Andrew R. Smith
Appalachian State University
Department of Psychology
Boone, NC, 28608
AppState Web Page
Broadly speaking, my research concerns factors that influence judgment and decision processes. Imagine, for example, that John's doctor suggested that he take a medication designed to reduce his heart attack risk. While John's impression of his heart-attack risk-and his decision about taking the drug-should primarily reflect his individual risk, it is clear that he can be influenced by a variety of factors. John might be motivated to view himself as low risk because of his desire to avoid a heart attack. Or, John might compare his risk with a group of high-risk friends and, consequently, feel better about his lower chances. Given the multitude of influences on John's decision, it is important to understand what these factors are, their specific mechanisms, and what (if anything) can be done to mitigate their biasing influence.
My research tends to focus on three general types of influences: non-motivated, motivated, and social comparative influences. I have looked at these influences across a wide variety of domains including health and perceptions of risk, consumer decisions, general-knowledge estimates (e.g., the populations of countries), aesthetic preferences, and predictions about competitive outcomes. Furthermore, I have conducted research using students, local community residents, and online samples. This broad approach is useful because it highlights the generality of the particular phenomena of interest, as well as provides insights into domains or populations that might be uniquely susceptible (or immune) to certain biases.
More specifically, my areas of interest include: wishful thinking, comparative optimism (and pessimism), the sample size bias (group size effect), anchoring and adjustment, affective and behavioral forcasting, and perceptions of risk and likelihood.
Smith, A. R. & Windschitl, P. D. (2011). Biased calculations: Numeric anchors influence answers to math equations. Judgment and Decision Making, 6, 139-146. [Full Article]
Smith, A. R. & Price, P. C. (2010). Sample size bias in the estimation of means. Psychonomic Bulletin & Review, 17, 499-503. [Full Article]
Windschitl, P. D., Smith, A. R., Rose, J. P., & Krizan, Z. (2010). The desirability bias in predictions: Going optimistic without leaving realism. Organizational Behavior and Human Decision Processes, 111, 33-47. [Full Article]
Windschitl, P. D., Rose, J. P., Stalkfleet, M. T., & Smith, A. R. (2008) Are people excessive or judicious in their egocentrism? A modeling approach to understanding bias and accuracy in people's optimism within competitive contexts. Journal of Personality and Social Psychology, 95, 253-273. [Full Article]
Price, P. C., Smith, A. R., & Lench, H. C. (2006). The effect of target group size on risk judgments and comparative optimism: The more the riskier. Journal of Personality and Social Psychology, 90, 382-398. [Full Article]
Smith, A. R., Windschitl, P. D., & Bruchmann, K. (under review). Knowledge matters: Anchoring effects are moderated by knowledge level.
Smith, A.R. Windschitl, P.D., & Rose, J.P. Biases in comparative and probability judgments: Under one theoretical framework.
Smith, A.R. What do spoilers spoil?
Smith, A.R. If only things were different: Overestimating the influence of counterfactual situations.
Smith, A.R. & Windschitl, P.D. The (lack of) downstream consequences of comparisons with anchors.
Smith, A.R. & Windschitl, P.D. The consequences of anchoring for WTP and purchase likelihood judgments.
Smith, A.R. & Windschitl, P.D. Exploring the relationship between knowledge and anchoring effects: Is the type of knowledge important?