SOCY3700 Selected Overheads for the Final Exam Prof. Backman Spring 2008

1. SOCY3700 Selected Overheads for the Final ExamProf. BackmanSpring 2008

2. Stages in Field Research • Choosing the site • Start where you are • Getting in • Being accepted • Anonymity • Getting on • With self, “the folk”, conscience and colleagues • Gathering data • Logging • Interviews • Focusing • Analysis • Write up • (Adapted from Lofland and Lofland)

3. Street Corner Society: The Social Structure of an Italian Slum William Foote Whyte, 1943 (third edition, 1981)

4. Whyte Bio • Educated middle class upbringing • Loved to write • Attended Swarthmore in suburban Philadelphia • Engaged in some reform activities in college, but engaged even more in writing • Wrote a novel, decided it was lousy because he didn’t have enough to say • Got a Junior Fellowship at Harvard – three years just to hang around and do whatever research took his fancy (sort of)

5. The Research Problem • Whyte came to Harvard knowing mainly that he wanted to study slums and somehow improve the world • Social scientific literature was just beginning to appear. He read lots of it • Other folks at Harvard had done similar work and were developing some theoretical ideas about group process • One would not think one would go to a slum to study group process, but in the end that was a big part of what Whyte did • Many of the ideas Whyte when he started his work came to naught • “We set out on the frontiers of our personal knowledge and began exploring beyond those frontiers” (Whyte 1984:63)

6. “Cornerville” • In the usual fashion, Whyte gave his city and neighborhood a psuedonym. Cornerville refers to the slum, now known to be Boston’s North End. He called Boston “Eastern City.” • At the time (around 1937) Cornerville was suffering the effects of The Great Depression • Predominately Italian in a city whose big politicians were mostly Irish • Many residents spoke only Italian

7. Getting In • Wandered around Boston, settled on Cornerville because it “looked like” his vision of a slum • Could observe from the outside, but wanted to observe from the inside • After various failed schemes, introduced to Doc by the social worker in charge of girls’ programs at the local settlement house • Moved into the neighborhood

8. Doc • Doc (a psuedonym for Ernest Pecci) is probably the most famous informant in sociology • A pretty good sociologist himself for someone who never had a sociology course • Late 20s, mostly unemployed guy from the neighborhood • Informal leader of a group of similarly underemployed age mates • Interested in making things better

9. Doc and Bill • Doc’s famous response to Whyte’s first rambling description of what Whyte was trying to do in Cornerville: “Do you want to see the high life or the low life?” • Doc served as Whyte’s sponsor, guide, and “member validator” • Having a sponsor can be a problem in settings with a great deal of conflict, as you may be seen as being on your sponsor’s side • “Member validator”: insider who reviews the sociologist’s analysis from an insider’s point of view

10. Getting On • Whyte moved into Cornerville, taking a room with a family • Whyte tried to learn Italian • Though never got proficient, he felt his efforts gave him a great deal of credibility, especially with the older generation • Joined various clubs, becoming secretary of at least one • Hung out with Doc’s gang • Returned regularly to Harvard for baths and brainstorming with other social scientists

11. Going Native • When you start to act like and especially to think like the people you are studying, you have gone native • Quite common occurrence • It is difficult to completely go native • Whyte’s efforts to swear like the other guys weren’t successful, partly because they wanted him to be himself • Can get you in trouble • Whyte voted illegally • Whyte almost inadvertently got engaged because he didn’t understand as much of native practice as he thought • The natives aren’t always grateful

12. Street Corner Society: Sources Whyte, William F. [1943] 1981. Street Corner Society. 3rd ed. Chicago, IL: University of Chicago Press. Whyte, William F. 1984. Learning From the Field: A Guide from Experience. Newbury Park, CA: Sage. Whyte, William F. nd. Various personal and classroom communications.

13. Bernard on Unstructured Interviews • H. Russell Bernard – cultural anthropologist from U of Florida, author of a research methods text I have used in advanced research methods courses • As surveys are to sociologists, so unstructured (and semi-structured) interviews are to cultural anthropologists • As a researcher, journal editor, and methods text author, Bernard has been given credit for strengthening the rigor of anthropological research Source: Bernard, H. Russell. 1995. Research Methods in Anthropology: Qualitative and Quantitative Approaches. 2nd ed. Walnut Creek, CA: AltaMira. Mostly Chapter 10, pp. 208-36.

14. Bernard on Unstructured Interviews (2):Continuum of Interview Situations Since the researcher is an outsider, the locals will generally be aware that any contact is likely to involve information gathering • Continuum of situations based on how much the interviewer controls the situation • Informal interview – more or less normal conversation • Typical early in research • Useful for rapport • Useful later for finding topics that might have been overlooked

15. Bernard on Unstructured Interviews (3):Continuum of Interview Situations (2) • Unstructured interview – not just normal conversation, but with minimal control over the responses of the interviewee • Semi-structured – like unstructured but with an interview guide • Interview guide: written list of topics, probes, etc. intended to be covered in the interview • More formal than unstructured • Structured – questions (and often answer choices) established ahead of time by the interviewer - For example, standard survey interviews, self-administered questionnaires

16. Bernard on Unstructured Interviews (4):Starting the Interview • Assure anonymity • Explain their importance to your understanding • Ask for permission to record the interview and to take notes • The value of the interview much lower if you can’t record or take notes • Even with recorder it helps to take occasional notes

17. Bernard on Unstructured Interviews (5):Let the Informant Lead Rule # 1: get an informant on the topic and get out of the way • You pick the topic, interviewee provides the content • In general, it is the interviewee’s ideas you are interested in, not yours • This rule is not always slavishly followed • Interviewee may stray off topic • You may have ideas you want responded to

18. Bernard on Unstructured Interviews (6):Probes • Use probes to guide interview • Probe (Bernard definition): stimulating an informant to give more information without injecting yourself so much into the interaction that you get only a reflection of yourself in the data • There are many types of probes • Our textbook definition: a neutral request to clarify an ambiguous answer, to complete an incomplete answer, or to obtain a relevant response (p. 192 in Neuman 2007)

19. Bernard on Unstructured Interviews (6):Types of Probes 1 • Silent probe – don’t say anything when the interviewee stops • Difficult to do appropriately • Culturally sensitive since different cultures have different rules about silence • Echo probe – repeat the last thing the interviewee said • Signals that you are interested in what was said without saying why or suggesting what to say

20. Bernard on Unstructured Interviews (7):Types of Probes 2 • Uh-huh (neutral) probe – make regular affirmative noises, as one often does in normal conversation to indicate you are still listening and are interested • Keeps the interviewee talking Rule #2: In general, more talking by the respondent is better • Hence, longer responses are better

21. Bernard on Unstructured Interviews (8):Types of Probes 3 • The long question probe – instead of keeping a question short and to the point, asking a long roundabout question • You’re modeling the kind of long answer you want to get back • The trick is not to guide the answer as you ask the question

22. Bernard on Unstructured Interviews (9):Types of Probes 4 • Probe by leading – ask a leading question as a way of focusing provoking the interviewee • Usually we try not to lead, but sometimes respondents seem to be avoiding a topic or conclusion • Can be used to ask about more specific incidents or about what happens when things don’t work out as expected • Often based on earlier interviews

23. Bernard on Unstructured Interviews (10):Types of Probes 5 • Phased assertion (baiting) probe – you take some information that may or may not be true and ask questions as if it were true • For example, “I guess Hilary and Barak are friends again. I wonder why.” • This is a favorite ploy of gossip-mongers

24. Bernard on Unstructured Interviews (11):Verbal Respondents; Equipment • Verbal respondents – don’t be afraid to interrupt a long winded respondent who is wandering away from your topic. Try to be graceful about it • Equipment – always make sure that your tape recorder is ready before the interview (fresh tapes and batteries)

25. Bernard on Unstructured Interviews (12):Uses of Unstructured Interviews • A primary source of raw data • Preparation for semi-structured interviews • To get info from people unlikely to give more formal interviews • Developing rapport • Studying sensitive topics • E.g., hot political topics, sexuality, racial prejudice • Conflict: you can get wide range of information from multiple interviewees

26. Bivariate Relationships with Integer-level Variables Preliminaries to multiple regression

27. Steps in Analysis of Bivariate Relationships Between Integer-level Variables • Look at scatterplot • Dependent variable as the Y (vertical) axis • Independent variable as the X (horizontal) axis • Make best-fit line • Since it is a line, we call it linear regression • Since we have only one independent variable, we call it simple linear regression • Calculate slope (b) • Calculate goodness of fit (r)

28. Interpretation of Simple Regression Results Equation: Dependent = intercept + coefficient * independent + error • Coefficient (aka b, beta, or regressioncoefficient) tells how many units of the dependent variable go with the increase of one unit on the independent variable • Mathematically, the slope

29. Interpretation of Simple Regression Results (2) • Correlation coefficient (aka r, Pearson’sr) – a measure of how well the line fits the data, usually interpreted as how strong the relationship is • Measures the “goodness of fit” • The higher the absolute value of r, the better the fit • Ranges between -1 and 1 • Positive coefficient means there is a positive relationship between the two variables (high on the independent goes with high on the dependent) • Negative coefficient means there is a negative relationship between the two variables (high on the independent goes with low on the dependent)

30. Interpretation of Simple Regression Results (3) • Intercept – how many units of the dependent variable you would be expected to have with 0 units of the independent • Mathematically, it is where the line crosses the vertical axis • Error – the difference between what was actually measured for the dependent variable for a particular case and the measurement predicted by the equation for the line

31. Interpretation of Simple Regression Results (4) • Statisticalsignificance – tests how sure we are that the regression coefficient is not zero OR that the correlation coefficient is not zero • Conventionally we use the 95 percent confidence level • At the 95 percent confidence level, the probability of a false positive is less than 5 percent, usually written as p<.05

32. Interpretation of Simple Regression Results (5) Example Dependent variable: violent crimes per 100,000 population Independent variable: percent of population 15 and up who are currently divorced Correlation coefficient = 0.24 There is a positive relationship Regression coefficient = 38.6 For every additional 1 percent to the percent divorced of the population 15+ there is an increase in the violent crime rate of 39 Intercept = 160 If no one in the population were divorced, there would be 160 violent crimes per 100,000 The relationship is significant at the p<.048 level

33. Multiple Regression • Multiple regression is multiple because it allows the use of more than one independent variable • This is nice since so much of social life has multiple causes • Multiple regression is probably the most important statistical tool in use in sociology today • There are many similarities between simple regression and multiple regression

34. Multiple Regression (2):Similarities with Simple Regression • The key mathematical operation is fitting a line to the data points • The method is the same: choose the line that minimizes the squared distances between the points and the line • Called the method of least squares; the line is sometimes called the least squares line. Sometimes it is called the ordinary least squares (OLS) line • There is a statistic for the overall fit of the line to the data points • Each independent variable gets its own regression coefficient

35. Multiple Regression (3):Differences from Simple Regression • Scatterplots are in hyperspace • That is, for each variable, including the dependent, there is another dimension in the graph • They’re really hard to draw! • The goodness of fit statistic doesn’t tell you the direction of the relationships • We use R (not r) as its symbol • Actually, we usually use R2 • R2 tells us the proportion of variation in the dependent variable that is accounted for by the independent variables

36. Multiple Regression (4):Interpretation of Regression Coefficients • New term: ceteris paribus – all other things being equal • A regression coefficient tells us how much change in the dependent variable is associated with a change of one unit in the coefficient’s independent variable, ceteris paribus

37. Multiple Regression (5):The Regression Equation • Multiple regression is based on the matrix equation Y = XB + e where Y is the dependent variable, X is the matrix of dependent variables, B is a vector of regression coefficients (and the intercept), and e is the error

38. Multiple Regression (6):Varieties of Multiple Regression • Ordinary regression makes certain assumptions about the relations between the independent variables and about the errors • These assumptions are not always met • Ordinary regression is limited to only one dependent variable • There are a large number of modifications to ordinary regression that overcome some of its limitations and to loosen the assumptions

39. Multiple Regression (7):The General Linear Model • The collection of modifications and extensions to ordinary regression is called the general linear model • The GLM is based on the equation given earlier • It brings together a wide range of statistical methods, some of which had been invented independently • The GLM is a conceptual and methodological breakthrough paralleled in its importance for quantitative social science only by the discovery of sampling theory