. These systemic problems require teams of expert scientists to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes. Despite the essential need for such interdisciplinary interactions, little research has examined the impact of scientific team support measures like training, facilitation, team building, and expertise. The literature is clear that solving complex problems requires more than contributory expertise, expertise required to contribute to a field or discipline. It also requires interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group. These forms of expertise are often tacit and therefore difficult to access, and studies about how they are intertwined are nearly non-existent. Most of the published work in this area utilises archival data analysis, not individual team behaviour and assessment. This study addresses the call of numerous studies to use mixed-methods and social network analysis to investigate scientific team formation and success. This longitudinal case-based study evaluates the following question: How are scientific productivity, advice, and mentoring networks intertwined on a successful interdisciplinary scientific team? This study used applied social network surveys, participant observation, focus groups, interviews, and historical social network data to assess this specific team and assessed processes and practices to train new scientists over a 15-year period. Four major implications arose from our analysis: (1) interactional expertise and contributory expertise are intertwined in the process of scientific discovery; (2) team size and interdisciplinary knowledge effectively and efficiently train early career scientists; (3) integration of teaching/training, research/discovery, and extension/engagement enhances outcomes; and, (4) interdisciplinary scientific progress benefits significantly when interpersonal relationships among scientists from diverse disciplines are formed. This case-based study increases understanding of the development and processes of an exemplary team and provides valuable insights about interactions that enhance scientific expertise to train interdisciplinary scientists.

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Introduction

Scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges (Read et al., 2016 ; Stokols et al., 2008 ). These systemic problems require interdisciplinary teams to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes (Barge and Shockley-Zalabak, 2008 ; De Montjoye et al., 2014 ; Fiore, 2008 ). Thus, to successfully approach complex research questions, scientific teams must synthesise knowledge from different disciplines, create a shared terminology, and engage members of a diverse research community (Matthews et al., 2019 ; Read et al., 2016 ). Despite significant time, energy, and money spent on collaboration and interdisciplinary projects, little research has examined the impact of scientific team support measures like training, facilitation, team building, and team performance metrics (Falk-Krzesinski et al., 2011 ; Klein et al., 2009 ).

Studies examining the development of scientific teaming skills that result in successful outcomes are sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The earliest studies of collaboration in science used bibliometric data to search for predictors of team success such as team diversity, size, geographical proximity, inter-university collaboration, and repeat collaborations (Borner et al., 2010 ; Cummings and Kiesler, 2008 ; Wuchty et al., 2007 ). Building from these studies, current research focuses on team processes. Literature suggests that to successfully frame a scientific problem, a team must also engage emotionally and interact effectively (Boix Mansilla et al., 2016 ) and that scientific collaboration involve consideration of the process, collaborator, human capital, and other factors that define an scientific collaboration (Bozeman et al., 2013 ; Hall et al., 2019 ; Lee and Bozeman, 2005 ). Similarly, Zhang et al. ( 2020 ) used social network analysis to examine how emotional intelligence is transmitted to team outcomes through team processes. Still more research is needed, and Hall et al. ( 2018 ) called for team science studies that use longitudinal designs and mixed-methods to examine project teams as they develop in order to move beyond bibliometric measures of success and to explore the complex, interacting features in real-world teams.

Fiore ( 2008 ) explained that much of what we know about the science of team science (SciTS), training scientists and team learning in productive team interactions, is anecdotal and not the result of systematic investigation (Fiore, 2008 ). Over a decade later there is still a paucity of research on how scientific teams develop the type of expertise they need to create new knowledge and further scientific discovery (Bammer et al., 2020 ). Bammer et al. ( 2020 ) has identified and defined two types of expertise: (1) contributory expertise, expertise required to make a contribution to a field or discipline (Collins and Evans, 2007 ); and (2) interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group (Bammer et al., 2020 ). These forms of expertise are often tacit, codified by “learning-by-doing,” and augmented from project to project; therefore, they are difficult to measure and rarely documented in literature (Bammer et al., 2020 ).

Wooten et al. ( 2014 ) outlined three types of evaluations—developmental, process, and outcome—needed to understand how teams develop and to provide information about their future success (Wooten et al., 2014 ). A developmental evaluation focuses on the continuous process of team development, and a process evaluation focuses on team interactions, meetings, and engagement (Patton, 2011 ). Both development and process evaluations have the common goal of understanding the team’s future success or failures, also known as the team’s outcomes (e.g., grants, publications, and awards) (Patton, 2011 ). The majority of published work on outcome metrics is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). Albeit informative, these studies are based upon limited outcome metrics such as publications and represent only a selective sampling of teams that have achieved success. To collect these three types of evaluation data, it is recommended to engage mixed-methods research such as a combination of social network analysis (SNA), participant observation, surveys, and interviews, although these approaches have not been widely employed (Bennett, 2011 ; Borner et al., 2010 ; Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ).

A few key studies have provided insight into successful collaboration strategies. Duhigg ( 2016 ) found that successful teams provided psychological safety, had dependable team members, and relied upon clear roles and structures. In addition, successful teams had meaningful goals, and team members felt like they could make an impact through their work on the team (Duhigg, 2016 ). Similarly, Collins ( 2001 ) explained that in business teams, moving from “Good to Great” required more than selecting the right people; the team needed development and training to achieve their goals (Collins, 2001 ). Woolley et al. ( 2010 ) found that it is not collective intelligence that builds the most effective teams, but rather, how teams interact that predicts their success (Woolley et al., 2010 ). The three traits they identified as most associated with team success included even turn-taking, social sensitivity, and proportion female (when women’s representation nears parity with men) (Woolley et al., 2010 ). Finally, Bammer et al. ( 2020 ) recommended creating a knowledge bank to strengthen knowledge about contributary and interactional expertise in scientific literature to solve complex problems. Collectively, these studies argue that the key to collective intelligence is highly reliant on interpersonal relationships to drive team success.

This article reports on a longitudinal case-based study of an exemplary interdisciplinary scientific team that has been successful in typical scientific outputs, including competing for research awards, publishing academic articles, and training and developing scientists. This analysis examines how scientific productivity, advice, and mentoring networks intertwined to promote team success. The study highlights how the team’s processes to train scientists (e.g., developing mentoring and advice networks) have propelled their scientific productivity, fulfilled the University’s land grant mission (i.e., emphasises research/discovery; education/training; and outreach/engagement) and created contributory and interactional expertise on the team. Team dynamics were evaluated by social network surveys, participant observation, focus groups, interviews, and historical social network data over 15 years to develop theory and evaluate complex relationships contributing to team success (Dozier et al., 2014 ; Greenwood, 1993 ).

Case study selection

The [BLIND] Science of team science (SciTS) team consisted of scientists trained in four different disciplines and research administrators. The SciTS team monitored twenty-five interdisciplinary teams at [BLIND] for 5 years from initiation of team formation to identify team dynamics that related to team success. This case is thus presented as part of an ongoing study of the 25 teams, supported by efforts through the [BLINDED] to encourage and enhance collaborative, interdisciplinary research and scholarship. Team outcomes were recorded annually and included extramural awards, publications, presentations, students trained, and training outcomes. An exemplary case-based study is appropriate when the case is unusual, the issues are theoretically important, and there are practical implications (Yin, 2017 ). Further, cases can illustrate examples of expertise and provide guidance to future teams (Bammer et al., 2020 ). An “exemplary team designation” was given to this team by the SciTS evaluators. Metrics used to designate an exemplary team included: team outcomes; highly interdisciplinary research; longevity of the team; fulfilment of all aspects of the land grant mission (research/discovery; education/training; and outreach/engagement); integration of team members; and use of external reviewers.

Social network survey

The exemplary team included Principle Investigators (PIs), postdoctoral researchers (postdocs), graduate students, undergraduate students, and active collaborators external to the University. The entire team was surveyed annually 2015–2019 about the extent and type of collaboration with other team members. In 2015, the team was asked about prior collaborations, and in subsequent years they were asked about additional interactions since joining the team. Possible collaborative activities included research publications, scientific presentations, grant proposals, and serving on student committees. Team members were also asked the types of relationships they had with each team member, including learning, leadership, mentoring, advice, friendship, and having fun (Supplementary 2 ). Data were collected using a voluntary online survey tool (Organisational Network Analysis Surveys). All subjects were identified by name on the social network survey but are not identified in any network diagrams or analyses. SNA software programmes R Studio (R Studio Team, 2020 ) and UCINET (Borgatti et al., 2014 ) were used to analyse data and Visone (Brandes and Wagner, 2011 ) was used to create visualisations. The response rate for the survey was 94% in 2015, 83% in 2016, 95% in 2017, and 81% in 2018. All data collection methods were performed with the informed consent of the participants and followed Institutional Review Board protocol #19-8622H.

Data from the social network survey were combined to create three different network measures: scientific productivity, mentoring, and advice. The scientific productivity network was a combination of four survey measures: research/consulting, grants, publications, and serving on student committees. Scientific productivity represents a form of cognitive or contributory expertise: expertise required to contribute to a field or discipline (Bammer et al., 2020 ; Boix Mansilla et al., 2016 ). The mentoring and advice networks were created from social network survey questions: “who is your mentor?” and “who do you go to for advice?”, respectively. Mentor and advice are tacit forms of interactional expertise: socialised knowledge that includes socialisation into the practices of an expert group (Collins and Evans, 2007 ). Other studies have also found a connection between social characteristics of interdisciplinary work and other factors like productivity, career paths, and a group’s ability to exchange information, interact, and explore together (Boix Mansilla et al., 2016 ).

Social network data were summarised using average degree, sometimes split into indegree and outdegree. Outdegree is a measure of how many team members a given individual reported getting advice, or mentorship, from. Similarly, the indegree of an individual is a measure of how many other team members reported receiving advice, or mentorship, from that person. Average degree is the average number of immediate connections (i.e., indegree plus outdegree) for a person in a network (Giuffre, 2013 ; R. Hanneman and Riddle, 2005 a, 2005 b). To further explore the mentoring and advice networks, we calculated the average degree/outdegree/indegree of postdocs, graduate students, and faculty separately to directly compare demographic groups.

The advice, mentoring, and scientific productivity networks were directly compared using the Pearson correlation between the corresponding network adjacency matrices. We predicted a positive correlation between the advice, mentoring, and scientific productivity matrices. Statistical significance ( p  < 0.05) of correlations was assessed with the network permutation-based method Quadratic Assignment Procedure (QAP) (R. A. Hanneman and Riddle, 2005 a, 2005 b).

Historical social network data

A historical network survey was created to determine how the connections in the network formed, developed, and changed from project-to-project. The historical social network was constructed from three forms of data: interviews with the PIs, a historical narrative written by the PIs describing the team formation process, and team rosters that listed the 81 team members since the inception of the team.

Retrospective team survey

A retrospective team survey was administered at the end of the study to determine what skills team members developed and codified through participating on the team, how membership on the team supported members personally and professionally, and their favourite aspects of the team. The survey was sent to 22 members from the 2018 team roster using Qualtrics (Qualtrics Labs, 2005 ) with an 86% response rate.

Two semi-structured, one-hour interviews were conducted with two PIs in 2018 to learn about the history of the team. The interviews were digitally recorded and transcribed.

Participant observations

Participant observation was conducted from 2015–2019 at four annual three-day, off-campus retreats and 1–2 additional meetings each year. Students, PIs, external collaborators, and families were all invited to attend the retreats and meetings. Field notes about team interactions were recorded immediately after each interaction. The analytic field notes captured how team members interacted across disciplines, tackled scientific problems, and engaged with others at different career stages. Analysis occurred as field notes were written, during observations, and again during data analysis.

An exemplary team

The SciTS Team identified one team from the larger study and designated it as exemplary based on six (tacit and non-tacit) elements. First, the team had outstanding team outcomes. From 2004–2018, notable accomplishments include 33 extramural awards totalling over $5.6 million, including two large federal awards totalling over $4.5 million; 58 peer-reviewed publications with 39 different universities, 13 state agencies, and 11 other organisations; 141 presentations, 21 graduate students and 15 postdocs trained; and receipt of an [BLIND]institution-wide Interdisciplinary Scholarship Team Award. Participants received many individual honours, including one of the PIs being named to the National Academy of Sciences.

Second, this interdisciplinary team combined scientific expertise from many different backgrounds, including ecologists, wildlife biologists, evolutionary biologists, geneticists, veterinarians, and numerous collaborators. Principal Investigators were housed in five main universities: Colorado State University, University of Wyoming, University of Minnesota, University of California-Davis, and University of Tasmania. They also engaged collaborators from national and international universities, federal, state, and local governmental agencies, veterinary centres, and animal shelters. Collectively, team members represented 39 different universities, 11 federal agencies, 13 state agencies, and 11 other organisations listed on their peer-reviewed publications. The team has published globally with co-authors from every continent but Antarctica.

The third element identified was the team’s 15-year history and how they evolved project-to-project (Supplementary Video S1 ). In 2003, a graduate student proposed a collaborative research project between two faculty members who became two of the founding team PIs (Fig. 1 ). The team was formed in 2004 with four members—two faculty PIs, a postdoc, and a Ph.D. student (Fig. 1 ). Initial grant proposals submitted in 2005 and 2006 were not funded; however, in 2007, the team received a large federal research award from the US National Science Foundation (NSF). The team roster increased from four to nine, and a second large expansion occurred after receipt of another NSF award in 2012. By 2014, membership increased to 31 people, and at the end of analysis in 2018, the roster comprised 43 members. Over the course of observation, 81 different individuals, including students, faculty, and collaborators, had participated in research activities supported by the team.

Significant events occurring over 15 years during the development and formation of an exemplary team.

The fourth reason this team was deemed exemplary was because it intertwined the components in the Land Grand mission, including research/discovery, teaching/training, and extension/engagement (Fig. 2 ). The team included undergraduates conducting research and presenting at conferences, graduate students working in multiple labs, and postdocs mentoring all the researchers in the lab. An external advisor said at the end of a retreat, “It’s really cool that students are part of the conversations that are both good/bad/ugly etc. It is not just good. It is not just one-on-one conversations. They hear it all.” A Ph.D. student wrote in the Retrospective Survey about the skills he developed: “I have developed the ability to talk about my research to people outside my field. I have also worked on broadening my understanding of disease ecology as a whole. I have been given the opportunity [to] begin placing my work in the larger framework of ecosystem health.” Faculty also wrote about what they learned, “[I] Learned from leadership of team (especially [blinded], and other PIs) how to develop and conduct research team work well - am using what I am learning to develop new research teams…. how to develop and nurture and respect interpersonal relationships and diversity of opinions. This has been an amazing experience, to be part of a well-functioning team, and to examine why and how that is maintained”

The team grew from 4 members in 2004 to 42 members in 2018. Much of the growth occurred by the addition of students and external collaborators.

Fifth, the team was effective at onboarding and integrating new members. To do so, they used two key strategies (Fig. 3 ). First, 15 of the students held co-advised graduate research positions. This shared model of mentorship provided students with opportunities to work in multiple labs, collaborate with additional team members, and gain a broader academic experience. A Ph.D. student wrote in the Retrospective Survey about the skills she learned from being a member of the team: “Leadership skills, communicating science to those in other fields, scientific writing skills, technical laboratory skills, interpersonal communication skills, data sharing experience, and many others.” The shared model supported the team’s interdisciplinary mission by providing opportunities to train future scientists to communicate, network, and conduct research across disciplines. Second, as team members developed through participation on the team, they assumed more mature scientific roles. Fourteen members of the team changed positions within the team. Many of these transitions were from undergraduate student to Ph.D. student or Ph.D. student to postdoctoral researcher. In 2012, one postdoc became a PI on the grant.

Social network diagrams of team growth and development from 2004–2018. This network reports onboarding and integration of all members, including their primary position when they joined the team. The nodes are sized by average degree (see text). Colours denote different roles on the team.

Finally, the 2018 team retreat included external reviewers. At the end of 2018 team retreat, they were asked if they had any feedback for the team. An external reviewer said: “You can check all of the boxes of a good team.”; “This is a dream team.”; “I am really impressed.”. Another external reviewer said:

The ambitiousness to execute the scope of the project, to have this many PIs, to be able to communicate; the opportunities for new insights; and the opportunities it presents for trainees are rare. There are a lot of people exposed in this. This is a unique experience for someone in training. And it extends to elementary school. I don’t think there are many projects that have this type of scope. I was impressed with just the idea that scientists are taking this across such a great scope and taking on such great questions.

Scientific productivity network

Prior to 2016, the average degree of the scientific productivity network was 8.8 (Fig. 4 ). In 2016, four faculty nodes were in the core of the network, and the periphery nodes included graduate students, postdocs, and external collaborators (Fig. 5 ). The average degree dropped slightly to 6.2 when the team integrated new members and re-formed around new roles and responsibilities on a new grant (Fig. 4 ). In 2017, the average degree peaked at 9.7 (Fig. 4 ) and faculty were still core, but graduate students and postdocs were more central than before (Fig. 5 ). During this time, productivity was at its highest as team members were working together to meet the objectives of a 5-year interdisciplinary NSF award. The network evolved further in 2018; two of the postdoc nodes overlapped with the faculty nodes in the core of the network (Fig. 5 ).

Average degree of social networks diagrams (mentoring, advice, scientific productivity) indicated strong social ties among team members.

Social network measures of productivity (research/consulting, grants, publications, and serving on student committees) were recorded over time. Each node represents a person on the team, and nodes are sized by average degree (see text). Colours denote different roles on the team. The node label indicates the number of years a person has been part of the team.

Mentoring is integral in the collaborative network

Team members reported between an average of 2.4–3.1 mentors (average outdegree) each year on the team (Fig. 6 ). More specifically, graduate students reported 6.0–7.7 mentors, whereas postdocs reported 2.4–3.5 mentors (Table 1 ). Faculty team members reported having an average of 2.2 to 4.3 mentors on the team (Table 1 ), with the highest average outdegree in 2018.

This diagram was created by using participant answers to the social network question, “who is your mentor?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

The highest indegree for an individual was the lead PI, with an indegree ranging from 13 to 14 each year (i.e., each year, 13–14 team members reported this individual provided mentorship). In response to an interview question about this PIs favourite part of the team, this individual said, “…and of course, I really like the mentorship of the students…They are initially naive, and some people are initially underconfident, but eventually they become fluent in their subject area.” Many students wrote about the mentoring they received from the team. An undergraduate student wrote:

I have improved my communication skills after needing to collaborate with several mentors across different time zones. I’ve also improved willingness to ask questions when I don’t understand a concept. I’ve also learned what concepts I find basic in my field that others outside my discipline are less familiar with.

Faculty also wrote about the mentoring they received, such as, “I continually learn from members in the team and mentorship by the more experienced members has supported my own career progression.”

Advice is integral in the collaborative network

In the 2015–2017 advice network diagrams, the faculty were tightly clustered (Fig. 7 ). In 2018, the cluster separated as postdocs and graduate students joined the centre of the network. On average, team members reported 5.1 to 6.4 people they could go to for advice (Fig. 4 ).

This diagram was created by using participant answers to the social network question, “who do you go to for advice?” Each circle or node represents a person on the team. The nodes are sized by outdegree to show who reported receiving mentorship. Node size indicates how many mentors an individual reported, and arrows indicate nodes that served as mentors. Colours denote different roles on the team.

In a survey, faculty responded to the question, “How has the team supported you personally and professionally?” One faculty member wrote: “Just today I asked three members of the team for professional advice! And got a thoughtful and prompt response from all.” Another team member wrote: “Being a member of the…team has allowed me to develop skills in statistical analysis, scientific writing, and critical thinking. This team has opened my eyes to what is possible to achieve with science and has provided me with opportunities to network and expand my horizons both within the field of study and outside of it.” These quotes further suggest that the mentoring and advice from a large interdisciplinary team were important to train future scientists.

Interpersonal relationships as driver for scientific productivity

The mentoring and advice networks supported and built on the scientific productivity network and vice versa. The correlation between the collaboration, mentoring, and advice networks would not be possible if the networks were not intertwined. In the retrospective survey, a faculty member described how tacit interpersonal relationships were correlated with their scientific productivity:

Being a part of this grant has helped me both personally and professionally by teaching me new skills (disease ecology, team dynamics), developing friendships/mentors from the team, and strengthening my CV and dossier for promotion to early full professorship.

A Ph.D. student also described how the relationships on the large team propelled their research.

Membership on this team has provided me with a lot of mentorship that I would not otherwise receive were I not working on a large multi-disciplinary for my doctoral research. It has also allowed me to network more effectively.

Between 2015 and 2018, the mentor and advice networks were significantly correlated with the scientific productivity network, demonstrating that personal relationships are associated with scientific collaboration (Table 2 ).

To date, the literature examining successful interdisciplinary scientific team skills that result in successful outcomes is sparse (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). The majority of published work in this area is evaluated by archival data analysis, not individual team behaviour and assessment (Hall et al., 2018 ). This study answers the call of numerous researchers to use mixed-methods and SNA to investigate scientific teams (Bennett, 2011 ; Borner et al., 2010 ; Hall et al., 2018 ; Woolley et al., 2010 ; Wooten et al., 2015 ). Our case-based study also increases understanding of the development and processes of an exemplary team by providing valuable insights about how the interactions that enhance scientific productivity are synergistic with the interactions that train future scientists. There are four major implications of our findings: (1) interactional and contributory expertise are intertwined; (2) team size, tacit knowledge gained from previous project, and interdisciplinary knowledge were used to effectively and efficiently train scientists; (3) the team increased scientific productivity through interpersonal relationships; and (4) the team fulfilled the land grant mission of the University by integrating teaching/training, research/discovery, and extension/engagement into the team’s activities.

Interactive and contributory expertise are intertwined

Previous literature on scientific teams has found that great teams are not built on scientific expertise alone, but on the processes and interactions that build psychological safety, create a shared language, engage members emotionally, and promote effective interactions (Boix Mansilla et al., 2016 ; Hall et al., 2019 ; Senge, 1991 ; Woolley et al., 2010 ; Zhang et al., 2020 ). The team highlighted in this report created a shared language and vision through the mentoring and advice networks that helped fuel the team’s scientific productivity (Hall et al., 2012 ). To solve complex problems requires more than contributory expertise, it also requires interactional expertise (Bammer et al., 2020 ). These forms of expertise are often tacit and internalised through the process of becoming an expert in a field of study (Collins and Evans, 2007 ). Learning-by-doing is augmented from project-to-project, with expertise codified over time (Bammer et al., 2020 ). Further, cognitive, emotional, and interactions are key components of successful collaborations (Boix Mansilla et al., 2016 ; Bozeman et al., 2013 ; Zhang et al., 2020 ). Using social network analysis, our case-based analysis found that the mentoring and advice ties were intertwined with the scientific productivity network.

Training scientists to be experts

The Retrospective Survey asked what personal and professional skills respondents learned from being a member of a team. We hypothesised that many respondents would report tangible skills. Surprisingly, 82% of the open-ended responses were about tacit skills. Students frequently had co-advised graduate research positions, worked in multiple labs, and communicated regularly with practitioners. Moreover, the team translated research to different disciplines within the team, mentored others, and managed interpersonal conflicts. These interactions built expertise because training was not limited to research in a single lab or only in an academic setting. Simple, discrete, and codified knowledge is relatively easy to transfer; however, teams need stronger relationships to gain complex and tacit knowledge, (Attewell, 1992 ; Simonin, 1999 ). On this team, interactions and the ability to practice communication were especially influential for students, junior scientists, and new members. These individuals provided survey responses reporting they learned a wide variety of skills ranging from leadership, scientific and interpersonal communication, networking across disciplines, scientific writing, laboratory techniques, and data sharing standards. Further, respondents noted they had gained experience in developing, nurturing, and respecting interpersonal relationships and diversity of opinions. This was reinforced with participant observation data. In other interdisciplinary groups studied in conjunction with this exemplary team, students were not typically exposed to the inner workings of the team such as leadership meetings. On this team, students were exposed to all conversations, which became an important component of the mentoring and advice structure, serving to train future scientists in all aspects of team integration and leadership development. Belonging to this large interdisciplinary team was effectively training, building, and structuring the team.

Interpersonal relationships increase scientific productivity

Longevity of relationships is an important factor in creating social cohesion, reducing uncertainty, and increasing reliability and reciprocity (Baum et al., 2007 ; Gulati and Gargiulo, 1999 ; Phelps et al., 2012 ). Previous literature has, however, rarely documented the importance of time in building the structure of the network (Phelps et al., 2012 ) and few longitudinal studies of scientific teams exist. Further, it has long been hypothesised that greater interaction among people increases the quality and innovativeness of ideas generated, which may in turn increase productivity (Cimenler et al., 2016 ). Our case-based study found that the mentoring and advice ties existed in a symbiotic relationship with the scientific productivity network where the practices of the team were simultaneously training scientists. This aligns with social network literature that interactions can structure the social network and the network structure influences interactions (Henry, 2009 ; Phelps et al., 2012 ). Second, intentional mentoring programmes have demonstrated a positive relationship between interdisciplinary mentoring and increased research productivity outcomes such as grant funding and publications (Spence et al., 2018 ). Finally, this finding also aligns with literature on the generation of new knowledge (Phelps et al., 2012 ). Knowledge creation has traditionally been framed in terms of individual creativity, but recent studies have placed more emphasis on how the contribution of social dynamics are influential in explaining this process (Boix Mansilla et al., 2016 ; Csikszentmihalyi, 1998 ; Phelps et al., 2012 ; Sawyer, 2003 ; Zhang et al., 2009 ). Thus, while we might think that science drives the team, in this case-based study, the team’s interpersonal relationships were the driver of the team’s scientific productivity.

Fulfilling the land grant mission

As noted above, this exemplary team fulfilled all three goals of the land grant mission. First, the team was training scientists at all levels, from undergraduate students, to graduate students, postdocs, new faculty, and external collaborators, including community partners. In many instances, the training and mentoring was structured in a vertically integrated manner. For example, postdocs were training graduate and undergraduate students, typical of many teams. In addition to the “top-down” scenarios, however, the team also encouraged training that went from the bottom up as well. Effectively, this is a hallmark of successful teams in other sectors such as emergency responders and elite military teams – whomever has the knowledge to drive the issue at hand is the effective “leader” in that mission (Kotler and Wheal, 2008 ). Second, the team excelled in research and discovery, partnering with a diversity of external collaborators to do so. This created a network structure wherein the team clearly utilised the collaborators for mentoring and advice. Organisations with a core-periphery network structure like this team have been reported to be more creative because ties on the periphery, such as external collaborators, can span boundaries and access diverse information (Perry-Smith, 2006 ; Phelps et al., 2012 ). Finally, because the team’s collaborators included community partners and practitioners, they were also influencing policy and practice. This resulted in an overall greater impact for the team’s science and allowed them to tailor their research to best meet the needs of society (Barge and Shockley-Zalabak, 2008 ).

Future research

This study provides a unique contribution to team science literature because it longitudinally studied the development and processes of a successful interdisciplinary team (Wooten et al., 2014 ). Future research on the elements of effective interdisciplinary teaming is required in five key areas. First, identification of best practices that inhibit or support teams is necessary (Fiore, 2008 ; Hall et al., 2018 ; Wooten et al., 2014 ). Second, previous research has found that small teams are best at disrupting science with new ideas and opportunities (Wu et al., 2019 ); however, practices large teams use to create new knowledge have been poorly documented. Third, successful training concepts for graduate students and postdoctoral researchers need additional consideration (Knowlton et al., 2014 ; Ryan et al., 2012 ; Sarraj et al., 2017 ). Fourth, we hypothesise that graduate students act as bridges in teams to connect scientific disciplines and prevent clustering the network. Future research should investigate the role of graduate students in creating knowledge through interdisciplinary teams. Finally, additional research is needed to better recognise and reward scientists who undertake integration and implementation (Bammer et al., 2020 ).

Data availability

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Acknowledgements

A special thank you to Elizabeth Scodfidio for helping with data, images and more!. The research reported in this publication was supported by Colorado State University’s Office of the Vice President for Research Catalyst for Innovative Partnerships Programme. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Office of the Vice President for Research. Supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535. Contents are the authors’ sole responsibility and do not necessarily represent official NIH views. Funding and support were provided by grants from the National Science Foundation’s Ecology of Infectious Diseases Programme (NSF EF-0723676 and NSF EF-1413925).

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HBL conceptualised the study, developed the methodology, curated the data, analysed the data, conducted the investigation, worked as the project manager, managed the software, validated the data, created visualisations, reviewed and edited the paper; BF conceptualised the study, developed the methodology, curated the data, analysed the data, managed the software, validated the data, supervised all aspects of the research, created visualisations, reviewed and edited the paper; JC conceptualised the study, developed the methodology, acquired funding, supervised data collection, and reviewed and edited the paper; KC and SV wrote the paper, secured funding, reviewed and edited the paper; and ERF conceptualised the study, developed the methodology, supervised all aspects of the research, acquired funding, created the visualisations, reviewed and edited the paper; All authors reviewed the manuscript.

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Theory, Research and Practice

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• Published: 16 October 2012

The exemplar methodology: An approach to studying the leading edge of development

• Kendall Cotton Bronk 1  

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The exemplar methodology is a useful, but to date underutilized, approach to studying developmental phenomena. It features a unique sample selection approach whereby individuals, entities, or programs that exemplify the construct of interest in a highly developed manner form the study sample. Studying a sample of highly developed individuals yields an important view of the leading edge of development that cannot be gleaned using other methodologies. A picture of the full range of development requires not only an understanding of typical and deficient growth, as provided by existing methodologies, but also of complete or nearly complete development, as provided by the exemplar methodology. Accordingly, the exemplar methodology represents a critical tool for developmental psychologists. In spite of this, because it has rarely been written about, the exemplar methodology has only been used to study a relatively narrow range of developmental constructs. Therefore, the present article defines the exemplar methodology, addresses key conceptual issues, and briefly outlines steps to utilizing the approach.

A wide range of methods exists for studying developmental constructs, and each has its own strengths and weaknesses, but historically no methodology has existed that could yield a picture of the leading edge of development, predict the next steps in development for typical individuals, or feature instances of complete- or nearly complete- development of a particular characteristic, and without this view, a complete understanding of construct development is impossible (Damon & Colby [ in press ]). The ability to view the upper ends of development in practice represents the unique contribution of the exemplar methodology.

The exemplar methodology is a sample selection technique that involves the intentional selection of individuals, groups, or entities that exemplify the construct of interest in a highly developed manner. This definition is derived from the empirical studies in developmental psychology that have employed this approach. In using the exemplar methodology, researchers deliberately select a sample of individuals or entities that exhibit a particular characteristic in a highly developed manner. The exemplar methodology features participants who are rare, not from the perspective of the characteristics they exhibit, but in the intensity with which they demonstrate those particular characteristics. For example, most people demonstrate care and compassion at times and with some level of sincerity, but care exemplars exhibit this characteristic more consistently and more intensely then more typical individuals. While exemplars differ from more typical individuals in terms of the way they exhibit a particular characteristic, they tend to be similar to more typical individuals in most other ways (Colby & [ Damon 1992 ]). For instance, a creative genius could be considered an exemplar, but just because this individual boasts particularly creative ideas does not mean that he or she is necessarily kinder, more sociable, or more athletic than other individuals. Exemplars serve as highly developed examples of the construct of interest, but in other ways their development may be typical or even deficient.

Studying individuals who exemplify different elements of development in highly developed ways is crucial; only by doing so are we able to witness advanced development in the real world. Exemplars trace the steps of where more typical individuals are likely to go, if growth continues (Colby & [ Damon 1992 ]). We call the individuals who exhibit a particular characteristic in an intense manner exemplars , and we call the methodology used to study these individuals the exemplar methodology .

History of the exemplar methodology

Although developmental psychologists interested in positive psychology have only relatively recently begun using the exemplar methodology (e.g. Colby & [ Damon 1992 ]), the methodology has been in existence since Aristotle. In Nicomachean Ethics Aristotle wrote, “We approach the subject of practical wisdom by studying the persons to whom we attribute it” ([ Aristotle 1962 ], 6.5 1140a25). In other words, to understand how a complex construct functions and develops, it is useful to examine that construct in the lives of individuals who exhibit it in a highly developed manner. Along these same lines, [ Maslow (1971 ]) was one of the earliest scholars to actually employ the exemplar methodology, though he never called it that. Education, he claimed, “is learning to grow and learning what to grow toward” (p. 169). If we want to learn about ultimate human potential, he argued, we should study highly functional and enlightened individuals.

Use of the exemplar methodology has increased in the past twenty years in conjunction with the growth of the positive psychology movement (Seligman & [ Csikszentmihalyi 2000 ]; Sheldon & [ King 2001 ]; [ Damon 2004 ]). Historically, psychologists were primarily concerned with understanding what could go wrong with regards to human behavior, emotions, social interactions, and cognition. The focus of this one-sided, however important, field of human functioning has brought about a highly developed understanding of people’s mental vulnerabilities, deficiencies, and illnesses. However, this focus has essentially ignored issues of human thriving and flourishing (Benson et al. [ 2006 ]; Bundick et al. [ 2010 ]; Lerner et al. [ 2003 ]; [ Seligman 2011 ]). Leaders in the field of psychology, recognizing the need for additional knowledge of, research into, and practical methods to sustain people’s inner strengths and overall well-being, helped establish the new paradigm of positive psychology.

As the number of studies based on this new paradigm has increased, so too has use of the exemplar methodology (e.g. Matsuba et al. [ in press ]). The exemplar methodology lends itself to the study of optimal human development. Using a methodology that is focused on the upper ends of development is appropriate in an area concerned with ideal states of being.

Conceptual issues

The exemplar methodology represents a complex approach to studying human development, and as such, there are many issues to consider when applying it. First, the decision of whether to include a comparison sample can be contentious. Many effective exemplar studies have included matched samples (e.g. [ Bronk 2008 ]; Hart & [ Fegley 1995 ]; Matsuba & [ Walker 2004 ]; Matsuba & [ Walker 2005 ]; [ Reimer 2003 ]; Walker & [ Frimer 2007 ]), but other valuable studies have not (e.g. [ Bronk 2011 ]; [ Bronk 2012 ]; Colby & [ Damon 1992 ]; [ Mastain 2008 ]). Including a comparison sample allows the researcher to draw conclusions regarding ways in which the exemplar sample is distinct from more typical individuals, and this would seem to be an important benefit of the exemplar methodology. However, it can also be argued that what we glean by studying exemplars alone is sufficient to describe what these individuals are like with regards to their development in a particular area. If a characteristic or experience is evident among this sample, then it is evident. A comparison with a matched sample is not needed. Ultimately, the decision to use a comparison sample should be made in light on the claims that the researcher hopes to make. Without a comparison sample, researchers can claim that exemplary individuals possess certain characteristics and share particular experiences, but they cannot claim that these characteristics and experiences differentiate the exemplary individuals from more typical people.

Another complex issue with regards to the exemplar methodology has to do with the nature of exemplarity itself. How can researchers ascertain what constitutes exemplarity in any particular domain? Whose conception of exemplarity is most valid? Researchers conducting studies of moral exemplars regularly wrestle with what constitutes morality. Some studies of moral exemplars rely on lay conceptions (e.g. Walker & [ Pitts 1998 ]) and others on expert conceptions (e.g. Colby & [ Damon 1992 ]). Still others rely on behavioral manifestations of different aspects of morality (e.g. Walker & [ Frimer 2007 ]). The expert approach has the potential advantage of including a thoughtful and unbiased perspective on morality, but in some cases it ends up yielding a fairly narrow and often unrepresentative view of the construct (Matsuba & [ Walker 2005 ]). The lay perspective is likely to be broader and more representative, but it may also runs the risk of being diffuse and biased. The outcome approach can include engagement in particular acts (e.g. harboring Jews during the holocaust as a sign of altruism exemplarity; Oliner & [ Oliner 1988 ]) or winning special awards (e.g. winning national awards for care or bravery as signs of care or bravery exemplarity; Walker & [ Frimer 2007 ]). The outcome approach is likely to yield a more homogeneous sample, at least with regards to certain experiences around the construct of interest, but because it is narrower it may miss individuals who would meet the criteria, but who did not have the opportunity to engage in the exemplar qualifying acts. Ultimately, the best approach depends on the aims of the study, but regardless of the approach selected, it is important to consider the ways in which the chosen definition of exemplarity is likely to influence the study’s findings.

Finally, it is important to consider how exemplar study findings can be generalized. Findings reveal the leading edge of development, but what does this tell us about more typical development of the construct? Correlational exemplar studies predict the experiences and characteristics that are likely to accompany the construct of interest, but we cannot claim that these experiences and characteristics cause exemplarity. Furthermore, it is important to bear in mind that while the exemplars are highly developed in one particular area, they are not necessarily highly developed in others, so it is important to bracket findings to the construct of interest.

The discussion that follows outlines the steps involved in conducting an exemplar study. These conceptual issues, including the use of comparison studies, discernment of exemplarity, and generalization of findings, undergird and guide this discussion.

Steps in conducting exemplar research

To date, the exemplar methodology has largely been confined to studies or moral and ethical development. Researchers in this area, familiar with Colby & Damon’s ([ 1992 ]) influential study of moral exemplars, have applied the methodology in related studies of ethical development (e.g. care exemplars, Hart & [ Fegley 1995 ]; spiritual exemplars, [ King 2010 ]; environmental exemplars, [ Pratt 2011 ]; moral exemplars, Walker & [ Frimer 2007 ]). While the methodology is certainly useful in this area, it should be applied more broadly to studies of positive psychology. Use has been limited to a narrow sliver of the positive psychology space in large part because the methodology has rarely been written about. To encourage broader use of this methodology, guidelines for its implementation are needed. Therefore, following is a discussion of the steps involved in carrying out an effective exemplar study.

Successful use of the exemplar methodology requires special attention to sample selection. Individuals who exhibit signs of full or nearly full development in the area of interest are included in the study. To determine which individuals demonstrate complete or nearly complete development, most studies rely on carefully designed nomination criteria and thoughtfully selected nominators who use the nomination criteria to select appropriate exemplar participants.

Nomination criteria

One of the first steps to utilizing the exemplar methodology is to devise the criteria by which the exemplars are to be identified. Nomination criteria represent the standards used to qualify exemplars.

Researchers vary widely in terms of the rigor they apply to devising nomination criteria. One of the earliest and most influential exemplar studies employed a very thorough method in this regard. Colby & [ Damon (1992 ]) used an iterative process that relied on moral experts, including moral philosophers, theologians, ethicists, historians, and social scientists from different cultural and ethnic backgrounds, to develop their nomination criteria for moral exemplars. Interviews were conducted with the moral experts, in which they were presented with a preliminary list of criteria that the researchers believed offered a basis for identifying moral exemplars. Experts were encouraged to edit the list as they saw fit. Eventually, the following set of five criteria emerged:

Moral exemplars exhibit a sustained commitment to moral ideals or principles that include a generalized respect for humanity, or a sustained evidence of moral virtue,

a disposition to act in accord with their moral ideals or principles implying also a consistency between their actions and intentions and between the means and ends of their actions,

a willingness to risk their self-interest for the sake of their moral values,

a tendency to be inspiring to others and to move them to moral action, and

a sense of realistic humility about their own importance relative to the world at large, implying a relative lack of concern for their own egos (p. 29).

Because the study sought to understand how morality developed and because people’s ideas of what constitutes morality vary, it was important to have in place a systematic process for devising nomination criteria.

The expert approach to devising nomination criteria has been used in many exemplar studies. For example, a study of care exemplars by Hart & [ Fegley (1995 ]) asked community leader experts, including church and youth group leaders, to come together to develop criteria for care exemplars (Hart & [ Fegley 1995 ]). Their collaboration yielded the following criteria:

Youth care exemplars are involved in community, church, or youth group activities that benefit others,

have unusual and admirable family responsibilities,

exhibit a willingness to help those in need,

volunteer their time to help others,

display emotional and social maturity,

lead others,

practice open-mindedness about others,

demonstrate a willingness to look beyond the difficulties of living in an urban locale to a better future,

show compassion,

display a sense of humility about his/her aid to others, and/or

demonstrate a commitment to friends and family (p. 1350).

Relying on community leaders who oversaw youth engaged in care-oriented, volunteer work was appropriate since Hart and Fegley were interested in understanding the correlates of a highly developed sense of caring. A subsequent study of adolescent moral maturity (Reimer et al. [ 2009 ]) used similar criteria to identify young moral exemplars, or “adolescent exemplars as paragons of moral maturity” (p. 380).

The outcome approach represents another effective technique for devising nomination criteria. Interested in identifying bravery and care exemplars, for example, [ Walker and Frimer (2007 ]) used awardees who were nationally recognized for demonstrating exceptional levels of bravery or care. Similarly, [ Oliner and Oliner (1988 ]) explored the highly developed altruistic personality by examining the lives of individuals who harbored Jews in Germany during the Holocaust. Individuals qualified as altruism exemplars a through their actions, which (1) were directed toward helping another, (2) involved high risk or sacrifice to the actor, (3) did not earn them any award, and (4) were voluntary (p. 6).

Still other studies have relied on existing literature and definitions of the construct to devise nomination criteria. For example, recent studies of youth purpose ([ Bronk 2008 ]; [ Bronk 2011 ]; [ Bronk 2012 ]; [ Damon 2009 ]) based their nomination criteria on the definition of purpose. Consistent with the definition, which states that a purpose in life is a stable and generalized intention to accomplish something that is at once meaningful to the individual and at the same time leads to productive engagement with some aspect of the world beyond the individual ([ Damon 2009 ]; Damon et al. [ 2003 ]), these studies relied on the following nomination criteria:

Youth purpose exemplars demonstrate enduring commitments to long-term aims,

are actively engaged in working toward their long-term aims and have plans for continuing to do so in the future,

maintain long-term aims that are personally meaningful and central to their sense of self, and

demonstrate commitment to these aims in large part because pursuing them allows the youth to positively impact the broader world, including groups of people, causes, artistic endeavors, etc. ([ Bronk 2008 ]; [ Bronk 2012 ], pp. 83–84).

Nomination criteria for a phenomenological study of moral commitment (Mac[ Renato 1995 ]) similarly relied on definitional components, including: (1) At least three years of sustained service to others outside of one’s work life (2) and a demonstrated tendency to inspire others to engage in similar service.

The expert approach, the outcome approach, and the definitional approach represent three useful ways of devising nomination criteria. Exactly which approach is best is determined by the particular study aims, but effective exemplar studies share certain features, including a systematic and thoughtful approach to devising nomination criteria. This is key because who the exemplars are and which criteria are considered will significantly impact the study’s findings. Nomination criteria should also draw useful boundaries around the construct; they need to be at once narrow enough to be descriptive of a particularly highly developed group of individuals but at the same time broad enough to capture the range of experiences and characteristics of highly developed individuals.

Second, researchers need to be mindful of the nomination criteria when reporting conclusions from their study to avoid reporting findings that were determined by the nomination criteria. For example, if the nomination criteria stipulate that civic exemplars demonstrate an other-oriented perspective, then experiences or characteristics that reveal a concern for the world beyond-the-self should not be reported as findings. In this way, hypotheses regarding likely study outcomes can help guide the creation of useful nomination criteria.

Third, because the aim of the criteria is to help nominators readily identify and agree upon potential exemplars, effective nomination criteria are concrete and quantifiable. For example, criteria that include visible behaviors or irrefutable phenomena are preferable to criteria that are vague or open to interpretation.

Additionally, some studies include demographic criteria. Researchers may choose to select a sample balanced for gender, age, ethnicity, income, or by other demographic variables.

Nominating exemplars

Once nomination criteria have been established, a process needs to be instituted for using the criteria to select the exemplars. Generally a small group of individuals is invited to serve as nominators. In some cases, the same individuals who created the criteria serve as nominators (Colby & [ Damon 1992 ]; Hart & [ Fegley 1995 ]). This expert approach has the benefit of ensuring that nominators fully understand and can apply the nomination criteria. However, the nominators may be too close to the project and may give extra weight to certain criteria. Of course, new nominators run this same risk, but there is less chance they would feel as invested in the criteria, and therefore would be more likely to recommend individuals who meet the stated criteria. Alternatively, using the same individuals to create the nomination criteria and select exemplars could be advantageous, since these individuals may struggle to verbalize some of their criteria, but may apply them in selecting relevant individuals.

Many exemplar studies rely on a new set of nominators to avoid having nominators who are too close to the project or because there are no outside individuals involved in the process of developing the nomination criteria. For instance, in the earlier referenced study of youth purpose, the nomination criteria were largely based on the definition of purpose, and youth practitioners, including coaches, teachers, youth ministers, etc., were called upon to nominate youth who met the criteria ([ Damon 2009 ]; [ Bronk 2008 ]; [ Bronk 2011 ]; [ Bronk 2012 ]).

Other exemplar studies use practitioners as nominators. These studies may invite leaders of local human service organizations such as shelters, orphanages, schools, and outreach programs to nominate altruism exemplars ([ Mastain 2008 ]), leaders of community-service organizations such as The Ronald McDonald House, AIDs organizations, and churches to nominate moral exemplars (Matsuba & [ Walker 2005 ]), or youth workers to nominate care exemplars (Reimer & Wade-[ Stein 2004 ]) based on their nomination criteria.

Still other exemplar studies employ laypeople as nominators. For example, Walker & [ Matsuba (2005 ]) asked members of social organizations to nominate young adults within their organizations who had demonstrated “extraordinary moral commitments” (p. 276). An interpretation of what constituted an extraordinary moral commitment was left up to the lay nominator. The decision to use relatively vague nomination criteria and to include lay individuals as nominators was an intentional one; the research team was concerned that expert nominators and more prescriptive nomination criteria would reflect too narrow a conception of morality, and the team sought to identify exemplars who reflected a “folk” conception of moral excellence (Walker & [ Matsuba 2005 ], p. 281).

Comparison samples

In addition to devising appropriate nomination criteria and selecting suitable nominators, effective exemplar studies often employ comparison samples (e.g. [ Bronk 2008 ]; [ Bronk 2011 ]; Frimer & [ Walker 2011 ]; Hart & [ Fegley 1995 ]; Matsuba & [ Walker 2004 ]; Oliner & [ Oliner 1988 ]; Reimer et al. [ 2009 ]; Walker & [ Frimer 2007 ]). Ideally, comparison samples should be demographically matched with the exemplars to isolate the variable of interest. While particular demographics of interest may vary based on the nature of the study, age, gender, ethnicity, social status, educational level, and hometown are useful variables to consider for matching purposes. Without a comparison sample, it is difficult, if not impossible, to make strong claims about unique attributes or experiences associated with exemplarity. However, when researchers do not seek to distinguish exemplar from more typical cases, comparison samples are not needed (e.g. [ Bronk 2012 ]; Colby & [ Damon 1992 ]; Midlarsky et al. [ 2005 ]).

Data analysis

An exemplar study is determined by its participants rather than by its data analysis strategy. As such, data analysis options vary. In some exemplar studies, researchers administer surveys to relatively large numbers of exemplar participants and data analysis is quantitative in nature (Matsuba & [ Walker 2004 ]; Walker & [ Frimer 2007 ]). In other cases, fewer participants are included and data analysis is qualitative in nature ([ Bronk 2008 ]; [ Bronk 2011 ]; [ Bronk 2012 ]; Colby & [ Damon 1992 ]; [ Damon 2009 ]; Hart & [ Fegley 1995 ]; [ King 2010 ]; Matsuba & [ Walker 2005 ]; Walker & [ Frimer 2007 ]). In line with historical uses of the exemplar methodology (e.g. Colby & [ Damon 1992 ]), qualitative exemplar studies rely on semi-structured, case study style interview protocols that are used for gathering both nomothetic and idiographic data. In still other exemplar studies, mixed methods data analysis approaches are employed (Matsuba & [ Walker 2004 ]; Walker & [ Frimer 2007 ]).

Most exemplar research is qualitative in nature. One reason may have to do with the history of this kind of research; early exemplar studies were qualitative in nature (Colby & [ Damon 1992 ]; Hart & [ Fegley 1995 ]). A second reason likely has to do with the difficulty of securing a sample that is at once exemplary and at the same time large enough for statistical analyses. Typically quantitative exemplar studies are required to lower the threshold of exemplarity to garner a sufficient sample size, but the benefits of doing so may be great enough to warrant such a decision.

Generalizing findings

Examining exemplars’ lives allows us to understand and even predict the developmental trajectory of the construct of interest. Exemplars characterize the leading edge of development, and their lives can be generalized in a developmental sense, but not from a conduct perspective. For example, when more typical individuals manage to act in an altruistic manner every now and again, they do so in the same way that altruism exemplars, including those who dedicated their lives to saving Jews from the holocaust, managed to do consistently. However, while the process of developing altruism may be the similar for exemplars and typical individuals, the conduct of exemplars is likely to differ from the conduct of typical individuals; there are of course many ways of acting altruistically.

Conducting exemplar research can add a great deal to our understanding of positive developmental phenomena. It provides a picture of complete or nearly complete development, and adding this understanding to research on deficient and typical growth allows us to examine the full spectrum of development; accordingly, the exemplar methodology is a critical research tool (Damon & Colby [ in press ]). However, as spelled out here, the methodology needs to be rigorously and thoughtfully applied.

As discussed in the introduction, [ Maslow (1971 ]) was one of the earliest psychologists to conduct exemplar research. Interested in understanding what constituted self-actualization, Maslow identified twenty-one highly self-actualized individuals and studied them. In spite of the influential results generated by this investigation, some have argued that Maslow’s use of the exemplar methodology was not scientific in nature (Sommers & [ Satel 2006 ]). Had Maslow followed the guidelines set forth by existing exemplar studies, he could have avoided these critiques.

First, Maslow identified individuals he believed were self-actualized. He then read essays, books, and other papers written by the exemplars of self-actualization and tried to infer personality characteristics from the written documents. Not surprisingly, he has been roundly criticized for his vague and potentially biased process of exemplar identification (Sommers & [ Satel 2006 ]). Including a rigorously selected panel of nominators and including clearly stated nomination criteria would have clarified the basis on which these individuals qualified as exemplary self-actualizers and would have minimized the potential influence of personal bias.

Second, not only did Maslow draw conclusions about the characteristics of self-actualizers based on his sample, but he also drew conclusions regarding the nature of self-actualization. Here Maslow committed one of the common errors in exemplar studies, and one that is warned against in this paper. Maslow drew conclusions based on his inclusion criteria, and doing so is tautological in nature. Had he clearly stated what his nomination criteria were, it would have been easier to avoid drawing conclusions that were predetermined by them.

Finally, it is possible that individuals other than self-actualizers possess the same characteristics as the exemplars. Because Maslow was interested not only in understanding more about the concept of self-actualization, but also in differentiating self-actualizers from more typical individuals, a comparison sample would have strengthened his study. Comparison samples are called for when the researcher intends to draw distinctions between exemplary and more typical individuals.

In sum, Maslow acknowledged that his methodology was lacking, and he tried to draw tentative conclusions regarding his findings. His hope was that future researchers would extend his ideas and investigate them more rigorously, but by following the aforementioned methodological guidelines, he could have avoided these shortcomings altogether. The fact that his findings are still so highly regarded and regularly debated underscores the interesting and important findings that exemplar research- even flawed exemplar research- can uncover.

Given that the exemplar methodology has become an increasingly popular means of investigating ethical constructs such as morality, spirituality, activism, care, bravery, environmental activism, and purpose in life, the time has come to outline the parameters of and draw boundaries around the reaches of this useful methodology. Additionally, the present article has attempted to outline key components of effective exemplar methodologies, including the importance of devising thoughtful nomination criteria, identifying appropriate nominators, using matched comparison samples, analyzing data, and generalizing findings. Clearer guidelines regarding how to carry out an exemplar study should allow a broader range of researchers to make use of this approach. Doing so will be reveal a fuller understanding not only of what is , but also of what is possible in terms of human development.

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Chapter 11 methods for descriptive studies.

Yulong Gu and Jim Warren .

11.1. Introduction

Descriptive studies in eHealth evaluations aim to assess the success of eHealth systems in terms of the system planning, design, implementation, use and impact. Descriptive studies focus on describing the process and impact of eHealth system development and implementation, which often are contextualized within the implementation environment (e.g., a healthcare organization). The descriptive nature of the evaluation design distinguishes descriptive studies from comparative studies such as a before/after study or a randomized controlled trial. In a 2003 literature review on evaluations of inpatient clinical information systems by van der Meijden and colleagues, four types of study design were identified: correlational, comparative, descriptive, and case study ( van der Meijden, Tange, Troost, & Hasman, 2003 ). This review inherited the distinction between objectivist and subjectivist studies described by Friedman and Wyatt (1997) ; in the review, van der Meijden and colleagues defined descriptive study as an objectivist study to measure outcome variable(s) against predefined requirements, and case study as an subjectivist study of a phenomenon in its natural context using data from multiple sources — quantitatively or qualitatively ( van der Meijden et al., 2003 ). For simplicity, we include case study under the descriptive study category in this chapter, and promote methodological components of qualitative, quantitative, and mixed methods for designing eHealth evaluations in this category. Adopting this wider scope, the following sections introduce the types of descriptive studies in eHealth evaluations, address methodological considerations, and provide examples of such studies.

11.2. Types of Descriptive Studies

There are five main types of descriptive studies undertaken in eHealth evaluations. These are separated by the overall study design and the methods of data collection and analysis, as well as by the objectives and assumptions of the evaluation. The five types can be termed: qualitative studies, case studies, usability studies, mixed methods studies, and other methods studies (including ethnography, action research, and grounded theory studies).

11.2.1. Qualitative Studies

The methodological approach of qualitative studies for eHealth evaluations is particularly appropriate when “we are interested in the ‘how’ or ‘why’ of processes and people using technology” ( McKibbon, 2015 ). Qualitative study design can be used in both formative and summative evaluations of eHealth interventions. The qualitative methods of data collection and analysis include observation, documentation, interview, focus group, and open-ended questionnaire. These methods help understand the experiences of people using or planning on using eHealth solutions.

In qualitative studies, an interpretivist view is often adopted. This means qualitative researchers start from the position that their knowledge of reality is a social construction by human actors; their theories concerning reality are ways of making sense of the world, and shared meanings are a form of intersubjectivity rather than objectivity ( Walsham, 2006 ). There is also increasing uptake of critical theory and critical realism in qualitative health evaluation research ( McEvoy & Richards, 2003 ). The assumption for this paradigm is that reality exists independent of the human mind regardless of whether it can be comprehended or directly experienced ( Levers, 2013 ). Irrespective of the different epistemological assumptions, qualitative evaluations of eHealth interventions apply similar data collection tools and analysis techniques to describe, interpret, and challenge people’s perceptions and experiences with the environment where the intervention has been implemented or is being planned for implementation.

11.2.2. Case Studies

A case study investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident ( Yin, 2011 ). Case study methods are commonly used in social sciences, and increasingly in information systems ( is ) research since the 1980s, to produce meaningful results from a holistic investigation into the complex and ubiquitous interactions among organizations, technologies, and people ( Dubé & Paré, 2003 ). The key decisions in designing a case study involve: (a) how to define the case being studied; (b) how to determine the relevant data to be collected; and (c) what should be done with the data once collected ( Yin, 2011 ). These decisions remain the crucial questions to ask when designing an eHealth evaluation case study. In eHealth evaluations, the fundamental question regarding the case definition is often answered based on consultation with a range of eHealth project stakeholders. Investigations should also be undertaken at an early stage in the case study design into the availability of qualitative data sources — whether informants or documents — as well as the feasibility of collecting quantitative data. For instance, eHealth systems often leave digital footprints in the form of system usage patterns and user profiles which may help in assessing system uptake and potentially in understanding system impact.

Case study design is versatile and flexible; it can be used with any philosophical perspective (e.g., positivist, interpretivist, or critical); it can also combine qualitative and quantitative data collection methods ( Dubé & Paré, 2003 ). Case study research can involve a single case study or multiple case studies; and can take the strategy of an explanatory, exploratory or descriptive approach ( Yin, 2011 ). The quality of eHealth evaluation case studies relies on choosing appropriate study modes according to the purpose and context of the evaluation. This context should also be described in detail in the study reporting; this will assist with demonstrating the credibility and generalizability of the research results ( Benbasat, Goldstein, & Mead, 1987 ; Yin, 2011 ).

11.2.3. Usability Studies

Usability of an information system refers to the capacity of the system to allow users to carry out their tasks safely, effectively, efficiently and enjoyably ( Kushniruk & Patel, 2004 ; Preece, Rogers, & Sharp, 2002 ; Preece et al., 1994 ). Kushniruk and Patel (2004) categorized the usability studies that involve user representatives as usability testing studies and the expert-based studies as usability inspection studies. They highlighted heuristic evaluation ( Nielsen & Molich, 1990 ) and cognitive walkthrough ( Polson, Lewis, Rieman, & Wharton, 1992 ) as two useful expert-based usability inspection approaches. Usability studies can evaluate an eHealth system in terms of both the design and its implementation. The goals of usability evaluations include assessing the extent of system functionality, the effect of interface on users, and identifying specific problems. Usability testing should be considered in all stages of the system design life cycle. The idea of testing early and often is a valuable principle for having a good usable system (e.g., to get usability evaluation results from early-stage prototypes including paper prototypes). Another principle, although challenging for eHealth innovations, is to involve users early and often — that is, to keep real users close to the design process. The interaction design model ( Cooper, 2004 ) recommends having at least one user as part of the design team from the beginning, so that right from the formulation of the product its concept actually makes sense to the type of users it’s aimed for; and the users themselves should participate in the usability testing.

A classic usability study is done through user participation, either in a laboratory setting or in the natural environment. There is also a suite of techniques that are sometimes called “discount” usability testing or expert-based evaluation (as they are applied by usability experts rather than end users). The most prominent expert-based approach is heuristic evaluation ( Nielsen & Molich, 1990 ). Whichever approach is taken for usability studies, the target measures for usability are similar:

• How long is it taking users to do the task?
• How accurate are users in doing the task?
• How long does it take users to learn to do the task with the system?
• How well do users remember how to use the system from earlier sessions?
• And, in general, how happy are users about having worked the task with the tool?

A usability specification can combine these five measures into requirements, such as: at least 90% of users can perform a given task correctly within no more than five minutes one week after completing a 30-minute tutorial.

11.2.4. Mixed Methods Studies

Increasing uptake and recognition of mixed methods studies, which combines qualitative and quantitative components in one research study, have been observed in health sciences and health services research ( Creswell, Klassen, Plano, & Smith, 2011 ; Wisdom, Cavaleri, Onwuegbuzie, & Green, 2012 ). Mixed methods studies draw on the strength of utilizing multiple methods, but have challenges inherent to the approach as well, such as how to justify diverse philosophical positions and multiple theoretical frameworks, and how to integrate multiple forms of data. A key element in reporting mixed methods studies is to describe the study procedures in detail to inform readers about the study quality.

Given the nature of eHealth innovations — often new, complex and hard to measure — a mixed methods design is particularly suitable for their evaluations to collect robust evidence on not only their effectiveness, but also the real-life contextual understandings of their implementation. For instance, the system transactional data may indicate the technology uptake and usage pattern; and end user interviews collect people’s insights into why they think certain events have happened and how to do things better.

11.2.5. Other Methods (ethnography, action research, grounded theory)

In addition to the above four main categories of designs used in eHealth evaluation studies, this section introduces a few other relevant and powerful approaches, including ethnography, action research, and grounded theory methods.

• With origins in anthropology, an ethnographic approach to information systems research aims to provide rich insights into the human, social and organizational aspects of systems development and application ( Harvey & Myers, 1995 ). A distinguishing feature of ethnographic research is participant observation, that is, the researcher must have been there and “lived” there for reasonable length of time ( Myers, 1997a ). Interviews, surveys, and field notes can also be used in ethnography studies to collect data.
• Similarly, multiple data collection methods can be used in an action research study. The key feature of action research design is its “participatory, democratic process concerned with developing practical knowing” ( Reason & Bradbury, 2001 , p. 1). Action research studies naturally mix the problem-solving activities with research activities to produce knowledge ( Chiasson, Germonprez, & Mathiassen, 2009 ), and often take an iterative process of planning, acting, observing, and reflecting ( McNiff & Whitehead, 2002 ).
• Grounded theory is defined as an inductive methodology to generate theories through a rigorous research process leading to the emergence of conceptual categories; and these concepts as categories are related to each other as a theoretical explanation of the actions that continually resolve the main concern of the participants in a substantive area ( Glaser & Strauss, 1967 ; Rhine, 2008 ). In the field of information systems research, grounded theory methodology is useful for developing context-based, process-oriented descriptions and explanations of the phenomena ( Myers, 1997b ). A 2013 review found that the most common use of grounded theory in Information Systems studies is the application of grounded theory techniques, typically for data analysis purposes ( Matavire & Brown, 2013 ).

It is worth noting that the use of the above methods does not exclude other designs. For instance, ethnographic observations can be undertaken as one element in a mixed methods case study ( Greenhalgh, Hinder, Stramer, Bratan, & Russell, 2010 ).

11.3. Methodological Considerations

There are a range of methodological issues that need to be considered when designing, undertaking and reporting a descriptive eHealth evaluation. These issues may emerge throughout the study procedures, from defining study objectives to presenting data interpretation. This section provides a quick guide for addressing the most critical issues in order to choose and describe an appropriate approach in your study.

11.3.1. Study Objectives and Questions

The high-level goals of an eHealth evaluation study are often planned in the initial phase of the study. The goals define what the study is meant to reveal and what is to be learned. These may be documented as a multilevel statement of high-level intentions or questions. This statement is then expanded in the methodology section of the final study report with specific aspects of the purpose of the evaluation: that is, things you want to find out. For instance, if the innovation were an electronic referral (e-referral) system:

• The acceptance of e-referrals by all impacted healthcare workers.
• The impact of e-referrals on safety, efficiency and timeliness of healthcare delivery.
• The key problems and issues emerging from a technical and management perspective in implementation of e-referrals.

Some of the above specific statements may be expressed as testable hypotheses; for example, “Use of e-referrals is widely accepted by General Practitioners ( gp s).” A good use of expanded objectives is to state specific research questions; for example, we might ask, “Do gp s prefer e-referrals to hard copy referrals?” as part of the “acceptance” assessment objective above.

11.3.2. Observable and Contextual Variables

In many cases, eHealth evaluation will be linked to (as part of, or coming after) a health is implementation project that had a business case based on specific expected benefits of the technology, and specific functional and non-functional requirements as critical success factors of the project. These should be part of the evaluation’s benefits framework. International literature (e.g., the benefits found with similar technology when evaluated overseas) may also inform the framework. The establishment of benefits framework in an eHealth evaluation will dictate the study design and variables selection, as well as the methods of data collection and analysis. For instance, observable variables to measure system outcome may include: mortality, morbidity, readmission, length of stay, patient functional status or quality of health/life.

One of the strengths of descriptive studies is that the study findings are contextualized within the system implementation environment. Hence, it is a good practice to explain in the methodology what system(s) is evaluated, including the technologies introduced, years and geography of implementation and use, as well as the healthcare delivery organizations and user groups involved in their use. Contextual variables also include those detailing the evaluation parameters such as research study period and those contextual conditions that are relevant to the system implementation success or failure, for example, organizational structure and funding model.

11.3.3. Credibility, Authenticity and Contextualization

The philosophy of evaluation that is taken along with the detailed research procedures should be described to demonstrate the study rigour, reliability, validity and credibility. The methods used should also be detailed (e.g., interviews of particular user or management groups, analysis of particular data files, statistical procedures, etc.). Data triangulation (examining the consistency of different data sources) is a common technique to enhance the research quality. Where any particularly novel methods are used, they should be explained with reference to academic literature and/or particular projects from which they have arisen; ideally, they should be justified with comparison to other methods that suit similar purposes.

Authenticity is regarded as a feature particular to naturalistic inquiry (and ethnographic naturalism), an approach to inquiry that aims to generate a genuine or true understanding of people’s experiences ( Schwandt, 2007 ). In a wider sense of descriptive eHealth evaluation studies, it is important to maintain research authenticity — to convey a genuine understanding of the project stakeholders’ experiences from their own point of view.

Related to the above discussion on credibility and authenticity, the goal of contextualizing study findings is to support the final theory by seeing whether “the meaning system and rules of behaviour make sense to those being studied” ( Neuman, 2003 ). For example, to draw a “rich picture” of the impact of the evaluated eHealth implementation, the study may inquire and report on “How has it impacted the social context (e.g., communications, perceived roles and responsibilities, and how the users feel about themselves and others)?”

11.3.4. Theoretical Sampling and Saturation

Theoretical sampling is an important tool in grounded theory studies. It is to decide, on analytic grounds, what data to collect next and where to find them ( Glaser & Strauss, 1967 ). This requires calculation and imagination from the analyst in order to move the theory along quickly and efficiently. The basic criterion is to govern the selection of comparison groups for discovering theory based on their theoretical relevance for furthering the development of emerging categories ( Glaser & Strauss, 1967 ).

In studies that collect data via interviews, ideally the interviewing should continue, extending with further theoretical sampling, until the evaluators have reached “saturation” — the point where all the relevant contributions from new interviewees neatly fit categories identified from earlier interviews. Often time and budget do not allow full saturation, in which cases the key topics of interest and major data themes need to be confirmed, for example, by repeating emphasis from individuals in similar roles.

11.3.5. Data Collection and Analysis

Descriptive studies may use a range of diverse and flexible methods in data collection and analysis. Detailed description of the data collection methods used will help readers understand exactly how the study achieves the measurements that are relevant to your approach and measurement criteria. This includes how interviewees are identified, and sources of documents and electronic data, as well as pre-planned interview questions and questionnaires.

In terms of describing quantitative data analysis methods, all statistical procedures associated with the production of quantitative results need to be stated. Similarly, all analysis protocols for qualitative data should be clarified (e.g., the data coding methods used).

11.3.6. Interpretation and Dissemination

Key findings from descriptive studies should provide answers to the research objectives/questions. In general, these findings can be tabulated against the benefits framework you introduced as part of the methodology. Interpretation of the findings may characterize how the eHealth intervention enabled a transformation in healthcare practices. Moreover, when explaining the interpretation and implications drawn from the evaluation results, the key implications can be organized into formal recommendations.

In terms of evaluation dissemination, the study findings should reach all stakeholders considering uptake of similar technology. Evaluation and dissemination as iterative cycles should be considered. Feedback from dissemination of interim findings is a valuable component of the evaluation per se. A dissemination strategy should be planned, specifying the dissemination time frame and pathways (e.g., conventional written reporting, face-to-face reporting, Web 2.0, commercial media and academic publications).

11.4. Exemplary Cases

This section illustrates two descriptive eHealth evaluation studies, one case study as part of the commissioned evaluation on the implementation and impact of the summary care record ( scr ) and HealthSpace programmes in the United Kingdom, and the other study from Canada as a usability evaluation to inform Alberta’s personal health record ( phr ) design. These two examples demonstrate how to design a descriptive study applying a range of data collection and analysis methods to achieve the evaluation objectives.

11.4.1. United Kingdom HealthSpace Case Study

Between 2007 and 2010, an independent evaluation was commissioned by the u.K. Department of Health to evaluate the implementation and impact of the summary care record ( scr ) and HealthSpace programmes ( Greenhalgh, Stramer et al., 2010 ; Greenhalgh, Hinder et al., 2010 ). scr was an electronic summary of key health data drawn from a patient’s gp -held electronic record and accessible over a secure Internet connection by authorized healthcare staff. HealthSpace was an Internet-accessible personal organizer onto which people may enter health data and plan health appointments. Through an advanced HealthSpace account, they could gain secure access to their scr and e-mail their gp using a function called Communicator.

This evaluation undertook a mixed methods approach using a range of data sources and collection methods to “capture as rich a picture of the programme as possible from as many angles as possible” ( Greenhalgh, Hinder et al., 2010 ). The evaluation fieldwork involved seven interrelated empirical studies, including a multilevel case study of HealthSpace covering the policy-making process, implementation by the English National Health Service ( nhs ) organizations, and experiences of patients and carers. In the case study, evaluators reviewed the national registration statistics on the HealthSpace uptake rate (using the number of basic and advanced HealthSpace accounts created). They also studied the adoption and non-adoption of HealthSpace by 56 patients and carers using observation and interview methods. In addition, they interviewed 160 staff in national and local organizations, and collected 3,000 pages of documents to build a picture of the programme in context. As part of the patient study, ethnographic observation was undertaken by a researcher who shadowed 20 participants for two or three periods of two to five hours each at home and work, and noted information needs as they arose and how these were tackled by the participant. An in-depth picture of HealthSpace conception, design, implementation, utilization (or non-use and abandonment, in most cases) and impact was constructed from this mixed methods approach that included both quantitative uptake statistics and qualitative analysis of the field notes, interview transcripts, documents and communication records.

The case study showed that the HealthSpace personal electronic health record was poorly taken up by people in England, and it was perceived as neither useful nor easy to use. The study also made several recommendations for future development of similar technologies, including the suggestion to conceptualize them as components of a sociotechnical network and to apply user-centred design principles more explicitly. The overall evaluation of the scr and Health­Space recognized the scale and complexity of both programmes and observed that “greatest progress appeared to be made when key stakeholders came together in uneasy dialogue, speaking each other’s languages imperfectly and trying to understand where others were coming from, even when the hoped-for consensus never materialised” ( Greenhalgh, Hinder et al., 2010 ).

11.4.2. Usability Evaluation to Inform Alberta’s PHR Design

The Alberta phr was a key component in the online consumer health application, the Personal Health Portal ( php ), deployed in the Province of Alberta, Canada. The phr usability evaluation ( Price, Bellwood, & Davies, 2015 ) was part of the overall php benefit evaluation that was embedded into the life cycle of the php program throughout the predesign, design and adoption phases. Although using a commercial phr product, its usability evaluation aimed to assess the early design of the phr software and to provide constructive feedback and recommendations to the phr project team in a timely way so as to improve the phr software prior to its launch.

Between June 2012 and April 2013, a combination of usability inspection (applying heuristic inspection and persona-based inspection methods) and usability testing (with 21 representative end users) was used in Alberta’s phr evaluation. For the persona-based inspection, two patient personas were developed; for each persona, scenarios were developed to illustrate expected use of the phr . Then in the user testing protocol, participants were asked to “think aloud” while performing two sets of actions: (a) to explore the phr freely, and (b) to follow specific scenarios matching the expected activities of the targeted end users that covered all key phr tasks. Findings from the usability inspection and testing were largely consistent and were used to generate several recommendations regarding the phr information architecture, content and presentation. For instance, the usability inspection identified that the phr had a deep navigation hierarchy with several layers of screens before patient health data became available. This was also confirmed in usability testing when users sometimes found the module segmentation confusing. Accordingly, the evaluation researchers have recommended revising the structure and organization of the modules with clearer top-level navigation, a combination of content-oriented tabs and user-specific tabs, and a “home” tab providing a clear clinical summary.

Usability evaluation can be conducted at several stages in the development life cycle of eHealth systems to improve the design — from the earliest mock-ups (ideally starting with paper prototypes), on partially completed systems, or once the system is installed and undergoing maintenance. The Alberta phr study represents an exemplary case of usability evaluations to inform the development of a government-sponsored phr project. It demonstrates the feasibility and value of early usability evaluation in eHealth projects for having a good usable system, in this case avoiding usability problems prior to rollout.

11.5. Summary

Descriptive evaluation studies describe the process and impact of the development and implementation of a system. The findings are often contextualized within the implementation environment, such as — for our purposes — the specific healthcare organization. Descriptive evaluations utilize a variety of qualitative and quantitative data collection and analysis methods; and the study design can apply a range of assumptions, from positivist or interpretivist perspectives, to critical theory and critical realism. These studies are used in both formative evaluations and summative evaluations.

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Case Studies: Exemplary Procedure

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In this chapter, we shall delve into the practical application and implementation of consulting governance. To this end, we will present two case studies that showcase the spectrum of individual implementation, from a highly complex project to a smaller one with less complexity. These case studies aim to illustrate the adaptability of consulting governance in addressing project-specific challenges. The implementation process is guided by an agreement reached between the client and the contractor, which is tailored to the specific framework conditions of each project.

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What Makes an Exemplary Case Study?

In all of case study research, one of the most challenging tasks is to define an exemplary case study. Although no direct evidence is available, some specula­tions seem an appropriate way of concluding this book. 3

The exemplary case study goes beyond the methodological procedures already highlighted throughout this book. Even if you, as a case study investi­gator, have followed most of the basic techniques—using a case study protocol, maintaining a chain of evidence, establishing a case study database, and so on—you still may not have produced an exemplary case study. The mastering of these techniques makes you a good technician but not necessarily an esteemed social scientist. To take but one analogy, consider the difference between a chronicler and a historian: The former is technically correct but does not produce the insights into human or social processes provided by the latter.

Five general characteristics of an exemplary case study are described below. They are intended to help your case study to be a lasting contribution to research.

EXERCISE 6.5 Defining a Good Case Study

Select a case study that you believe is one of the best you know (again, the selection can be from the BOXES in this book). What makes it a good case study? Why are such characteristics so infrequently found in other case stud­ies? What specific efforts might you make to emulate such a good case study?

1. The Case Study Must Be Significant

The first general characteristic may be beyond the control of many investi­gators. If an investigator has access to only a few “cases,” or if resources are extremely limited, the ensuing case study may have to be on a topic of only marginal significance. This situation is not likely to produce an exemplary case study. However, where choice exists, the exemplary case study is likely to be one in which

• the individual case or cases are unusual and of general public interest,
• the underlying issues are nationally important—either in theoretical terms or in policy or practical terms, or
• your case meets both of the preceding conditions.

For instance, a single-case study may have been chosen because it was a revelatory case—that is, one reflecting some real-life situation that social scientists had not been able to study in the past. This revelatory case is in itself likely to be regarded as a discovery and to provide an opportunity for doing an exemplary case study. Alternatively, a critical case may have been chosen because of the desire to compare two rival propositions; if the propositions are at the core of a well-known debate in the literature—or reflect major differ­ences in public beliefs—the case study is likely to be significant. Finally, imagine the situation in which both discovery and theory development are found within the same case study, as in a multiple-case study in which each individual case reveals a discovery but in which the replication across cases also adds up to a significant theoretical breakthrough. This situation truly lends itself to the production of an exemplary case study.

In contrast to these promising situations, many students select nondistinc- tive cases or outmoded theoretical issues as the topics for their case studies. This situation can be avoided, in part, by doing better homework with regard to the existing body of research. Prior to selecting a case study, you should describe, in detail, the contribution to be made, assuming that the intended case study were to be completed successfully. If no satisfactory answer is forthcoming, you might want to plan another case study.

2. The Case Study Must Be “Complete”

This characteristic is extremely difficult to describe operationally. However, a sense of completeness is as important in doing a case study as it is in defin­ing a complete series of laboratory experiments (or in completing a symphony or finishing a painting). All have the problem of defining the boundaries of the effort, but few guidelines are available.

For case studies, completeness can be characterized in at least three ways. First, the complete case is one in which the boundaries of the case—that is, the distinction between the phenomenon being studied and its context—are given explicit attention. If this is done only mechanically—for example, by declar­ing at the outset that only arbitrary time intervals or spatial boundaries will be considered—a nonexemplary case study is likely to result. The best way is to show, either through logical argument or the presentation of evidence, that as the analytic periphery is reached, the information is of decreasing relevance to the case study. Such testing of the boundaries can occur throughout the analytic and reporting steps in doing case studies.

A second way involves the collection of evidence. The complete case study should demonstrate convincingly that the investigator expended exhaustive effort in collecting the relevant evidence. The documentation of such evidence need not be placed in the text of the case study, thereby dulling its content. Footnotes, appendices, and the like will do. The overall goal, nevertheless, is to convince the reader that little relevant evidence remained untouched by the investigator, given the boundaries of the case study. This does not mean that the investigator should literally collect all available evidence—an impossible task—but that the critical pieces have been given “complete” attention. Such critical pieces, for instance, would be those representing rival propositions.

A third way concerns the absence of certain artifactual conditions. A case study is not likely to be complete if the study ended only because resources were exhausted, because the investigator ran out of time (when the semester ended), or because she or he faced other, nonresearch constraints. When a time or resource constraint is known at the outset of a study, the responsible inves­tigator should design a case study that can be completed within such con­straints, rather than reaching and possibly exceeding his or her limits. This type of design requires much experience and some good fortune. Nevertheless, these are the conditions under which an exemplary case study is likely to be produced. Unfortunately, if in contrast a severe time or resource constraint suddenly emerges in the middle of a case study, it is unlikely that the case study will become exemplary.

3. The Case Study Must Consider Alternative Perspectives

For explanatory case studies, one valuable approach is the consideration of rival propositions and the analysis of the evidence in terms of such rivals (see Chapter 5). The citing of rival claims or alternative perspectives also should be part of a good abstract for your case study (Kelly & Yin, 2007). Even in doing an exploratory or a descriptive case study, the examination of the evidence from different perspectives will increase the chances that a case study will be exemplary.

For instance, a descriptive case study that fails to account for different per­spectives may raise a critical reader’s suspicions. The investigator may not have collected all the relevant evidence and only may have attended to the evidence supporting a single point of view. Even if the investigator was not purposefully biased, different descriptive interpretations might not have been entertained, thereby presenting a one-sided case. To this day, this type of problem persists whenever studies of organizations appear to represent the perspectives of management and not workers, or when studies of social groups appear to be insensitive to issues of gender or multiculturalism, or when studies of youth programs appear to represent adult perspectives and ignore those of youths.

To represent different perspectives adequately, an investigator must seek those alternatives that most seriously challenge the assumptions of the case study. These perspectives may be found in alternative cultural views, different theories, variations among the stakeholders or decision makers who are part of the case study, or some similar contrasts. If sufficiently important, the alterna­tive perspectives can appear as alternative renditions covering the same case, using the comparative structure of composition described earlier in this chapter as one of seven possible structures. Less prominently but still invalu­able would be the presentation of alternative views as separate chapters or sections of the main case study (see BOX 46).

Adding Alternative Perspectives, Written by a Case Study’s Participants, as Supplements to a Case Study

Edgar Schein’s (2003) single-case study tried to explain the demise of a computer firm that had been among the country’s top 50 corporations in size (see BOX 28, Chapter 5, p. 142). The contemporary nature of the case study meant that the firm’s former executives were still available to offer their own rendition of the firm’s fate. Schein supported his own explanation with much documentation and interview data, but he made his case study distinctive in another way: He also included sup­plementary chapters, each giving a key executive the opportunity to present his own rival explanation.

Many times, if an investigator describes a case study to a critical listener, the listener will immediately offer an alternative interpretation of the facts of the case. Under such circumstances, the investigator is likely to become defensive and to argue that the original interpretation was the only relevant or correct one. In fact, the exemplary case study anticipates these “obvious” alternatives, even advocates their positions as forcefully as possible, and shows—empiri­cally—the basis upon which such alternatives might be rejected.

4. The Case Study Must Display Sufficient Evidence

Although Chapter 4 encouraged investigators to create a case study data­base, the critical pieces of evidence for a case study must still be contained within the case study report. The exemplary case study is one that judiciously and effectively presents the most relevant evidence, so that a reader can reach an independent judgment regarding the merits of the analysis.

This selectiveness does not mean that the evidence should be cited in a biased manner—for example, by including only the evidence that supports an investigator’s conclusions. On the contrary, the evidence should be presented neutrally, with both supporting and challenging data. The reader should then be able to draw an independent conclusion about the validity of a particular interpretation. The selectiveness is relevant in limiting the report to the most critical evidence and not cluttering the presentation with supportive but sec­ondary information. Such selectiveness takes a lot of discipline among inves­tigators, who usually want to display their entire evidentiary base, in the (false) hope that sheer volume or weight will sway the reader. (In fact, sheer volume or weight will bore the reader.)

Another goal is to present enough evidence to gain the reader’s confi­dence that the investigator “knows” his or her subject. In doing a field study, for instance, the evidence presented should convince the reader that the investigator has indeed been in the field, made penetrating inquiries while there, and has become steeped in the issues about the case. A parallel goal exists in multiple-case studies: The investigator should show the reader that all of the single cases have been treated fairly and that the cross-case conclusions have not been biased by undue attention to one or a few of the entire array of cases.

Finally, the display of adequate evidence should be accompanied by some indication that the investigator attended to the validity of the evidence—in maintaining a chain of evidence, for example. This does not mean that all case studies need to be burdened with methodological treatises. A few judicious footnotes will serve the purpose. Alternatively, some words in the preface of the case study can cover the critical validating steps. Notes to a table or figure also will help. As a negative example, a figure or table that presents evidence without citing its source is an indication of sloppy research and cautions the reader to be more critical of other aspects of the case study. This is not a situ­ation that produces exemplary case studies.

5. The Case Study Must Be Composed in an Engaging Manner

One last global characteristic has to do with the composition of the case study report. Regardless of the medium used (a written report, an oral presen­tation, or some other form), the report should be engaging.

For written reports, this means a clear writing style, but one that con­stantly entices the reader to continue reading. A good manuscript is one that “seduces” the eye. If you read such a manuscript, your eye will not want to leave the page, and you will continue to read paragraph after paragraph, page after page, until exhaustion sets in. Anyone reading good fiction has had this experience. This type of seduction should be the goal in composing any case study report.

The production of such seductive writing calls for talent and experience. The more often that someone has written for the same audience, the more likely that the communication will be effective. However, the clarity of writ­ing also increases with rewriting, which is highly recommended. With the use of electronic writing tools, an investigator has no excuse for shortcutting the rewriting process.

Engagement, enticement, and seduction—these are unusual characteristics of case studies. To produce such a case study requires an investigator to be enthusiastic about the investigation and to want to communicate the results widely. In fact, the good investigator might even think that the case study con­tains earth-shattering conclusions. This sort of inspiration should pervade the entire investigation and will indeed lead to an exemplary case study.

Source: Yin K Robert (2008), Case Study Research Designs and Methods , SAGE Publications, Inc; 4th edition.

13 Aug 2021

23 Oct 2019

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• awe-inspiring
• awe-inspiringly
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• meritorious
• meritoriously
• the toast of something idiom

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Home > Dissertations > 105

Dissertations

Meaning makers: a mixed-method case study of exemplary police chiefs and the behaviors they use to create personal and organizational meaning.

Rose Nicole Villanueva , Brandman University Follow

Date of Award

Spring 4-3-2017

Document Type

Dissertation

Degree Name

Doctor of Education (EdD)

Organizational Leadership

First Advisor

Dr. Keith Larick

Second Advisor

Dr. Cindy Petersen

Third Advisor

Dr. Jim Cox

The purpose of this thematic, mixed-method case study was to identify and describe the behaviors that exemplary police chiefs use to create personal and organizational meaning for themselves and their followers through character, vision, relationships, wisdom, and inspiration. Additionally, this study surveyed the followers of these exemplary leaders to evaluate the degree of importance to which these followers believe a leader uses character, vision, relationships, wisdom, and inspiration to create personal and organizational meaning. Police chiefs were interviewed for this study regarding their insight in the use of the behaviors associated with character, vision, relationships, wisdom, and inspiration. There have been studies that have focused on character, vision, relationships, wisdom, and inspiration. However, there has not been a study that has included all five variables in the research that identify and describe behaviors that exemplary leaders use to create personal and organizational meaning. The literature and the findings support use of the five variables in the behaviors that create meaning. The findings of this research show that exemplary leaders use all five variables throughout their leadership. Additionally, exemplary police chiefs agree that all five variables are needed, and one variable does not offset the others. Their followers also concur that the five variables are important exemplary leadership behaviors that help create personal and organizational meaning. Further research is recommended for this area of study by replicating this study in other law enforcement agencies focusing on either elected sheriffs, school district police, or special district police chiefs. In addition, a limited case study is recommended, locating three female police chiefs and looking at their pathways to the chief position. By identifying and describing the behaviors that exemplary police chiefs use to create personal and organizational meaning for themselves and their followers and the degree of importance which followers perceive the behaviors through character, vision, relationships, wisdom, and inspiration, help to create personal and organizational meaning, researchers can provide the necessary strategies and tools for improving these five variables in leaders in order to create successful and meaning leadership.

Recommended Citation

Villanueva, Rose Nicole, "Meaning Makers: A Mixed-Method Case Study of Exemplary Police Chiefs and the Behaviors They Use to Create Personal and Organizational Meaning" (2017). Dissertations . 105. https://digitalcommons.umassglobal.edu/edd_dissertations/105

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On March 29, 2024, the U.S. Environmental Protection Agency (EPA) announced a final rule, “Greenhouse Gas Emissions Standards for Heavy-Duty Vehicles – Phase 3,” that sets stronger standards to reduce greenhouse gas emissions from heavy-duty (HD) vehicles beginning in model year (MY) 2027. The new standards will be applicable to HD vocational vehicles (such as delivery trucks, refuse haulers, public utility trucks, transit, shuttle, school buses, etc.) and tractors (such as day cabs and sleeper cabs on tractor-trailer trucks).

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