I'm a South Dakota native who earned my PhD in social psychology with a minor in statistics and quantitative methods at Ohio State University working with Dr. Russ Fazio. I'm currently a postdoctoral researcher of marketing at Northwestern University in the Kellogg School of Management.
I'm interested in a fundamental question: How do we navigate our worlds? Pivotal to any decision we make is the positivity and negativity we have associated with the items important to that decision. In general, then, I'm interested in evaluations and opinions (attitudes), how they affect what we do, and how we can better understand them.
I use a variety of viewpoints for asking and answering questions related to evaluations and opinions including a traditional consumer behavior perspective as well as natural language, neurobiological, and cognitive perspectives. I use a variety of methods to answer my questions including in-laboratory experiments, pharmacological treatments, longitudinal interventions, and “big data” naturalistic text analysis.
Feel free to get in touch with me by e-mailing me here.
We began with a very simple question: why do we have so many words to express our likes and dislikes?
Why use the word "amazing" versus "perfect"? Why "fantastic" versus "excellent"? While these words all appear to be quite positive, we have the intuition that they differ. But in what way?
Our work reveals that these words have the ability to signal different degrees of emotionality, valence, and extremity. Based on this observation, we created the Evaluative Lexicon (EL): a quantitative linguistic tool that measures these facets of people's opinions, with a special focus on emotionality.
Using the EL, we have found that attitude emotionality is predictive of more extreme final judgments in over 15 million real-world online reviews (Rocklage and Fazio 2015; Rocklage, Rucker, and Nordgren in press; Rocklage and Fazio under review), greater consistency in expressing opinions across contexts (Rocklage and Fazio 2016), greater ability for consumers to quickly indicate their opinion – due in part to its ability to provide an important signal to them (Rocklage and Fazio in press; Rocklage, Durso, Way, and Luttrell in prep), and even the future success of restaurants and Super Bowl commercials (Rocklage, Rucker, and Nordgren in prep).
We are currently developing easy-to-use software for the EL, but click here to download the EL 2.0 wordlist and please me e-mail me for further help on how to use it in the meantime.
I use beans to understand people's biases.
No really, I do.
Imagine the following scenario: You're playing a game and you've learned that one kind of bean is positive (you gain +10 points when you approach it), but you've learned another kind of bean is negative (you lose -10 points when you approach it). What do you do when you see a new bean that equally resembles both the positive and negative one you saw earlier?:
We use an in-lab game, affectionately dubbed BeanFest, to measure this bias. And it turns out that the way people decide whether that novel bean is positive or negative has a big impact on how they live their lives. People who give greater weight to the positive bean when categorizing the novel bean are less impacted by negative word-of-mouth, more likely to try new things, make riskier decisions, less likely to ruminate after a negative experience, less sensitive to the possibility of being rejected by others, and even make more friends over time (e.g., Rocklage and Fazio 2014; Rocklage, Pietri, and Fazio 2017; Fazio, Pietri, Rocklage, and Shook 2015).
Interestingly, people don't seem to be able to report this weighting bias: self-beliefs about our weighting bias don't correspond to the measurement we obtain in the lab.
If you're interested in using the BeanFest paradigm, click here to download the Python version of BeanFest I created, or if you want more information, check out our BeanFest home page here.
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