论文写作data analysis

1、1 Data Analysis WANG Yan School of International Studies, UIBE Data Collection Data collection is the process of gathering and measuring information on targeted variables in an established systematic fashion, which then enables one to answer relevant questions and evaluate outcomes. The goal for dat

2、a collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answer to questions that have been posed. A formal data collection process is necessary as it ensures that data gathered are both defined and accurate and that

3、subsequent decisions based on arguments embodied in the findings are valid. Data Collection Importance of data collection accurate data collection is essential to maintaining the integrity of research. Both the selection of appropriate data collection instruments (existing, modified, or newly develo

4、ped) and clearly delineated instructions for their correct use reduce the likelihood of errors occurring. Consequences from improperly collected data: Inability to answer research questions accurately Inability to repeat and validate the study Impact of faulty data Distorted findings result in waste

5、d resources and can mislead other researchers to pursue fruitless avenues of investigation. 2 Types of data A fundamental distinction between two types of data: qualitative and quantitative. The data is called quantitative if it is in numerical form and qualitative if it is not. Qualitative data cou

6、ld be much more than just words or text, and it also includes photographs, videos, sound recordings and so on. The superiority of one kind of data over the other? Quantitative: hard, rigorous, credible, and scientific; Qualitative: sensitive, nuanced, detailed, and contextual. Types of data This kin

7、d of polarized debate obscures the fact that qualitative and quantitative data are intimately related to each other. All quantitative data are based upon qualitative judgments; and all qualitative data can be described and manipulated numerically. What may look like a simple, straightforward, cut-an

8、d- dried quantitative measure is actually based on lots of qualitative judgments made by lots of different people. On the other hand, all qualitative information can be easily converted into quantitative data The simplest way to do this is to divide the qualitative information into units and number

9、them! Types of data Qualitative data Quantitative data For instance, the text information (say, excerpts from transcripts) can be grouped into piles of similar statements and describe the results quantitatively. 3 Types of data Qualitative Data Quantitative Data Non-numerical, requiring classificati

10、on into categories Numerical and quantified Analysis through conceptualization Analysis through use of diagrams and statistics Meanings derived from words and pictures Meanings derived from numbers Types of data 1. Surveys: Standardized paper-and-pencil or phone questionnaires that ask predetermined

11、 questions. 2. Interviews: Structured or unstructured one-on-one directed conversations with key individuals or leaders in a community. 3. Focus groups: Structured interviews with small groups of like individuals using standardized questions, follow-up questions, and exploration of other topics that

12、 arise to better understand participants Types of data 4. Textual data written texts books, papers, etc. oral texts speech, conversation, theatre plays, etc. iconic texts drawings, paintings, icons, etc. audio-visual texts TV programs, movies, videos, etc. hypertexts one or more of the texts above,

13、on the Internet 4 Analyzing Qualitative Data Data transcription Data collected in the oral or audio-visual form need to be transcribed into written texts Time consuming Verbatim transcription Detailed transcription including hesitations, repetitions and laughter, etc. Transcription under time and ma

14、npower limitations Using a transcriber (person or machine) Analyzing Qualitative Data Data analysis Systematically searching and arranging the data collected will enable the researcher to come up with findings Put your data in the classified files chronologically Read the data at least twice before

15、working on it Write your notes/comments while reading Break the data into manageable units Look for clues and code them with labels Organizing them into categories (synthesizing) Developing your findings Analyzing Qualitative Data Data analysis Summarizing (condensation) Produce a summary of the key

16、 points from the transcript Condense the meaning into short passages to perceive the themes in the data Categorization (grouping) Develop categories and put chunks of data under them Revise the categorization to find patterns in the data Structuring (ordering) Narrative structuring follows a story l

17、ine, with a beginning, middle and end. Organize the writing in a perceptible order 5 Analyzing Qualitative Data Coding categories Means to helping the researcher sort out the data Determined by the research questions and theoretical framework you adopt for this project Looking for patterns and regul

18、arities and give them your labels Analyzing Qualitative Data Coding categories Families of codes Setting/Context/Situation Perspectives held by informants about themselves Informants ways of thinking about others/object Categories of codes Process/Activity/Event Narrative Methods Strategies Relation

19、ship and social structure Analyzing Qualitative Data Segmenting and displaying data The transcripts can be segmented with reference to each specific research questions so that the irrelevant data can be put aside. Data display is defined as a segmented data file with further reduction and synthesizi

20、ng, which can be presented in a table or in a diagram, or a structured summary 6 Analyzing Qualitative Data Data interpretation: Developing ideas about your findings and relating them to the literature reviewed and broader concerns and concepts Explaining your findings based on your experiences Fram

21、ing your ideas in relation to the theory and literature you have read Both theories and experiences are important in developing your own understanding and interpretation of the data Showing the significance of your findings in relation to the current scholarship as well as the experiences e.g. Inter

22、views data collection and interpretation Develop effective and analytical questions Open-ended questions: concerning process and meaning, rather than cause and effect Make adjustment to your data collection plan if something new is found to be important during the process Take notes which may help y

23、our understanding of the interview contents Memos or summaries of your feelings and ideas/ Visual devices /Observers comments Referring to literature for stimulations to new understanding Try out your impressions/ideas/tentative conclusions on key informants e.g. Interviews segmenting and displaying

24、 data Research question: What is your perception of a good teacher? Group 1: Experienced teachers SusanCatherineWendy Student-centered learning Character molder Help students develop academically but also spiritually Humble, honest, consistent Update the knowledge of the subject Positive attitude to

25、wards students Honest, enthusiastic, dedicated Group 2: Novice teachers CeciliaLucyRebecca Not a commander but friendly and helpful Knowledgeable Teach systematically and efficiently Fair grading system Patience, love students Fluent spoken English Good communication skills with adolescents Good exp

26、lanation ability Enthusiastic for teaching Care for students without prejudice Prepare before lessons Continually learn and improve oneself 7 Analyzing Quantitative Data Quantitative data can be used to produce descriptive statistics and inferential statistics Descriptive statistics: data analysis t

27、echniques that enable the researcher to meaningfully describe many scores with a small number of numerical indices. Inferential statistics: data analysis techniques for determining how likely it is that the results based on a sample or samples are the same results that would have been obtained for a

28、n entire population. Analyzing Quantitative Data Steps in analyzing quantitative data: Designing the data layout Coding Entering data Doing statistical analysis Analyzing Quantitative Data Designing the data layout A data matrix developed for further process by computer IDVariable 1Variable 2Variabl

29、e 3Variable 4 Case 11131882 Case 22222075 Case 33241990 8 Analyzing Quantitative Data Coding Converting data into codes so as to compare, process and analyze them with statistical software Numerical data: use actual numbers e.g. : scores, salary Ranked data: code in a logical order e.g.: 1 for never

30、, 2 for occasionally, 3 for sometimes, 4 for often, and 5 for always Categorical data: assign each category a number e.g.: 1 for male, 2 for female; 1 for teachers, 2 for doctors, 3 for lawyers, etc. Analyzing Quantitative Data Entering data Analyzing Quantitative Data Doing statistical analysis Des

31、cribe and compare variables numerically Using software programs, e.g. Excel, Access, SPSS, etc. Central Tendency: e.g. Mean Dispersion: to measure how scattered the data are, e.g. Standard Deviation 9 Presenting the Results Presenting non-statistical results Non-statistical data can be transformed i

32、nto statistical data by summarizing the number of cases in each category. In other cases, the results of analysis can be presented in narrative and descriptive text Raising questions and giving answers Putting forward as critical opinion Illustrating cause and effect (e.g. using figures) Using self-

33、designed diagrams to show complex logical relationships Presenting the Results Presenting statistical results Use tables to show numbered values Use pie charts and percentage component bar charts to show proportions Us bar charts, multiple bar charts, column charts, and histograms to show highest an

34、d lowest values Use line graphs and multiple line graphs to show trends over time 10 Presenting the Results Presenting statistical results Even with the assistance of tables and diagrams, you still need to use your own language to discuss in your thesis the main features shown in those tables and di

35、agrams You need to interpret these figures and call readers attention to them Discourse analysis Discourse is a tangible form of communication act that is semantically and pragmatically coherent, and can be rendered in either written form or spoken form and is used to achieve a certain communicative

36、 goal. Discourse analysis (DA), or discourse studies, is a general term for a number of approaches to analyze written, vocal, or sign language use, or any significant semiotic event. Discourse analysis What to analyze? The objects of discourse analysis discourse, writing, conversation, communicative

37、 event are variously defined in terms of coherent sequences of sentences, propositions, speech, or turns-at-talk. Contrary to much of traditional linguistics, discourse analysts not only study language use beyond the sentence boundary, but also prefer to analyze naturally occurring language use, and

38、 not invented examples. Text linguistics is a closely related field. The essential difference between discourse analysis and text linguistics is that discourse analysis aims at revealing socio-psychological characteristics of a person/persons rather than text structure. 11 Discourse analysis Multi-d

39、isciplinary in nature Discourse analysis has been taken up in a variety of social science disciplines, including linguistics, education, sociology, anthropology, social work, cognitive psychology, social psychology, area studies, cultural studies, international relations, human geography, communicat

40、ion studies, and translation studies, each of which is subject to its own assumptions, dimensions of analysis, and methodologies. Discourse analysis Topics of interest The various levels or dimensions of discourse, such as sounds (intonation, etc.), gestures, syntax, lexicon, coherence, style, rhetoric, meaning, speech acts, moves, strategies, turns, and other aspects of interaction Genres of discourse (various types of discourse in politi