Expanding the Conversation: Multiplier Effects From a Deliberative Field Experiment

Abstract

Do formal deliberative events influence larger patterns of political discussion and public opinion? Critics argue that only a tiny number of people can participate in any given gathering and that deliberation may not remedy—and may in fact exacerbate—inequalities. We assess these criticisms with an experimental design merging a formal deliberative session with data on participants’ social networks. We conducted a field experiment in which randomly selected constituents attended an online deliberative session with their U.S. Senator. We find that attending the deliberative session dramatically increased interpersonal political discussion on topics relating to the event. Importantly, after an extensive series of moderation checks, we find that no participant/nodal characteristics, or dyadic/network characteristics, conditioned these effects; this provides reassurance that observed, positive spillovers are not limited to certain portions of the citizenry. The results of our study suggest that even relatively small-scale deliberative encounters can have a broader effect in the mass public, and that these events are equal-opportunity multipliers.

Keywords:

• deliberation
• social networks
• political discussion
• field experiment

Notes

1. While some prominent studies have avoided calling discussion a form of participation (Burns, Schlozman, & Verba, 2001; Verba et al., 1995), the (sizable) literature on interpersonal networks (e.g., Huckfeldt & Sprague, 1995; Mutz, 2006), studies it as both an independent and dependent variable, treating it as a central feature of democratic politics (for a related—though non-network-focused—examination of “everyday deliberation” as participation, see Jacobs, Cook, & Delli Carpini [2009]).

2. In “hidden profile” experiments, participants in small group decision-making sessions are given different levels of information to solve a common problem prior to face-to-face discussion (see Stasser & Titus, 2003, for an overview).

3. For discussions of motives as they relate to political discussion, see Eveland and colleagues (2011) and Lyons and Sokhey (2014).

4. Descriptive statistics and a cursory comparison of network characteristics between our study and several nationally representative ones appear in the online Supplemental Material.

5. We administered the baseline survey July 18–25, 2008. In line with many egocentric network studies (e.g., Mutz’s Spencer Foundation Study), network size was censored at three individuals. This likely limits the number of weaker ties in our data set (see Sokhey & Djupe, 2014, for a discussion of name-generator methodology).

6. These background materials—along with data, code, and other supporting information (SI) analyses—are available on the Connecting to Congress Dataverse (thedata.org).

7. We administered the posttreatment survey August 5–8, 2008.

8. The question of “who participates” is in itself an important one, which we directly examine in another article using two distinct, yet related studies (Neblo et al., 2010). For present purposes we treat this question as a methodological problem. Compliance among the partial-control (information-only) group was not monitored.

9. As in many observational (survey-based) studies, we also have some non-response/missing data. Across all conditions in the initial sample, 70% of individuals responded to the survey one week after the session. These response rates are calculated using AAPOR RR6, which is appropriate for opt-in survey panels (Callegaro & DiSogra, 2009, p. 1022). Critically, we find no statistically significant differences in survey non-response in the post-survey based on group assignment. Our analyses use conventional list-wise deletion, though in conducting robustness checks—for example, using matching (see footnote 11)—we employed multiple imputation techniques. Table 1 results are robust to these choices.

10. We present summary statistics/balance tests for all control variables—across assignment groups—in the SI materials (available at the Dataverse). We also tested whether there were any differences in propensities to discuss detention issues, asking about the number of people—in an open-ended format—with whom respondents discussed U.S. detention policy in the pre-survey (we found no statistically significant differences). We also used matching to further improve balance and to address model dependence (Ho, Imai, King, & Stuart, 2007a); this produced the same substantive conclusions.

11. The F-statistic is calculated as:

where SSR stands for the sum of squared residuals, H₀ is the model without the interaction, and H_A is the model with the interaction. Error bounds on the interaction can be somewhat misleading (since an interaction can be statistically significant while only marginally decreasing (or even increasing) the sum of squared residuals).

12. The SI document presents additional results concerning disagreement (broadly conceived). Specifically, we include 3-way interactions conditioning on (a) session participation, dyad type (spouse), and dyad-disagreement, and (b) disagreement in the session versus disagreement in respondent networks. We find some evidence that disagreement with the session (affect toward Levin; respondent partisanship) conditions the aforementioned network disagreement effects.

Additional information

Notes on contributors

David M. Lazer

David M. Lazer is a professor of political science and computer science at Northeastern University and Harvard University. Anand E. Sokhey is an associate professor of political science at the University of Colorado at Boulder. Michael A. Neblo is an associate professor of political science at The Ohio State University. Kevin M. Esterling is a professor of political science at the University of California at Riverside. Ryan Kennedy is an associate professor of political science at the University of Houston.

Anand E. Sokhey

David M. Lazer is a professor of political science and computer science at Northeastern University and Harvard University. Anand E. Sokhey is an associate professor of political science at the University of Colorado at Boulder. Michael A. Neblo is an associate professor of political science at The Ohio State University. Kevin M. Esterling is a professor of political science at the University of California at Riverside. Ryan Kennedy is an associate professor of political science at the University of Houston.

Michael A. Neblo

David M. Lazer is a professor of political science and computer science at Northeastern University and Harvard University. Anand E. Sokhey is an associate professor of political science at the University of Colorado at Boulder. Michael A. Neblo is an associate professor of political science at The Ohio State University. Kevin M. Esterling is a professor of political science at the University of California at Riverside. Ryan Kennedy is an associate professor of political science at the University of Houston.

Kevin M. Esterling

David M. Lazer is a professor of political science and computer science at Northeastern University and Harvard University. Anand E. Sokhey is an associate professor of political science at the University of Colorado at Boulder. Michael A. Neblo is an associate professor of political science at The Ohio State University. Kevin M. Esterling is a professor of political science at the University of California at Riverside. Ryan Kennedy is an associate professor of political science at the University of Houston.

Ryan Kennedy

David M. Lazer is a professor of political science and computer science at Northeastern University and Harvard University. Anand E. Sokhey is an associate professor of political science at the University of Colorado at Boulder. Michael A. Neblo is an associate professor of political science at The Ohio State University. Kevin M. Esterling is a professor of political science at the University of California at Riverside. Ryan Kennedy is an associate professor of political science at the University of Houston.