Difference between revisions of "Case Study Guidelines"

Revision as of 01:46, 4 November 2011

DRAFT ideas for discussion at Leuven meeting.

What is a case study?

Excerpted from Wikipedia Case Study:

Rather than using samples and following a rigid protocol (strict set of rules) to examine limited number of variables, case study methods involve an in-depth, longitudinal (over a long period of time) examination of a single instance or event: a case. They provide a systematic way of looking at events, collecting data, analyzing information, and reporting the results. As a result the researcher may gain a sharpened understanding of why the instance happened as it did, and what might become important to look at more extensively in future research. Case studies lend themselves to both generating and testing hypotheses.

From Yin(2003):

• typically used to investigate "how" or "why" questions about a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident

Purpose(s) of FORSYS Case Studies

Our first and most important question is what are the main purposes of our case studies. These purposes will influence the many methodological choices outlined below.

General ideas

Three general types of case studies:

1. Exploratory: explores a relatively unknown issue, generally unstructured, develop hypotheses for further research
2. Descriptive: some important features of the case(s) have been identified beforehand and are used to structure the case write-up
3. Explanatory: a cause-effect hypothesis is stated, and the case is structured to test this relationship

Specific examples

• (Forsys general purpose) Produce guidelines for the development and use of forest decision support systems
• In the FORSYS application for the COST Action the CS were said to be selected based on their relevance to the European forest sector and the Strategic Research Agenda (SRA). In the SRA the Strategic Objective 4 "Meeting the multifunctional demands on forest resources and their sustainable management" is most relevant. It contains the research areas: 4-1 Forests for multiple needs, 4-2 Advancing knowledge on forest ecosystems and 4-3 Adapting forestry to climate change. If we are turning to these areas we might enter into DSS that are quite new and not put into practices to any larger extent but it might be important as well.
• Other FORSYS structures
  • Working groups (architecture, models, knowledge mgt, participation)
  • Problem types

Exploratory

• What are the concrete impacts of DSS in sustainable forest management?
• How can DSS support EU Strategic Research Agenda:
  • 4-1 Forests for multiple needs
  • 4-2 Advancing knowledge on forest ecosystems
  • 4-3 Adapting forestry to climate change.

Descriptive

• How do DSS contribute to different stages in decision making?
  • The Participatory planning case template describes the roles of participation and DSS in particular cases using a descriptive structure based on Simon's (1960) stages of decision making: organization-intelligence-design-choice-monitoring
• Does the adoption and use of DSS vary by problem type?
• What are lessons learned by WG themes (architecture, models, knowledge mgt, participation)?

Explanatory

The participatory case study group extracted success criteria for participation (e.g. fairness, accessibility, transparency) from the literature and then assessed the degree to which DSS features might influence these participation criteria.^[1]

A more general example of an explanatory research question is: What factors make a forest DSS successful? For a DSS to be successful, a simple model could consider three stages: 1) development of the DSS itself > 2) adoption of the DSS by users > 3) use of the DSS. A case could focus on one of these stages or try to cover all three.

DSS development factors

The use of iterative prototyping and feedback from potential users is an often-cited success factor in DSS development.

DSS adoption (by users) factors

A commonly used perspective on the adoption of technologies, such as DSS, relies on theories of innovation diffusion. Rogers (2003) text on innovation diffusion theory states that adoption rates depend on five attributes of innovations: relative advantage, compatibility, complexity, triability and observability.

DSS use factors

Once the decision is made to use a DSS, both technical and social factors are likely to influence its success. DeLone and McLean (1992, 2003) have developed a widely cited framework for technical factors, which begins with: system quality, information quality, and service quality. Gordon (2006) reviewed social success factors, and reduced them to DSS effects on: participation, communication, translation, and mediation.

Methods - Case definition options

There are actually many ways in which "a case" can be defined - it is important to explicitly make a choice:

• The application of a single DSS to a single problem (probably the most common type of case definition)
• A particular DSS over its lifetime (its application to multiple problems)
• The application of DSS (possibly multiple) to a particular problem domain (eg fire) or to a problem type

Methods - Selecting studies

General

• Purpose will drive selection, examples:
  • Exploratory study of DSS for climate change would select DSS most relevant to climate change

• Replication strategy
  • similar to replicating an experiment
  • type of replication depends again on purpose
    • same DSS & same context OR same DSS & different contexts OR different DSS & same context, etc
• How many cases do we undertake?
  • Depends on the detail needed (cases could be short or long, depending on purposes)

Specific

• Representative sample of problem types
• For WG4 more interest on case specific studies (e.g. Valencia) while other groups looks for good general representation in terms of region, development approach, context etc.
• Most significant DSS identified by country reports

Methods – Data sources & collection methods

• six general sources for case studies: documents, archival records, interviews, direct observation, participant observation, physical artifacts

Methods - improving validity

• Case study is known as a triangulated research strategy. Snow and Anderson (cited in Feagin, Orum, & Sjoberg, 1991) asserted that triangulation can occur with data, investigators, theories, and even methodologies. Stake (1995) stated that the protocols that are used to ensure accuracy and alternative explanations are called triangulation. The need for triangulation arises from the ethical need to confirm the validity of the processes. In case studies, this could be done by using multiple sources of data (Yin, 1984). (Tellis 1997)

Specific

• How to guarantee the “fairness” in evaluating case studies?
  • Each of us works only on CS in which is not directly involved; widening the group; …; the problem does not exist

Reporting results

General

• Possible audiences: academic, DSS developers, forest managers, DSS funders

Specific

• Add to wiki
• Publish most developed case studies as journal articles (seek special issue?)

Other issues

References

1. ↑ http://fp0804.emu.ee/pdf/STSM_scientific_report_Nordstrom.pdf