Lecture 23: New Methods and Future Directions

Question 1

social network analysis

Answer 1

The process of investigating social structures through the use of networks. It characterizes networked structures or individuals in terms of nodes (individual actors) and the ties, edges, or links (relationships) that connect them

Question 2

node

Answer 2

entity in a network (dot)

Question 3

edge/ tie/ link

Answer 3

connection between nodes (line)

Question 4

directed edge

Answer 4

edge that has an orientation (ex. An arrow indicating popularity)

Question 5

distance

Answer 5

the smallest number of edges needed to connect two notes

Question 6

centrality

Answer 6

importance of a node in the network

Question 7

advantages of social network analyses

Answer 7

• Can provide insight that people may not be able to self-report
• Can identify popular nodes to target for possible interventions

Question 8

limitations of social network analyses

Answer 8

• Analyses will only be as good as how much of the network you cover
• People belong to multiple networks simultaneously so effects in one network may or may not carry over

Question 9

Paluck et al., 2016 social network analysis study aims

Answer 9

used a social network approach to design and assess the effectiveness of an anti-bullying intervention. They identified social referents and they reasoned that these social referents would receive more attention from other kids and be looked to for information about group norms.

Question 10

social referents

Answer 10

kids in a school network that had many connections with other kids

Question 11

Paluck et al., 2016 social network analysis study method

Answer 11

56 middle schools were randomly assigned to a control or an intervention condition. A random sample of students was selected in each school to receive the intervention, which required meeting with a research assistant every other week and discussing common conflict behaviours at school. Throughout the intervention, these seed students are encouraged to become the public face of opposition to these conflicts

Question 12

Paluck et al., 2016 social network analysis study findings

Answer 12

schools in the treatment condition saw less conflict than those in the control condition. The intervention was also more effective when a greater number of the seed students in the intervention condition were also social referents

Question 13

Parkinson et al., 2017 social network analysis study aims

Answer 13

used a social network analysis to see how people encode social networks automatically.

Question 14

Parkinson et al., 2017 social network analysis study method

Answer 14

• A cohort of business school students was surveyed about their social network
• A subset of participants was passively shown images of other members of the social network while in an fMRI scanner. These faces varied in the degree to which they were distant from the participant in the network

Question 15

Parkinson et al., 2017 social network analysis study findings

Answer 15

• Distance in the social network was consistently related to activation in three brain areas
• The current results indicate that when encountering familiar individuals, humans may retrieve knowledge of where they are located relative to oneself in a social network

Question 16

distributional language analysis

Answer 16

Reviewing large bodies of text from culture to identify what words are most likely to co-occur with one another

Question 17

core assumption of distributional language analysis

Answer 17

the meaning of a word can be described by the words it co-occurs with (words occurring in similar contexts tend to have similar meanings)

Question 18

advantages of distributional language analysis

Answer 18

• Can provide insight that people may not be able to self-report
• Allows for possible historical analyses of associations that may have existed before modern measures

Question 19

limitations of distributional language analysis

Answer 19

• Requires a lot of data
• May be dependent on the type of text you use
• Cannot know whether the text is supporting or refuting certain associations

Question 20

Lewis & Lupyan, 2020 distributional language analysis study aims

Answer 20

investigated the association between the strength of the gender-career stereotype in distributional language analysis and the strength of gender-career IAT effect among participants speaking that language from Project Implicit

Question 21

Lewis & Lupyan, 2020 distributional language analysis study findings

Answer 21

• Language may provide a distinct source of information for learning about pre-existing stereotypes
• Implicit gender associations of participants in a country is correlated with the gender associations embedded in the dominant language spoken in that country

Question 22

what type of analysis is the Lewis & Lupyan, 2020 study?

Answer 22

correlational

Question 23

big data

Answer 23

archival analysis that uses a dataset too large to code by hand and instead relies on automatic data collection and analysis

Question 24

advantages of big data

Answer 24

• Same strengths as archival research but with a greater scope
• Often measures impactful, real-world behaviour, can have access to large amounts of data that make any findings unlikely to be a fluke

Question 25

limitations of big data

Answer 25

• The same limitations of archival research but now with less ability to notice possible errors in data collection
• Analysis is dependent on the data that are made available and often hard to get at issues of causality

Question 26

Obermeyer et al., 2019 big data study aims

Answer 26

investigated enrollment in a “high-risk care management program,” which provides additional resources to patients with complex health needs. Enrollment in the program is primarily determined by a risk algorithm based on patient records, with higher scores leading to automatic enrollment in the program

Question 27

Obermeyer et al., 2019 big data study findings

Answer 27

even when Black and White participants had the same risk scores as determined by the algorithm, Black patients had worse objective health measures. For Black and White patients to earn the same risk score, Black patients had 26% more chronic illnesses

Question 28

health costs

Answer 28

how much patients spend to maintain their health) to predict overall health

Question 29

why did the algorithm in the Obermeyer et al., 2019 study produce biased results?

Answer 29

The algorithm was using health costs to predict overall health. Treating health costs as a proxy for health needs introduced a racial disparity; Black patients generate less healthcare costs than White patients

Question 30

intersectionality

Answer 30

the interconnected nature of social categorization such as race, class, and gender, regarded as creating overlapping and interdependent systems of discrimination; a theoretical approach based on such a premise

Question 31

New York Stop and Frisk Data intersectionality findings

Answer 31

racial disparities between the number of Black men stopped per White man increases with height

Question 32

algorithmic bias

Answer 32

systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others

Question 33

coded bias movie

Answer 33

“The past dwells within our algorithms”

Question 34

2 main future directions of intergroup relations

Answer 34

intersectionality & algorithmic bias

Question 35

key feature of the Paluck et al., 2016 social network analysis study

Answer 35

This intervention lacked an educational or persuasive unit regarding adult-defined problems at school