The goal of this paper is to make modeling and quantitative testing accessible to behavioral decision researchers interested in substantive questions. We provide a novel, rigorous, yet very general, quantitative diagnostic framework for testing theories of binary choice. This permits the nontechnical scholar to proceed far beyond traditionally rather superficial methods of analysis, and it permits the quantitatively savvy scholar to triage theoretical proposals before investing effort into complex and specialized quantitative analyses. Our theoretical framework links static algebraic decision theory with observed variability in behavioral binary choice data. The paper is supplemented with a custom-designed public-domain statistical analysis package, the QTest software. We illustrate our approach with a quantitative analysis using published laboratory data, including tests of novel versions of “Random Cumulative Prospect Theory.” A major asset of the approach is the potential to distinguish decision makers who have a fixed preference and commit errors in observed choices from decision makers who waver in their preferences.
Birnbaum (2011, 2012) questioned the iid (independent and identically distributed) sampling assumptions used by state-of-the-art statistical tests in Regenwetter, Dana and Davis-Stober’s (2010, 2011) analysis of the “linear order model”. Birnbaum (2012) cited, but did not use, a test of iid by Smith and Batchelder (2008) with analytically known properties. Instead, he created two new test statistics with unknown sampling distributions.Our rebuttal has five components: 1) We demonstrate that the Regenwetter et al. data pass Smith and Batchelder’s test of iid with flying colors. 2) We provide evidence from Monte Carlo simulations that Birnbaum’s (2012) proposed tests have unknown Type-I error rates, which depend on the actual choice probabilities and on how data are coded as well as on the null hypothesis of iid sampling. 3) Birnbaum analyzed only a third of Regenwetter et al.’s data. We show that his two new tests fail to replicate on the other two-thirds of the data, within participants. 4) Birnbaum selectively picked data of one respondent to suggest that choice probabilities may have changed partway into the experiment. Such nonstationarity could potentially cause a seemingly good fit to be a Type-II error. We show that the linear order model fits equally well if we allow for warm-up effects. 5) Using hypothetical data, Birnbaum (2012) claimed to show that “true-and-error” models for binary pattern probabilities overcome the alleged short-comings of Regenwetter et al.’s approach. We disprove this claim on the same data.
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