1. Research Methods School of Economic Information Engineering Dr. Xu Yun Office ： Phone ： Email ： michael.xuyun@gmail.com xuyun@swufe.edu.cn

2. Case Study Procedure develop theory design data collection protocol write individual case report write cross- case report develop policy implications modify theory draw cross- case conclusions Conduct remaining case study conduct2 nd case study conduct 1st case study select cases

3. Case Study Procedure (Yin, 2003)  Preparation for Data Collection  Collecting data  Analyzing data  Writing paper 2015-10-103

4. Preparation for Data Collection  Prior skills of the investigators  The training and preparation for the specific case study  The development of a case study protocol  The conduct of a pilot case study  Remember: case study research is among the hardest types of research to do

5. Desired Skills (Investigators)  Case study is easy?? case be mastered without difficult have to learn only a minimal set of technical procedures any deficiencies in formal, analytic skills will be irrelevant allow them simply to “tell it like it is”

6. Difficulties  The data collection procedures are not routinized  Little room for traditional research assistant  The continuous interaction between the theoretical issues being studied and the data being collected  Take advantage of expected opportunities  Care against potentially biased procedures

7. Basic Required Skills  Ask good questions - and to interpret the answers  Be a good listener and not be trapped by his/her own ideologies or preconception  Be adaptive and flexible (new = opportunity; not always as planned)  Have firm grasp of the issues being studied (relevant events and information)  Not mechanical recording  Recognize deviations, contradictions  Should be unbiased by preconceived notions  Be open to contrary findings

8. Case Study Protocol  An overview of the case study project objectives, issues, relevant readings  Field procedure credentials & access to “sites,” sources of information, procedure reminders  Case study questions the specific questions  A guide for the case study report outline, format for narrative etc.

9. Data Collection 2015-10-109

10. inf5220 - 27 October 200510 Sources of evidence  Documentation  Archivial records  Interviews  Direct observations  Participant-observation  Physical artifacts (technological devices, tools or instruments, a work of art)

11. inf5220 - 27 October 200511 Three Principles of data collection Principle 1:Use multiple sources of evidence  Single source: problems of accuracy and trustworthiness  Triangulation: rationale for using multiple sources of evidence (data sources, investigator, theory, methodological)  Construct validity  More expensive/time consuming/need different skills

12. inf5220 - 27 October 200512 Three Principles of data collection Principle 2: Create a case study database  Need to separate between collected evidence and final report  Increases reliability  Contents: notes, documents, quantitative data, narratives  Other people should be able to access data

13. inf5220 - 27 October 200513 Three Principles of data collection Principle 3: Maintain a chain of evidence  To allow an external observer to follow the derivation to any evidence  Trace steps From conclusions to research questions From research questions to conclusions  Final report ↔ database ↔ evidence and circumstances ↔ procedures and questions in protocol ↔ initial research questions

14. Various types of qualitative interviews  Structured interview: There is a complete script that is prepared beforehand. There is no room for improvisation. These types of interviews are often used in surveys where the interviews are not necessarily conducted by the researcher.  Unstructured or semi-structured interview. There is an incomplete script. The researcher may have prepared some questions beforehand, but there is a need for improvisation.  Group interview. In a group interview two or more people are interviewed at once by one or more interviewers. This type of interview can be structured or unstructured. Interview Protocol

15. Problems and pitfalls  Artificiality of the interview  Lack of trust  Lack of time  Level of entry  Elite bias  Hawthorne effects  Constructing knowledge  Ambiguity of language  Interviews can go wrong 2015-10-1015

16. Suggested guidelines for interviewer  Situating the researcher as actor. Because the interview is a social encounter and the data gathered from interviews are idiographic, the interviewer should situate themselves as well as the interviewee.  Minimize social dissonance As the interview is a social encounter, it is important to minimize social dissonance i.e. minimize anything that may lead to the interviewee to feel uncomfortable. 2015-10-1016

17. Suggested guidelines for interviewer  Represent various ‘‘voices’’ In qualitative research it usually necessary to interview a variety of people within an organisation.  Use Mirroring in questions and answers. Focus on the subjects’ world and uses their language rather than imposing yours. 2015-10-1017

18. Suggested guidelines for interviewer  Flexibility Semi-structured and unstructured interviewing uses an incomplete script and so requires flexibility, improvisation, and openness.  Confidentiality of disclosures It is important for researchers to keep transcripts, records and the technology confidential and secure. 2015-10-1018

19. Data Analysis 2015-10-1019

20. Data analysis strategies Relaying on theoretical propositions Thinking about rival explanations Developing a case description 2015-10-1020

21. Data analysis and interpretation  Describe the case and its setting in detail  Stake (1995) suggests four forms Categorical aggregation Direct interpretation Establish patterns Develop naturalistic generalizations  Researcher-developed generalizations Relate back to literature review and research questions Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

22. Computer tools for qualitative data analysis  Text retrievers: Metamorph  Text-based manager: askSam  Code and retrieve: The Ethnograph  Code-based theory builders: NUD*IST  Conceptual network builders: Inspiration Weitzman, E. A., & Miles, M. B. (1995). Computer programs for qualitative data analysis. Thousand Oaks, CA: Sage.

23. Narrative and Metaphor  Narrative is defined by the Concise Oxford English Dictionary as a "tale, story, recital of facts, especially story told in the first person." There are many kinds of narrative, from oral narrative through to historical narrative.  Metaphor is the application of a name or descriptive term or phrase to an object or action to which it is not literally applicable (e.g. a window in Windows 95). 2015-10-1023

24. Hermeneutics - 解释学  Hermeneutics is primarily concerned with the meaning of a text or text-analogue (an example of a text-analogue is an organization, which the researcher comes to understand through oral or written text). The basic question in hermeneutics is: what is the meaning of this text? 2015-10-1024

25. Semiotics - 符号学  Semiotics is primarily concerned with the meaning of signs and symbols in language. The essential idea is that words/signs can be assigned to primary conceptual categories, and these categories represent important aspects of the theory to be tested. The importance of an idea is revealed in the frequency with which it appears in the text. 2015-10-1025

26. Semiotics  One form of semiotics is "content analysis." Krippendorf (1980) defines content analysis as "a research technique for making replicable and valid references from data to their contexts." The researcher searches for structures and patterned regularities in the text and makes inferences on the basis of these regularities. Krippendorf (1980)  Conversation analysis  Discourse analysis 2015-10-1026

27. Content analysis—analyzing and coding the content of various communication media, i.e., existing written, audio, or visual communication, in an effort draw conclusions about the characteristics, attitudes, and behaviors of the individuals or groups that produced it and/or the surrounding culture. Content analysis is used by both qualitative and quantitative researchers. Positivistic/quantitative Classifying and counting occurrences Deploying preconceived coding categories Emphasis on manifest content. Focus on hypothesis testing Interpretive/qualitative Classifying and categorizing responses Developing categories during data analysis that fit the data Emphasis on latent content Focus on theory building Content Analysis

28. Doing Quantitative Content Analysis Identifying, locating, and gathering data sources Choosing media What is your research question and/or hypotheses? What is the availability of the desired media? Sampling and selection Stratified, multistage, cluster sampling Sampling levels Measuring and coding data Selecting indicators and developing a coding guide Counting occurrences of indicators Coding manifest versus latent content Reliability and validity. Multiple coders and inter-rater reliability

29. Content analysis 2015-10-1029

30. Coding process - example  The interview transcripts and other documents were first read by one of the authors who used a data reduction and presentation technique for analyzing, triangulating, and documenting the contents of the transcripts and documents to identify relevant quotes and events representing the constructs.  The entire coding process was repeated by another coder  The coders compared their codes and no significant differences were identified.  Minor disagreements were discussed and resolved.  After this initial coding was completed, a finer grained coding was performed by the same coders where the data within each broad category were coded into constructs associated with the theoretical mechanisms. 2015-10-1030

31. Analysis - example  The quantitative analysis included a content analysis of the interview data using the NUD ∗ IST software.  Nodes were created in the software to represent the coding categories identified in the coding process.  The software provided counts of statements related to each of our theoretical mechanisms. 2015-10-1031

32. Case Writing 2015-10-1032

33. Suggestions for writing up results  Begin with a story or first-person narratives  Describe all data and procedures, processes, and tools used  Discuss results related to literature  Recommend future research topics and investigative methods

34. Case-Study Reports  Compose report (format) early Select form at design time  Wider audience  Often comprehensive ”Book-sized” Part of multi-method studies

35. Targeting Reports  Audience Academia Popular science Thesis committees Research funders  Different versions of report  Avoid egocentric perspective Understand audiences and their needs

36. Report Formats - Classic Single-Case Study  Narrative  Augment using Tabular Pictorial displays  Typically Book-sized

37. Report Formats - Multiple-Case Studies  A set of narrative single Case Studies  One CS/chapter  One [several] cross-case chapter[s]

38. CS Reports as Part of Multi Method Studies  CS encompasses other methods Typically chapter in large study  In overall conclusions: CS strengthen evidence from other methods  Triangulation  CS share the same research questions “Independent studies show the same result”

39. Illustrative Structures for CS Compositions  Six types of structures Linear analytic structures Comparative structures Chronological structures Theory-building structures Suspense structures Unsequenced structures  All may be used for multiple- or single- case studies

40. Linear Analytic Structures  The classical approach for composing research reports Problem formulation Relevant prior literature Methods used Findings Conclusions  Suitable for all types of case studies Explanatory, descriptive, or exploratory

41. Comparative Structures  Repeat the same study two or more times From different perspectives Using different descriptive/explanatory models  An example of pattern-matching at work  Suitable for Explanatory and descriptive studies Not Exploratory???

42. Chronological Structures  Present evidence in chronological order  Sections represent phases of the study  Suitable for explanatory case studies Explain = show causal relationships A cause always occurs before an effect Maybe also descriptive and exploratory???  Pitfall: overemphasize early phases Advisable to draft report backward

43. Theory-Building Structures  Sequence of chapters follows some theory-building logic  May produce very compelling arguments  Suitable for Explanatory studies: build causal arguments Exploratory: debate the value of further investigating hypotheses and propositions

44. Suspense Structures  Present the outcome of the study first  Reveal evidence afterwards  The inverse of linear analytical structures  Suitable for explanatory studies

45. Unsequenced Structures  The ordering of the sections or chapters is not important  Suitable for descriptive studies

46. What Makes an Exemplary CS?  The case study must be significant be “complete” consider alternative perspective display sufficient evidence be composed in an engaging manner

47. Improving the quality of case studies  Define research questions, anchor into theory  Use within and between case analysis  Be clear about sampling  Validate/test your instruments  Identify natural controls  Use multiple sources of data for triangulation  Search for alternative explanations  Report your data collection process, threats to reliability, method bias  Use all data, including field notes  User study protocol and methods to map data to constructs  Use quotes or other field data 2015-10-1047

48. Backup 2015-10-1048

49. Analyzing case-study evidence  Pattern Matching  Explanation Building  Time-Series Analysis  Logic Models  Cross-Case Synthesis

50. Pattern Matching  Predict a pattern Or more than one (rival hypothesis) – mutually exclusive! ”OO increases productivity and quality”  Match outcome to pattern(s) Several variables (productivity, quality, …) Define measures and data collection method for each If not entire pattern is supported, you need to question initial hypothesis

51. Pattern Matching  Replication Theoretical replication (different treatment - different outcome?)  E.g.: ”OO not introduced, quality increased” Literal replication (same treatment - same outcome?)  ”OO introduced, quality increased”

52. Pattern Matching  Low level of precision -> interpretation  Fewer variables -> more dramatic differences required ”Quality increased with 300%”  Other events than the hypothesis ”At the same time, there was a new boss, all computers were exchanged, and the office windows were eventually washed”

53. Explanation Building  Special type of pattern matching  Stipulate a presumed set of causal links  Often iterative Test hypothesis Refine hypothesis  Could also be a hypothesis- generating process, to develop ideas for further study

54. Time Series Analysis  Sample variables over time Trends, variations, statistical analysis Simple time series – rising or declining trend Complex time series – more complex behavior  ”Natural disaster -> disruption on short term, no disruption on long term (population, economic growth, etc.)” Pattern matching on predicted behavior – rival hypotheses

55. Time Series Analysis  Start and end points?  Cross-case comparison (same as pattern matching) Theoretical replication (different treatment - different outcome?) Literal replication (same treatment – same outcome?)

56. Time Series Analysis  Chronologies Not descriptive only, but also explanatory  Compare chronology with theory Sequence of events (cause before effect)  ”Quality increases after OO is introduced” Time intervals between events  ”Quality increase visible after one year” Time periods, classes of events (Pattern matching again…)

57. Logic Models  Not what we in this room would call ”logic”!  More advanced predictive model than in time series At least one figure looked like a state machine Pattern matching Strength: if shown to be correct, the model describes opportunities to intervene and break undesired transitions  ”Starts Drugs” to ”Joins Gang”

58. Logic Models  Individual-level One case = one person Trace one person’s path through the model  Organizational-level One case = one organization  Program level One case = one government program/reform  Final goal of a reform? Continuous improvement…

59. Cross-Case Synthesis  Said before… Theoretical replication (different treatment - different outcome?) Literal replication (same treatment - same outcome?)  Large number of cases – quantitative analysis  Modest number of cases – qualitative analysis Word tables (?) Argumentative interpretation