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Modeling Mindless Choice

Abstract Details

2017, Master of Arts, Ohio State University, Psychology.
Experiment participants sometimes do not pay attention to the task that ex- perimenters provide. To manage this problem, researchers often implement data preprocessing techniques to discriminate “good data” from “bad data”. The most commonly used data preprocessing technique is a removal of data points. Such pre- processing is problematic for two reasons. First, deciding what is “bad data” is based on a researcher’s subjective criterion. Second, “bad data” might be generated by an underlying process which is theoretically interesting, and knowing about that process might help to understand the data better. Discarding data without deep considera- tion, then, wastes time, money and potentially useful information. In this project, I am suggesting an alternative to discarding data. The “bad data” generating process should be modeled explicitly. The key concept behind this modeling is a Markov process which can describe choice dependency between trials. Using Bayesian hier- archical modeling, I am suggesting that stimulus-unrelated response processes can be separated from the stimulus-based response process, instead of discarding outlier data points.
Trisha Van Zandt (Advisor)
Paul DeBoeck (Committee Member)
Brandon Turner (Committee Member)
80 p.

Recommended Citations

Citations

  • Choi, S. W. (2017). Modeling Mindless Choice [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512125771227293

    APA Style (7th edition)

  • Choi, Seo Wook. Modeling Mindless Choice. 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1512125771227293.

    MLA Style (8th edition)

  • Choi, Seo Wook. "Modeling Mindless Choice." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512125771227293

    Chicago Manual of Style (17th edition)