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Social Research Methods Qualitative and Quantitative Approaches.pdf
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这份资料是社会研究方法重点资料，兼顾定性与定量两类研究范式。整个资料包共17部分，完整覆盖研究设计、数据采集、数据分析与成果撰写全流程。资料没有单独设立伦理章节，而是将科研伦理融入多个章节展开论述。内容依次讲解社会研究基础术语、研究方案撰写、实验逻辑、量表构建、概率与非概率抽样；介绍访谈、问卷、社会网络数据、田野观察等数据收集手段；阐述扎根理论、内容分析、话语分析等文本分析方法；讲解单变量、双变量、多变量以及社会网络数据分析；最后介绍成果写作与发表。资料面向社科学习者，帮助读者掌握完整的社会科学研究实操体系。
Social Research Methods Qualitative and Quantitative Approaches There are 17 chapters in this resource, covering research design, data collection, data analysis, and write-up. There is no chapter devoted to ethics in this resource because ethics is too important to deal with that way. It’s part of everything we do in social science, and we deal with this all-important topic at length, in several chapters. Look for references to all those discussions under “ethics” in the index. The first three chapters are about what goes on before we collect or analyze any data: Chapter 1 is on the language of social research—the basic vocabulary, including levels of measurement and tests for reliability and validity of measurements. Chapter 2 is on setting up a research project: defining a research question, and doing the kind of literature search required. And Chapter 3 is on research design and on writing proposals for research. We feel strongly that students practice the craft of proposal writing as part of their basic instruction on the methods of social research. Chapter 4 introduces students to the logic of the experimental method: formulating a hypothesis, measuring the independent and dependent variables, and controlling for threats to validity. Students who never conduct an experiment need to control this logic in order to design and conduct convincing research. Chapter 5 is on scaling: how to use scales and how to build them. A single question on a questionnaire is technically a scale if it lets you assign the people you’re studying to categories of a variable. Many interesting variables in social science, however, are complex and can’t easily be assessed with single indicators. Chapter 5 covers methods for developing and testing composite measures of complex concepts. Methods covered include Guttman scales, Likert scales, and semantic differential scales. Chapter 6 is about the basics of probability sampling, including the central limit theorem, and determining the size of representative samples. Chapter 7 is on nonprobability sampling, including quota sampling, snowball and respondent driven sampling, purposive sampling, and convenience sampling. Together, Chapters 6 and 7 address the question: Given that my findings are valid, how far can I generalize them beyond the people (or countries, or court cases) I actually studied? Chapters 8, 9, 10, and 11 are on the real how-to of collecting social science data. Chapter 8 deals with unstructured and semistructured interviewing, including focus groups. In unstructured interviewing, the idea is to get people to open up and to let them express themselves in their own terms and at their own pace. Semistructured interviewing follows a written list of questions and topics that need to be covered in a particular order. Focus groups—a kind of semistructured interview—are recruited to discuss a particular topic—like people’s reaction to a television commercial or their attitudes toward a social service program. Survey researchers may use focus groups to help design a questionnaire and also to help interpret the results of surveys. Response effects are measurable differences in interview data that are predictable from characteristics of respondents, interviewers, and environments. They are a problem in all interviewing and Chapter 8 ends with a review of this important problem. In fully structured interviews, like questionnaires, people are asked to respond to as nearly identical a set of stimuli as possible. Chapter 9 covers questionnaire design, improving response rates, asking questions about sensitive topics, using interviewers in team research, and translating questionnaires from one language to another. Chapter 10 is about collecting social network data. Most social science data are about characteristics of individuals: their gender, their age, their income, their answers to questions about their behavior and feelings. … Network data are about relations among people: who they like and who they interact with. We deal with some of the special techniques for analyzing network data in Chapter 16. Chapter 11 is on methods for field research: direct observation and participant observation. Direct observation involves watching people and recording their behavior, using methods like continuous monitoring, spot sampling, and experience sampling. Direct observation is reactive when people know that you are watching them (people can play to the observer), so some researchers use unobtrusive observation. This involves deception, which raises obvious ethical issues. Participant observation turns fieldworkers into instruments of data collection and data analysis. This requires certain skills, which include learning the local language, dialect, or jargon; developing explicit awareness; building memory; maintaining naiveté; learning to hang out and build rapport; maintaining objectivity; and learning to write clearly. Chapters 12 and 13 cover the analysis of texts. Most of the recoverable information about human thought and behavior is in naturally occurring text—diaries, property transactions, recipes, correspondence, song lyrics, billboards, artifacts, still and moving images, advertisements. . . . Chapter 12 is about the inductive method of grounded theory and the deductive methods of content analysis. Both methods involve coding, so this chapter includes a method for testing intercoder reliability. Chapter 13 covers conversation analysis, narrative analysis, phenomenology, language in use, and critical discourse analysis. Chapters 14, 15, and 16 are on quantitative data analysis. Chapter 14 deals with univariate analysis—that is, statistics that describe a single variable, without making any comparisons among variables—and bivariate analysis—statistics that describe relationships in pairs variables. Chapter 15 is an introduction to multivariate analysis—statistics that describe relationships in larger sets of variables and that let you test hypotheses about what causes what. Chapter 16 is about analyzing network data. In addition to the specialized techniques required for analyzing network composition and network structure, this involves multidimensional scaling, cluster analysis, and cultural consensus analysis, so these methods of analysis are explained here, rather than in the chapter on multivariate statistics. If you want to become comfortable with statistical analysis, you need more than a basic course; you need a course in regression and applied multivariate analysis and a course (or a lot of hands-on practice) in the use statistical software like R, SPSS®, SAS®, STATA®, and SYSTAT®. Neither the material in this resource nor a course in the use of statistical software is a replacement for taking statistics from professional instructors of that subject. Nevertheless, after working through the materials in Chapters 14 and 15, students should be able to use basic statistics to describe their data and be able to take their data to a professional statistical consultant—and understand what she or he suggests. Finally, Chapter 17 is about the last big piece of the research puzzle: writing up the results of all your work and getting it published.
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