###### Abstract
In light of this, why not directly collect the information in a binary \"like/dislike\" format? This would make it easier for students to fill out the questionnaire. In addition, students are typically familiar with the binary format since it is used in a large number of social networks where a binary opinion is asked (\"thumb up/thumb down\"). Will a binary survey provide the same information as the usual Likert-scale survey? There is a possibility that the results derived from binary response formats may differ from those obtained with multi-category ordinal formats, be less reliable, or be perceived as more difficult to obtain because respondents are less familiar with multi-category scales. Hypothesis testing is the statistical method commonly adopted in psychological research. Over the last century, several proposals have emerged to perform hypothesis testing in a statistically valid way. One of the best-known approaches is the null hypothesis statistical significance test (NHST) of [12], an approach situated within the frequentist school of thought [13]. In it, the evidence against a null hypothesis (\(H_{0}\): no effect or difference) is quantified using a probability. When a predetermined threshold \(\alpha\) is exceeded, it is rejected. Usually, a 5% threshold is used. This is expressed as a level of 0.05 and the null hypothesis is rejected when the \(p\)-value is less than that value. The \(p\)-value quantifies the probability of obtaining data equal to or more extreme than the results observed, under the assumption that the null hypothesis is true. With the rejection of \(H_{0}\), the test is said to be significant. For a given sample size, the lower the \(p\)-value, the greater the evidence against \(H_{0}\). Possible causes for a non-significant contrast include (1) sampling variability or poor luck in sampling that prevents rejecting \(H_{0}\), even though the intervention contemplated in the experiment produces a change; and (2) the intervention actually produces no effect. As the sample size increases, the problem of sampling variability can be solved. However, even if significant \(p\)-values indicate evidence against \(H_{0}\), non-significant \(p\)-values do not allow one to conclude that the data support the null hypothesis. This is elegantly summarized as \"absence of evidence is not evidence of absence\" [14]. Bayesian thinking is rapidly gaining popularity among psychologists and neuroscientists [15] for reasons such as flexibility, higher accuracy of data with noise and small samples, lower tendency for type I errors or false positives, the possibility of introducing prior knowledge into the analysis, and the intuitive and simple interpretation of results [16; 17; 18; 19]. In recent years, as discussed, frequentist thinking has become associated with the \(p\)-value approach and null hypothesis significance testing (NHST). The misinterpretation and misuse of \(p\)-values, so-called \"p-hacking\" [20], has contributed to the reproducibility crisis in psychological science [21]. Regarding Bayesian hypothesis testing as a replacement or alternative to NHST and \(p\)-values, there are a variety of a posteriori indices that have been proposed in the statistical literature [19]. Conceptually, these indices are all based on the posterior distribution in some form, and are employed to test a null hypothesis such as \(H_{0}\) against an alternative \(H_{1}\). The mathematical theory behind each of the proposed posterior indices differs substantially, and examples include the Bayes factor [22; 23], the region of practical equivalence (ROPE) [24], the direction probability (DP), and the Full Bayesian Significance Test (FBST) [25]. Some works have compared the results of these indices in models such as linear regression or two-sample parametric tests [26]. For this work, we decided to use the Bayes factor mainly because it is able to support or reject the null hypothesis, in addition to being one of the most traditional metrics.