SOC101Y
Introduction to Sociology
Professor Robert Brym
Lecture #5
Networks, Groups & Bureaucracies
10 Oct 12
Test #1 – 17 Oct 12
 Check “Class Announcements” on course website for
test format (updated) and locations.
 Bring non-programmable calculator and Englishforeign language dictionary.
 Review last year’s test #1 in “Tests and Exam” section
of course website.
 Responsible for all material covered in readings,
lectures, handouts, movies, and tutorials.
 Test covers rote knowledge of the material and ability
to apply it to novel problems and scenarios.
 Few if any questions on names and percentages;
many questions on trends, ideas and interpretations.
Why Most People Conform
 Norms of solidarity
demand conformity.
 Structures of authority
tend to render people
obedient.
 Bureaucracies in particular
are highly effective
structures of authority.
Obedience to Authority Increases with
Separation from the Negative Effects
of One’s Actions
Percent of
subjects who
60
administered
maximum
shock
40
Same room;
hand forced
Same room
Different rooms;
see and hear
20
Different rooms;
see but not hear
0
Milgram’s experiment supports the view that separating people from the negative effects of their actions increases the likelihood of
compliance. When subject and actor were in the same room and the subject was told to force the actor’s hand onto the electrode,
30% of subjects administered the maximum, 450-volt shock. When subject and actor were merely in the same room, 40% of
subjects administered the maximum shock. When subject and actor were in different rooms but the subject could see and hear the
actor, 62.5% of subjects administered the maximum shock. When subject and actor were in different rooms and the actor could be
seen but not heard, 65% of subjects administered the maximum shock.
Bureaucracy
A bureaucracy is a large, impersonal
organization composed of many clearly
defined positions arranged in a hierarchy.
It has a permanent, salaried staff of
qualified experts and written goals, rules,
and procedures. Staff members always
try to find ways of running their
organization more efficiently. Efficiency
means achieving the bureaucracy’s goals
at the least cost.
 A social group is composed of two or people who
identify with one another, routinely interact, and adhere
to defined norms, roles, and statuses.
 A social category is composed of two or more
people who share similar status but do not routinely
interact or identify with one another.
 In a primary group, norms, roles, and statuses are
agreed upon but are not put in writing. Social
interaction creates strong emotional ties, extends over a
long period, and involves a wide range of activities.
 A secondary group is larger, and social interaction is
more impersonal, creates weaker emotional, extends
over a shorter period, and involves a narrow range of
activities.
The Asch experiment: Which line in card
2 is the same length as the line in card 1?
Card 1
Card 2
1. Line 1
2. Line 2
3. Line 3
The likelihood of conformity...
 increases as group size increases to three
or four members;
 increases in more cohesive groups;
 is higher among people of low status;
 is higher in collectivist than in
individualistic cultures;
 is greater if it appears that nobody is
willing to dissent.
Groupthink
 Groupthink is the
tendency to conform to
group norms despite
individual misgivings.
Bystander apathy
 Bystander apathy is the tendency
of witnesses not to get involved in
wrongdoing. As the number of
bystanders increases, the likelihood
of any one bystander helping
decreases because the greater the
number of bystanders, the less
responsibility any one individual feels.
In-group and out-group
 In-group members are those who
belong to a group.
 Out-group members are those
who do not belong to the group.
 An in-group typically draw symbolic,
spatial and interactive boundaries
separating itself from an out-group
and tries to keep out-group
members from crossing the
boundaries.
Correlation
 A variable is a concept that can have more
than one value.
 A correlation is the relationship or
association between two variables.
 The correlation coefficient (r) measures
the strength of the association between two
variables. Its value ranges from -1 to +1, with
-1 indicating a perfect negative linear
association, +1 indicating a perfect positive
linear association, and 0 indicating no
association.
Correlations
Each red diamond in the graphs below indicates a case’s value on two
variables, x and y. The straight black lines are “regression lines” that show the
linear trend in the data. Each line minimizes the sum of the squared distances
between each data point and the line. The correlation coefficient measures the
amount of “scatter” around the line.
Variable y
60
r = .85
Variable y
Variable y
60
60
r = -.92
(little scatter, strong
positive correlation)
40
40
20
20
0
0
0
2
4
6
8
Variable x
(even less scatter,
stronger negative
correlation)
r=0
(lots of scatter, no correlation)
40
20
0
0
2
4
6
8
Variable x
0
2
4
6
8
Variable x
1. Positive Correlation 2. Negative Correlation 3. No Correlation
Correlations may be coincidental
 Events, including correlations, may be due to
coincidence.
 For example, you may toss a coin and get heads, and
then toss it again and get heads. The chance of this
happening is ½ x ½ = ½2 = ¼. Common sense
suggests that these successive events are likely just a
coincidence.
 However, if you tossed a coin 10 times and got all
heads, the chance of this happening is 1/210 =
1/1,024. This is a sufficiently rare event that we
suspect the event is not due to chance.
 We study statistics partly to know the chance that
events, including correlations, are likely to be real or
coincidental.
Correlations may be spurious
 A correlation may also be spurious (or
phony) if it is preceded by the real cause
of the correlation.
Frequency of
stork sightings
Frequency of
stork sightings
Correlation
Correlation
No correlation
Rurality
Correlation
Fertility rate
SPURIOUS RELATIONSHIP
Fertility rate
NON-SPURIOUS RELATIONSHIP
Statistics
 We must eliminate (or at least
sharply reduce) the chance of
coincidence and spuriousness
before we can conclude that a
correlation signifies a causal
relationship.
 Statistics teaches us how to do
these things.