23- Methods and Future Directions Flashcards
1- Social Network Analysis
Social network analysis (SNA) is 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
Node: Entity in a network (dot).
Edge (tie, link): Connection between nodes (line).
‘Directed Edge’: Edge that has an orientation (e.g.,
an arrow indicating popularity).
Distance: Smallest number of ‘edges’ needed to connect two nodes.
Centrality: Importance of a node in the network
(e.g., how many edges a node contains or how many cross-group edges a node has).
Advantages of social network analyses include:
- Can provide insight that people may not be able to self-report
(e.g., who is actually most popular versus who is perceived to be most popular).
- Can identify popular nodes to target for possible interventions.
Some limitations:
- Analyses will only be as good as how much of the network you cover.
- People belong to multiple networks simultaneously (work, family, school,
clubs, sports, etc.) so effects in one network may or may not carry over.
Paluck, Shepherd and Aronow (2016) used a social network approach to
design and assess the effectiveness of an anti-bullying intervention.
They identified ‘social referents’, meaning kids in a school network that
had many connections with other kids, and they reasoned that these
social referents would receive more attention from other kids and be looked to for information about group norms.
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 behaviors at school.
Throughout the intervention, these “seed students” were then encouraged to become the public face of opposition to these conflicts.
Notably, this intervention lacked an educational or persuasive unit regarding adult-defined problems at school.
Results:
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 ‘social referents’ (aka popular?)
Parkinson et al. (2017) used a social network analysis to see how people encode social networks automatically.
An entire cohort of business school students were surveyed about their social network (who they like to spend time with).
A subset of participants were 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 (friend vs. friend of a friend vs. friend of a friend of a friend)
Distance in the social network was consistently related to activation in
three brain areas (you do not need to remember these): lateral superior temporal cortex (STC), inferior parietal lobule (IPL), medial prefrontal
cortex (MPFC).
The current results indicate that when encountering familiar individuals, humans may spontaneously retrieve knowledge of where they are
located, relative to oneself, in a mental map of ‘social space’.
2- Distributional Language Analysis
Reviewing large bodies of text from a culture to identify what words are most likely to co-occur with one another.
The core assumption of these models is that 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.
The word “dog”, for example, is represented as more similar to “cat” than to “banana” because contexts containing “dog” are more similar to contexts containing “cat” than to contexts containing “banana”.
Advantages of distributional language analysis:
- Can provide insight that people may not be able to self-report (e.g., what kinds of patterns exist in what we read).
- Allows for possible historical analyses for associations that may have
existed before more modern measures.
Some limitations:
- Requires a lot of data (tens of millions of words).
- May be dependent on the type of text you use.
- Cannot know whether the text is supporting (“men are better at work”), reflecting (“the culture believes men are better at work”) or refuting (“it’s impossible that men are better at work”) certain associations.
Across 25 languages. Lewis & Lupyan (2020) investigated the association between:
1) The strength of the gender-career stereotype in a distributional language
analysis (e.g., how closely “man”/”career” and ”woman”/”home” cooccur vs. the opposite pairing)….with all of wikipedia of a country…all subtitles from something
2) The strength of the gender-career IAT effect among participants speaking
that language from Project Implicit.
Results:
Language may not only reflect pre-existing stereotypes, but may also
provide a distinct source of information for learning about them.
We find that the implicit (but not explicit) gender associations of
participants in a country is correlated with the gender associations embedded in the dominant language spoken in that country.
This is only a correlational analysis, so hard to determine causation.
To what extent does the language we make or see create implicit gender
stereotypes and to what extent do our implicit gender stereotypes create changes in our language?
3- Big Data
In the social sciences, a ’big data’ approach typically refers to an archival analysis that uses a dataset too large to code by hand and
instead relies on automated data collection and analysis (for example, webscraping of tweets).
Advantages of ‘big data’ approaches:
- Same strengths as archival research but just with greater scope.
- Often measures impactful, ‘real-world’ behavior, can have access to large
amounts of data that make any findings unlikely to be a “fluke”
Some limitations:
- Basically, same limitations as archival research but now with less ability to notice possible errors in data collection.
- Analysis dependent on the data that are made available and often hard to get at issues of causality.
One ‘big data’ approach has been applied to medical records and specifically
treatment algorithms.
The researchers (Obermeyer et al., 2019) worked with a large hospital to track the
treatment records of 50,000 patients over three years.
Specifically, they investigated enrollment in a “high-risk care management program”,
which provide 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.
Results:
Results found that even when Black and White patients had the same risk score as determined by the algorithm, Black patients had worse objective health measures.
- For instance, for a Black and White patient to earn the same risk score to
gain eligibility for the program, Black patients typically had 26% more
chronic illnesses (had to be more sick to be treated)
Why?
Unlike most instances of research on algorithmic bias, the researchers actually had
direct access to how the algorithm was working. The algorithm does not factor in
patient race.
When looking at the algorithm, they found that it was using health costs (how much patients spend to maintain their health) to predict overall health.
But there is a difference between “receiving health care” (health costs) and “needing health care” (objective health). Treating health costs as a proxy for health needs introduced a racial disparity; Black patients generate less healthcare costs than
White patients.
But Let’s Not Forget…
These ‘big data’ approaches to issues of intergroup relations are very
impressive and impactful.
But we will always want to test these theories under conditions of high control of various variables, and we will always want to know
how these effects may work. For that, we’ll need lab studies. The laboratory study isn’t dead yet!
4- Intersectionality
Meaning:
The interconnected nature of social categorizations such as
race, class, and gender, regarded as creating overlapping and interdependent systems of discrimination or disadvantage; a theoretical approach based on such a premise.
Current Thinking: Taller is Better
New York Stop and Frisk Data
2006-2012
Black and White men (non-Hispanic)
64-76 inches photo identification only
1,073,536 valid targets
At different heights, how many Black men are stopped per White man?
Even though being taller for men has usually been considered better, taller BLACK men are at higher risk of being arrested.
5’‘4’: 81.81% were black men
5’‘10’: 84.61% were black men
6’‘4’: 86.7% were black men
5- Algorithmic Bias
Meaning:
Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
Ex: Coded bias movie (new technology tested in a black neighborhood… cameras and face recognition…makes them feel like animals being tracked)
“The past dwells within our algorithms.”
