Assessing the Effects of Partisan Bias at the Group Level of Analysis: A Hidden Profile Experiment

• Author: Daniel E. Bergan,Brian Manata,Gwen M. Wittenbaum,Franklin J. Boster
• DOI: 10.1177/1532673X18788052
• Published date: 01 November 2019
• Date: 01 November 2019
• Subject Matter: Articles

https://doi.org/10.1177/1532673X18788052

American Politics Research

2019, Vol. 47(6) 1283 –1302

© The Author(s) 2018

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DOI: 10.1177/1532673X18788052

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https://doi.org/10.1177/1532673X18788052

American Politics Research

2019, Vol. 47(6) 1283 –1302

© The Author(s) 2018

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DOI: 10.1177/1532673X18788052

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Article

Assessing the Effects

of Partisan Bias at the

Group Level of Analysis:

A Hidden Profile

Experiment

Brian Manata1, Franklin J. Boster2,

Gwen M. Wittenbaum2, and Daniel E. Bergan2

Abstract

Although there is some evidence in the political arena that pooling

information can overcome individual biases to improve decision-making

accuracy, research from the group communication and psychology arenas

suggests otherwise. Specifically, research on the hidden profile, a group-

level decision-making problem, suggests that groups are decidedly biased

when making decisions. This laboratory experiment tested whether or not

partisan biases manifest at the group level of analysis. In the main, it was

found that groups composed of either all Republican or all Democratic

group members were likely to make a decision that was consonant with their

party’s political ideology, which ultimately impacted hidden profile solution

rates (i.e., decision accuracy). Moreover, supplemental analyses suggest that

Republican and Democratic groups reached their biased decisions through

different means. A discussion is provided in which the implications of these

results are considered.

Keywords

motivated reasoning, partisan bias, decision-making, hidden profile, groups

1Portland State University, OR, Portland, USA

2Michigan State University, East Lansing, MI, USA

Corresponding Author:

Brian Manata, Portland State University, P.O. Box 751, Mailstop SP-COMM, Portland,

OR 97207-0751, USA.

Email: manata@pdx.edu

788052APRXXX10.1177/1532673X18788052American Politics ResearchManata et al.

research-article2018

1284 American Politics Research 47(6)

Despite concerns about biased reasoning (Kunda, 1990; Taber & Lodge,

2006), there is some evidence in the political arena that pooling information

appears to overcome individual biases to improve decision-making. For

example, the forecasting successes of political markets, in which markets

pool private information to provide accurate election forecasts, suggest

that, in the aggregate, people’s judgments are accurate (Berg, Forsythe,

Nelson, & Rietz, 2008; Wolfers & Zitzewitz, 2004). Similarly, classic work

in public opinion suggests that in spite of the idiosyncrasies of individual

political attitudes (Converse, 1964), in the aggregate, public opinion is sta-

ble and responds to real world events in a rational manner (Page & Shapiro,

1992).

Ultimately the ability to discuss and share information in groups could be

important in reducing the impact of individual biases in political decision-

making. Deliberation forums, for instance, in which people discuss contro-

versial political issues in a structured setting, have been shown to influence

people’s attitudes (Fishkin & Luskin, 2005). Moreover, allowing people to

consider multiple dimensions of controversial issues rather than considering

only one salient dimension has been shown to benefit decision-making qual-

ity (Druckman & Nelson, 2003).

Nevertheless, literature on the hidden profile, a group-level decision-mak-

ing paradigm (Stasser & Titus, 1985), suggests that, under some general con-

ditions, groups fail to overcome individual biases. As a decision-making

problem, the hidden profile task typically unfolds in two phases. First, groups

are required to assess the favorability of multiple hypothetical decision alter-

natives (Wittenbaum, Hollingshead, & Botero, 2004). Second, members are

asked to convene and choose the group’s preferred alternative (e.g., best job

candidate, Cruz, Boster, & Rodriguez, 1997). Notably, some alternatives are

assigned more positive attributes (optimal alternatives) when compared with

others (suboptimal alternatives), which implies that, by weight of the evi-

dence, some alternatives are better options than others (Wittenbaum, 2010).

Presumably, when all information about each of the alternatives is avail-

able and pooled, choosing the optimal alternative is a simple task. In desig-

nating candidate attributes as either shared (known by all members) or

unshared (known by a single member), however, the difficulty of solving the

hidden profile task increases substantially. Specifically, despite the existence

of an optimal solution, information about each of the alternatives is largely

unshared and dispersed throughout the group (Stasser & Titus, 2003;

Wittenbaum, 2010; Wittenbaum et al., 2004). Hence, solving the hidden pro-

file and thus making an optimal decision require that members exchange

unshared information and engage in the “systematic and balanced explora-

tion of relevant issues” (Stasser & Titus, 1985, p. 1467).