자료분석 & 질적 연구의 타당성과 평가

1. 자료분석 & 질적 연구의 타당성과 평가 2011. 12. 09. 광주대학교 유아교육과 김 승 희 교수

2. Dataanalysis guidelines • Analysis is not the last phase in the research process; it is concurrent with data collection or cyclic. • The analysis process is systematic and comprehensive, but not rigid. • Attending to data includes a reflective activity that results in a set of analytic notes that guide the process. • Data are ‘segmented,’ i.e. divided into relevant and meaningful ‘units.’ • The data segments are categorized according to an organizing system that is predominantly derived from the data themselves.

3. Dataanalysis guidelines • The main intellectual tool is comparison. • Categories for sorting segments are tentative and preliminary in the beginning: they remain flexible. • Manipulating qualitative data during analysis is an eclectic activity; there is no one right way. • The procedures are neither ‘scientific’ nor ‘mechanistic.’ • The results of the analysis is some type of higher-level synthesis.

4. Examples of different types of qualitative data analysis • Domain analysis • Grounded theory • Discourse analysis • Feminist analysis • Critical analysis • Phenomenogical analysis • Power-knowledge reading • Rhizo-analysis • Archeology

5. Domain analysis(Spradley, 1980) • Based upon discovery of parts or elements of cultural meaning • Interested in how those elements are organized • Steps of domain analysis • Select a single semantic relationship • Prepare a domain analysis work sheet • Select a sample of fieldnote entries • Search for possible cover terms and included terms that fit the semantic relationship

6. Formal grounded theory(Glaser and Strauss, 1967) • Doing a great deal of analysis in the field • Developing theoretical questions and answers as you move from site to site in order to generate theory • Phase one: open coding(개방 코딩) • Phase two: axial coding(축 코딩) • Phase three: selective coding(선택 코딩)

7. Open coding(개방 코딩) • Take apart words, lines, sentences, paragraphs • Line-by-line coding keeps researchers studying data

8. Axial coding(축 코딩) • Putting data back together by making connections between the codes • Making connections between a category and its subcategory

9. Selective coding(선택 코딩) • Establishing core categories • Integration categories into a substantive theory • Selective coding uses initial codes that reappear frequently to sort large amounts of data • These codes account for the most data and categorize them more precisely

10. Discourse analysis • Select a small interaction that will illustrate an important issue. • Pick some key words and phrases. • Determine their situated meanings knowing the overall context in which the data occurred. • Consider the cultural models these meanings appear to implicate.

11. Discourse analysis • Identify motifs. • Significance: how and what different things mean? • Activities: description of activities, how activities are components of situations? • Identities: how identities are enacted and recognized? • Relationships: how relationships are described? • Politics: distribution of social goods, how authority is viewed? • Connections: how things are connected, relevant or irrelevant to each other? • Sign systems and knowledge: how sign systems and knowledge become operational, valued, and disvalued? • Make stories and frames within stories, including stanzas. • Explain the story frame. Use examples.

12. Narrative analysis • Seeks complex patterns and descriptions of identity, knowledge, and social relations from specific cultural points of view. • Examines social histories that influence identity and development. • Generates unique insights into the range of multiple, intersecting forces that order and illuminate relations between self and society. • Permits the incursion of values and evaluation into the research process.

13. Assessing the quality of research • Validity(타당성) • Reliability(신뢰성) • Generalizability(일반성)

14. Validity • Is consistently concerned about truth. • Deals with the notion that what you say you have observed is, in fact, what is really happening.

15. Internal validity • Deals with the question of how research findings match reality. • Hinges on the meaning of reality. • Reality is no longer considered to be single-faceted. • Reality is holistic, multidimensional, and ever-changing.

16. Six basic strategies to enhance internal validity • Triangulation – using multiple investigators, multiple sources of data, or multiple methods to confirm the emerging findings • Member checks – taking data and tentative interpretations back to the people from whom they were derived and asking them if the results are plausible • Long-term observation at research site or repeated observations of the same phenomenon – gathering data over a period of time in order to increase the validity of the findings • Peer examination – asking colleagues to comment on the findings as they emerge • Participatory or collaborative modes of research – involving participants in all phases of research from conceptualizing the study to writing up the findings • Researcher’s biases – clarifying the researcher’s assumptions, worldview, and theoretical orientation at the outset of the study

17. Reliability • Reliability is considered the consistency or dependability of the results obtained from the data rather than getting the same results. • Reliability is about the question of whether findings will be found again and whether the results are consistent with the data collected.

18. 신뢰도를 높이기 위한 전략들 • Triangulation • 방법의통합 • 연구자의 통합 • 이론의 통합 • 자료의 통합 • 자료 수집, 분석, 절차에 대한 심층 기술 • 반성적 주관성 • 연구자의 편견이 신뢰도를 위협하는 가장 큰 요인

19. Generalizability • Interested in the particular situation of a single case or a small nonrandom sample rather than finding out what is generally true • Concerned with depth than breadth • Evaluated in terms of its representativeness, for example, the question of whether researchers have sampled a broad enough spectrum of informants, whether researchers have gone to a variety of settings, or whether the conclusions are supported by a large enough body of data

20. Strategiesto increase the generalizability • Rich, thick descriptions • Provide enough description so that readers will be able to determine how closely their situations match the research context and thus, whether findings can be transferred. • Typicality • Describes how typical the program, event, or individual is compared with others in the same class in order for users to be able to make comparisons with their own situations. • Maximum variation • Refers to using several sites, cases, or situations, especially those that maximize diversity in the phenomenon of interest, and such purposeful variation or diversity in sample selection enables researchers to find a greater range of application of the findings.

21. 연구윤리 • 자발적 동의 • 기밀유지 • 상호 호혜성 • Spradly(1975) • 정보제공자를 가장 우선으로 고려할 것 • 정보 제보자의 권리, 관심, 그리고 예민성을 보호할 것 • 연구 목표를 명확하게 잘 전달할 것 • 정보 제보자의 사생활을 보호할 것 • 정보 제보자를 착취하지 말 것 • 연구 결과를 참여자에게 알려줄 것 출처: 김영천(2010). 질적연구방법론 1. 서울: 민음사.