Lecture 3 core concepts
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


Lecture 3 core concepts

Uploaded on


  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 82

http://mj89sp3sau2k7lj1eg3k40hkeppguj6j-a-sites-opensocial.googleusercontent.com 82

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide
  • This list is not exhaustive. I excluded, for example, the obvious combination of #2 and #3, which in statistics is sometimes called “panel” data analysis. There is also comparative statics, which is like taking cross-sectional studies taken at two different times (like snapshots) and comparing them. The object of investigation is called the explanandum, more commonly known as the dependent variable (Y).
  • Comparisons are made with the assumption that events in the test condition have not affected events in settings where the test condition is absent.
  • Comparisons are made with the assumption that events in the test condition have not affected events in settings where the test condition is absent.
  • A “fact” is something that does exist or did happen. Therefore a counter-fact is something that does not exist or did not actually happen.
  • Here are some definitions found in textbooks on sociology [You do not need to remember these!]:“Scientific study of ‘Society’” [But what is ‘society’?]“systematic study of human groups.”“scientific study of human groups”“scientific study of human behavior, social groups, and society”“systematic study of society and human behavior.”
  • There are two levels here to evaluate: what is going on, and what people think is going on; the facts, and perceived facts; the world of physical, material objects and the world of meanings ascribed to these objects. The relation between these two levels is often complicated. For example, a sufficient sociological explanation would not only explain to people that what they believe to be true is in fact only partially true or false, but also, to explain what about the real world leads to their being deluded about it in the first place!
  • See pages 227-8 in your book!
  • See pages 227-8 in your book!
  • Methodological Individualism: the idea that society can be explained entirely by the individuals that make up society.
  • Adam Smith published his famous Wealth of Nations in 1776.
  • If we assume homogeneity of preferences (i.e. each individual has the same threshold dissatisfaction, say 30%), then about as many new moves are caused as the number of initial moves, displacements. We get significantly more sorting or segregation than any particular individual wanted! The amount of segregation goes up even more, however, if we assume heterogeneity, i.e. each person has a different movement rule.
  • When people are connected and interdependent, critical states can emerge. In these critical states, small changes can generate disproportionate (nonlinear) ‘domino effects’, ‘chain reactions’, social cascades, snowballing, etc.  
  • Granovetter is perhaps most famous for his concept of the ‘small worlds’ such as in the popular game, 6 degrees of separation from Kevin Bacon.
  • Granovetter is perhaps most famous for his concept of the ‘small worlds’ such as in the popular game, 6 degrees of separation from Kevin Bacon. We will cover this later in the semester!
  • Granovetter is perhaps most famous for his concept of the ‘small worlds’ such as in the popular game, 6 degrees of separation from Kevin Bacon. We will cover this later in the semester!


  • 1. CORE CONCEPTS in Sociology John Bradford, Ph.D.
  • 3. Three types of Studies • There are 3 different types of studies that correspond to 3 different sorts of dependent variables (Y), or objects of investigation… 1. Case study (what causes an event or condition) – Often we aren’t interested in Y itself as a fact or event, but changes in Y across time (longitudinal study) or differences in Y across space (cross-sectional study). 2. Cross-sectional study (comparison across space) 3. Longitudinal study (comparison across time)
  • 4. Feedback Two types of Feedback: 1. Positive (reinforcing, amplifying): Initial changes become amplified or magnified over time; patterns are reinforced. – Examples: exponential population growth; nuclear explosion; ‘rich getting richer’, etc. 2. Negative (counteracting, balancing): Initial changes are counteracted or balanced out, so that conditions remain relatively stable. – Examples: homeostasis; a thermostat; “what goes up, must come down”, etc. Births + Population + Force of Gravity - Jump up + Positive Feedback Negative Feedback
  • 5. Positive vs. Negative Association of Variables • Positive association i. as values of X go up, values of Y go up. ↑↑ ii. as X goes down, Y goes down. ↓↓ • Negative association – as X goes up (down), Y goes down (up) ↓↑ or ↑↓.
  • 6. Positive vs. Negative Association of Variables • Notes: i. “Positive” and “Negative” associations are averages! Examples that don’t fit the general pattern will always exist. ii. ‘Associations’ refer to relationships between 2 or more variables, not a single variable in itself. iii. Example: height and weight are positively associated (on average)
  • 7. Independent (X) vs. Dependent (Y) Variables • Independent variable (X) = the ‘cause.’ Variable that influences. • Dependent variable (Y) = the ‘effect.’ Variable that is influenced by the cause; it is dependent on the cause. • INCA: the INdependent variable is the CAuse.
  • 8. Independent (X) vs. Dependent (Y) Variables • Examples: – Gender (X) is thought to influence occupation (Y) – Religious affiliation (Y) is thought to be influenced by income. – Educational attainment (X) is thought to influence income (Y). – Age (X) is thought to influence attitudes towards using computers (Y) – Income (Y) is thought to be influenced by race (X)
  • 9. Sampling 1. A Sample is a portion of the larger population that you will study to make inferences about the larger population. 2. General rule: the more diverse a population is, the larger the sample needs to be! 3. Samples should be random (equally probable). Randomness means that every element in the population has the same probability of being in the sample.
  • 10. Experiments • An experiment involves manipulating the independent variable (X) and observing the effect on the dependent variable (Y) • Experiments are the only means by which we can explore causal relationships; only way we can know for sure if changes to X cause changes in Y. • Experimenter needs two dependent variable (Y) groups of Y: 1. Experimental group- receives ‘treatment’ of independent variable (X) 2. Control group- does not receive treatment; is left alone.
  • 11. Experiments • Imagine a scientist testing the effect that some drug, X, has on growth of rats, Y. • To see how the drug effects rat growth, the experimenter will compare growth in two groups of rats: Y₁ , the group of rats that gets the drug (X) and a group of rates Y₂ that will not. • Y₁ is the experimental group, and Y₂ is the control group.
  • 12. Experiments • One assumes separation or isolation between the setting where X is applied and the control, where X isn’t applied. • It is important that rats which receive the drug and rats which do not be alike in all relevant characteristics and conditions, so that any observed differences between rats which receive the drug (the experimental group) and those that do not (the control group) can be attributed only to the drug (X), and not to something else.
  • 13. Experiments • Random Assignment to condition- is the process whereby all participants have an equal chance of taking part in any condition of the experiment. • The purpose is to ensure that any potentially relevant differences between the experimental and control groups are distributed evenly and therefore won’t affect the outcome (i.e. will cancel each other out)
  • 14. Experiments • A counter-factual refers to something that did not happen, but could have or would have occurred. • We use the ‘control group’ to make a counterfactual argument, which says that: “in the absence of X, this is how Y₁ would have behaved.” We assume that Y₁ would have behaved like Y₂, the control. • Why? Because they are alike in all relevant characteristics so any difference we observe must be a result of the independent variable, X.
  • 15. Experiments 5 Rules for Doing True Experiments 1. Have at least two groups (control and experiment) 2. Randomly assign people to groups 3. Treat the experimental group by manipulating the independent variable 4. Observe the effect of the treatment on the dependent variable in the experimental group 5. Compare the dependent variable differences (the outcome of treatment) in the experimental and control groups
  • 17. Stanley Milgram and Obedience • One of the most famous experiments of the 20th century. • What explains the Holocaust? Are Germans just inherently more obedient than other people? • The Milgram experiment measured the willingness to obey an authority figure who instructed them to perform acts that conflicted with their personal conscience.
  • 18. Stanley Milgram and Obedience Experiment: • Three roles: – an experimenter (man in white lab coat); – a volunteer (the ‘teacher’); – and the shockee (the ‘learner’). All are actors except the volunteer. • Responding to a newspaper ad, a volunteer was told he would be participating in an experiment testing the effects of negative reinforcement (punishment) on learning. The volunteer was told that a ‘teacher’ (giving electric shocks) and ‘learner’ (receiving electric shocks) were to be picked at random.
  • 19. Stanley Milgram and Obedience Experiment: • In reality, the experiment was to see how much electroshock the teacher would give as punishment, when told it was part of an experiment. Everyone but the ‘teacher’ was acting and knew the true purpose of the experiment. No electric shocks were actually administered, but the volunteer believed he was administering them. • The ‘learner’ would go into another room and a tape recording was played of scripted answers. For each wrong answer, the teacher was supposed to give a shock to the learner, with the voltage increasing in 15-volt increments for each wrong answer.
  • 20. Stanley Milgram and Obedience Findings: • BASELINE STUDY (most famous): 65% of volunteers ‘go all the way’ and are willing to shock the subject to death! • Milgram also studied 20-40 variants of this experiment with different results:
  • 21. Stanley Milgram and Obedience Findings: • Experiment #3: The Shockee is placed in the same room so that the volunteer can see him; obedience drops to 40%. • Experiment #4: The volunteer must physically restrain the shockee; obedience drops to 30%. • Experiment #14 : If experimenter is not a scientist in a white lab coat, then obedience drops to 20%. • Experiment #17: Volunteer and two other participants (both actors); if other actors refuse to continue the experiment, obedience drops to 10%
  • 22. Stanley Milgram and Obedience Findings: • Experiment #15: *If there are two other experimenters in white lab coats (both actors) who disagree about what to do, then obedience drops to ZERO! • As soon as participants are told that they “have no choice”, obedience drops to ZERO! • These results were confirmed in 2006.
  • 23. Stanley Milgram and Obedience QUESTION: What does all this mean? Why did so many people go along with the experiment, if they only did so long as they were NOT ordered to do so?
  • 24. Stanley Milgram and Obedience • This study does NOT show that people ‘obey orders’! • They are participating because they believe they are promoting the ‘greater good’, a noble cause: science. • They are shocking innocent strangers not because they believe they have to, but because they believe they ought to.
  • 25. Zimbardo’s Stanford Prison Experiments Experiment: • 70 volunteers selected; • by flip of coin, half are chosen as guards, other half as prisoners • Participants make up their own rules; not pre-determined • Each participant was paid $15 a day
  • 26. Zimbardo’s Stanford Prison Experiments • Findings: • Experiment ended after 6 days! • Could no longer distinguish reality (the experiment) from the roles they adopted as prisoners and guards • “There were dramatic changes in virtually every aspect of their behavior, thinking and feeling…. We were horrified because we saw some boys (guards) treat others as if they were despicable animals, taking pleasure in cruelty, while other boys (prisoners) became servile, dehumanized robots….” (141)
  • 27. Zimbardo’s Stanford Prison Experiments • Findings: • About 1/3 of guards became ‘corrupted by the power of their roles’ (142) • “*T+he mere act of assigning labels to people and putting them into a situation where those labels acquire validity and meaning is sufficient to elicit pathological behavior” (Zimbardo, pg. 143)
  • 28. ‘On Being Sane in Insane Places’ • Can we always distinguish ‘normal’ from ‘abnormal’ people? The ‘sane’ from the ‘insane’? • How objective are these labels? 1. Are ‘insane’ behaviors caused by innate characteristics of these individuals or are they elicited from external environments? 2. Do observers see the ‘same’ behavior differently in different circumstances? Scene from One Flew Over the Cuckoo’s Nest (1975)
  • 29. ‘On Being Sane in Insane Places’ • Rosenhan undertakes groundbreaking study: will sane people (‘pseudo-patients’) be recognized as sane by hospital staff in a psychiatric ward? • Experiment – 8 sane people admitted into 12 hospitals; 3 women, 5 men – Initially complained of ‘hearing voices’ of an ‘existential nature’: – Symptoms chosen because there were zero reports of ‘existential psychoses in the literature’ – After being admitted, pseudo-patients behaved normally – Length of stay ranges from 7 to 52 days, average of 19 days D. L. Rosenhan
  • 30. ‘On Being Sane in Insane Places’ • Findings: The normal are not detectably sane! – Pseudo-patients were never detected • Other patients (but not doctors and staff) sometimes detected that they were not insane. – Each was discharged with a diagnosis of schizophrenia “in remission” – Normal behaviors were often interpreted as abnormal because of the diagnosis! D. L. Rosenhan
  • 31. Labels and Perception Label (diagnosis) Perception of behavior • “Once a person is designated abnormal, all of his other behaviors and characteristics are colored by that label” (280). 1. Observers perceive normal behavior as crazy; our expectations thus reinforce our initial impressions 2. Patients can even begin to see themselves as ‘crazy’, and thus act crazy (self-fulfilling prophecy)
  • 32. Asch’s Conformity Experiments • Question: Which of the lines on the second card (A, B, or C) is the same length as the line on the first card? • “That we have found the tendency to conformity in our society so strong that reasonably intelligent and well-meaning young people are willing to call White Black is a matter of concern. It raises questions about out ways of education and about the values that guide out conduct” (95) Solomon Asch (1907 – 1996)
  • 34. What is Sociology? • Definition #1: Sociology is the scientific study of interactions and relations among human beings (p. 3). – Socius (Latin) = ‘associate’; logy (Greek) = ‘study’ • Definition #2: Sociology explains the intended and unintended consequences of human influence.
  • 35. What is Sociology? • Sociology studies the PATTERNS that people generate as they interact, influence, and relate to one another. • In short: THINK PATTERNS, NOT PEOPLE! (at least not individual people)
  • 36. What is an explanation? • An Explanation of anything is always: 1. An answer some Why-question, and 2. A comparison (or contrast) – “Why is the sky blue and not orange?” – “Why does social inequality exist, instead of not existing?” • Often this comparison is not stated explicitly – {NOTE: In English we can express this contrast in a variety of ways. For example: Why A rather than B? Why A, as opposed to B? Why A instead of, or in contrast to B? }
  • 37. What is an explanation? Additional Vocabulary: • Explanandum (Latin) = the object of explanation; whatever it is you are trying to explain • Explanans (Latin) = the explanation; the thing that explains the explanandum.
  • 38. What is an explanation? • Example: “Why is it 85 degrees?” • Explanandum = 85 degrees. • Possible Explanations: a) “Because we use the Fahrenheit scale instead of Celsius.” b) “Because of our approximate distance from the sun.” c) “Because it is summer time.” d) “Because the air conditioner is not working.”
  • 39. What is an explanation? • The explanandum is really not an object at all, but a comparison! • Example: “Why is it 85 degrees?” • Each explanation (explanans) of ‘85 degrees’ addresses a different explanandum: a) 85 degrees (Fahrenheit, rather than Celsius) b) 85 degrees (on earth, as opposed to another planet or without the sun) c) 85 degrees (in summer, in comparison to temperatures in other seasons) d) 85 degrees (inside, instead of 72 in most buildings)
  • 40. What is an explanation? • Why-Question: “Why do you rob banks?” • Willie Sutton: “Because that’s where the money is!”
  • 41. What is an explanation? • Intended Explanandum: The priest meant by his question: ‘Why do you rob banks {vs not rob banks}?’ • Reinterpreted Explanandum: ‘Why do you rob banks {vs. rob some other place}?
  • 42. What is an explanation? • Question: “Why do ducks fly south for the winter? “ • Answer: “Because its too far to walk.” – Intended explanandum: Why do ducks fly south for the winter {vs not migrate south for the winter}? – Reinterpreted explanandum: Why do ducks fly {vs walk} south for the winter?
  • 43. What is an explanation? • Detective asks the suspect: “Why did the man die?” • Suspect answers: “Well, he had to go sometime!” – Intended explanandum: Why did the victim die now {vs. die at some other time}? – Reinterpreted explanandum: Why did the victim die at all {vs. live forever}?
  • 44. What is an explanation? • Making different comparisons has led to scientific revolutions... • Physics: – pre-Newtonian: Why does an object {move/not move}? – Newton: Why does an object have a {given acceleration/ some other acceleration}? • Biology: – Aristotle: Why does {this species/ some other species} exist? – Darwin: Why did this species {survive/become extinct}?
  • 45. What is an explanation? • In a nutshell, “Thinking without comparison is unthinkable.” (Swanson 1971: 145).
  • 46. The Sociological Imagination • Sociology attempts to explain facts about groups of people, and then to relate these social facts to our individual lives. • The study of how our lives are influenced by our larger historical and social circumstances is called the sociological imagination.
  • 47. The Sociological Imagination “Neither the life of an individual nor the history of a society can be understood without understanding both.” C. Wright Mills (1916-1962)
  • 48. The Sociological Imagination • To understand one side, you have to understand the other. • The ability to understand history and its relation to biography is called the sociological imagination by C. Wright Mills. Man/Woman Society Biography History Self World Personal “Troubles of milieu” Public “Issues of social structure”
  • 49. “Men make their own history, but they do not make it as they please; they do not make it under self-selected circumstances, but under circumstances existing already, given and transmitted from the past. The tradition of all dead generations weighs like a nightmare on the brains of the living.” Karl Marx (1818-1883)
  • 50. What is Social REALITY? • Thomas theorem: "If people define situations as real, they are real in their consequences“ • To understand human inter-actions and relations, sociologists have to understand both reality, and perceived reality. W. I. Thomas 1863 - 1947
  • 51. • Social relations are often real because we act AS IF they are real. The social world concerns not only the material world, but the meanings we ascribe to the material objects, meanings which are themselves non-physical and non-material. Examples: 1. Nations 2. Money
  • 52. Self-fulfilling and Self-negating prophecies • Robert K. Merton also coined the terms – ‘self-fulfilling prophecy’ and – ‘role model’ • A self-fulfilling prophecy is something that becomes true because it is believed to be true. – Example: bank run, placebos, psychic predictions, etc… • A self-negating prophecy is a belief that causes its own falsehood. Explanation: it is something that, once believed to be true or expected to happen, cannot happen (or becomes less likely to happen). Robert K. Merton (1910 – 2003)
  • 53. The Power of Expectations • Pygmalion Effect (aka Rosenthal effect): the greater the expectation placed upon people, the better they perform. – According to legend, Pygmalion was the king of Cyprus who fell in love with a beautiful woman (Galatea) he sculpted out of ivory.
  • 54. The Power of Expectations • In the 1960s Robert Rosenthal and Lenore Jacobson hypothesized that teacher expectations influenced children’s performance. • Study: they randomly assigned 1 out of 5 children to the ‘spurter/bloomer’ group, but told teachers these students were selected to the group based on test performances that indicated future success. • Findings: the kids who were expected to ‘spurt’ made larger improvements than nonspurters.
  • 55. Cascades and ‘Tipping’ points • Social Cascades = TIPPING = a domino effect or chain reaction. – Occurs when a small event triggers a large event or when the actions of a few trigger the actions of many. – Basic idea: small or few  large and many • What explains this? We are always paying attention to and being influenced by the behavior of other people.
  • 56. Cascades and ‘Tipping’ points • Diversity + Connectedness = ‘Tipping’ – Example: There are 100 people in the mall and you see a few of them running! How many of them have to be running out of the mall before you run out of the mall also? • Assume you have no understanding of why they are running! Crowded mall
  • 57. Cascades and ‘Tipping’ points • Diversity and Connectedness lead to ‘Tipping’ • Consider two scenarios: – Scenario 1: Homogeneity. Everyone has the same threshold, or tipping point. Everyone will run out of the mall if they see 20 other people run out of the mall. What happens? NOTHING! No one will leave unless 20 other people leave! – Scenario 2: Heterogeneity (Diversity). Everyone is numbered from 1 to 100; their number is also the number of people they need to see running before they also run: their threshold. What happens? First person leaves, then the second, then the third, etc. This generates a chain reaction, aka a CASCADE! Person 0 Begins to run Person 1 runs only if 1 other person runs Person 2 runs only if 2 other people run 3 4 5 6
  • 58. Cascades and ‘Tipping’ points • Mark Granovetter devised this threshold model initially to describe RIOTS: – one person will definitely riot; another will riot only if one other person riots; a third will riot only if two others riot; etc…. – We are much more likely to riot ourselves if we see others rioting.
  • 59. Cascades and ‘Tipping’ points • The threshold model explains: 1. Why social changes can be abrupt, discontinuous, and sudden. 2. Why they are so unpredictable. – One person in a chain can either cause or prevent a collective chain reaction, or social cascade. • Other examples: clapping, birth rates, dancing at parties…