자료분석 & 질적 연구의 타당성과 평가

2011. 12. 09. 광주대학교 유아교육과

김 승 희 교수

Data analysis 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 comprehen-sive, 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.

Data analysis 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.

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

Domain analysis(Spradley, 1980)

• Based upon discovery of parts or ele-ments of cultural meaning

• Interested in how those elements are or-ganized

• 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

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 )

Open coding( 개방 코딩 )

• Take apart words, lines, sentences, paragraphs

• Line-by-line coding keeps researchers studying data

Axial coding( 축 코딩 )

• Putting data back together by mak-ing connections between the codes

• Making connections between a cate-gory and its subcategory

Selective coding( 선택 코딩 )

• Establishing core categories• Integration categories into a substan-

tive 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 pre-cisely

Discourse analysis

• Select a small interaction that will il-lustrate 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.

Discourse analysis

• Identify motifs.• Significance: how and what different things mean?• Activities: description of activities, how activities are compo-

nents 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 irrele-

vant to each other?• Sign systems and knowledge: how sign systems and knowl-

edge become operational, valued, and disvalued?• Make stories and frames within stories, including stanzas. • Explain the story frame. Use examples.

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 evalua-tion into the research process.

Assessing the quality of re-search

• Validity( 타당성 )• Reliability( 신뢰성 ) • Generalizability( 일반성 )

Validity

• Is consistently concerned about truth.

• Deals with the notion that what you say you have observed is, in fact, what is really happening.

Internal validity

• Deals with the question of how re-search 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.

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 ob-servations 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 find-ings 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

Reliability

• Reliability is considered the consis-tency 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 consis-tent with the data collected.

신뢰도를 높이기 위한 전략들 • Triangulation–방법의 통합–연구자의 통합–이론의 통합–자료의 통합

• 자료 수집 , 분석 , 절차에 대한 심층 기술 • 반성적 주관성–연구자의 편견이 신뢰도를 위협하는 가장 큰 요인

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 re-searchers have sampled a broad enough spec-trum of informants, whether researchers have gone to a variety of settings, or whether the conclusions are supported by a large enough body of data

Strategies to increase the gen-eralizability

• 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, espe-

cially 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.

연구윤리 • 자발적 동의• 기밀유지• 상호 호혜성• Spradly(1975)– 정보 제공자를 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). 질적연구방법론1. 서울 : 민음사 .