Twitter Adoption in U.S. Legislatures: A Fifty-State Study James Cook @JamesCookUMA University of Maine at Augusta materials at jamescookuma.com

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Twitter Adoption Rates: U.S. Senators: 100% U.S. Representatives: 96.6% source: Josh Shpayher, govsm.com @JamesCookUMA

Look Down: Why State Legislatures? Gridlock vs. considerable policy activity

Look Down: Why State Legislatures? Gridlock vs. considerable political activity # people (1 vs 100 vs 435 vs 7,378)

Look Down: Why State Legislatures? Gridlock vs. considerable political activity # people (1 vs 100 vs 435 vs 7,378) # organizations (1 vs 1 vs 1 vs 50) general theory from individual case? replication structural variation

Association of Twitter Adoption with Independent Variables (U. S Association of Twitter Adoption with Independent Variables (U.S. Legislative Studies to Date) Variable Negative Assoc. No Association Positive Assoc. Status: Woman US (‘10) US (‘12), WI (‘13) Status: Legislative Veteran US (‘12) US (‘10, ‘10, ‘10), WI (‘13), NE (‘12) Status: Republican WI (‘13) USS (‘10), US (‘12), TX (‘11), NE (‘12) US (‘09, ’09, ‘10, ‘10, ‘10) Status: Leadership Status: Upper Chamber US (‘09, ‘12) @JamesCookUMA

Association of Twitter Adoption with Independent Variables (U. S Association of Twitter Adoption with Independent Variables (U.S. Legislative Studies to Date) Variable Negative Assoc. No Association Positive Assoc. District: Younger US (‘10), USS (‘10) US (‘10), USH (‘10) District: Higher Income USH (‘10) US (‘10, ‘10) WI (‘13) District: Higher Education US (’10, ‘10), USS (‘10), WI (‘13) State: % w/ Internet Access -- State: Population per Legislator State: Legislature Professionalism State: Instability (Party Switch/Split) @JamesCookUMA

Current Research Dependent Variables Analyses 50 State Legislatures 7,378 Legislators Dependent Variables Twitter Account? (65.1%) Active on Twitter ‘14-’15? (55.7%) Analyses 50 Separate Bivariate Analyses for Individual States 1 Pooled Multivariate Analysis of All States @JamesCookUMA

studied just one state legislature? Analysis 1: What would we find if we studied just one state legislature? @JamesCookUMA

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{ { { Consistently positive, Inconsistent statistical significance Inconsistent direction, Inconsistent statistical significance { Consistently negative, Inconsistent statistical significance @JamesCookUMA

What patterns emerge if we pool data, Analysis 2: What patterns emerge if we pool data, enabling the use of multivariate analysis and state-level structural variables? @JamesCookUMA

Logistic Regression Models (DV=Adoption) Logistic Regression Models (DV=Activity) Independent Variable Model 1 Model 2 Republican 0.125 0.139 Majority Party -0.076 -0.079 Woman 0.329*** 0.386*** Upper Chamber 0.333** 0.340*** Legislative Leader 0.444*** 0.383** Legislative Veteran -0.089 -0.169* District % BA+ 0.022*** 0.025*** Median Income ($thousands) 0.007 0.003 Median Age -0.049** -0.039*** % Households with Internet Access in State   -0.022 Party Switch/Split 10-15 0.062 Population/Legislator in State (thousands) 0.016*** State Squire Index 1.834* Intercept 1.710** 1.960 N 7277 Pseudo-R2 0.063 0.166 Independent Variable Model 1 Model 2 Republican 0.002 0.045 Majority Party -0.121 -0.133 Woman 0.228*** 0.273*** Upper Chamber 0.331** 0.341*** Legislative Leader 0.437*** 0.386*** Legislative Veteran -0.332** -0.421*** District % BA+ 0.017*** 0.020*** Median Income ($thousands) 0.014 0.013 Median Age -0.047** -0.039*** % Households with Internet Access in State   -0.016 Party Switch/Split 10-15 0.059 Population/Legislator in State (thousands) 0.013*** State Squire Index 2.113** Intercept 1.309* 1.317 N 7277 Pseudo-R2 0.066 0.165 * p<.05, ** p<.01, ***p<.001

So What?

Future Work Structural Interactions with Main Effects Network Analysis Other Clearly Bounded, Exhaustive and Mutually-Exclusive Groups System Size Group Size Your ideas??? Homophily Tie Strength Registered Lobbyists Chambers of Commerce Trade Associations Faculties* Boards of Directors * See Philippe Mongeon et al, Session 1C