• Preferred
  
• "Ben" or "Benjamin"
• Young, male sociologist from California--it's all good
• Appropriate Formalities
  
• "Professor Lind," "Prof Lind," "Dr Lind"
• American-appropriate, yet not Russian-appropriate
• Poor Formalities
  
• "Mr Lind"

• Goal
• Build upon previous course
• Provide a comprehensive understanding of advanced and specialized research design strategies
• Instructional style
• American, conversational and informal
• Emphasis on interaction
• De-emphasis on physical documents
• Timing
• One week full-lecture
• One week odds and ends
• Language vow

1. State your name
   
2. Favorite methodological subject
3. Last item bought

• Nine subjects
  
• Experiments, sampling, case studies, content analysis, secondary data, historical analyses, social network analysis, simulations, and synthesis
• Readings
• < 50 pages on subject
• General approach
• One general intro
• One empirical work
• Students present empirical work

1. Attendance (15%)
2. Participation (15%)
3. Group presentation (15%)
1. Review (3.75%)
2. Evaluate (3.75%)
3. Relate (3.75%)
4. Dialog (3.75%)
4. Assignments (15%)
5. Final examination (40%)

1. Hypothesis
   
2. Modify situation
   
3. Compare with and without modification
   

1. Temporal Order
2. Association
3. No Alternative Explanation

• Which units of analysis would this requirement preclude?
  
• Which types of questions would it preclude due to issues of ethics and practicality?

• What is meant by random assignment?
• How are assignments determined?

1. Random assignment
2. Control group
3. Experimental group
4. Pretest
5. Treatment / Independent variable
6. Posttest
7. Dependent variable

• What are some examples?
• What are some ethical considerations?
• Role of debriefing subjects.

• O = Dependent variable
• O1 = Pretest
• O2 = Posttest
• X = Treatment
• X1 = First treatment
• X2 = Second treatment
• ...
• R = Random assignment
• Rows represent groups
• Z = Confounding factor (factorial design)

• To pretest or not to pretest?
• Benefits of pretest
• Benefits of avoiding pretest
• Which designs lends themselves to macro inquiry?
  
• How can we identify them?
• How strong is their causal claim?

"[T]he ability to eliminate alternative explanations of the dependent variable" Neuman (2007:212)

• Selection bias (if no random assignment)
• Contamination between subjects
• Historical circumstances
  
• Testing (i.e., pretest effects)
  
• Instrumentation (i.e., slow equipment failure)
  
• Experimenter effects
• Maturation
• "Mortality"
• Statistical regression

• Reactivity
• "Hawthorne effect"
• Subject awareness of experiment changes how they respond
• Field experiments
• Natural settings
• Verify external validity
• Greater generalization, but limited control

• I will e-mail you a link to take an online survey
  
• Results will be kept confidential
• Please take it seriously
• I will use your responses throughout the course
• Please do not discuss it with your peers
• Fill it out in a private setting
• Do not talk to your classmates about its
• Questions
• Format
• Images
• You must complete it by Friday

• What did the survey ask?
• What were the researcher's main interests?
• How did you know?
• Was the survey an experiment?
• Can one conduct an experiment with a survey?
• What leads you to believe it was or was not?
• If so, what was the treatment?
• Did any questions stand out as unusual?
• How did you recognize them as unusual?
• What do you think their purposes were?

• Yes, it was an experiment
• Randomly selected 50% of students for treatment*
• Two e-mails, two mostly identical surveys
• Some questions were repeated.
• Examples?
• Why ask the same questions a few times?
• Reliability
  
• Pretest/Posttest
• Treatment must occur between them
• What was the treatment?

• No, it was not about cats!
• Sociologists don't care about people buying cats
• Pretest/post test on that was a ruse
• Slightly interested in cultural consensus
• "Lenin Cat" and "Hitler Cat"
• Human emotion ascription

• "Can additional survey questions affect the number of peer nominations?"
  

• Two-tailed hypothesis
• H0: No difference between the control and the treatment
• H1: Difference between the control and the treatment
• Privacy concerns -> fewer nominations
• Interest in appearing thorough -> more nominations

• "Over the past two weeks, have you..."
  
• "...gone on a river safari with a student from our class?"
• "...been enrolled in the same course with at least two students from our class?"

Change in Nominations

• Did the treatment affect the posttest?
• What are the implications?

• All subjects had already received a reactivity treatment!
  
• Which question(s)?
• Are surveys even appropriate to test reactivity?
• Deception came later, not well-integrated
• Small sample and absolutely no external validity

• Loading Data
  
• Commands
  

• ...universe?
  
• ...population?
• ...sampling frame?

• ...universe?
• ...population?
• ...sampling frame?

• Convenience Sampling
• Quota Sampling
• Purposive Sampling
• Sequential Sampling
• Deviant Case Design
• Snowball Sampling

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

1. Categories: Females and Males
2. Quotas: Four women and one man
3. Convenience: First to fill out survey

m <- sort(studentdat$timerank[which(studentdat$male)])[1] #First male time ranks
f <- sort(studentdat$timerank[which(!studentdat$male)])[1:4] #First four females time ranks
quota.samp <- which(studentdat$timerank %in% c(m, f)) #Samples
quota.param <- mean(studentdat$commties[quota.samp]) #Measures sample
cat("\n  Population parameter = ", pop.param, ", Sample parameter = ", quota.param, "\n")

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

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

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
   

set.seed(666) #Standardizes random number generator
sb.samp <- function(n = 5, rr = .5, nseeds = 1, net = studentel){
  recruit.wave <- rep(NA, times=vcount(studentel))
  orig.seeds <- sample.int(nrow(studentdat), nseeds)
  new.seeds <- orig.seeds
  ncount <- nseeds
  wave <- 1
  recruit.wave[orig.seeds] <- wave
  deadend <- FALSE
  while ((ncount < n) & !deadend){
    wave <- wave + 1
    new.seeds <- unlist(sapply(new.seeds, neighbors, graph = net))
    if (all(new.seeds %in% orig.seeds))
      deadend <- TRUE
    else{
      new.seeds <- new.seeds[which(new.seeds %in% orig.seeds == FALSE)]
      recruited <- which(rbinom(length(new.seeds), 1, rr)==1)
      new.seeds <- new.seeds[recruited]
      if ((length(recruited) == 0) | (length(new.seeds) == 0))
        deadend <- TRUE
      else{
        orig.seeds <- c(orig.seeds, new.seeds)
        recruit.wave[new.seeds] <- wave
        ncount <- length(orig.seeds)
        }
      }
    }
  return(recruit.wave)
  }

#General plot of "population" sociogram
library(RColorBrewer)
plot.wrap <- function(...)
  plot(studentel, edge.width=7, vertex.label.family="sans", vertex.label.cex=1.25, vertex.label.dist=.85, vertex.label.degree=-1.9*pi/4, ...)
plot.wrap.sb <- function(sb.recruited){
  sb.param <- mean(degree(studentel)[which(!is.na(sb.recruited))])
  sb.maintitle <- "Snowball Sample Simulation"
  sb.sub <- paste("Population Parameter = ", round(pop.param, 3), ", Sample parameter = ", round(sb.param, 3), sep="")
  sb.recruits.color <- rev(brewer.pal(max(sb.recruited, na.rm=TRUE), "Blues"))[sb.recruited]
  sb.recruits.color[is.na(sb.recruited)] <- "white"
  plot.wrap(vertex.color = sb.recruits.color, main = sb.maintitle, sub = sb.sub)  
  }
plot.wrap.sb(sb.samp(5))
plot.wrap.sb(sb.samp(10, rr = .3, nseeds = 2))
plot.wrap.sb(sb.samp(10, rr = .8, nseeds = 2))

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

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

#Sample size of 10, ranging from 1 to 1000set.seed(666)
random.samp <- sample.int(1000, 10)
random.samp
random.samp.meanfun <- function(ss)
  return(mean(studentdat$commties[sample.int(vcount(studentel), ss)]))
cat("\n  Population parameter = ", pop.param, ", Sample parameter = ", round(random.samp.meanfun(5), digits = 3), "\n")

#To demonstrate the central limit theorem
random.mean.5samp.1k <- replicate(1000, random.samp.meanfun(5))
mean.across.randomsamples <- mean(random.mean.5samp.1k)
mean.across.randomsamples <- round(mean.across.randomsamples, digits = 3)
hist(random.mean.5samp.1k, main = "Frequency of Mean Communication Ties\nin 1000 Random Samples of 5", sub = paste("Population Parameter = ", pop.param, ", Mean Across Distributions = ", sep=""), xlab = "Mean Number of Communication Partners")

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

# To demonstrate systematic samples
start.point <- sample.int(vcount(studentel), 1)
k <- 4
first.half <- seq(from = 1, to = start.point, by = k)
second.half <- seq(from = start.point, to = vcount(studentel), by = k)
syst.samp <- unique(c(first.half, second.half))
syst.samp
syst.samp.mean <- mean(studentdat$commties[syst.samp])
syst.samp.mean <- round(syst.samp.mean, digits = 3)
cat("\n  Population parameter = ", pop.param, ", Sample parameter = ", syst.samp.mean, "\n")

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

• Better representation than simple random sampling
  
• Why?

• 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

• Should you use it?
  

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

• Role of telephones in social life
  
• Nonresponse
  
• Privacy
  

• "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?"

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

• 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

• 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

• 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

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

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

• Dependent Variable
• Outcome of Interest

• Independent Variable
• Explanatory Variable/Factor

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

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

• 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

• 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

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

• Language
• Data availability
• Theoretical background

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

1. Spreadsheet
2. Text editor
3. Automated

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

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

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

• "Dead"
• Reliability
• Replication

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

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

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

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

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

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

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

• 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

• 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.

• All participants understood the data would be analyzed
• (Admittedly, for a different purpose)
• The data was collected unobtrusively
• The data was 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"

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

• 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
  

• "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)

• Less gender equity in comics without CCA stamp
• Niche market and limited oversight
• (Not applicable for Dell Comics)
• Less gender equity in comics with CCA stamp
• Code recreate 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

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

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

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

• 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

• 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
  

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

• Primary Sources
• Secondary Sources
• Running Records
• Recollections

• Originality
• Basis of historical knowledge

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

• General understandings
• Broad descriptions

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

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

• Accuracy and recollection
• Sensitivity

• Wider range of observations
• Test cultural sensitivity behind theories

• Laborious
• Sampling
• Limited generalizations

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

• 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

• Lexical
• Contextual
• Conceptual
• Measurement

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

"Number of edges incident upon a node"

"Proportion of observed edges in a network"

• Sociogram

• Sociogram
• Matrix

• Sociogram
• Matrix
• Edgelist

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

• 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)

• 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
  

Path

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

1. Degree
2. Betweenness
3. Closeness

• 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

• Ties going inward
• Ties going outward
• Total ties

1. Homophily
2. Diffusion
3. Tie formation models

1. Generalized
2. Differential
3. Matching

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

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

• 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

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

• Cumulative Advantage
  
• Matthew Effect (Merton)
  

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

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

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

1. High clustering
2. Short path lengths

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

• "Connected caveman graph"
• Random ties 
  

• 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?

• 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.
  

1. Tie formation
2. Tie dissolution

1. Actors do not join or leave
2. Actor attributes remain fixed

• tie formation?
• tie dissolution?

• edges + group homophily
• triangles
• two-paths (dissolution only)

• Edges + Homophily
  
• Probability of a within group tie forming: 0.24
• Probability of a between group tie forming: 0.08
• Probability of a within group tie persisting: 0.80
• Probability of a between group tie persisting: 0.44
• Edges + Homophily + Triangle
• Tie formation
• No significant triangle effect
• Probability of a within group tie forming: 0.18
• Tie dissolution
• Probability of a triangle persisting: 0.71
• No significant homophily effect now

• Differential models
• Formation: Edges + Homophily
• Dissolution: Edges + Triangles + Two-Paths
• Results
• Formation
• Probability of within group tie formation: 0.22
• Probability of between group tie formation: 0.08
• Dissolution
• Probability of a triangle persisting: 0.96
• Probability of a two-path persisting: 0.78

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

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

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

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

1. Calculate an initial measurement
2. Resample within the responses with replacement
3. Recalculate  the measurement
   
1. Repeat Steps 2 and 3 many times (thousands)
4. Construct a confidence interval
1. Infer the population parameter

• 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

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

• I intend to select <A>, but I instead select <B>
• Assumed constants
• Number of vertices, edges
• A student's degree
• Mistakes are random

• 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

• Why or why not?
• Under which conditions?

• The number of vertices in a network
• The number of edges in a network

• Transitivity decreased over time
• Number of edges increased
• Number of vertices stayed the same
• Homophilous networks
• Can interact with transitivity

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

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

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

• Homophily
• Transitivity
• Density
• Relational stability (persistence)
• Reciprocity

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

• Cost
• Respondent cooperation
• Ethical

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

1. Review one network model
2. Review one diffusion process
3. Formulate three hypotheses
4. Simulate network and diffusion process
5. Evaluate hypotheses

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

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

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

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

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

• 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 constant

• 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

• 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.

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

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

             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

            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

                    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

                    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

• 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

1. Fixed set of actors*
2. Monotonic causal flow
3. Univocal meaning*
4. No sequential effects
5. Casewise independence
6. Context independent*

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

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

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

• 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

• 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

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

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