Designing Case Studies

1. Designing Case Studies Grupp 2 Jukka Mäki-Turja, Johan Andersson, Joel Huselius

2. Case Studies from Chapter 1 • A case study is an empirical inquiry that • Investigates a contemporary phenomenon within its real-life context, especially when • the boundaries between phenomenon and context are not cleraly evident • When to use a CS? • Many more variables of interest than data points • Relies of multiple sources of evidence • Benefits from prior theoretic propositions, guiding data collection and analysis. • In answering ”how” and ”why” questions

3. Outline – Research Design • What is a Research Design? • The role of Theory • Criteria for high quality research design • Single vs. Multiple case design • Conclusion and Advice

4. What is a Research Design • Research Design is a difficult part of doing Case Studies • No roadmaps exists… • Logical plan to go from A to B • A = initial set of question to be answered • B = conclusions of study • Logical, not a logistical problem! • Research design can be seen as a blueprint of research • What question to study? • What data are relevant? • What data to collect? • How to analyze the results? • Case studies require its own research design • Not a special case of, e.g., experiment.

5. 5 Components of Research Design • Questions • Propositions • Unit of analysis • Linking data to propositions • Criteria for interpreting the findings

6. Questions and Propositions • Questions • The high level questions of the Case Study. • Case studies suitable for ”how” and ”why” questions. • Propositions • Possible (partial) answers (a.k.a hypotheses) • Directs attentions on what to examine in the study • More concrete than questions • Forces the study in the “right” direction • In exploratory studies - no propositions • State purpose instead

7. Unit of Analysis • What is the ”case”? • An individual? • A decision? • A program? • Relates to research questions and proposition • Without clear propositions, one might be tempted to cover “everything”. • Non-favoring research questions – too vague or too numerous • Different units of analysis requires different research design and data collection strategy.

8. ”no effects” pattern Observation ”effects” pattern Linking data to propositions • Least well developed • Pattern Matching • Identify effects/no effects patterns • Which pattern matches best?

9. The criteria for Interpreting the findings • How close does a match have do be in order to be considered a match? • No general solution… • Hope that patterns of rival propositions are sufficiently constrasting

10. Outline – Research Design • What is a Research Design? • The role of Theory • Criteria for high quality • Single vs. Multiple case design • Conclusion and Advice

11. The Role of Theory • Covering these 5 aspects force you to begin constructing a preliminary theory. • Important to have a theoretical framework providing guidance • Existing work • Analytical vs. Statistical generalisation • Replication

12. Criteria for high quality • Judging the quality of Research Design • Four tests • Construct Validity • Internal Validity • External Validity • Reliability

13. Construct Validity • ”Establishing correct operational measures for the concepts being studied” • Case studies are often criticized that subjective judgement is used collecting data. • To meet Construct Validity, e.g. • Select the specific type of changes that are to be studied. • Demonstrate that the selected measures of these changes do indeed reflect the specific type of change that have been selected.

14. Internal Validity • “Establishing a causal relationship, whereby certain conditions are shown to lead to other conditions, as distinguished from spurious relationships” • For explanatory or causal studies only. • Inferring theory • Study x leads to y • What happens if unknown z affects y?

15. External Validity • ”Establishing the domain to which a studies findings can be generalized” • Critics state that single cases offer a poor basis for generalization. • Analytical generalization rather than statistical • Generalization by replication • Replication logic same as for experiments

16. Reliability • ”Demonstrating that the operations of a study can be repeated with the same results” • The goal of reliability is to minimize the errors and biases in a study. • Case study protocols to document • General approach: conduct research ”as if someone were always looking over your shoulder” • compare with accounting

18. Case Study Designs • Single vs. Multiple case • Single case appropriate in certain conditions • Multiple case design better in general • Embedded vs. Holistic • Holistic = one unit of analysis • Emdedded = several units of analysis

19. Context Context Context Case Case Case Context Context Case Case Context Context Context Case Case Case U1 U2 U1 U2 Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 Context Context Case Case U1 U2 U1 U2 Basic types of Designs Single-case Designs Multiple-case Designs Holistic (single unit of analysis) Embedded (multiple units of analysis)

20. Single-case Design • Five rationales • Critical case: clear set of propositions • Extreme/unique case • Representative/typical case • Revelatory case • Previously inaccessible phenomena • Longitudinal case • Same things at different points in time • Assumes that conditions changes over time • As a pilot case for multiple case studies • Not considered as a case study of its own

21. Embedded vs. Holistic Designs • Holistic design • When no logical subunits can be identified. • study might be conducted on a too abstract level • Research question slippage • Embedded design • Avoids slippage • Extensive analysis • Might focus too much on subunits, loses higher level (holuistic) aspects.

22. Multiple-case Designs • More robust results and compelling arguments • Require more resources • Replication rather than ”sampling” logic • Each case can be holistic or embedded

23. Replication vs. Sampling logic • Replication – analytical generalization • Analogous to that used in multiple experiments • Goal is to duplicate results from previous work • Convergent evidence is saught • ”Sampling”– statistical • Analogous to that used in surveys • Goal is to gather general information from large amounts of data

24. Literal vs. Theoretical Replication • Literal replication • Similar results • Theoretical replication • Contrasting results for predictable reasons • If cases are contradictory initial proposition must be revised • Without redesign, you can be accused of distorting or ignoring the discovery to accommodate your design. • A prerequisite of successful replication is a rich theoretical framework • Number of cases is very fuzzy.

25. Rationale for a multiple case design • Comes from understanding theoretical and literal replication • Simplest multiple case design • Literal replication among two cases • More complicated multiple case design • Theoretical replication between different types of conditions • Literal replication within each type of condition

26. Conclusion and Advice • When you have a choice (and resources) choose multiple case design • Two cases is significatly better than a single one – allows for replication. • Drastical improvment of generalizability • Theoretical replication even stronger argument • Avoids critisism and skepticism • If you use single case • prepare to make an extremly strong argument in justifying your choice of case.