Introductions

*Introductions

*Introductions

Course

Students

Syllabus

Syllabus

Syllabus

Grading

Presentation Assignments

Presentation Assignments

Seating Assignments

If needed

Churikova, Ekaterina; Arzyamova, Dasha; Kanter, Daria; Egorova, Anastasia; Dergunova, Ekaterina; Skubko, Anfisa; Fomenkova, Anastasia; Tambasov, Eugene; Nogay, Anastasia; Somkova, Daria; Sherman, Elina; Skopintseva, Valentina; Papishvili, Anastasia; Smagina, Elizaveta; Komareeva, Tatiana; Pavelko, Ekaterina; Melianova, Kate; Naryan, Svetlana; Shubenicheva, Liya; Lebanova, Anastasia; Uchaneyshuili, Iya; Kovalenko, Olga; Klimeshova, Julia; Remizova, Yuliana; Tkachuk, Dmitry; Kudryavtseva, Maria; Nyagina, Maria; Novazilova, Ekaterina; Chukina, Nina; Lyalina, Nadya; Bugaeva, Anastasija; Lukina, Anastasia

Experiments

Experiments

Experiments are the Best for a Causal Test

Experiments Require an Intervention

E.g.,

E.g., 

Etc.

What are some ways we can intervene?

Random Assignment

The Seven Parts to a True Experiment

Why use a control?

Deception

Types of Experimental Designs

Debates

Internal Validity

External Validity

"[T]he ability to generalize experimental findings to events and settings outside the experiment itself"  Neuman (2007:216)

Assignment 1

Was it about cats?

If You're Not Paying for It; You're the Product

Kramer et al. 2014. "Experimental evidence of massive-scale emotional contagion through social networks." PNAS 111:24:8788-90.

Was this assignment an experiment on you?

• What was the design?
• What was the intervention?
• What were the pretest and posttest questions?

Student Presentation

Sampling

Materials

Fundamental Terms

The broad class of units that are covered in a hypothesis. All the units to which the findings of a specific study might be generalized. (Neuman 2007)

"The name for the large group of many cases from which a researcher draws a sample and which is usually stated in theoretical terms." (Neuman 2007)

Fundamental Terms

A list of cases in a population, or the best approximation of it. (Neuman 2007)

A smaller set of cases a researcher selects from a larger pool and generalizes to the population. (Neuman 2007)

Fundamental Terms

Other Terms

The number of sampled cases divided by the size of the population they represent

A characteristic of the population, typically estimated with statistics

The difference between the measured parameter in a sample and the population parameter

Statistical Terms

As the number of random samples on a measurement increase, their average approaches the population parameter

An interval in which a research claims, with a given degree of certainty, includes the population parameter

A distribution created by drawing many random samples from the same population" (Neuman 2007)

The Law of Large Numbers

Probability vs Nonprobability Sampling

Is there a known probability of a case being selected?

Nonprobability Samples

Types

Nonprobability Samples

Haphazard/Accidental/Convenience Sampling

Photo courtesy of Anneli Salo

Nonprobability Samples

Haphazard/Accidental/Convenience Sampling

Poll at opensource.com

Nonprobability Samples

Haphazard/Accidental/Convenience Sampling

Link to Tweet

Nonprobability Samples

Haphazard/Accidental/Convenience Sampling

What are the limitations to this sampling method?

When should this sampling method be used for substantive knowledge?

Nonprobability Samples

Quota Sampling

Photo courtesy of BrianZim

Nonprobability Samples

Quota Sampling

Steps

1. Determine categories
2. Determine how many to sample from each category
3. Sample haphazardly until quotas are met

Nonprobability Samples

Purposive/Judgmental Sampling

When is it appropriate?

1. Different information from unique cases
2. Population is generally inaccessible
3. More detailed information on a targeted group

Continues until data or research exhaustion

Nonprobability Samples

Purposive/Judgmental Sampling

Nonprobability Samples

Purposive/Judgmental Sampling

Variant: Sequential Sampling

Continues until no new information or sample diversity attained

Nonprobability Samples

Purposive/Judgmental Sampling

Variant: Deviant Case

("extreme" case)

To be discussed during our lessons on case studies.

Nonprobability Samples

Snowball Sampling 

("network," "chain referral," or "reputational" sampling)

Photo courtesy of Kamyar Adl

Nonprobability Samples

Snowball Sampling 

Steps

1. Begin with seed(s) 
2. Referrals from seed(s) 
3. Sample referrals 

Nonprobability Samples

Snowball Sampling

Nonprobability Samples

Snowball Sampling

Simulating the process

1. Start with seeds 
2. Seeds refer peers 
3. Peers might not respond 
4. If peers respond, gain more referrals 
5. Continue until sample size met or hit dead ends 

Nonprobability Samples

Snowball Sampling

Which social phenomena is this method good for studying?

Who are we more likely to reach in this population?

Who are we least likely to reach in this population?

Probability Samples

Types

• Simple Random Sampling
• Systematic Sampling
• Stratified Sampling
• Cluster Sampling

Probability Samples

Simple Random Sampling

Steps

1. Acquire a reasonable sampling frame
2. Determine sample size
3. Randomly sample cases from the sampling frame
   1. Repeat until the sample size is met
   2. Sample without replacement

Photo courtesy of saschapohflepp

Webpages

http://www.random.org/ 

Probability Samples

Systematic Sampling

Steps

1. Begin with a non-cyclical sampling frame
2. Select a starting case at random
3. Move up and down the list by every k cases

How would this method compare to random sampling?

How could a cyclical sampling frame affect your results?

Probability Samples

Stratified Sampling

Steps

1. Identify mutually exclusive strata
   1. E.g., geographical units
2. Randomly sample within each strata
3. Weight to balance representation

Main benefits

• Better representation than simple random sampling
  • Why?

Probability Samples

Stratified Sampling

Consider our example "population"

(i.e., students in our class)

How could we construct a stratified sample?

Probability Samples

Cluster Sampling

(aka, "multistage sampling")

Steps

• Identify mutually exclusive strata
• Randomly sample strata
  • Identify mutually exclusive strata within
  • Randomly sample these strata
    • Identify mutually exclusive strata within
    • Randomly sample...
• Weight to balance representation

Probability Samples

Cluster Sampling

Consider our example "population"

(i.e., students in our class)

How could we construct a cluster sample?

Probability Samples

Cluster Sampling

Advantages

• Cost
• Speed

Disadvantages

• Less accurate than simple random sampling
• Requires detailed sampling frames

Tradeoff on cluster numbers and cluster size

Laboratory

Who carries more money on hand?

Using the Telephone

Photo courtesy of Takkk

Using the Telephone

Is there a sampling frame?

• Should you use it?

Random digit dialing as cluster sampling

• What is it?
• Why is it cluster sampling?

Words of caution

• Role of telephones in social life
• Nonresponse
• Privacy

Weights

What do weights do?

Why are weights sometimes needed?

On which criteria should respondents be weighted?

Hidden Populations

What are hidden populations?

Capture-Recapture

Respondent-Driven Sampling

Scale-up Methods

Hidden Populations

Photo courtesy of Oldmaison

Hidden Populations

Photo courtesy of Todd Huffman

Hidden Populations

Photo courtesy of Orangeadnan

Hidden Populations

Photo courtesy of AdamCohn

Hidden Populations

Hidden Populations

Photo courtesy of maxintosh

Hidden Populations

Photo courtesy of kargaltsev

Hidden Populations

Photo courtesy of T-Hino

Hidden Populations

Lack a Sampling Frame

Characteristics

• Population members interact with each other 
  • Isn't this true for all "populations?"
• Often illegal or stigmatized, though not always 

• E.g., No clear organizational hierarchy or authority, 
• no written rules or neutral governing body, 
• no expert training, and 
• no meritocratic advancement 

Hidden Populations

Capture-Recapture

Photo courtesy of Mickey Samuni-Blank

Hidden Populations

Capture-Recapture

Lincoln-Petersen Method 

Two Capture Sweeps

• M = First sweep, captured and marked
• R = Recaptured with marks
• C = All captured during second sweep
• N = Estimated total population size

N = M * C  / R

R / M = C / N

How do we ethically "capture" and "mark" humans?

Hidden Populations

Scale-up Methods

Hidden Populations

Scale-up Methods

• "How many incarcerated people do you know?"
• "How many licensed pilots do you know?"
• "How many people with the first name 'David' do you know?"
• ...
• "How many people do you know who died in the September 11, 2001 attacks?"

Basic points:

• Determine how many people respondent knows
• Extrapolate to a hidden population

Hidden Populations

Respondent Driven Sampling

(Heckathorn and Jeffri 2001)

• Location sampling
  • Problem: Locations must be large & public
• Institutional samples
  • Problem: Requires affiliation with institution
• Chain referrals ("snowball")
  • Problem: Nonrandom "seeds"
  • Problem: Volunteerism
  • Problem: Differential recruitment
  • Problem: Popularity effects
  • Problem: Homophily and in-group effects

Hidden Populations

Respondent Driven Sampling

(Heckathorn and Jeffri 2001)

Address Problems of Chain Referrals

• Law of large numbers and Markov chains
  • After enough waves, starting seeds don't matter
  • Transition states and equilibrium
• Pay your respondents and make them comfortable
  • Incentivize both the recruiter and the recruit
    • E.g., Dropbox, plasma donation
  • Use a limited number of "coupons"
  • Respondents should come to the researcher

Hidden Populations

Respondent Driven Sampling

(Heckathorn and Jeffri 2001)

Address Problems of Chain Referrals

• Apply weights
  • Understand who is likely/unlikely to be recruited
  • Understand who is likely to recruit whom
• Homophilous recruitment as structure
  • Recruitment is a behavioral network
  • Can indicate communities and inequality
• Boundaries
  • Screening process

Crowd Sampling

Photo courtesy of Roland zh

Crowd Sampling

Steps

• Understand event geography
• Break into teams
• Section crowd into geographical regions
• Interview every k person
  • Record their responses
  • Record basic information on refusals

(Milkman, Luce, and Penny. 2012. "Changing the Subject: A Bottom-Up Account of Occupy Wall Street in New York City." Appendix B.)

Case Studies

Picture courtesy of Bill Ebbesen

The following lesson relies upon and draws heavily from Gerring (2007)

Definitions

Case connotes a spatially delimited phenomenon (a unit) observed at a single point in time or over some period of time. Gerring (2007:19)

A case study may be understood as the intensive study of a single case where the purpose of that study is -- at least in part --  to shed light on a larger class of cases (a population). Gerring (2007:20)

Definitions

At the point where the emphasis of a study shifts from the individual case to a sample of cases, we shall say that a study is cross-case.  Gerring (2007:20)

An observation is the most basic element of any empirical endeavor. Gerring (2007:20) 

Typically, "N " refers to the number of observations

Population > Sample > Case  ≥ Observation

Case Studies are Research Designs

• No prescribed data format
• No prescribed method of analysis
• No upper limits on the N

Neither inherently qualitative nor quantitative.

There are certain affinities, though.

Population typically difficult to discern.

Definitions

A single observation may be understood as containing several dimensions, each of which may be measured ...as a variable. Gerring (2007:20)

Y

• Dependent Variable
• Outcome of Interest

X

• Independent Variable
• Explanatory Variable/Factor

Y ~ X

Data Organization

Data Frames and Matrices

• Rows represent observations
• Columns represent variables
• Grouping variable (could) represent cases

Typically done in a spreadsheet

Research Design Typology

Research must examine variation across cases or units

Dimensions of variation

• Number of cases
  • One, "several," or "many"
• Form of variation
  • Spatial and/or temporal
• Location of variation
  • Within case and/or across cases 

Research Design Typology

Why Case Studies?

Research Goals

Empirical Considerations

Case Study Research Goals

• Role of hypotheses
  • Generating, rather than testing
  • Needed to study new phenomena
• Validity
  • Internal, rather than external
  • Difficult to speak outside of sample
• Causality
  • Focus on
    • Mechanisms, rather than effects
  • Inference
    • Deep, rather than broad

Effects

Picture courtesy of @alexlundry

Kosinskia, Stillwella, and Graepelb. 2013. "Private traits and attributes are predictable from digital records of human behavior." PNAS.

Mechanisms

Griffin (1993:1110) AJS

Mechanisms

Griffin (1993:1110) AJS

Case Studies & Empirical Considerations

Tendencies

• Population of cases
  • Heterogeneous, rather than homogeneous
• Causal relationship
  • Strong, rather than weak
• Useful variation
  • Rare, rather than common
• Data availability
  • Concentrated, rather than disperse

Case Selection Strategies

• Typical
• Diverse
• Extreme
• Deviant
• Crucial test
• Pathway
• Most-similar
• Most-different

Practical Reasons

• Language
• Data availability
• Theoretical background

Cross-National Income Inequality

Let's focus our strategy to this concept for an example. 

We'll pretend we're going to investigate the causes in rich institutional detail.

Cross-National Income Inequality

Cross-National Income Inequality

Typical Case Selection

Representation

Hypothesis testing

Diverse Case Selection

Values range rather than distribution

Hypothesis testing

Hypothesis generation

Extreme Case Selection

Outliers

Representative only relative to larger sample of cases

Hypothesis generation

Deviant Case Selection

Outlier due to nonconforming relationship

Identify alternative relationships

Hypothesis generation

Crucial Test

Case least likely to exhibit relationship

• Explain Y
• Typical explanations X1, X2, X3, etc
  • None explain Y  for this case
• New variable, X4 works

Representativeness questionable

Hypothesis testing

Pathway

Select cases based on covariational patterns (Combinations)

Interests

• Mechanisms 
• Dependencies 

Hypothesis testing

Most-Similar

Select very similar cases with different outcomes

Cases should have only one independent difference

That difference is the key variable

Maybe representative

Hypothesis testing and generating

Most-Different

Select very different cases with similar outcomes

Cases should have only one independent commonality

That commonality is the key variable

Maybe representative

Hypothesis testing and generating

Assignment

1. Break into five groups
2. Each group is a case study design
3. Create a "research design" with
   1. Unit of analysis
   2. Observation(s)
   3. Case(s)
   4. Outcome of interest, Y
   5. Explanatory factors, X1 and X2

Data Entry

Source

Data Entry

Typical Ways to Enter Data

1. Spreadsheet
2. Text editor
3. Automated

Demonstration

Let's create some simple data!

Typical Layout

• Units/Cases represented in rows
• Variables represented in columns

Delimited Text Files

• Advantages
  • Cross-platform
  • Easy to read
• Disadvantages
  • Size
  • Somewhat limited detail

Exercise

Without looking at your neighbor's responses, write down

No questions.

1. Your name
2. Your gender
3. Your date of birth
4. Your study group
5. How many hours you spent studying this week
6. How cats make you feel (Happy, Neutral, or Unhappy)
7. One word to describe your class with Prof. Flores
8. Who is your favorite character in Game of Thrones?

Now write it on the board, exactly as you wrote it on paper.

Typical Sociology Data Types

• Boolean
• Numeric
  • Integer vs. Real
• Date and Time
  • Numeric, Text, Other Formats
• Ordinal
  • Numeric or Text
• Text
  • Factors or Descriptive

What are examples of each form of data type?

Missing Values

Missing Values

Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns -- the ones we don't know we don't know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones.

 

- Donald Rumsfeld, 2002

Missing Values

• Types of missingness
• Representation
• Handling
  • Additional data (Best)
  • Mean substitution (Bad)
  • Case-wise deletion (Conventional)
  • (Multiple) Imputation (Good)
    • Random ("Hot deck")
    • Nearest neighbor
    • Estimation

Content Analysis

Much of the material from this lesson draws from

Krippendorff  (2004) Content Analysis: An Introduction to Its Methodology

Content Analysis

How to Collect Data from Texts

What is a "text?"

What sort of projects is this method good for?

Advantages

• "Dead"
• Reliability
• Replication

What are some disadvantages?

Content Analysis

Steps

1. Unitizing
2. Sampling
3. Recording/Coding
4. Reducing data
5. Inferring context
6. Narration

Provide an example of a text.

Krippendorff (2004:83)

Unitizing

Which unit will you be recording?

Unitizing

Even though large tracts of Europe and many old and famous states have fallen or may fall into the grip of the Gestapo and all the odious apparatus of Nazi rule, we shall not flag or fail. We shall go on to the end, we shall fight in France, we shall fight on the seas and oceans, we shall fight with growing confidence and growing strength in the air, we shall defend our Island whatever the cost may be, we shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets, we shall fight in the hills; we shall never surrender.

How could this text be unitized?

Unitizing

Sampling

Which samples could these examples represent?

Which limitations would these samples face?

How could we sample within these examples?

Recording/Coding

Let's use a panel as our unit. What could we code?

Daredevil Battles Hitler 001

Reducing Data

E.g.,

What proportion of panels portray violence?

What proportion of panels with violence display violence directed against one or more Nazis?

What proportion of nouns in Churchill's speech were first person plural?

Inferring Context

What would be the context for these two examples?

Which sociological topics do they speak to?

Narration

Natural Language Processing

• Object character recognition (OCR)
• Named entity recognition
  • Relationship extraction
• Sentiment analysis

Suggested Application

Data Science Toolkit

Object Character Recognition

Images of text are not machine-readable

Software required to convert text images

(Always check for quality.)

Object Character Recognition

Named Entity Recognition

Latoya Ammons, from Gary Indiana, and her three children claimed to have been possessed by evil spirits....Recently, the priest who dealt with the actual exorcisms of this family, Rev. Michael Maginot has signed with Evergreen Media Holdings to make his account of the story into a movie.

Latoya Ammons [person], from Gary Indiana [location], and her three children claimed to have been possessed by evil spirits....Recently, the priest who dealt with the actual exorcisms of this family, Rev. Michael Maginot [person] has signed with Evergreen Media Holdings [organization] to make his account of the story into a movie.

Source: Exorcism in Gary Indiana by Wikinews

Named Entity Recognition

What are some possible uses for sociologists?

Which relationships do you think could be extracted?

Sentiment Analysis

What is the emotional state of the author?

<Words associated with happiness> :D

<Words associated with unhappiness> :'(

Positive values suggest happiness

Negative values suggest unhappiness

Sentiment Analysis

Remember when I asked you to describe yourself?

Sentiment Analysis

Let's test a hypothesis!

Photo Analysis

Can a computer detect...

• The number of people?
• Their gender?
• If they are smiling?
• If they have mustaches?

Photo by nosound

Photo Analysis

Application
Jetpac

Secondary Data

Data is all around us...

Secondary Data

Where to find it?

• Libraries and electronic archives
• Statistical abstracts
• Published articles
  • Bibliographies
  • Tables and figures
  • Ask the authors

Libraries and Electronic Archives

Some examples

• ICPSR
• Archive.org
• Datahub
• Quandl
• State released
  • data.gouv.fr
  • data.gov
  • data.Seattle.gov
  • data.mos.ru

Statistical Abstracts

Organized by subject

Offers description and reference to contemporary data

Publication of the Statistical Abstract of the United States stopped in 2012 due to budget cuts

Published Articles

Read the friggin' bibliography!

Andrews (2001:91)

Andrews (2001:92, 94-5)

Figures

McAdam (1983:739)

Figures

Marshakova, Irina V. 1981. Scientometrics 3, 1: 13-26.
Marshakova, Irina V. 1973. Scientific and Technical Information Serial of VINITI 6: 3-8

Figures

How would you convert a time series like McAdam (1983) into data?

Ask the Authors

Their contact information is provided for a reason!

Reasons they say "no":

• Privacy
• Proprietary restrictions
• Future intentions with data
• Data lost
• Busy
• Dishonesty

Reasons they say "yes":

• It helps science progress
• It strengthens the community
• Demonstrates honesty and finding integrity

Who creates and releases data?

Image courtesy of Nicknilov

Questions

Typical units with public data?

Image courtesy of Brion VIBBER

Decades

...1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000...

Years

...2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011....

Divisions within a Year

Quarter 1
Quarter 2
Quarter 3
Quarter 4

Module I
Module II
Module III
Module IV

Months of the Year

January
February
March
April
May
June
July
August
September
October
November
December

What type of units are these?

Beware the ecological fallacy!

Combining Datasets

"Mashups"

• Benefits
  • Greater context
  • Additional insight and contribution
  • Often free
• Requires common identifier
  • Geographical unit
  • Time
  • Industry, school, other organizations
• Doesn't require common unit of analysis
  • Multilevel models

Limitations

• Have the analyses been done before?
• Is the data appropriate for the research question?
• Do you know the details of measurement and collection?
• Limited to the original variables and constructs.
• Are the items measured a proxy for your interests?
• No control over data collection.
• What information isn't collected?
• Administrative capacity.

Challenge

Characteristics of the data behind the next diagram:

• All participants understood the data would be analyzed
  • (Admittedly, for a different purpose)
  • The data was collected unobtrusively
  • The data was non-reactive
• All members of our class are participants
• All members of our class
  • Are capable of collecting it--no researcher privilege
  • Have seen the original data--no privacy violation
• The data was free and quick to collect
• Data is a proxy measurement for "association"

Implications

• Accuracy
  • Behavioral
  • Proxy requires researcher inference
• Ethical
  • Informed consent
  • Anonymity
• Examples of related data?
  • Building pass
  • Assignment completion time
  • Credit card purchases
  • Metro rides...

Historical Research

The following lesson relies upon and draws heavily from Neuman (2007), Chapter 12.

Content Analysis Assignment

Debriefing and Introduction to Historical Social Research

Content Analysis Assignment

• Two comics
  • Linda Lark: Student Nurse #1
  • Nurse Betsy Crane Vol 2 #13
• Similar books
  • Subject matter
    • Young, white female nurses
    • Romance
  • Audience
  • Time: Oct - Dec & Oct 1961
• Different publishers
  • Price
  • Comic Code Authority policy

Historical Circumstances

• "Golden Age" ~ 1938-1956
• 1954
  • Comics Code Authority established
  • Seduction of the Innocent
• "Silver Age" ~ 1956-1971
  • Comics and gender
  • Fear and anxiety
• Second Wave of Feminism ~ 1960s-1980s
  • Feminine Mystique (1963)

Hypotheses

• Less gender equity in comics without CCA stamp
  • Niche market and limited oversight
• Less gender equity in comics with CCA stamp
  • Code recreated existing unequal discourse
  • Code reduced creative outlet
    • Emphasis on male-centric themes
• No difference in gender equity
  • Non-objectionable content, no need for code
  • Mass popularity
    • Cultural regression to the mean

Operationalization

How did we measure gender equity?

Results

Linda Lark (Dell, no CCA) relative to Nurse Betsy Crane (Charlton, CCA)

• Linda Lark is ~9 times more likely to have a woman in the panel
  • But, the odds she is standing is half that of Nurse Betsy
• Nurse Betsy is ~twice more likely to have a close-up of a man

Weak support CCA less gender equitable hypothesis

Assignment Questions

What was the case selection strategy? 

What was the sampling strategy employed? 

What are the suggested historical implications for gender socialization?

What are the other limitations to this exercise?

Historical Research:
Introduction

Purpose

• Challenge existing explanations and assumptions
• Expand subject of inquiry to new settings
• Specify or generalize

Difficulties

• Requires rich knowledge base on both
  • Culture
  • History

Steps

1. Conceptualization
2. Locating evidence
3. Evaluating evidence
4. Organizing evidence
5. Synthesizing findings
6. Narrative

Conceptualization

• Loose theoretical models
  • Read some existent theoretical literature
  • Imagine plausible models
• Background materials on case
  • Encyclopedias
  • Chronologies
  • Generalist histories

Locating Evidence

• Bibliographies
  • Additional literature
  • Data sources
    • Periodicals, reports, and white papers 
    • Datasets
    • Archival materials
• Specialist libraries and archives
  • Identify the important sites
  • Locate pertinent and related materials
  • Follow the archive's rules
  • Record information and citation details
    • Voice, notes, photocopies, scans, etc

Evaluating Evidence

• Authenticity
  • Original vs. secondary
  • Assess probabilistically
    • E.g., date created vs. date occurred
• Reliability
  • Internal and external consistency
  • Literal vs. real meanings
  • Created for researcher purposes?
• Author's ability to be truthful
  • Socially able
  • Physically able

(Milligan, JD. [1979] History and Theory 18:2:177-96.)

Organizing Evidence

• Create a system of organization
  • Spreadsheets
  • Tagging systems
• Schemes
  • Theoretical
  • Chronological
  • Case variation

Synthesis

How does your research fit into the existent literature?

Narration

Tell a compelling story for readers.

Evidence Types

• Primary Sources
• Secondary Sources         
• Running Records
• Recollections

Primary Sources

Uses

• Originality
• Basis of historical knowledge

Downsides

• Laborious
• Often inaccurate
• Biases
  • Document retention
  • Organization
  • Literacy skills

Examples?

Example: Diaries

Northrop, John Worrell. 1904. Chronicles from the diary of a war prisoner in Andersonville and other military prisons of the South in 1864.  Wichita, KS. p. 66.

Secondary Sources

Uses

• General understandings
• Broad descriptions

Downsides

• Subjectivity
• Selective inclusion/exclusion
• Causality
  • Organization
  • Narration
  • Multiple and interactive effects
• Varying empirical strength

Running Records

Files and statistical documents produced by organizations.

Recollections

Individuals recounting their past experiences.

 

Uses

• Counteracts elite bias
• Absence of documentation
  • People
  • Activities

Downsides

• Accuracy and recollection
• Sensitivity

Example: Oral History

Dora Strong Dennis. 1995. Behind the Veil. 

Comparative Research

Uses

• Wider range of observations
• Test cultural sensitivity behind theories

Downsides

• Laborious
• Sampling
• Limited generalizations

Units of Comparison

• Cultural regimes. Boundaries?
• Nation-states. Appropriate?

Comparative Research: Data

• Field research
  • Primary
  • Secondary
    • Human Relations Area Files
    • Ethnographic Atlas
• Survey research
  • Primary
  • Secondary
    • E.g., World Values Survey
• Content analysis
• Governmental statistical records
• Historical materials

Comparative Research:
Equivalence

Is the comparison appropriate?

Types of equivalence

• Lexical (e.g., "брат" and "cousin")
• Contextual (e.g., "наука" and "science")
• Conceptual (e.g., "race")
• Measurement (e.g., body weight in US, UK, & Russia)

Social Network Analysis

Time 1, colored by study group

Social Network Analysis

Time 2, colored by study group

Social Network Analysis

Time 3, colored by study group

Features

1. Intuition of social structure as ties bonding social actors
2. Informed by systematic empirical data
3. Visualization plays a substantial role
4. Requires mathematical and/or computational models

(Freeman 2004:3, 5)

What is a social network?

A finite set or sets of actors and the relation or relations defined on them
(Wasserman and Faust 1994:20)

What is an actor?

Actors are social entities 

Actors do not necessarily have the ability to act

Actors (typically) are all of the same type

Formal terms for actors 

• Vertex 
• Node

Examples?

Actors may also have attributes

(e.g., age, sex, ethnicity)

What are relations?

Social ties link pairs of actors

Relations collect a specific set of ties among group members

Related formal terms

• Edges
• Arcs

Conceptualizing Relations 

• Directed undirected?
• Weighted or unweighted?
  • Nominal, ordinal, interval, or ratio scale?
• Signed or unsigned?
• Loops?
• Time sensitivity?
  • Static
  • Moving window
  • Real-time
  • Accumulation and decay

Relations may also have attributes

Two Basic Measurements

Degree

Number of edges incident upon a node

• Undirected
• Directed
  • Indegree
  • Outdegree
  • Total (Freeman) Degree

Density

Proportion of observed edges in a network

Two Basic Measurements

Two Basic Measurements

Ways to Express a Social Network

• Sociogram

Ways to Express a Social Network

• Sociogram
• Matrix

Ways to Express a Social Network

• Sociogram
• Matrix
• Edge list

Social Network Data

• Surveys
  • Free response
  • Roster
  • Ego networks
• Field observations
• Documents
  • Official reports
  • Content analysis
  • Published papers
• Websites
  • Social media
  • Links

Subgraphs

A set of nodes and edges within a graph

• Node-generated subgraphs
• Edge-generated subgraphs

Dyad Census

Dyad Census & Graph Properties

Undirected

• Density (i.e., tie probability) 

Directed 

• Density (i.e., tie probability) 
• Reciprocity

Dyad Census & Graph Properties

Directed 

• Density (i.e., tie probability)
• Reciprocity 
  • Conceptual questions
    • Are null ties reciprocal?
    • Defined by edges or dyads? 
  • Common measurements
    • Edgewise
      • 2* M  / (2* M  +  A )
    • Dyadic
      • ( M + N ) / ( M + A + N )
    • Dyadic, non-null ("ratio")
      • M / ( M +  A )

Triad Census, Undirected

• Brokerage
  • Characterized by only two ties among three actors
• Transitivity, "clustering," triadic closure​
  • Your friends are often friends with each other​
  • Typically = (3 * Triangles) / (Connected Triples)

Triad Census, Undirected

Triad Census, Directed

Triad Census, Directed

• Brokerage
  • i → j → k , i  ↛ k,  k  ↛ i 
• Transitivity
  • Weak (most common)
    • i  →  j   →  k, if  i  → k 
  • Strong 
    • i → j → k, iff i → k
• Cycles
  • i  →  j   →  k  →  i 

Walks

A walk is a sequence of nodes and lines, starting and ending with nodes, in which each node is incident with the lines following and proceeding it in the sequence.
-Wasserman and Faust (1994:105)

Trail

A walk such that every edge traversed is unique

(yet not necessarily every node )

Path

A trail such that every vertex traversed is distinct

There could be zero, one, or multiple walks, trails, and paths between any two vertices!

Seven Bridges of Königsberg

Problem: Walk must cross every bridge only once

Euler (1735) proved there is no solution for the walk 

• Land masses are nodes, bridges are edges 
• Would need zero or two nodes of odd degree 

(Image modified by Bogdan Giuşcă)

Distance Measurements

Pairwise

Path length: Number of edges traversed between two nodes

Geodesic: Shortest path between two nodes

Geodesic distance: Length of the shortest path between two nodes

Graph and Subgraph 

Average path length

Mean geodesic distance

Diameter: Longest  geodesic distance

Courtesy of A.Savin

Distance Measurements

Cycles

A walk "that begins and ends at the same node" and has "at least three nodes in which all lines are distinct, and all nodes except the beginning and ending node are distinct." 
Wasserman and Faust (1994:107-8)

 

Cycles have a length

Connectivity and Components

If a path exists between each pair of vertices in a graph, then the graph is connected

• Strong connectivity: preserves path directionality
• Weak connectivity: ignores path directionality

A component is a maximally connected subgraph

An isolate is the smallest possible component: a single vertex without any ties to other vertexes in the graph

How many components?

Connectivity and Components

A bridge is an edge that, if removed, creates more components

A cutpoint is a node that, if removed, creates more components

Find Bridges and Cutpoints

Centrality and Centralization

Centrality: Nodal measurement

Who are the most important actors in a network?

Centralization: Graph measurement

How much difference in "importance" is there between actors within a network?

Generally, compares the observed network's centralization against the theoretical maximum

Centrality and Centralization

1. Degree
2. Betweenness
3. Closeness

(Freeman 1979)

Betweenness

How many geodesics go through a node (or edge)?

Variations

Edge weighted 
Edge betweenness 
Proximity, Scale Long Paths, and Cutoff 
Endpoints

Random walk

Time 1

Colored by group, scaled by betweenness

Time 2

Colored by group, scaled by betweenness

Time 3

Colored by group, scaled by betweenness

Closeness

Q: What is closeness?

A: The inverse of farness!

Q: What is farness?

If connected, the sum of a node's geodesic distances to all other nodes

Variations

Unconnected graphs

Edge weighted

Random walk

Time 1

Colored by group, scaled by closeness

Time 2

Colored by group, scaled by closeness

Time 3

Colored by group, scaled by closeness

Cohesive Subgroups

the forces holding the individuals within the groupings in which they are
 - Moreno and Jennings (1937:137)
 

Cohesive groups tend to

• Interact relatively frequently
• Have strong, direct ties within themselves
• Display high internal density
• Share attitudes and behaviors within themselves
• Exert pressure and social norms internally

Cliques

A maximally complete subgroup - Luce and Perry (1949)

~In other words~

Everyone has a tie to everyone else in the subgroup (complete)

No other, smaller subgroups include only a subset of the same actors (maximal)

Critique: Too stingy!

4-Cliques (t1)

Can you identify the 3-cliques?

4-Cliques (t2)

Can you identify the 3-cliques?

4-Cliques (t3)

Can you identify the 3-cliques?

Clique Distribution

k-cores

Cohesive "seedbeds" nested within a network 

Minimum #ties (k) each member of a subgroup has to other subgroup members

Directed graphs may measure k -cores through 

• Ties going inward
• Ties going outward
• Total ties

Alvarez-Hamelin et al. (2006); Seidman (1983) 

1-Core

Time 1

1 and 2-cores

Time 1

1, 2, and 3-cores

Time 1

1, 2, and 3-cores

Time 2

1, 2, and 3-cores

Time 3

Shift in Cohesion

Hierarchical Cohesion, Time 1, Vertices Colored by Study Group

Hierarchical Cohesion, Time 2, Vertices Colored by Study Group

Hierarchical Cohesion, Time 3, Vertices Colored by Study Group

Major Research Topics in Brief

1. Homophily
2. Diffusion
3. Tie formation models

Homophily

("Assortativity")

Homophily

Categorical vs. continuous variables

Sources?

Which relationships?

Felds's Foci

Forms of homophily

1. Generalized
2. Differential
3. Matching

Intervening considerations

1. Population effects
2. Degree correlated attributes
3. Triadic closure

Homophily

E-I Index

One (of many) measurements

EI = ( E - I ) / ( E + I )

E = #Ties between subunits

I = #Ties within subunits

Range: [-1, 1]

Lower values: More homophily

Higher values: Less homophily

Krackhardt (2003) The Journal of Applied Behavioral Science

Diffusion

The spread of a behavior or attribute

Diffusion

Requirements

1. An artifact
2. A sender
3. A receiver
4. A channel

Diffusion

Relationship to previous adopter increases a receiving node's propensity to adopt

Diffusion

Considerations

• Account for homophily
• Theorizing channels and artifacts
  • What are some artifacts that could diffuse?
  • Which channels could diffuse these artifacts?
• Conceptualizing time
  • Adoption rate
  • Decay
• Inhibitors

Modeling

How do ties form?

• Preferential attachment
• Homophily / assortativity
• Block models 
• Small world
• Other network evolution models

Preferential Attachment

• Cumulative Advantage 
• Matthew Effect (Merton) 

"For everyone who has will be given more, and he will have an abundance. Whoever does not have, even what he has will be taken from him." (Matthew 25:29)

• Friendship Paradox (Feld 1991) 
  • Sensor research & epistemology

P(X=x) ~ x^(-alpha) 

Nodes are of degree greater than or equal to x 

P (X=x)  is the probability of observing a node with degree x  or greater 

alpha is the scalar

(Barabási and Albert 1999) 

Blockmodels

Focus upon positions or "roles," not actors

Comprised of

1. Discrete subsets of actors into "positions"
2. Relationships within and between positions

Potential hypotheses

1. Relationship between positions and attributes
2. Structure of relationships

The following examples from Wasserman and Faust (1994:423)

Cohesive Subgroups

Center-Periphery

Centralized

Hierarchy

Transitivity

Block Modeling

Which hypotheses do we have regarding the blocked structure of our class?

Time 1, Reduced Block Model, Vertices Scaled by Number of Students in each Block

Time 2, Reduced Block Model, Vertices Scaled by Number of Students in each Block

Time 3, Reduced Block Model, Vertices Scaled by Number of Students in each Block

Small World

 Watts and Strogatz (1998)

Properties

1. High clustering
2. Short path lengths

Inspired by Milgram's "small world" experiment

Small World

 Watts and Strogatz (1998)

1. Begin with a ring-type graph ("lattice")
2. Connect each node to k others
3. Rewire (switch) the edges with β probability

Reality somewhere between

• "Connected caveman graph"
• Random ties 

Small World

Picture courtesy of Arpad Horvath

Other Evolutionary Models

Key questions as time proceeds:

• Actors
  • Can they join a network?
    • If so, do they form a tie upon joining?
  • Can they exit a network?
• Edges
  • Can they form? Dissolve? Rewire?
• Mechanisms
  • Under which circumstances do...
    • Actors join or exit a network?
    • Edges form, dissolve, or rewire?
  • Actor-oriented or tie-based?

Other Evolutionary Models

References

• Robins and Pattison. 2001. "Random graph models for temporal processes in social networks." Journal of Mathematical Sociology, 25:5-41.
• Toivonen et al. 2009. "A comparative study of social network models: Network evolution models and nodal attribute models." Social Networks, 31:4:240-54.
• Snijders et al. 2010. "Introduction to stochastic actor-based models for network dynamics." Social Networks 32:1:44-60.
• Krivitsky and Handcock. 2014. "A separable model for dynamic networks." Journal of the Royal Statistical Society, 76:1:29-46.

E.g., Separable Temporal Exponential Random Graph Models

(STERGM)

• Models two separate processes
  • Tie Formation
  • Tie Persistence
• Constants
  • Actors neither enter, nor leave
  • Actor attributes

STERGM

• Which hypotheses do you have regarding
  • Tie formation?
  • Tie persistence?
• My hypotheses
  • Tie formation: homophily
  • Tie persistence: triangles (+), two-paths (-)

How did I do?

Tie Formation

Tie Persistence

Model Summary

Observed t1

Observed t2

Observed t3

Simulated t2

Simulated t3

Simulated t4

Network Homework Assignment

Exercise

Produce a research design with the following elements

• A research question
• A social network with clearly defined
  • Actors
  • Relationships
• Two hypotheses
• A means to test them with network
  • Data
  • Measurements

Simulations and Agent-Based Models

Why would a social scientist conduct computer simulations?

Ideas?

An Anecdote

FiveThirtyEight.com Predicts the 2012 US Presidential Election

Image from Kyle Hill

An Anecdote

FiveThirtyEight.com

Methodology

1. Weighted Polling Average
2. Adjusted Polling Average
3. FiveThirtyEight Regression
4. FiveThirtyEight Snapshot
5. Election Day projection
6. Error analysis
7. Simulation

Why would a social scientist conduct computer simulations?

Ideas?

• Methodological Robustness
  • Data limitations
  • Measurement
  • Model fit
• Theory
  • Hypothesis testing
  • Hypothesis generating
• Prediction

Data Limitations

What are some typical data problems?

(Assuming the operationalization matches the conceptualization.)

• Response rate issues
  • Low response rate
  • Unrepresentative respondents
• Missing data
• Error
  • Respondent error
  • Interviewer error

Response Rate Issues

• Low response rates are often addressed with bootstrapping
• Steps to bootstrap a measurement
  • Calculate an initial measurement
  • Resample within the responses with replacement
  • Recalculate  the measurement
    • Repeat two above steps many times (thousands)
  • Construct a confidence interval
    • Infer the population parameter

Observed = 0.14 (orange)

95% Bootstrapped Confidence Interval = [-0.36, 0.62]

Missing Data

Why worry about it?

Techniques

• Case removal (default)
  • Some problems, though...
• Imputation Methods
  • Additional data and inference (best)
  • Random value from within the data
    • Completely at random
    • Similar case
• Model missingness and estimate
• Multiple imputation
  • Impute -> Measure -> Repeat -> Combine

Error

How does respondent error enter a dataset?

How does interviewer error enter a dataset?

Error

Practically all data contain some error

1. Take original measurement
2. Introduce error to your data
3. Remeasure from error-riddled data
4. Repeat Steps 2 and 3 many times
5. Repeat Step 4, increasing the simulated error
6. Compare simulated error to observed

Simulated Experiment

What are some forms of respondent error in our peer communication networks?

Simulated Experiment

Let's focus on transitivity

• For each time point we introduce more selection error
  • Error rates: 5%, 10%, 15%, 20%, 25%
• At each time point and error rate we "rewire" edges
  • "Rewiring" means edges randomly trade partners
  • The proportion of edges rewired equals the error
• We measure transitivity using these faulty networks
• We repeat each step a thousand times

0% Rewired Edges

5% Rewired Edges

10% Rewired Edges

25% Rewired Edges

Results of the Simulation

Is transitivity on our network at time one robust against random selection error?

• Why or why not?
• Under which conditions?

Measurement

What if your measurement results from structural properties?

• E.g., Is a transitivity score of 0.44 high or low?
• It certainly depends upon both
  • The number of vertices in a network
  • The number of edges in a network

Let's Simulate a Homophily Network!

Constant parameters

• Number of nodes
• Density (approximately)
• Number of groups and their sizes
• Density within and between groups

Varying parameters

• Random edge assignment, given above parameters
• Simulated networks modeled after each time point

One thousand simulations for each time point

Time 1, Observed

Time 1, Simulated

Time 2, Observed

Time 2, Simulated

Time 3, Observed

Time 3, Simulated

Model Fit

Does the model adequately produce the outcome of interest?

Tie Formation

Tie Persistence

Agent-Based Models

Exercises in Theory

Portions of the next few slides draw from Macy and Willer (2002)

Agent-Based Models

Four Common Assumptions

1. Agents are autonomous decision makers
2. Agents are interdependent and influence one another 
3. Agents follow simple behavioral rules
4. Agents adaptive and look back to the past

Agent-Based Models

Simplicity and generality are key to good models.

Agent-Based Models

Two major questions

1. Emergent structure, e.g.,
   1. Convergence vs. differentiation
   2. Influence and diffusion
2. Emergent social order, e.g.,
   1. Adaptation from interactions
   2. Yields cooperation, trust, collective action 

Agent-Based Models

Common explanatory factors

1. Homophily
2. Transitivity
3. Density
4. Relational stability (persistence)
5. Reciprocity

Agent-Based Models

Quality models should...

1. Be simple
2. Avoid relying upon biological metaphors
3. Provide rigorous experiments
4. Be robust to changes
5. Exhibit external validity
6. Test the validity of the field's claims
7. Emphasize affects resulting from social factors

Benefits of Simulation Methods

• Cost
• Respondent cooperation
• Ethical

Limitations of Simulation Methods

• Often lacks of empirical data
• Assumptions and complexity reduction
  • Or is it a benefit?

Demonstration

• Review one network model
• Review one diffusion process
• Formulate three hypotheses
• Simulate network and diffusion process
• Evaluate hypotheses

One Network Model

Alexei Vazquez. 2003. "Growing Network with Local Rules: Preferential Attachment, Clustering Hierarchy, and Degree Correlations."  Physical Review E 67, 056104

Network Growth

1. Upon each turn
   1. Networks add a vertex (probability [1 - u])
      1. New vertex ties to random old vertex
      2. Potential ties created from new two-paths
   2. A random two-path closes (probability u)
2. Turns continue until N vertices introduced

One Network Model

Vazquez (2003)

Parameters

1. N: Number of vertices
2. u: Density & transitivity

Properties

1. Skewed degree distribution
2. Transitivity
3. Degree correlation

One Network Model

Vazquez (2003)

We're going to add one slight modification for added realism...

Randomly rewire edges with probability p.

Implications:

1. Constant
   1. Degree distribution
   2. Density
   3. Number of vertices
2. As p increases
   1. Transitivity decreases
   2. Number of components increases

One Diffusion Process

Mark Granovetter. 1978. "Threshold Models of Collective Behavior." American Journal of Sociology 83:6:1420-43.

Outcome of interest: binary decisions

Examples

• Innovation adoption
• Rumors and diseases
• Strikes
• Voting
• Educational attainment
• Leaving social occasions and migration
• Jumping onto the  dance floor

One Diffusion Process

Mark Granovetter. 1978. "Threshold Models of Collective Behavior." American Journal of Sociology 83:6:1420-43.

Two main ideas

• Each Individual has a threshold
  • A minimum proportion of people engaging in collective action required for the individual engage
  • Varies across vertices from 0 to 1
    • We'll be using a random uniform distribution
• Network ties can encourage or discourage engagement
  • Granovetter says they count twice
    • We'll be varying this "peer effect" constant

One Diffusion Process

Mark Granovetter. 1978. "Threshold Models of Collective Behavior." American Journal of Sociology 83:6:1420-43.

F(thresholdi, xit) = Decision of i to engage at time t

F(thresholdi, xit) = xit > thresholdi  

xit = Engagedit / (Engagedit   + Unengagedit )

Engagedit  = EngagedPeersit * (PeerEffect - 1) + AllEngagedt

Unengagedit  = UnengagedPeersit * (PeerEffect - 1) + Allunengagedt

Example

• Network
  • N = 100
  • u = 0.35
  • Rewiring probability, p, = 0.20
• Diffusion process
  • Initially engaged = 5%
  • Thresholds = random uniform distribution
  • Peer effect = 2
  • Number of trials = 5

Research Question

Given these theories on network tie formation and decision-making, why do some collective action episodes escalate more quickly than others?

Hypotheses

• Density and transitivity
  • As density increases along with transitivity, the rate of collective action engagement will increase.
• Randomization
  • As the proportion of rewired edges increases, transitivity will decrease and the number of components will increase, preventing collective action growth.
• Strength of peer effect
  • Increasing the effect from one's network ties will increase the rate of collective action growth.

Our Simulation

Code available here:

http://pastebin.com/u0dNiizy

Steps:

1. Create a random network
   1. Use Vazquez model with N = 100 and u
   2. Randomly rewire edges with probability p
2. Set thresholds with a random uniform distribution
3. Set a random 5% of population as engaged
4. Run through threshold model for five trials
5. Measure average change from one trial to the next
6. Repeat 100 times
7. Regress effects on average change

Density and Transitivity Experiment

Parameters

• N = 100
• Rewiring probability (p) = 0.20
• Number of trials = 5
• Peer effect = 2
• Randomly vary u from 0 to 1

Findings

Implications: Positive density (or transitivity) effect

             Estimate Std. Error t value Pr(>|t|)    
(Intercept)    4.795      1.069   4.487 1.97e-05 ***
u              7.434      1.991   3.733 0.000317 ***
Multiple R-squared:  0.1245,    Adjusted R-squared:  0.1156

Randomization Experiment

Parameters

• N = 100
• u = 0.35
• Number of trials = 5
• Peer effect = 2
• Randomly vary rewiring probability, p, from 0 to 1

Findings

Implications: Transitivity has no effect aside from density

            Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.855      1.113   7.057 2.45e-10 ***
p             -2.234      1.846  -1.210    0.229
Multiple R-squared:  0.01472,   Adjusted R-squared:  0.004666

Both Experiments

Parameters

• N = 100
• Number of trials = 5 
• Peer effect = 2
• Randomly vary rewiring probability, p, from 0 to 1
• Randomly vary u from 0 to 1

Findings

Implications: Transitivity has no effect aside from density

                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)            6.594      1.294   5.094 1.73e-06 ***
u                      3.880      1.651   2.350   0.0208 *  
p                     -2.790      1.750  -1.595   0.1141    
Multiple R-squared:  0.07746,   Adjusted R-squared:  0.05844

Peer Effect Experiment

Parameters

• N = 100
• u = 0.35
• Rewiring probability (p) = 0.20
• Number of trials = 5
• Randomly vary peer effect from 1 to 5

Findings

Implications: Peer effect does not affect growth rate.

                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)           8.2278     1.5214   5.408  4.5e-07 ***
Peer Effect          -0.4018     0.4621  -0.869    0.387    
Multiple R-squared:  0.007653,  Adjusted R-squared:  -0.002473

Conclusions

• We have simulated two processes
  • Network tie formation
  • Interdependent decision making
• We tested the "rate of engagement"
  • Averages change over time in engagement
• We have confirmed one hypothesis and rejected two
  • Confirmed: Density increases growth rate
  • Rejected:
    • Transitivity has no effect after controlling density
    • Peer effect does not affect growth rate
      • Somewhat contradictory finding

Discussion

How would you improve upon this model?

• Varying peer effect
  • Across actors
  • Over time
• Add and remove edges

Social Visualizations

"Carte figurative des pertes successives en hommes de l'Armée Française dans la campagne de Russie 1812-1813" by Charles Joseph Minard

What do these datasets have in common?

Image courtesy of Schutz

The Grammar of Graphics

 Wilkinson, Anand, and Grossman (2005)

• Graphics manual supporting SYSTAT
  • What's SYSTAT? A statistics package Wilkinson wrote later bought by SPSS
• Inspired ggplot2, d3.js, Gadfly, and others
  • I have used Gadfly for most of our visualizations
  • Many interactive online charts use d3.js
    • E.g., today's visualizations with Plot.ly
• Common themes across all data visualizations

Grammar tells you the structure of a language. Graphics also have a similar structure.

The Grammar of Graphics

Wickham. 2010. J Comp & Graphical Stats, p. 5-6

The Grammar of Graphics

Wickham. 2010. J Comp & Graphical Stats, p. 7

"Facet" = "face"

Same plot as earlier, but with two facets

The Grammar of Graphics

Wickham. 2010. J Comp & Graphical Stats, p. 8

Graphics typically have layers

One layer placed on top of another

Parts of a plot

• Dataset, variables, and aesthetic mappings
• One or more layers containing
  • Geometric objects
  • Statistical transformations
  • Positioning
  • Aesthetic mappings
• A scale for each aesthetic mapping
• Coordinate system
• Facet specifications

The Grammar of Graphics

What are some of the layers here?

The Grammar of Graphics

What are some of the aesthetic mappings in the layers?

The Grammar of Graphics

Which geometric objects did Minard use? Are they zero, one, two, or three dimensional?

The Grammar of Graphics

Was the data transformed statistically? If so, how?

The Grammar of Graphics

Which examples of scaling did Minard use?

The Grammar of Graphics

Which coordinate systems did Minard use?

The Grammar of Graphics

How many facets did Minard use?

Characteristics of Excellent Visualizations (Tufte 2000:13)

• Data shown
• Reader thinks primarily about the message
• No data distortion
• Many numbers in a small space
• Large datasets become clear
• Visual comparison
• Different levels of detail in the data, large and small
• There's an obvious purpose
• Closely reflects dataset

Two Common and Useful, but Limited, Social Visualizations

• Maps
• Time Series

Know the Underlying Features of your Data

Image courtesy of XKCD.

Image courtesy of Eric Fischer

Source

McAdam (1983:739)

U.S. Representative Outgoing Mail

Tufte (2000:37)

What are the limitations to maps and time series plots?

Visualizations that Shows Relationships are Much Better

An Example

A Qualitative Example

Zeeman 1976:67 reproduced in Tufte 2001:50

Multivariate Time Series

Showing an Historical Path

Tufte (2000:48)

Visualization Integrity

Visualization Integrity Follows these Principles
(Tufte 2000:77)

• Representation of numbers should be directly proportional to the numbers
• Use clear labels
• Data variation over design variation
• Standardize money in time series charts to avoid inflation
• Never use more dimensions in the visualization than dimensions in the data
• Don't cherry pick data out of context

The Lie Factor

What is the lie factor?

"Data-Ink"

• Keep the "ink" that represents data

• Reduce the ink that doesn't 

  • represent information about the data

  • introduce new information about the data

Chartjunk

Image courtesy of "self." (Not me!)

What went wrong?

Ross Richardson's BGP graph (rendered by Fan Chung)

What went wrong?

Tufte's (1983:121) Guidelines

• Forgo chartjunk, e.g.,
  • Moire Vibration
  • The Grid
  • The Duck

Moiré Vibration

Image courtesy of Fibonacci.

The Grid

Image courtesy of World24.

The Duck

Tuft (2000:117)

A Duck in the Wild

Chartjunk Debate

Multifunctioning Graphical Elements

Mobilize every graphical element, perhaps several times over, to show the data.

-Tufte 2001:139

Compare the next to graphics on US political polarization

Andris, Lee, Hamilton, Martino, Gunning, and Selden (2015)

Moody and Mucha 2013:120

Data Density

Image courtesy of Steve Jurvetson

What's the problem here?

Image courtesy of Christopher

Aesthetics

• Simple > Complex
  • Complexity needs to be accessible
  • Simple labels for small datasets
    • Order of labels matter
      • Alphabetical is horrible, "Alabama first!"
      • Order by an aspect of data
  • Simple legends
  • Tables > graphics for small datasets
• Using combination of words, numbers, drawings
• Dimensions
  • Wide > Tall
  • Golden ratio proportions are ideal

Aesthetics

Wilkinson et al. 2005:260 from Cleveland 1985

Aesthetics

Healy and Moody 2014:121

Social Network Visualizations

Aesthetics

• Vertices
  • Color: Hue, Saturation, Value (luminance)  and transparency (categorical or non-categorical)
  • Border: Color, width, and line type (solid or dashed)
  • Size (continuous) and shape (categorical)
• Edges
  • Width (non-categorical measure for tie strength)
  • Line type (categorical)
  • Color
  • Curve 
• Arrows: Width, length, color, distance from vertex
• Labels: Shown/not shown, font, type face, color, size, position

Vertex Layout

(coordinates)

• Force-directed layout
  • e.g., Fruchterman & Reingold and Kamada & Kawai
  • I.e. "spring-embedding"
  • Purpose
    • Edges roughly equal length
    • Minimize edge crossing
  • Intuitive interpretation
    • ​Central nodes in the center
    • Similar nodes close together
  • Coordinates are initially chosen at random
• Multidimensional scaling
  • Coordinates have meaning, yet edges likely cross
• Vertex attributes (e.g., geospatial information)

Same data

Network Dynamics

• Facets
• Animation

One week separated

Animation Example

An Application

Image courtesy of Kami888

How would you visualize this situation?

Data

• Main Variable of Interest (Source 1, 2, 3, 4, 5, 6)
  • Heads of State Participating in the 2015 Victory Day Parade, Moscow
  • Comparative Group: 2005 Victory Day Parade
• Other Variables of Interest
  • GDP per Capita, 2000-04 / 2010-14 ($2005)
  • Index of Democracy, 2012
  • NATO Member, 2015
  • Contributed Troops in the Iraq War
  • Post-Soviet State
  • Transitional Economy
• Missing Data
  • Interpolated Data on Somalia, North Korea, and Cuba

First Idea

Maybe it's about the economy, a change in a country's GDP per capita, and it's level of democracy?

• Red indicates attendance, blue otherwise, and yellow is unknown or not invited
• Border represents 2005 attendance
• Points scaled by a 2012 democracy index

• Red indicates attendance, blue otherwise, and yellow is unknown or not invited
• Border represents 2005 attendance
• Points scaled by a 2012 democracy index

What can we infer from this plot?

Things I don't like about it

• Wasted dimension--there's not much growth or decline
• Histograms aren't so useful
• Correlation between economy and democracy

Edits

• Keep
  • GDP per capita, 2010-2014
  • Colors to indicate participation
• Toss
  • GDP per capita, 2000-2004
  • Scaling points
  • Histograms
• Add
  • Shift democracy to the y-axis
  • Regression line
  • Participation Heatmap

What have we learned?

• More poorer countries
• Not necessarily more authoritarian after considering GDP per capita
• Biggest groups
  • Countries represented in 2005, but not 2015
  • Countries not invited in 2005, but attended in 2015
• But what about the type of country?

Applications

• Wizards
  • E.g., Excel, Google Spreadsheets
• Dialog boxes
  • E.g., SPSS's GUI
• Procedural languages
  • E.g., SAS, SPSS command line
• Object-Oriented Languages
  • E.g., R, Python, Julia, Javascript

Applications

Personal Favorites

• Wizard
  • Google Charts through Google Spreadsheets or Fusion
  • Plotly
• Wizard+ 
  • Wizard, then edit with GIMP, Inkscape, Photoshop
• Procedural languages
  • Maybe some are good, but I've never used them
• Object-Oriented Languages
  • Plotly or Google Charts, both in R, Python, Julia
  • ggplot2 in R, Gadfly in Julia, Seaborn in Python

Vector vs. Raster Image Format

Assignment

• Find two social visualizations published in a peer reviewed international journal
  • One good
  • One bad
• What do they
  • do well?
  • do poorly?
• Identify the layers, scaling, aesthetics, geometries, facets, and statistics used for each

Challenges & Directions

Types of Challenges

Methods Guiding Theory

Every Day Methodological Proliferation

Methods Guiding Theory

Abbott. 1988. “Transcending Generalized Linear Reality.” Sociological Theory.

Issues

• Fixed set of actors
• Monotonic causal flow
• Univocal meaning
• No sequential effects
• Casewise independence
• Context independent

Methods Guiding Theory

Abbott. 1988. “Transcending Generalized Linear Reality.” Sociological Theory.

y = X * b + u 

 X(t) = X (t - 1) * B + U

Better Reality Models

1. Demographic models
2. Sequential models
3. Network models

Everyday Methodological Proliferation

Savage and Burrows. 2007. “The Coming Crisis of Empirical Sociology." Sociology.

Savage and Burrows. 2009. “Some Further Reflections on the Coming Crisis of Empirical Sociology.” Sociology.

Sociologists no longer have a monopoly on social data.

Who collects the most data? How?

Everyday Methodological Proliferation

Savage and Burrows. 2007. “The Coming Crisis of Empirical Sociology." Sociology.

Savage and Burrows. 2009. “Some Further Reflections on the Coming Crisis of Empirical Sociology.” Sociology.

Survey Problems

1. Response rates
2. Homogeneous units
3. Commercial surveys

Related Search Terms

Everyday Methodological Proliferation

Savage and Burrows. 2007. “The Coming Crisis of Empirical Sociology."  Sociology.

Savage and Burrows. 2009. “Some Further Reflections on the Coming Crisis of Empirical Sociology.” Sociology. 

In-Depth Interview Problems

1. Mastery outside of sociology
   1. Who?
2. More commonplace
3. Less interesting

Why conduct interviews when...

Individual-level Public Data

• Advantages
  • Relatively accurate and detailed
  • Behavioral
  • Contextual, ripe for secondary data
    • Education
    • Occupation
    • Geography
    • Relationships
• Problems
  • Selective sample
  • Selective information
  • Same issues can be said of interviews

Demonstration

Netlytic.org

#NotAllMen / #YesAllWomen

Suggested Directions

• Continue to carefully construct good theories
• Collect data based upon its ability to address theory
• Case study designs
  • Field research
  • Historical cases
  • Comparative cases
• Triangulate data sources
• Be very wary of sampling issues
• Select (and create) analytic methods according to theory

New Method Examined:

Wiki Surveys

• Primary characteristics
  • Greedy
  • Collaborative
  • Adaptive
• Pairwise comparisons
  • Relative importance
  • Establishes rank order with uncertainty modeled 

Example

Which ethnic group is more successful in Russia?

Assignment

Which country is more closely aligned with Russia?

1. Take a screenshot after you cast your first vote
2. Cast a total of at least 75 votes
3. Take a screenshot after your last vote
4. Upload those screenshots to imgur.com
   1. Not required to create an account and log in
5. Complete this form before the end of June 3.