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Ego-centered Network Analysis Meredith Rolfe, Oxford University Using Sample Surveys to Study Social Networks Connect to wireless Download file

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1 Ego-centered Network Analysis Meredith Rolfe, Oxford University Using Sample Surveys to Study Social Networks Connect to wireless Download file

2 Whole Networks…. Source: Christakis and Fowler, 2008

3 ….vs. EgoNets

4 Pros and Cons of Egonets Whole Networks  Often limited samples (college/high school students, work groups)  Political attitudes are often tack-ons, if collected at all  Subjects enjoy  Lots of well-developed analysis methods  Nice pictures  Recall/Missing Data Ego Networks  General Sample  Included in widely available attitude and election studies (NES, GSS, South Bend, ILS, NCCS, BES, CCAP)  Respondent burden  Recall/Reporting errors  Sample attrition

5 EGO NETWORK MEASURES: 5 POLITICAL DISCUSSANTS The Traditional Approach

6 Surveys with Political Networks data  Columbia School (Elmira)  Detroit Area Studies  South Bend 1985 (Huckfeldt and Sprague 1985)  Indianapolis-St. Louis (1996 ILS) (Huckfeldt and Sprague 2000)  ANES (years)  Spencer Foundation 2000 (Mutz)  General Social Survey (1985/1987)  CNES 1992  Great Britain, Germany, Japan, Spain, and the United States  Snowball sample of Spouses (D2) and Other Discussants (D3)  CCAP (US, UK, Germany)

7 Political Discussion Name Generator

8 Structure of the Data: Original (Wide) Main Resp Name 1Name 2…Name 5D1.MaleD2.Male… 1JohnDean…NA11… 2SuePete…Aya01… 3KateSara…Ewan00…

9 Example: CNES & CCAP  Read in Data cnes.d1<- read.dta("http://dl.dropbox.com/u/ /cnes.d1.dta") cnes.d2<- read.dta("http://dl.dropbox.com/u/ /cnes.d2.dta") cnes.d3<- read.dta("http://dl.dropbox.com/u/ /cnes.d3.dta") ccap<-read.dta(http://dl.dropbox.com/u/ /ccap.dta)http://dl.dropbox.com/u/ /ccap.dta  Summary of Data summary(cnes.d1) colnames(cnes.d1) summary(cnes.d2) summary(cnes.d3) summary(ccap.nets)

10 Structure of the Data: Transformed to Dyadic (Long) Main Resp R.VoteD.NumNam e D.MaleD.EducD.Vote… 1Dem1John1CollRep… 1Dem2Dean1HSDem… 2Rep1Sue0HSRep… 2 2Pete1CollDK 2Rep…… 2 5Aya0CollRep 3DK1Kate0HSDem 3DK2Sara0HSDK 3 … 3 5Ewan1HSDem

11 Transform the Data to Long Format  Step 1 - “line up” the variables correctly shape<-c(seq(2,10,by=2),seq(3,11,by=2)) for(i in 12:23) shape<-c(shape,seq(i,i+48, by=12)) temp<-NULL for(i in seq(1,66,5)) temp<- c(temp,list(names(cnes.sm)[shape[i:(i+4)]]))  Step 2 – reshape cnes.long<-reshape(cnes.d1, varying=temp, idvar="caseid", timevar="discnum", v.names=c("d.given", "d.name", "d.relate", "d.cowork", "d.church", "d.nghbr", "d.friend", "d.close", "d.educ", "d.discpol", "d.disagree", "d.male", "d.expert", "d.vote"), direction="long", time=1:5)  Step 3 – Do any variable recoding (see polnet20011.R)

12 Personal Network Size  Discussant Name Given? (d1.given – d5.given) cnes.d1$netsize<-rowSums(cnes.d1[,grep(“d[1-5]_given”, colnames(cnes.d1))]==1, na.rm=TRUE) egen netsize = neqany(d1.given d2given d3given d4given d5given), values(1)  Analysis attach(cnes.d1) table(netsize) hist(netsize, breaks=6) ##also see lattice version summ netsize tab netsize

13 Non-response  No one to talk to tapply(married, netsize==0,mean.na ) 35% are married!  Nothing to talk about table(sp_talkpol, married)  Low political interest prop.table(table(polint, netsize==0),2) chisq.test(polint, netsize)  Forgetting  Non-compliance

14 Online survey issues: Invalid responses  Telephone and Face to Face surveys, no invalid answers that could be verified  Online – there could be many invalid answers! attach(ccap) table(b2.pn1.cat) table(b2.pn1[b2.pn1.cat=="missing (sure)"])  Invalid answers can increase/decrease depending on non-response format and forced/semi-forced choice options prop.table(table(b2.pnum==b2.pnum.orig))

15 Political Discussants Named

16 Network size: Political Discussion sub-network  Need to identify political discussants from important matters discussants table(d.discpol) table(tapply(d.discpol%in%c("often", "sometimes", "rarely"), caseid, sum.na))  Assign back to “wide” format file cnes.d1$pnum[order(cnes.d1$caseid)]<- tapply(d.discpol%in%c("often", "sometimes”), caseid, sum.na)  Can set different thresholds for discussion table(tapply(d.discpol%in%c("often", "sometimes”), caseid, sum.na)

17 Graphic: Network Size of Impt. Matters and Political Discussion Networks par(mfrow=c(2,2)) hist(cnes.d1$netsize, breaks=6, main="Important Matters", xlab="Network Size") hist(cnes.d1$pnum, breaks=6, main="Talk Politics Subnetwork", xlab="Network Size") hist(tapply(d.discpol%in%c("often", "sometimes"), caseid, sum.na), breaks=6, main="Talk Politics Sometimes Subnetwork",xlab="Network Size") hist(tapply(d.discpol%in%c("often"), caseid, sum.na), breaks=6, main="Talk Politics Often Subnetwork", xlab="Network Size") ###also see lattice alternative

18 EGO NETWORK DESCRIPTION: NETWORK COMPOSITION The Traditional Approach

19 Personal Network Composition  Who does R discuss politics with? Family, friends, coworkers?  Is political discussion primarily a male activity?  How politically interested are R’s discussants?  Does R disagree about politics with discussants?

20 Traditional Name Interpreters  How is [name 1] connected to you?  spouse or partner  other relative [specify]  unrelated  Is [name 1] a coworker?  Is [name 1] a member of same church?  Is [name 1] a neighbor?

21 CCAP Online Survey Political Discussion Name Interpreter

22 Structure of the Data: Original (Wide) Main Resp Name 1Name 2…Name 5D1.MaleD2.Male… 1JohnDean…NA11… 2SuePete…Aya01… 3KateSara…Ewan00…

23 Structure of the Data: Transformed to Dyadic (Long) Main Resp R.VoteD.NumNam e D.MaleD.EducD.Vote… 1Dem1John1CollRep… 1Dem2Dean1HSDem… 2Rep1Sue0HSRep… 2 2Pete1CollDK 2Rep…… 2 5Aya0CollRep 3DK1Kate0HSDem 3DK2Sara0HSDK 3 … 3 5Ewan1HSDem

24 Non-response: a potential issue  Respondents who name NO discussants  Omit Rs from all composition measures  Divide by netsize=0 or NA will omit R  Respondents who don’t provide one or more composition variables  Omit discussants with invalid or missing information from R’s netsize (must adjust manually for each characteristic)  table(d.given[is.na(d.relate)])

25 Personal Network Composition: Who does R discuss politics with? Family, friends, etc?  Number of discussants that are family members table(tapply(d.relate%in%c(”spouse", ”family”), caseid, sum.na))  Proportion of discussants that are family members x<-tapply(d.relate%in%c("spouse", "family"), caseid, sum.na)/tapply(d.given==1 & (d.relate%in%c("dk","rf"))==FALSE, caseid,sum.na) summary(x) mean.na(x==0) mean.na(x==1)

26 Personal Network Composition: Who does R discuss politics with? (continued)  Proportion of discussants that are coworkers x<-tapply(d.cowork=="yes", caseid, sum.na)/tapply(d.given==1 & (d.relate%in%c("dk","rf"))==FALSE & (d.cowork%in%c("dk","rf"))==FALSE, caseid,sum.na) summary(x) mean.na(x==0) mean.na(x>.5)

27 Personal Network Composition: Is political discussion primarily a male activity?  Proportion of discussants that are male prop.table(table(d.male)[1:2])  Proportion of POLITICAL discussants that are male prop.table(table(d.male[d.discpol%in%c("often", "sometimes", "rarely")])[1:2]) prop.table(table(d.male[d.discpol%in%c("often", "sometimes”)])[1:2]) x<-tapply(d.male=="male" & d.discpol%in%c("often", "sometimes"), caseid, sum.na)/tapply(d.given==1 & (d.male%in%c("dk","rf"))==FALSE & d.discpol%in%c("often", "sometimes"), caseid, sum.na)

28 Personal Network Composition: Is political discussion primarily a male activity? (cont.) summary(x) prop.table(table(x==0)) prop.table(table(x>.5)) prop.table(table(x==1))  Frequency of political discussion with male discussants print(prop.table(table(d.discpol, d.male)[,1:2]), digits=2) chisq.test(table(d.discpol, d.male)[,1:2]) t.test((4-unclass(d.discpol))~d.male, data=cnes.long[d.male%in%levels(d.male)[1:2],])

29 Personal Network Composition: How politically informed are R’s discussants? (cont.)  Distribution of expertise among all or political discussants print(prop.table(table(d.expert)[1:3]), digits=2) print(prop.table(table(d.discpol, d.expert)[,1:3],1), digits=2)  Do R’s prefer informed discussants? chisq.test(table(d.discpol, d.expert)[,1:3])  How many R’s have highly informed discussants? x<-tapply(d.expert=="Great deal", caseid, sum.na)/tapply(d.given==1 & (d.expert%in%c("dk", "rf"))==FALSE,caseid, sum.na) table(x==0) table(x>.5)

30 Political Network Composition

31 Network composition: Do R and discussants disagree about politics? Self-reported Disagreement When you discuss politics with [name], do you disagree table(d.disagree) print(prop.table(table(d.disa gree, d.vote!=vote)[1:4,],1), digits=2) Self-reported Vote Choice Differences Which candidate do you think [name] supported in the presidential election this year? table(d.vote, vote) prop.table(table(d.vote== vote))[2] print(prop.table(table(d.d isagree, d.vote!=vote)[1:4,],2), digits=2)

32 Misperception of Political Preferences of Discussants: Snowball Sample Huckfeldt & Sprague (1987) Non voter Reagan (Disc) Mondal e (Disc) Nonvote r.222 (9).790 (19).818 (11) Reagan.400 (20).912 (170).662 (65) Mondale.333 (15).547 (53).992 (90) Merge data files tp replicate detach(cnes.long) temp<- merge(cnes.d3,cnes.d2, by=c(colnames(cnes.d3)), all=TRUE) cnes.match<-merge(cnes.long, temp,by=c("caseid","discnum"), all=FALSE) cnes.match$correct<- cnes.match$d.vote== cnes.match$act.both.vote attach(cnes.match)

33 Misperception: Analysis  Does misperception depend on discussant vote? prop.table(table(d.vote==act.vote,d.vote),2)  Does misperception depend on respondent vote? prop.table(table(d.vote==act.vote,vote),2)  Does misperception depend on agreement? print(tapply(correct, list(vote, act.vote), mean.na), digits=2)

34 EGO NETWORK ANALYSIS: PARTICIPATION AND INFLUENCE The Traditional Approach

35 Personal Network Analysis: Traditional Approaches  One time wave (with snowball)  Instrument for discussant vote choice (H&S, 1991)  Two or more survey waves (change)  Kenny (1994)  Nested/hierarchical models  van Duijn, Busschbach and Snijders 1999  Lubbers et al 2010  de Miguel Luken and Tranmer 2010  Respondent driven sampling (snowball)  Gile and Handcock (2010), Goel and Salganik (2010)  Use in UCInet/whole network software if enough ties

36 Changes in Personal Networks (from Feld, Suitor and Existence of TiesNature of ties Dyadic TieWhich ties that come and go Examples: Selection How characteristics of ties change Example: Persuasion Personal Network Expansion and contraction of network Example: Stop discussing politics Change in overall characteristics of network Example: increase in average political disagreement

37 Political Participation  McClurg 2003  South bend, dyadic transformed  DV: index of political participation  Specification: negative binomial regression model3  Controls for socioeconomic status, politically-relevant attitudes, generalized civic engagement, and political mobilization  See Also: McClurg 2006 (“wide” data)  See Also: La Due Lake and Huckfeldt, 1998

38 Dyadic Particiapation:Spouses, Family, andFriends

39 “Wide” Participation model

40 Influence  Kenny (1994) “Microenvironment of Attitude Change” Journal of Politics  OLS on transformed dyadic data  DV is directional change in pid from pre-election to post-election (-6 to 6)  Includes national and local level context variables  Discussion network in 3 rd wave

41 Influence model

42 EGO NETWORK MEASURES: NETWORK MECHANISMS The Traditional Approach

43 Network Mechanisms & Measures  Weak ties  Neighbors or Coworkers  Strong ties  Contact frequency  transitive ties  reciprocated ties (snowball)  Centrality  Self-report  Social capital  Organisational memberships  Network size  Network diversity  Do you know a…  % who disagree  % with diff demographics

44 Network Styles: Activities

45 Network Style: Time Use

46 DRAWBACKS AND SIGNIFICANT ISSUES The Traditional Approach

47 Threats to analysis and inference  Mistaken perceptions of discussant political views (minimal)  Sample issues with snowball  Selection vs. influence (difficult to disentangle)  Loosely defined traditional mechanisms  Very narrow conception of how networks might impact political behavior  No network structure  Little thought to social cleavages & social groups (old school)

48 Solutions?  Longitudinal studies with shorter measures  Formal modeling (mathematical and simulation) identifies new network mechanisms  New questions to tap into the mechanisms & cleavages  Other test implications if we think interaction matters (usually with formal models)  Network structure measures


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