社會的研究方法雙語教學(xué)課件-專業(yè)-

1、社會研究方法雙語教學(xué)課件Chapter 1 introduction What is social research?Way of knowing social reality by direct experienceDefinitionThe differences between “social research” and “社會調(diào)查研究“ Functions or purposesof social researchDescription(描述)A major purpose of many social scientific studies is to describe situati

2、ons and events. The researcher observes and then describes what was observed. Since scientific observations is careful and deliberate, however, scientific descriptions are typically more accurate and precise than casual descriptions. (P73)Examples 北京汽車市場調(diào)查 農(nóng)村居民收入差距Explanation(解釋)The second general p

3、urposes of social scientific research is to explain things. Reporting the voting intentions of an electorate is a descriptive activity, but reporting why some people plan to vote for candidate A and others for candidate B is an explanatory activity. Reporting why some cities have higher crime rates

4、than others is a case of explanation, but simply reporting the different crime rates is a case of description. Prediction(預(yù)測)For example, the goal of regression analysis is find out the relationship between two or more variables. 2. Types of research methodsObjetctive dimensionA. Census(普查). An enum

5、eration(列舉) of the characteristics of some population(總體). A census is often similar to a survey, with the difference that the census collects data from all members of the population while the survey is limited to a sample. B. B. Sampling survey (抽樣調(diào)查)Careful probability sampling provides a group of

6、 respondents whose characteristics may be taken to reflect those of the larger population, and carefully constructed standardized questionnaires provide data in the same form from all respondents. C. Case study (個案研究)Take only several members from the population and study them in detail. Purposive D

7、imensiondescriptive studies (描述性研究)explanatory studies (解釋性研究) time dimension. Cross-sectional Study(橫剖研究). A study that is based on observations representing a single point in time.Longitudinal Study（縱貫研究）. A study design involving the collection of data at different points in time, as contrasted w

8、ith a cross-sectional study. Longitudinal studies are designed to permit observations over an extended period. Three types of longitudinal studies should be noted here. Trend studies (趨勢研究) are those that study changes within some general population over time. Examples would be a comparison of U. S.

9、 Census over time, showing growth in the national population, or a series of Gallup Polls during the course of an election campaign, showing trends in the relative strengths and standing of different candidates. Cohort Studies (同期群研究) examine more specific subpopulations (cohorts) as they change ove

10、r time. Typically, a cohort is an age group, such as those people born during the 1920s, but it can also be based on some other time grouping, such as people attending college during the Vietnam War, people who got married in 1964, and so forth. An example of cohort study would be a series of nation

11、al surveys, conducted perhaps every 10 years, to study the economic attitudes of the cohort born during the depression of the early 1930s. a sample of persons 20-25 years of age might be surveyed in 1960, and another sample of those 40-45 years of age in 1970. Although the specific set of people stu

12、died in each of those surveys would be different, each sample would represent the survivors of the cohort born between 1930 and 1935. Panel Studies(定組研究,追蹤研究) are similar to trend and cohort studies except that the same set of people is studied each time. One example would be a voting study in which

13、 the same sample of voters was interviewed every month during an election campaign and asked for whom they would intended to vote. Such a study would make it possible to analyze overall trends in voter preferences for different candidates, but it would have the added advantage of showing the precise

14、 patterns of persistence and change in intentions. For example, a trend study that showed that Candidates A and B each had exactly half of the voters on September first and on October first as well could indicate that none of the electorate had changed voting plans, that all of the voters had change

15、d their intentions, or something between. A panel study would eliminate this confusion by showing what kinds of voters switched from A to B and what kinds switched from B to A, as well as other facts.Procedures of social researchPreparatory stage(準(zhǔn)備階段)Data collection stage (收集資料階段)Analysis stage (分析

16、階段)Summary stage (總結(jié)階段)Chapter 2 research design1.Choose a research projecta)How to choose a research project b) Factors relating with research project choicec)Principles regarding research project choice2. Preliminary Exploration a)Literature review b) Filed observation 3.Research Project Design a)

17、Research hypothesisb) Research plan2.1 Literature Review1. Purposes of Literature ReviewTo avoid redundant research and try to make new contributionsTo provide bases for hypothesis To take other researches as references for your research plan2. How to Review Literature Snowball method: according to

18、the references and notes of the existing literature to look for more related literatureElectronic resources2.2 Field ObservationMethods: colloquia(座談會）, interview, refer to literaturePurpose1 : for questionnaire designExample: how to measure peasant family income into three levels: “high”, “medium”

19、and “l(fā)ow” Purpose 2: for hypothesisExample: EconomicdevelopmentImplementation of electoral systemVillagersparticipation3. Research Project Design3.1 Research HypothesisHypothesis: An expectation about the nature of things derived from a theory.Functions of hypothesis:To guild a researchTo relate the

20、oretical concepts with empirical dataTo explore new theoretical knowledgePrinciples for making hypothesisConsistent with existing theoriesConsistent with confirmed factsCan be verified by experience3.2 Research Project DesignPurposesPopulation and objectsSampling methodsMethods of data collection an

21、d data analysisOrganizationBudget and facilitiesWages, travelling expenses, expense for copying and printingFacilities: camera, tape recorder, computerTimetableChapter 3 Sampling3.1 Introduction to Sampling1. The history of sampling2. Sampling concepts and terminology3.2 Probability Sampling (隨機(jī)抽樣）1

22、. Simple random sampling (SRS) 簡單隨機(jī)抽樣2. Systematic sampling 系統(tǒng)抽樣3. Stratified sampling 分層抽樣4. Cluster sampling 整群抽樣5. Multi-stage sampling 多段抽樣3.3 Non-Probability Sampling（非隨機(jī)抽樣）1. Purposive or judgment sampling 立意抽樣2. Quota sampling 配額抽樣3. Snowball sampling 滾雪球抽樣3.1 Introduction to Sampling1. The h

23、istory of samplingPolitical polling by Literacy Digest In 1920, Digest editors mailed postcards to people in six states, asking them who they were planning to vote for in the presidential campaign between Warren Harding and James Cox. Names were selected for the poll from telephone directories and a

24、utomobile registration lists. Based on the postcards sent back, the Digest correctly predicted that Harding would be elected. In elections that followed, the magazine expanded the size of its poll, and made correct predictions in 1924, 1928, and 1932.In 1936, based on two million respondents answers

25、, the Digest predicted that Republican candidate Alf Landon would get 57% ballots and incumbent President Franklin Roosevelt would get only 43%. Two weeks later, voters gave Roosevelt a third term in office by the largest landslide in history, with 61 per cent of the vote. The problem lay in the sam

26、pling frame used: telephone subscribers and automobile owners. Such a sampling design selected a disproportionately wealthy people, especially coming on the tail end of the worst economic depression in the nation history.3.1 Introduction to Sampling(continued)In contrast to the Literacy Digest, Geor

27、ge Gallup correctly predicted that Roosevelt would beat Landon. Gallups success in 1936 hinged on his use of quota sampling.Quota sampling is based on a knowledge of the characteristics of the population being sampled: what proportion are men, what proportion women, what proportions are of various i

28、ncomes, ages, etc. People are selected to match the population characteristics. 3.1 Introduction to Sampling2. Sampling Concepts and Terminology (1) i. 1.Element（研究單位）. An element is that unit about which information is collected and which provides the basis of analysis. Typically, in survey researc

29、h, elements are people or certain types of people. However, other kinds of units can constitute the elements for social research; families, social clubs, or corporations might be the elements of a study. (Note: Elements and units of analysis are often the same in a given study, though the former ref

30、ers to sample selection while the latter refers to data analysis.)2. Sampling Concepts and Terminology (2)1.Population (總體). A population is the theoretically specified aggregation of study elements. For example, specifying the term “college students” would include a consideration of full-time and p

31、art-time students, degree candidates and non-degree candidates, undergraduate and graduate students, and similar issues.2. Study Population（研究總體）. A study population is that aggregation of elements from which the sample is actually selected. As a practical matter, you are seldom in a position to gua

32、rantee that every element meeting the theoretical definitions laid down actually has a chance of being selected in the sample. Even where lists of elements exist for sampling purposes, the lists are usually somewhat incomplete. Some students are always omitted, inadvertently, from student roster. So

33、me telephone subscribers request that their names and numbers be unlisted. The study population, then, is the aggregation of elements from which the sample is selected.2. Sampling Concepts and Terminology (3)3. Sampling Unit（抽樣單位）. A sampling unit is that element or set of elements considered for se

34、lection in some stage of sampling. In a simple, single-stage sample, the sampling units are the same as the elements. In more complex samples, however, different levels of sampling units may be employed. For example, you might select a sample of census blocks in a city, then select a sample of house

35、holds on the selected blocks, and finally select a sample of adults from selected households. 4. Sampling Frame（抽樣框）. A sampling frame is the actual list of sampling units from which the sample, or some stage of the sample, is selected.5. Observation Unit（觀察單位）. An observation unit, or unit of data

36、collection, is an elements from which information is collected. Again, the unit of analysis and unit of observation are often the samethe individual personbut that need not be the case. Thus the researcher may interview heads of households (the observation unit) to collect information about all fami

37、ly members of the households ( the units of analysis).2. Sampling Concepts and Terminology (4)6. Variable（變量）. A variable is a set of mutually exclusive attributes: sex, age, employment status, and so forth.7. Parameter（參數(shù)值）. A parameter is the summary description of a given variable in a population

38、. 8. Statistic（統(tǒng)計值）. A statistic is the summary description of a given variable in a sample. Sample statistics are used to make estimates of population parameters.9.Sampling Error（抽樣誤差）. Probability sampling methods seldom, if ever, provide statistics exactly equal to the parameters that they are us

39、ed to estimate. Probability theory, however, permits us to estimate the degree of error to be expected for a given sample design. 2. Sampling Concepts and Terminology (5)10. Confidence Levels and Confidence Intervals（顯著性水平與置信區(qū)間）. We express the accuracy of our sample statistics in terms of a level o

40、f confidence that the statistics fall within a specified interval from the parameter. For example, we may say we are 95 percent confident that our sample statistics are within plus or minus 5 percentage points of the population parameter.3.2 Probability Sampling (1)Simple Random Sampling (簡單隨機(jī)抽樣). A

41、 type of probability sample in which the units composing a population are assigned numbers, a set of random numbers is then generated, and the units having those numbers are included in the sample. Although probability theory and the calculations it provides assume this basic sampling method, it is

42、seldom used for practical reasons. 3.2 Probability Sampling (2)Systematic Sampling (系統(tǒng)抽樣). A type of probability sample in which every kth unit in a list is selected for inclusion in the sample: e.g., every 25th student in the college directory of students. K is computed by dividing the size of the

43、population by the desired sample size and is called the sampling interval. Within certain constraints, systematic sampling is a functional equivalent of simple random sampling and usually easier to do.Sampling interval = population size / sample size sampling ratio = sample size / population size 3.

44、2 Probability Sampling (3)Stratified sampling (分層抽樣): to organize the population into homogeneous subsets (with heterogeneity between subsets.) and to select the appropriate number of elements from each.3.2 Probability Sampling (4)Cluster Sampling (整群抽樣). A multistage sample in which natural groups

45、(clusters) are sampled initially, with the members of each selected group being subsampled afterward . For example, you might select a sample of U.S. colleges and universities from a directory, get lists of the students at all the selected schools, then draw samples of students from each. 3.3 Non-Pr

46、obability Sampling (1)Purposive or judgmental sampling(立意抽樣). A type of nonprobability sampling in which you select the units to be observed on the basis of your own judgment about which ones will be the most useful or reprsentative. 3.3 Non-Probability Sampling (2)Quota sampling (配額抽樣). A type of n

47、on-probability sampling in which units are selected into the sample on the basis of prespecified characteristics, so that the total sample will have the same distribution of characteristics as are assumed to exist in the population being studied.3.3 Non-Probability Sampling (3)Snowball sampling (滾雪球

48、抽樣). A non-probability sampling method often employed in filed research. Each person interviewed may be asked to suggest additional people for interviewing.3.4 Factors influencing sample sizeA. population size 樣本規(guī)模B. population heterogeneity 樣本異質(zhì)性 variance (方差)C. permited sampling error 允許抽樣誤差Chapte

49、r 4 Social Measurement4.1 Operationalization and Social MeasurementA. Operationalization of Research Project （研究課題的操作化）B. Social Measurement （社會測量）4.2 Levels of Social Measurement A. Nominal Measure （定類測量） B. Ordinal Measure （定序測量） C. Interval Measure （定距測量） D. Ratio measure （定比測量）4.3. Reliability a

50、nd Validity A. Reliability （信度）B. Validity （效度）C. Relations between reliability and validity4.1 Operationalization and Social MeasurementA. Operationalization of Research Projecta. Operational definition of conceptOperational definitiona definition that spells out precisely how the concept will be m

51、easured. Strictly speaking, an operational definition is a description of the “operations” that will be undertaken in measuring a concept.b. Choice of indexesExample: Economic development-annual income per capita; collective incomeIntelligence-Couple relation-c. Operationalization of hypothesisConce

52、pt: Industrialization-Human relationIndex: industrial output-times visiting each other phone subscribers B. Social Measurement Conceptualization Nominal definition Operational definitionmeasurements in the real worldDefinition:in order to understand the nature, characteristics and conditions of the

53、objects, we allocate some numbers or symbols to the objects according to some regulations. This process is called social measurement.Three elements of social measurementObjectsNumber or symbolsregulations4.2 Levels of Social MeasuremntA. Nominal MeasureVariables whose attributes have only the charac

54、teristics of exhaustiveness and mutual exclusiveness are nominal variables. Examples of these would be sex, religious affiliation, political party affiliation, birthplace, college major, and hair color. B. Ordinal MeasureVariables whose attributes may be logically rank-ordered are ordinal measures.

55、The different attributes represent relatively more or less of the variable. Variables of this type are social class, conservatism, alienation, prejudice, and the like.c. Interval MeasureFor the attributes composing some variables, the actual distance separating those attributes does have meaning. Su

56、ch variables are interval measures. For these, the logical distance between attributes can be expressed in meaningful standard intervals. A physical science example would be the Fahrenheit or Celsius temperature scale. The difference, or distance, between 80 degrees and 90 degrees in the same that b

57、etween 40 degrees and 50 degrees. However, 80 degrees Fahrenheit is not twice as hot as 40 degrees, since the zero point in the Fahrenheit and Celsius scales are arbitrary; zero degrees does not really mean lack of heat, nor does 30 degrees represent 30 degrees less than no heat.D. Ratio MeasuresIn

58、ratio measures , the attributes composing a variable, besides having all the structural characteristics mentioned above, are based on a true zero point.Examples from social scientific research would include age, length of residence in a given place, number of organizations belonged to, number of tim

59、es attending church during a particular period of time, number of times married, and number of Arab friends. Most of the social scientific variables meeting the minimum requirements for interval measures also meet the requirements for ratio measurements. 4.3 Reliability and Validity Precision and ac

60、curacy are obviously important qualities in research measurement, and they probably need no further explanation. When social scientists construct and evaluate measurements, however, they pay special attention to two technical considerations: reliability and validity.A.Reliability. That quality of me