社会研究方法双语教学课件.ppt

社会研究方法双语教学课件,Chapter 1 introduction,What is social research? Way of knowing social reality by direct experience Definition The differences between “social research” and “社会调查研究“,Functions or purposesof social research,Description(描述) A major purpose of many social scientific studies is to describe situations 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 北京汽车市场调查 农村居民收入差距,Explanation(解释),The second general purposes 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 than others is a case of explanation, but simply reporting the different crime rates is a case of description.,Prediction(预测),For example, the goal of regression analysis is find out the relationship between two or more variables.,2. Types of research methods,Objetctive dimension A. Census(普查). An enumeration(列举) of the characteristics of some population(总体). 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 (抽样调查),Careful probability sampling provides a group of 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 (个案研究),Take only several members from the population and study them in detail.,Purposive Dimension,descriptive studies (描述性研究) explanatory studies (解释性研究),time dimension.,Cross-sectional Study(横剖研究). A study that is based on observations representing a single point in time. Longitudinal Study（纵贯研究）. A study design involving the collection of data at different points in time, as contrasted with 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 (趋势研究) are those that study changes within some general population over time. Examples would be a comparison of U. S. 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 over 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 national 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 studied in each of those surveys would be different, each sample would represent the survivors of the cohort born between 1930 and 1935.,Panel Studies(定组研究,追踪研究) 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 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 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 changed 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 research,Preparatory stage(准备阶段) Data collection stage (收集资料阶段) Analysis stage (分析阶段) Summary stage (总结阶段),Chapter 2 research design,1.Choose a research project a)How to choose a research project b) Factors relating with research project choice c)Principles regarding research project choice 2. Preliminary Exploration a)Literature review b) Filed observation 3.Research Project Design a)Research hypothesis b) Research plan,2.1 Literature Review,1. Purposes of Literature Review To avoid redundant research and try to make new contributions To provide bases for hypothesis To take other researches as references for your research plan 2. How to Review Literature Snowball method: according to the references and notes of the existing literature to look for more related literature Electronic resources,2.2 Field Observation,Methods: colloquia(座谈会）, interview, refer to literature Purpose1 : for questionnaire design Example: how to measure peasant family income into three levels: “high”, “medium” and “low” Purpose 2: for hypothesis Example:,Economic development,Implementation of electoral system,Villagers participation,3. Research Project Design,3.1 Research Hypothesis Hypothesis: An expectation about the nature of things derived from a theory. Functions of hypothesis: To guild a research To relate theoretical concepts with empirical data To explore new theoretical knowledge Principles for making hypothesis Consistent with existing theories Consistent with confirmed facts Can be verified by experience,3.2 Research Project Design,Purposes Population and objects Sampling methods Methods of data collection and data analysis Organization Budget and facilities Wages, travelling expenses, expense for copying and printing Facilities: camera, tape recorder, computer Timetable,Chapter 3 Sampling,3.1 Introduction to Sampling 1. The history of sampling 2. Sampling concepts and terminology 3.2 Probability Sampling (随机抽样） 1. Simple random sampling (SRS) 简单随机抽样 2. Systematic sampling 系统抽样 3. Stratified sampling 分层抽样 4. Cluster sampling 整群抽样 5. Multi-stage sampling 多段抽样 3.3 Non-Probability Sampling（非随机抽样） 1. Purposive or judgment sampling 立意抽样 2. Quota sampling 配额抽样 3. Snowball sampling 滚雪球抽样,3.1 Introduction to Sampling,1. The history of sampling Political 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 automobile 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, 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 sampling 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, George 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 incomes, ages, etc. People are selected to match the population characteristics.,3.1 Introduction to Sampling2. Sampling Concepts and Terminology (1),i. 1.Element（研究单位）. An element is that unit about which information is collected and which provides the basis of analysis. Typically, in survey research, 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 refers to sample selection while the latter refers to data analysis.),2. Sampling Concepts and Terminology (2),1.Population (总体). 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 part-time students, degree candidates and non-degree candidates, undergraduate and graduate students, and similar issues. 2. Study Population（研究总体）. 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 guarantee 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. Some 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（抽样单位）. A sampling unit is that element or set of elements considered for selection 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 households on the selected blocks, and finally select a sample of adults from selected households. 4. Sampling Frame（抽样框）. 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（观察单位）. An observation unit, or unit of data 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 family members of the households ( the units of analysis).,2. Sampling Concepts and Terminology (4),6. Variable（变量）. A variable is a set of mutually exclusive attributes: sex, age, employment status, and so forth. 7. Parameter（参数值）. A parameter is the summary description of a given variable in a population. 8. Statistic（统计值）. 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（抽样误差）. Probability sampling methods seldom, if ever, provide statistics exactly equal to the parameters that they are used 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（显著性水平与置信区间）. We express the accuracy of our sample statistics in terms of a level of 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 (简单随机抽样). A 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 seldom used for practical reasons.,3.2 Probability Sampling (2),Systematic Sampling (系统抽样). 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 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,3.2 Probability Sampling (3),Stratified sampling (分层抽样): 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 (整群抽样). A multistage sample in which natural groups (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-Probability Sampling (1),Purposive or judgmental sampling(立意抽样). 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 (配额抽样). A type of non-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 (滚雪球抽样). 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 size,A. population size 样本规模 B. population heterogeneity 样本异质性 variance (方差) C. permited sampling error 允许抽样误差,Chapter 4 Social Measurement,4.1 Operationalization and Social Measurement A. Operationalization of Research Project （研究课题的操作化） B. Social Measurement （社会测量） 4.2 Levels of Social Measurement A. Nominal Measure （定类测量） B. Ordinal Measure （定序测量） C. Interval Measure （定距测量） D. Ratio measure （定比测量） 4.3. Reliability and Validity A. Reliability （信度） B. Validity （效度） C. Relations between reliability and validity,4.1 Operationalization and Social Measurement,A. Operationalization of Research Project a. Operational definition of concept Operational definitiona definition that spells out precisely how the concept will be measured. Strictly speaking, an operational definition is a description of the “operations” that will be undertaken in measuring a concept. b. Choice of indexes Example: Economic development-annual income per capita; collective income Intelligence- Couple relation- c. Operationalization of hypothesis Concept: Industrialization-Human relation Index: industrial output-times visiting each other phone subscribers,B. Social Measurement,Conceptualization Nominal definition Operational definition measurements in the real world Definition:in order to understand the nature, characteristics and conditions of the objects, we allocate some numbers or symbols to the objects according to some regulations. This process is called social measurement. Three elements of social measurement Objects Number or symbols regulations,4.2 Levels of Social Measuremnt,A. Nominal Measure Variables whose attributes have only the characteristics 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 Measure Variables whose attributes may be logically rank-ordered are ordinal measures. 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 Measure,For the attributes composing some variables, the actual distance separating those attributes does have meaning. Such 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 between 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 Measures,In 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 times 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 accuracy 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 measurement method that suggests that the same data would have been collected each time in repeated observations of the same phenomenon. Re-measurement reliability （再测信度） Duplicate reliability （复本信度） Folded reliability (折半信度）,B. Validity,Validity refers to the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration. Criterion-related validity（准则效度） is based on some external criterion. Content validity（内容效度） refers to the degree to which a measure covers the range of meanings included within the concept. For example, a text of mathematical ability cannot be limited to addition alone but would also need to cover subtraction, multiplication, division, and so forth. Construct validity （构造效度）is based on the way a measure relate to other variables within a system of theoretical relationships.,C. Relations between Reliability and Validity,a. reliable but not valid b. valid but not reliable C. valid and reliable,Chapter 5 Questionnaire,5.1 Types and Structure of Questionnaire 5.2 Questionnaire Construction 5.3 Attitudinal Scales,5.1 Types and Formats of Questionnaire (1),Questionnaire (问卷): a document containing questions and other types of items designed to solicit information appropriate to analysis. 1. Types of Questionnaire Self-administered questionnaire and interviewer-administered questionnaire(自填问卷与访问问卷) Questionnaires may be completed by the respondents themselves or by interviewers who read the items to respondents and record the answers.,5.1 Types and Formats of Questionnaire (2),Self-administered questionnaire Mailed questionnaire Distributed questionnaire,5.1 Types and Formats of Questionnaire (3),2. Formats of questionnaire items in a questionnaire Instruction Questions and an