TEMAP2.R: True and Error model analysis program in R

Quispe-Torreblanca, Edika; Birnbaum, Michael H.

doi:10.1017/s1930297500008706

Cited by 12 publications

(39 citation statements)

References 27 publications

(41 reference statements)

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“…One can also use Bayesian methods to analyze TE models to calculate posterior distributions of the parameters, given suitable priors. Although arguments can be made for possible advantages or disadvantages of these various techniques, analyses of empirical examples so far have yielded very similar solutions and conclusions from the different methods (Lee, 2018;Birnbaum, 2019;Birnbaum & Quispe-Torreblanca, 2018;Schramm, 2020). In this paper, both TE8x8_fit.xlsx and TE8x2_fit.R were applied, minimizing…”

Section: True and Error Fitting Modelmentioning

confidence: 82%

Reanalysis of Butler and Pogrebna (2018) using true and error model

Birnbaum

2020

Judgm. decis. mak.

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Butler and Pogrebna (2018) devised triples of three-branch gambles theorized to violate transitivity of preference according to a most probable winner model. According to this model, a person chooses the option that has the higher probability to yield a better outcome than the other alternative. They tested 11 triples with 100 participants and found cases that appeared to violate weak stochastic transitivity and the triangle inequality. But tests of weak stochastic transitivity and the triangle inequality do not provide a proper method to compare transitive and intransitive models that allow mixtures of preference patterns and random errors. Those older methods can yield false conclusions regarding transitivity, for example, if different participants have different true preferences or if different choice problems have different rates of error. This paper reanalyzes their data using a true and error (TE) model, which does not require these unrealistic assumptions, and which provides estimates of the incidence of transitive and intransitive behavior in a mixture. Reanalysis indicated that 3 of the 11 triples showed convincing evidence of violations of transitivity in the opposite direction of the predictions of the most probable winner model. Further, these and other triples showed other significant violations of the most probable winner model. Despite some violations of the true and error model, the data of Butler and Pogrebna appear to contradict not only transitive utility models but they also refute the most probable winner model as a descriptive theory of choice behavior.

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Section: True and Error Fitting Modelmentioning

confidence: 82%

Reanalysis of Butler and Pogrebna (2018) using true and error model

Birnbaum

2020

Judgm. decis. mak.

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“…However, we must admit that this statistical test does not really rule out EU, once we realize that random errors might True Error Response True Error Response F 1: True and Error Models for two choice problems. In TE4, all four error terms are free; TE2, assumes e = f and e ′ = f ′ ; TE1 assumes e = f = e ′ = f ′ produce inequality of these two types of reversals (Birnbaum & Quispe-Torreblanca, 2018). Birnbaum and Quispe-Torreblanca (2018) noted that the test of correlated proportions in such studies make an unreasonable assumption about errors.…”

Section: Test Of Correlated Proportionsmentioning

confidence: 99%

“…In TE4, all four error terms are free; TE2, assumes e = f and e ′ = f ′ ; TE1 assumes e = f = e ′ = f ′ produce inequality of these two types of reversals (Birnbaum & Quispe-Torreblanca, 2018). Birnbaum and Quispe-Torreblanca (2018) noted that the test of correlated proportions in such studies make an unreasonable assumption about errors. If the two choice problems have different rates of error, then it can easily happen that EU theory can hold and the two frequencies of reversal can be unequal; it can also happen that the two frequencies can be equal and yet EU does not hold.…”

Section: Test Of Correlated Proportionsmentioning

confidence: 99%

“…TEMAP2.R can be applied with relatively small samples, because it employs Monte Carlo simulation to construct the sampling distribution of the fit statistic, and it uses bootstrapping to estimate confidence intervals on the fitted parameters. Birnbaum and Quispe-Torreblanca (2018) used the data in Table 1 to illustrate their program, method, and the models.…”

Section: Index Of Fitmentioning

confidence: 99%

“…1 Introduction Birnbaum and Quispe-Torreblanca (2018) presented software for analysis of True and Error (TE) models (Birnbaum, 2008(Birnbaum, , 2012(Birnbaum, , 2013. If we allow that human responses contain random errors according to these models, it ,follows that standard statistical tests might easily lead to systematically wrong conclusions.…”

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Bayesian and frequentist analysis of True and Error models

Birnbaum

2019

Judgm. decis. mak.

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Birnbaum and Quispe-Torreblanca (2018) presented a frequentist analysis of a family of six True and Error (TE) models for the analysis of two choice problems presented twice to each participant. Lee (2018) performed a Bayesian analysis of the same models, and found very similar parameter estimates and conclusions for the same data. He also discussed some potential differences between Bayesian and frequentist analyses and interpretations for model comparisons. This paper responds to certain points of possible controversy regarding model selection that attempt to take into account the concept of flexibility or complexity of a model. Reasons to question the use of Bayes factors to decide among models differing in fit and complexity are presented. The partially nested inter-relations among the six TE models are represented in a Venn diagram. Another view of model complexity is presented in terms of possible sets of data that could fit a model rather than in terms of possible sets of parameters that do or do not fit a given set of data. It is argued that less complex theories are not necessarily more likely to be true, and when the space of all possible theories is not well-defined, one should be cautious in interpreting calculated posterior probabilities that appear to prove a theory to be true.

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Robust Modeling in Cognitive Science

et al. 2019

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In an attempt to increase the reliability of empirical findings, psychological scientists have recently proposed a number of changes in the practice of experimental psychology. Most current reform efforts have focused on the analysis of data and the reporting of findings for empirical studies. However, a large contingent of psychologists build models that explain psychological processes and test psychological theories using formal psychological models. Some, but not all, recommendations borne out of the broader reform movement bear upon the practice of behavioral or cognitive modeling. In this article, we consider which aspects of the current reform movement are relevant to psychological modelers, and we propose a number of techniques and practices aimed at making psychological modeling more transparent, trusted, and robust.Cognitive modeling | Reproducibility | Open science | Robustness | Model comparison You never want a serious crisis to go to waste . . . This crisis provides the opportunity for us to do things that you could not before.

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TEMAP2.R: True and Error model analysis program in R

Cited by 12 publications

References 27 publications

Reanalysis of Butler and Pogrebna (2018) using true and error model

Reanalysis of Butler and Pogrebna (2018) using true and error model

Bayesian and frequentist analysis of True and Error models

Robust Modeling in Cognitive Science

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