1. Data Analysis in Qualitative Research By: Ersa Tri Wahyuni, PhD Universitas Padjadjaran To be presented at Universitas Airlangga 10 August 2016

2. Agenda What is Qualitative Research? The Data in Qualitative Research Data Analysis : Ersa’s Thesis How to Evaluate the Quality of Qualitative Research? 6-2

3. Table 1.2, Qualitative and Quantitative Research Contrasted QUALITATIVEQUANTITATIVE Multiple realities Single reality Reality is socially constructed Reality is objective Reality is context interrelated Reality is context free Holistic Reductionistic Strong philosophical perspective Strong theoretical base Reasoning is inductive Reasoning is deductive and inductive Discovery of meaning is the basis of knowledge Cause-and-effect relationships are the bases of knowledge Develops theory Tests theory 6-3

4. 6-4© 2007 Pearson Education Canada Table 1.2, Qualitative and Quantitative Research Contrasted (continued) QUALITATIVEQUANTITATIVE Theory developed during study Theory developed a priori Meaning of concepts Measurement of variables Process oriented Outcome oriented Control unimportant Control important Rich descriptions Precise measurement of variables Basic element of analysis is words Basic element of analysis is numbers Uniqueness Generalization Trustworthiness of findings Control of error

5. 6-5 Figure 6.1, Steps in Quantitative and Qualitative Studies Quantitative (Linear) ↓ Define a Research Problem/Question ↓ Review the Literature ↓ Formulate Hypothesis or Refine Question ↓ Make Operational Definitions ↓ Design or Select Instruments for Data ↓ Obtain Ethical Approval ↓ Collect Data ↓ Analyze Data ↓ Interpret Findings – Refer to Literature Again ↓ Determine Implications – Draw Conclusions Source: Based on H.J. Streubert and D.R. Carpenter (1999). Qualitative Research in Nursing: Advancing the Humanistic Imperative. 2 nd ed. Philadelphia: J.B.Lippincott.

6. 6-6 Figure 6.1, Steps in Quantitative and Qualitative Studies (continued)

7. 6-7 Core Activities in Qualitative Research Qualitative approaches on: A. Literature review B. Explicating researcher’s beliefs C. Role of participants: subject or informant? D. Selection of participants E. Setting for data collection F. Approach to data analysis G. Saturation

9. Data in Ersa’s Thesis Data typesExamples How the information from the data is used in the study Observation of meetings Indonesian Financial Accounting Standard Board meetings To understand the nature of how the board make decisions in the adopting process IFRS Advisory Council meeting To understand the international response to the Japanese FSA presentation on their market competition mechanism World Standard Setters meeting To understand the relationship between the IASB and other national standard setters To learn how national standard setters use the platform to lobby IASB for their issues International Forum of Accounting Standard Setters Asian-Oceanian Accounting Standard Setters Group IFRIC meeting To understand how Indonesian national standard setters fought for their implementation issue “Accounting for Land” Interview Face-to-face at the respondent’s place of work To gather historical information on the adoption process and the issues debated in the process To validate information from the documents related to actors involved in the process To validate the analysis of the story Face-to-face outside respondent’s place (in London/Europe) Phone interviews 6-9

10. Data in Ersa’s Thesis DocumentsPublished speeches by actors As the major source of information to set up a timeline, identify the actors, identify the issues debated, and identify the actors’ points of view on the issue Discussion papers/ invitation for comments/reports issued by standard setters or regulators Comment letters to the standard setters or regulators Minutes of the meetings Report issues by international organisations such as World Bank ROSC Magazine articles Memoirs of the actors Books/articles/research published by the actor Articles from the web-based newspapers and the websites of professional associations Audio Materials Recording (podcast) of IFRS Advisory Council Meeting To understand the reaction of the international community toward the decision of Japan in 2011 Recording (podcast) of IASB meetingTo understand how IASB deals with implementation issues from new adopted countries 6-10

11. 6-11 Refers to a situation in data analysis where participants’ descriptions become repetitive and confirm previously collected data An indication that data analysis is complete When data analysis is complete, data collection is terminated Saturation : When do we stop collecting data?

12. 6-12 Data Collection Methods in Qualitative Studies Three data collection strategies introduced: 1. Participant observation 2. In-depth interviews 3. Focus group interviews Qualitative researchers may combine more than one method

13. 6-13 Data Collection Methods: 1. Participant Observation Intensive, usually long term, examination of a social group, an organization, etc. Researcher becomes a participant in the lives of group members Observes their behaviour and learns meaning systems (which are tied to language) Most closely associated with Ethnography, as developed in Classical Anthropology Now done in a variety of disciplines

14. 6-14 1. Participant Observation (cont’d) Many classic participant observations studies E.g., Asylums (Erving Goffman), Tally’s Corner (Elliot Liebow), Street Corner Society (William F. Whyte), etc. Today most ethnographers take an overt role I.e., their identity as a researcher is known to the people being studied Covert participation (i.e., identity concealed from participants) is fraught with ethical issues – e.g., Humphrey’s “Tearoom Trade”

15. 6-15 Steps Involved in Participant Observation Research A. Gaining entry into the group B. Developing and maintaining rapport C. Developing a method for taking field notes D. Integrating data collection and data analysis

16. 6-16 Steps in Participant Observation: A. Gaining Entry into the Group Take into consideration the type of group formal organizations require formal entry; involves letter writing, permission requests, etc. Informal groups – different strategy needed Access may be gained through a gatekeeper (an individual with special status) Want to involve key informants (those who are most knowledgeable about the group)

17. 6-17 Steps in Participant Observation: B. Developing/Maintaining Rapport Researcher must work hard to develop and maintain good relationships in the field E.g., be sure not to become associated with one faction in a group or organization Researcher could be blamed for problems that arise in the setting

18. 6-18 Steps in Participant Observation: C. Strategies for Taking Field Notes Field notes – integral to participant observation Include descriptions and interpretations of individuals, interactions, and events Distinguish descriptions from interpretations Record time and location of observations, as well as key information (weather, events happening and their significance) Keep theoretical memos – which are the tentative interpretations emerging and being assessed through further data collection

19. 6-19 Field Notes (cont’d) May not be possible or advisable to take notes while in the field Important that they be done as soon after field observation as possible Note-taking is time-consuming because it is integral to guiding the data collection and continuing the analysis E.g., field notes for When Prophecy Failed were well over 1,000 typed pages

20. 6-20 Steps in Participant Observation: D. Integrating Data Collection and Analysis Organizing field notes into different types of files facilitates data analysis Master field file – complete journal of field notes; number pages and include entry dates Background, history file – subfile organizing background material Key character files – subfiles on key players in the group or organization Analytic files – subfiles for different types of observations or relationships

21. 6-21 F. Data Analysis Researcher immerses self in data to bring order and meaning to vast narrative Come to truly understand what the data are saying Cyclical process – data collection occurs simultaneously with data analysis Analysis begins when data collection begins Reading, rereading, intuiting, analyzing, synthesizing, and reporting on data Sometimes called theoretical sampling (collect data until saturation is reached)

22. 6-22 Generalizations drawn from earlier interviews are returned to participants for clarification and elaboration Look for meaning in the data as it is gathered Data similar in meaning are clustered together into preliminary categories R equires an extensive amount of time F. Data Analysis (cont’d)

23. Data Reduction Process of Selecting, Coding, and Categorizing the Data. Coding is the process of analysing the qualitative data, to reduce, re-arrange, and integrated to form a theory Categorization is the process of organizing, arranging and classifying coding units. Theory can help in coding and categorization Or Grounded theory if the coding and category is inductively developed from the data 6-23

24. Data Analysis Ersa’s Thesis 6-24

25. First Coding of Interview The Interview was coded and three themes emerge: About the process About the Actors, Who are involved in the process About the arguments for and against IFRS adoption 6-25

26. Second Coding: Cross Country Cross Country Analysis between countries. Some countries have similar process, some don’t Categorization emerge from the second coding Harmonization Decision Transition Implementation 6-26

27. Model from second coding This is explaning the stages of IFRS adoption. Derived from the data. Many of other papers only highlights the harmonization and implementation period 6-27

28. Third Coding : Theoretical Categorization Using Institutional Work Theory, the data were categorize into: Institutional Creating Work Institutional Maintaining Work Institutional Distrupting Work Once again the data was scrutinised again and categorised A model was built using Institutional Work 6-28

29. Different forms of institutional work re the adoption of IFRS 29 Institutional work : “the purposive action of individuals and organizations aimed at creating, maintaining and disrupting institutions ”. (Lawrence and Suddaby 2006) (Lawrence and Suddaby 2006) Institutional work represents a new idea connecting, bridging and extending the work of institutional entrepreneurship, institutional change and innovation and deinstitutionalisation (Lawrence et al., 2009)Lawrence et al., 2009

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31. 6-31 Methods of Evaluating Qualitative Research Qualitative validity means that the researcher checks for the accuracy of the findings by employing certain procedures, while Qualitative reliability indicates that the researcher’s approach is consistent across different researchers and different projects How to improve the quality of qualitative research?

32. 6-32 Alternate Tests of Quality Description must be plausible and recognized by participants Enhanced by: Prolonged time in the field repeatedly observing and interacting with participants Using different data sources, methods, data type (Triangulation of Data) Conducting member checks Involving other investigators in the study Peer Review

33. Validity and Reliability of Ersa’s Thesis Country Selection Criteria : Very Clear why I choose those 6 countries Respondent Selection Criteria : Very selective Confirmatory Interview Coding and categorizing process in discussion with other researchers Presentation of findings in various FGD, workshop and conference 6-33

34. Challenges of conducting qualitative research in Islamic Economics Access to Data : Respondents are not very cooperative The quality of the data : Even when respondents are cooperative, they have some reservations in telling their story Using the theory to frame the data. Avoiding jargons to make the paper understandable. 34

35. Ersa’s Tips for Qualitative Researcher Keep your field notes close Write down your summary (memo) right after interview Collaboration with other colleagues Keep open minded, but honest and be aware of your own view Be prepared to get surprised Be prepared to be rejected by prospective respondents Leveraging your networks 6-35

36. Thank You 6-36