Percentage of nonoverlapping corrected data

In the present study, we proposed a modification in one of the most frequently applied effect-size procedures in single-case data analysis: the percentage of nonoverlapping data. In contrast with other techniques, the calculus and interpretation of this procedure are straightforward and can be easily complemented by visual inspection of the graphed data. Although the percentage of nonoverlapping data has been found to perform reasonably well in N = 1 data, the magnitude of effect estimates that it yields can be distorted by trend and autocorrelation. Therefore, the data-correction procedure focuses on removing the baseline trend from data prior to estimating the change produced in the behavior as a result of intervention. A simulation study was carried out in order to compare the original and the modified procedures in several experimental conditions. The results suggest that the new proposal is unaffected by trend and autocorrelation and that it can be used in case of unstable baselines and sequentially related measurements.     

Influence of deontological versus consequentialist orientations on act choices and framing effects: when principles are more important than consequences

A long tradition in decision making assumes that people usually take a consequentialist perspective, which implies a focus on the outcomes only when making decisions. Such a view largely neglects the existence of a deontological perspective, which implies that people are sensitive to moral duties that require or prohibit certain behaviors, irrespective of the consequences. Similarly, recent research has also suggested that people holding “protected values” (PVs) show increased attention to acts versus omissions and less attention to outcomes. The present research investigates the role of deontological versus consequentialist modes of thought and of PVs on framing effects and act versus omission choices. In a modification of Tversky and Kahneman's ( 1981 ) risky choice framing paradigm, we manipulated the framing of the outcomes (positive, negative), as well as whether the certain outcome was associated with an act or inaction. The main results suggest that act versus omission tendencies are linked to deontological focus and PVs. Framing effects, on the other hand, are driven by a consequentialist focus. Copyright © 2007 John Wiley & Sons, Ltd.     

Sinning Saints and Saintly Sinners: The Paradox of Moral Self-Regulation

ABSTRACT— The question of why people are motivated to act altruistically has been an important one for centuries, and across various disciplines. Drawing on previous research on moral regulation, we propose a framework suggesting that moral (or immoral) behavior can result from an internal balancing of moral self-worth and the cost inherent in altruistic behavior. In Experiment 1 , participants were asked to write a self-relevant story containing words referring to either positive or negative traits. Participants who wrote a story referring to the positive traits donated one fifth as much as those who wrote a story referring to the negative traits. In Experiment 2 , we showed that this effect was due specifically to a change in the self-concept. In Experiment 3 , we replicated these findings and extended them to cooperative behavior in environmental decision making. We suggest that affirming a moral identity leads people to feel licensed to act immorally. However, when moral identity is threatened, moral behavior is a means to regain some lost self-worth.     

Assigning and combining probabilities in single-case studies: A second study

The present study builds on a previous proposal for assigning probabilities to the outcomes computed using different primary indicators in single-case studies. These probabilities are obtained by comparing the outcome to previously tabulated reference values, and they reflect the likelihood of the results in the case that no intervention effect is present. In the present study, we explored how well different metrics are translated into p values in the context of simulation data. Furthermore, two published multiple-baseline data sets were used to illustrate how well the probabilities might reflect the intervention effectiveness, as assessed by the original authors. Finally, the importance of which primary indicator would be used in each data set to be integrated was explored; two ways of combining probabilities were used: a weighted average and a binomial test. The results indicated that the translation into p values worked well for the two nonoverlap procedures, with the results for the regression-based procedure diverging due to some undesirable features of its performance. These p values, when either taken individually or combined, were well aligned with effectiveness for the real-life data. These results suggest that assigning probabilities can be useful for translating the primary measure into the same metric, using these probabilities as additional evidence of the importance of behavioral change, complementing visual analysis and professionals’ judgments.     

Randomization tests for ABAB designs: comparing-data-division- specific and common distributions

Monte Carlo simulations were used to generate data for ABAB designs of different lengths. The points of change in phase are randomly determined before gathering behaviour measurements, which allows the use of a randomization test as an analytic technique. Data simulation and analysis can be based either on data-division-specific or on common distributions. Following one method or another affects the results obtained after the randomization test has been applied. Therefore, the goal of the study was to examine these effects in more detail. The discrepancies in these approaches are obvious when data with zero treatment effect are considered and such approaches have implications for statistical power studies. Data-division-specific distributions provide more detailed information about the performance of the statistical technique.     

Do not put the blame on me: Asymmetric responses to service outcome with autonomous vehicles versus human agents

The present study aims to examine customer responses to negative (vs. positive) service outcome with an autonomous vehicle (AV) vis-à-vis human agent. In the first study we manipulate the outcome of the service (negative vs. positive) and the type of service agent (AV vs. human) to evaluate customers' satisfaction with the service. Based on the results of the first study, in the second study we focus only on the negative outcome scenario and we examine how the perceived blame of the AV (vs. human agent) affects customers' satisfaction. Finally, in the third study we introduce another mediator, namely perceived competence, which explains why people attribute less blame to the AV (vs. human agent). Customers' satisfaction is higher for the AV only in the case of a negative service outcome, while no differences emerge when the service had a positive outcome. Additionally, AVs (vs. human agent) are perceived as less competent and blameworthy, leading to a higher customer satisfaction with the service in case of a negative outcome. These results show a customer under-reaction to negative service outcome with AVs. This has relevance for the design and policy implications related to AVs. Our results also provide insights into the psychological underpinnings of acceptance of AVs replacing human agents. This study extends previous literature by showing circumstances in which customers are more (or less) satisfied with an AVs (vs. human agents) and the psychological drivers of these asymmetrical responses.     

The Paradox of Abstraction: Precision Versus Concreteness

We introduce a novel measure of abstractness based on the amount of information of a concept computed from its position in a semantic taxonomy. We refer to this measure as precision. We propose two alternative ways to measure precision, one based on the path length from a concept to the root of the taxonomic tree, and another one based on the number of direct and indirect descendants. Since more information implies greater processing load, we hypothesize that nouns higher in precision will have a processing disadvantage in a lexical decision task. We contrast precision to concreteness, a common measure of abstractness based on the proportion of sensory-based information associated with a concept. Since concreteness facilitates cognitive processing, we predict that while both concreteness and precision are measures of abstractness, they will have opposite effects on performance. In two studies we found empirical support for our hypothesis. Precision and concreteness had opposite effects on latency and accuracy in a lexical decision task, and these opposite effects were observable while controlling for word length, word frequency, affective content and semantic diversity. Our results support the view that concepts organization includes amodal semantic structures which are independent of sensory information. They also suggest that we should distinguish between sensory-based and amount-of-information-based abstractness.     

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions about intervention effectiveness in single-case designs. Ordinary least square estimation is compared to two correction techniques dealing with general trend and a procedure that eliminates autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approach the nominal ones in the presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.     

Choosing among techniques for quantifying single-case intervention effectiveness

If single-case experimental designs are to be used to establish guidelines for evidence-based interventions in clinical and educational settings, numerical values that reflect treatment effect sizes are required. The present study compares four recently developed procedures for quantifying the magnitude of intervention effect using data with known characteristics. Monte Carlo methods were used to generate AB designs data with potential confounding variables (serial dependence, linear and curvilinear trend, and heteroscedasticity between phases) and two types of treatment effect (level and slope change). The results suggest that data features are important for choosing the appropriate procedure and, thus, inspecting the graphed data visually is a necessary initial stage. In the presence of serial dependence or a change in data variability, the nonoverlap of all pairs (NAP) and the slope and level change (SLC) were the only techniques of the four examined that performed adequately. Introducing a data correction step in NAP renders it unaffected by linear trend, as is also the case for the percentage of nonoverlapping corrected data and SLC. The performance of these techniques indicates that professionals' judgments concerning treatment effectiveness can be readily complemented by both visual and statistical analyses. A flowchart to guide selection of techniques according to the data characteristics identified by visual inspection is provided.     

Moral kinematics: The role of physical factors in moral judgments

Harmful events often have a strong physical component??for instance, car accidents, plane crashes, fist fights, and military interventions. Yet there has been very little systematic work on the degree to which physical factors influence our moral judgments about harm. Since physical factors are related to our perception of causality, they should also influence our subsequent moral judgments. In three experiments, we tested this prediction, focusing in particular on the roles of motion and contact. In Experiment 1, we used abstract video stimuli and found that intervening on a harmful object was judged as being less bad than intervening directly on the victim, and that setting an object in motion was judged as being worse than redirecting an already moving object. Experiment 2 showed that participants were sensitive not only to the presence or absence of motion and contact, but also to the magnitudes and frequencies associated with these dimensions. Experiment 3 extended the findings from Experiment 1 to verbally presented moral dilemmas. These results suggest that domain-general processes play a larger role in moral cognition than is currently assumed.