Disgust, Need for Affect, and Responses to Microbiome Research

Abstract

Contributing to the literature on affective processing and attitude formation, this study investigates the effects of a discrete emotion (disgust) and an emotional disposition (need for affect [NFA]) on support for regulation in the context of microbiome research. Data from a web-based experiment (N = 1,005) showed that experienced disgust mediated the effect of disgust-eliciting information on support for regulation. This mediated relationship was moderated by NFA. More specifically, NFA moderated the path between experienced disgust and regulatory attitudes but not that between message exposure and experienced disgust. The two dimensions of NFA played different moderating roles: Emotional approach amplified the relationship between disgust and the attitudinal outcome, whereas emotional avoidance attenuated it. The study furthers understanding about how NFA influences emotional processing and contributes to research on disgust as a relatively understudied discrete emotion.

Acknowledgments

This material is based on work supported by the American Society for Microbiology (ASM). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the ASM.

Notes

¹ Qualtrics partners with online market research panel providers and randomly selects respondents from them (Qualtrics, 2014; for more information on online panels, see the report of the American Association for Public Opinion Research, 2010).

² Individuals were notified via panel real-time software, e-mail, or text and invited to participate in the survey for an incentive. Incentives are given on a point system; points can be pooled and redeemed in the form of gift cards and credit for online purchases, among others. The points given for successfully taking the survey were given at variable amounts, depending on several factors. A total of 1,702 panelists started to participate in the survey; 1,343 completed the survey, yielding an initial completion rate of 78.9%. Because some potential respondents may have been invited by the panel system’s real-time software, it is not known how many individuals were actually invited to participate in the survey. As such, a response rate cannot be fully calculated. The data set provided by Qualtrics consisted of 1,282 responses after removal of responses that did not meet the quality control criterion (i.e., those who failed the “attention filter” question). We implemented another filter based on the completion time (i.e., removing 20 responses with the z score of completion time with 1 SD above or below the mean), resulting in a sample size of 1,262. From the analyses reported in this article, we removed the control condition (n = 257). In the control condition (122 words), participants were presented the basic information about microbiomes (i.e., the two paragraphs describing the definition of “microbiome” and the trend of microbiome-related research, which was also part of the material for all four experimental conditions). The control group was not included in the analyses reported in this article, as a fully crossed and balanced design allows for more effective testing and interpretation of interaction effects between the two experimental factors.

³ CFAs reported in this study were run using the “sem” package in Stata 14.0 (StataCorp, 2015). For the one-factor model, χ²(35) = 2548.4, p < .001, root mean square error of approximation (RMSEA) = 0.267, 90% CI [0.259, 0.276], comparative fit index (CFI) = 0.501, standardized root mean square residual (SRMR) = 0.231. For the two-factor model, χ²(34) = 240.68, p < .001, RMSEA = 0.078, 90% CI [0.069, 0.087], CFI = 0.959, SRMR = 0.062.

⁴ The one-factor model failed to yield a satisfactory fit: χ²(2) = 104.97, p < .001, RMSEA = 0.226, 90% CI [0.191, 0.264], CFI = 0.947, SRMR = 0.051. Modification indices suggested a strong correlation between disgust and distaste, as well as that between anger and fear. We then ran a two-factor CFA model with disgust and distaste loaded on one factor and anger and fear the other. The two-factor model was a much better fit to the data: χ²(1) = 6.42, p < .001, RMSEA = 0.073, 90% CI [0.028, 0.132], CFI = 0.997, SRMR = 0.006. The second model, compared to the first, was a significant improvement: Δχ²(1) = 98.55, p < .001.

⁵ Computations followed the process described in Hayes (2013, pp. 405–407) and Hayes (2015).

Additional information

Funding

This work was supported by the American Society for Microbiology.

Notes on contributors

Ye Sun

Ye Sun (Ph.D., University of Wisconsin–Madison, 2008) is an Associate Professor in the Department of Communication at the University of Utah. Her research addresses various questions related to individual perceptions of media effects, especially in the domain of health communication.

Sara K. Yeo

Sara K. Yeo (Ph.D., University of Wisconsin–Madison, 2014) is an Assistant Professor in the Department of Communication and a faculty affiliate with the Global Change and Sustainability Center and the Environmental Humanities Program at the University of Utah. Her research interests include science and risk communication, particularly focusing on information seeking and processing. She is also trained as a bench and field scientist with a M.S. in Oceanography from the University of Hawaii at Manoa.

Meaghan McKasy

Meaghan McKasy (M.A., University of Utah, 2011) is a PhD candidate in the Department of Communication at the University of Utah. Her research focuses on environmental communication with a specific interest in energy communication.

Erika Shugart

Erika Shugart (Ph.D., University of Virginia, 1997) is the CEO of the American Society for Cell Biology. Her professional career, including previous jobs at the National Academy of Sciences and the American Society for Microbiology, and research interests include her scientific background in biological research, skills in science communication, and leadership in marketing and strategic planning.