Although researchers often assume their participants are naive to experimental materials, this is not always the case. We investigated how prior exposure to a task affects subsequent experimental results. Participants in this study completed the same set of 12 experimental tasks at two points in time, first as a part of the Many Labs replication project and again a few days, a week, or a month later. Effect sizes were markedly lower in the second wave than in the first. The reduction was most pronounced when participants were assigned to a different condition in the second wave. We discuss the methodological implications of these findings.

While individual differences in decision-making have been examined within the social sciences for several decades, this research has only recently begun to be applied by computer scientists to examine privacy and security attitudes (and ultimately behaviors). Specifically, several researchers have shown how different online privacy decisions are correlated with the "Big Five" personality traits. However, in our own research, we show that the five factor model is actually a weak predictor of privacy preferences and behaviors, and that other well-studied individual differences in the psychology literature are much stronger predictors. We describe the results of several experiments that showed how decision-making style and risk-taking attitudes are strong predictors of privacy attitudes, as well as a new scale that we developed to measure security behavior intentions. Finally, we show that privacy and security attitudes are correlated, but orthogonal.

Despite the plethora of security advice and online education materials offered to end-users, there exists no standard measurement tool for end-user security behaviors. We present the creation of such a tool. We surveyed the most common computer security advice that experts offer to end-users in order to construct a set of Likert scale questions to probe the extent to which respondents claim to follow this advice. Using these questions, we iteratively surveyed a pool of 3,619 computer users to refine our question set such that each question was applicable to a large percentage of the population, exhibited adequate variance between respondents, and had high reliability (i.e., desirable psychometric properties). After performing both exploratory and confirmatory factor analysis, we identified a 16-item scale consisting of four sub-scales that measures attitudes towards choosing passwords, device securement, staying up-to-date, and proactive awareness.

In their study about the Dr. Fox lecture, Naftulin, Ware, and Donnelly (1973) claimed that an expressive speaker who delivered an attractive lecture devoid of any content could seduce students into believing that they had learned something significant. Over the decades, the study has been (and still is) cited hundreds of times and used by opponents of the measurement of student evaluations of teachers (SET) as empirical proof for the lack of validity of SET. In an attempt to formulate an alternative explanation of the findings, we replicated the 1973 study, using the original video of the lecture and following the exact methodology of the original study. The alternative explanations tested on several samples of students included (a) acquiescence bias (via a reversed questionnaire and a cognitive remedy); (b) ignorance bias (participants’ lack of familiarity with the lecture content); (c) status/prestige bias (presentation of the speaker as a world authority); and (d) a direct measurement of students’ reports about their presumed learning. The Dr. Fox effect was indeed consistently replicated in all samples. However, the originally proposed notion of educational seduction leading to presumable (illusory) student learning was ruled out by the empirical findings: Students indeed enjoyed the entertaining lecture, but they had not been seduced into believing they had learned. We discuss the relevance of metacognitive considerations to the inclusion of self-reported learning in this study, and to the wider issue of the incorporation of student learning in the contemporary measurement of SET.

Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.

Confessions are people’s way of coming clean, sharing unethical acts with others. Although confessions are traditionally viewed as categorical—one either comes clean or not—people often confess to only part of their transgression. Such partial confessions may seem attractive, because they offer an opportunity to relieve one’s guilt without having to own up to the full consequences of the transgression. In this article, we explored the occurrence, antecedents, consequences, and everyday prevalence of partial confessions. Using a novel experimental design, we found a high frequency of partial confessions, especially among people cheating to the full extent possible. People found partial confessions attractive because they (correctly) expected partial confessions to be more believable than not confessing. People failed, however, to anticipate the emotional costs associated with partially confessing. In fact, partial confessions made people feel worse than not confessing or fully confessing, a finding corroborated in a laboratory setting as well as in a study assessing people’s everyday confessions. It seems that although partial confessions seem attractive, they come at an emotional cost.

Research showed that given the opportunity, people behave dishonestly only to the degree that will allow them to maintain a positive self‐concept. These experiments did not include the probability of getting caught cheating, although in everyday life, this risk always exists. If it is shown that people behave more honestly when faced with an explicit risk of getting caught, the ecological validity of these experiments may be at risk. This study showed that explicit risk, framed as the probability of getting caught or as the complementary probability of not getting caught, did not reduce participants’ dishonest behavior relative to no risk. These findings support the ecological validity of previous research on unethical behavior. 

People generally overestimate the time they can save when increasing from a relatively high driving speed. Previous research suggested that people follow a Proportion Heuristic, calculating the time saved as the proportion of speed increase from the new higher speed. The present study suggests that drivers use another heuristic – the Percentage Heuristic – to calculate how much time they save by increasing speed. In the percentage heuristic, the initial (rather than higher) speed is used as the denominator. Using a discriminating set of questions, we classified participants' responses as normative (correct answer), as following the proportion or percentage heuristic, or some other strategy. We found that participants used the percentage heuristic more often, perhaps because it predicts linearly increasing values of time saved when increasing speed. In addition, we found that participants high in need for cognition (NFC) gave correct answers more often than low NFC participants who relied more on heuristics.