Revisiting the Gain–Loss Separability Assumption in Prospect Theory

A fundamental assumption of prospect theory is gain–loss separability (GLS)—the assertion that the overall utility of a prospect can be expressed as a function of the utilities of its positive and negative components. Violations of GLS may potentially limit the generalization of results from studies of single‐domain prospects to mixed prospects and systematically distort the predictions of the theory. Violations also have implications for how choices with positive and negative components should be presented to decision makers. Previous studies, using different elicitation methods, have documented different rates, and types, of systematic violations of GLS. We discuss the differences between two specific elicitation methods—binary choice and certainty equivalents—and report results of a new study of GLS using both methods and randomly generated prospects. We compare the extent and nature of GLS violations under the two elicitation methods using between‐subject and within‐subject analyses. We find (i) systematic violations of GLS under both methods, (ii) higher rates of violations under choice, (iii) higher sensitivity to the outcomes for the certainty equivalents, which is consistent with the predictions of the scale‐compatibility hypothesis, and (iv) different patterns of violations under the two methods, which are explained by method‐specific preferences. We discuss the psychological mechanisms underlying the findings and the implications for presenting information with gain and loss components. Copyright © 2012 John Wiley & Sons, Ltd.     

Mixed Generation Sensitivity Analysis: An Optimal Scenario Study

There are several generating units available to meet the load demand on any given day. The mixed‐integer programme case engages generating units that can be shut down or operated between minimum and maximum output levels within a specific time demand block. An examination of these conditions espouses the utilization of renewable energy technology as a reliable power generation source and its effectiveness and augmentation in a robust, heavily subjugated thermal generation system. The mixed‐integer problem has the predisposition to generate many subproblems during the computation. However, with careful selection of the parameters in the model, the challenges are easily overcome. Hydroelectric energy is significant from an operational standpoint as it requires little or no ramp‐up time, commonly found in many combustion technologies. The essential load‐following capability, peaking capacity and voltage stability attributes render hydroelectric energy as a significant part in ensuring reliable electricity service and in meeting customer needs in a market‐driven industry. When this renewable resource is combined with the thermal generation, an improved reliability function is apparent. Yet, in order to minimize the environmental effects inherent in this type of energy resource, the renewable power supply is expected to produce an effectual, consistent output. The integration of renewable generation within the electrical system entails intricate analysis of the transmission line upgrades throughout the system. These upgrades are designed to relieve congestion, improve reliability and provide voltage support by thoroughly evaluating the alternatives to accommodate the new expansion efforts. This paper examines the system integration of renewable generators, via simulated studies, in the context of electrical operations from the hydroelectric generation perspective. Copyright © 2014 John Wiley & Sons, Ltd.     

Application of the Within- and Between-Series Estimators to Non-normal Multiple-Baseline Data: Maximum Likelihood and Bayesian Approaches

In single-case research, multiple-baseline (MB) design provides the opportunity to estimate the treatment effect based on not only within-series comparisons of treatment phase to baseline phase observations, but also time-specific between-series comparisons of observations from those that have started treatment to those that are still in the baseline. For analyzing MB studies, two types of linear mixed modeling methods have been proposed: the within- and between-series models. In principle, those models were developed based on normality assumptions, however, normality may not always be found in practical settings. Therefore, this study aimed to investigate the robustness of the within- and between-series models when data were non-normal. A Monte Carlo study was conducted with four statistical approaches. The approaches were defined by the crossing of two analytic decisions: (a) whether to use a within- or between-series estimate of effect and (b) whether to use restricted maximum likelihood or Markov chain Monte Carlo estimations. The results showed the treatment effect estimates of the four approaches had minimal bias, that within-series estimates were more precise than between-series estimates, and that confidence interval coverage was frequently acceptable, but varied across conditions and methods of estimation. Applications and implications were discussed based on the findings.     

Children's preference for mixed‐ versus fixed‐ratio schedules of reinforcement: A translational study of risky choice

Laboratory research has shown that when subjects are given a choice between fixed‐ratio and bi‐valued mixed‐ratio schedules of reinforcement, preference typically emerges for the mixed‐ratio schedule even with a larger ratio requirement. The current study sought to replicate and extend these findings to children's math problem completion. Using an ABCBC reversal design, four fourth‐grade students were given the choice of completing addition problems reinforced on either a fixed‐ratio 5 schedule or one of three mixed‐ratio schedules; an equivalent mixed‐ratio (1, 9) schedule, a mixed‐ratio (1, 11) schedule with a 20% larger ratio requirement, and an equally lean mixed‐ratio (5, 7) schedule without the small fixed‐ratio 1 component. This was followed by a reversal back to the preceding phase in which preference for the mixed‐ratio schedule had been observed, and a final reversal back to the mixed‐ratio (5, 7) phase. Findings were consistent with previous research in that all children preferred the mixed‐ratio (1, 9) schedule over the equivalent fixed‐ratio 5 schedule. Preference persisted for the leaner mixed‐ratio (1, 11) schedule for three of the four children. Indifference or preference for the fixed‐ratio 5 alternative was observed in phases containing the mixed‐ratio (5, 7) schedule. These results extend previous research on risky choice to children's math problem completion and highlight the importance of a small ratio component in the emergence of preference for bi‐valued mixed‐ratio schedules. Implications of these results for arranging reinforcement to increase children's academic responding are discussed.     

有中介的调节变量和有调节的中介变量

研究了有中介的调节效应模型和有调节的中介效应模型,两种模型都涉及到中介变量和调节变量。分别讨论了这两种模型的分析方法,两者基本上都是按中介效应模型的分析步骤,但其中都有一步要做调节效应的分析。还讨论了同时包含有中介的调节变量和有调节的中介变量的混合模型。最后,作为示范例子,用混合模型分析儿童行为对同伴关系的影响,结果发现,如果混合模型中的调节都设定是线性的,那么调节变量对因果关系是二次调节     

Do seductive details do their damage in the context of graph comprehension? Insights from eye movements

In educational research, interesting but irrelevant materials are often considered seductive details, which are suspected to have detrimental effects on learning. Although seductive details have been mostly examined in the context of text comprehension, such elements are also used in graphs (e.g., depicting data points). In the present experiment, we investigated both seductive text and seductive pictures in the context of graph comprehension as well as the interaction of seductive details with spatial working memory capacity (SWMC). We recorded N = 68 students' eye movements, while they analyzed bar graphs in a within‐subject design. Data were analyzed with linear mixed‐effects models. Results show that seductive details did not affect students' graph reading performance but prolonged the task processing time. Eye‐fixation measures revealed that additional processing time was best explained by attention distraction towards the seductive material. SWMC did not affect the presence or the extent of the seductive details effect.     

What role does substance use play in the social world of Australian people who have experienced homelessness? A critical realist mixed‐methods exploration

Substance use is prevalent amongst Australians who have experienced homelessness, but the social impacts of using substances amongst this group are poorly understood. This study uses a relational and critical realist approach to understand the role of substance use on the social lives of people with lived experience of homelessness. A mixed‐methods design was used to explore this question and assess the experiences of people who have exited homelessness. Recruiting across services catering to marginalised populations, 110 participants completed a cross‐sectional survey. Findings showed substance use both socially connected and disconnected participants, and that attempting abstinence required participants to negotiate and alter their social networks. To participants, being a substance “user,” or “non‐user,” was a social identity that they actively negotiated to facilitate competing social, psychological and material/embodied needs. Participants chose to engage and not engage with others to craft a social identity they valued, which had the sometimes‐negative side‐effect of limiting potential relationships, leaving users feeling isolated from their non‐users and a continued imperative to keep using. The impacts of substance use on social networks were similar for currently and formerly homeless participants, suggesting transitioning into housing may not immediately prompt a reduction in substance use or influence from substance‐using peers. Interventions with substance users with lived experience of homelessness would benefit from taking a social identity approach that incorporates an understanding of the material/embodied aspects of addiction to provide a holistic model of care that best supports the relational needs of the client.     

Populist polarization in postcolonial Philippines: Sociolinguistic rifts in online drug war discourse

Social psychological scholarship on populism explains polarization processes in terms of individual differences and group‐level divisions. However, predominant approaches often elide wider historical contexts, implicitly assuming the structural features of Western settings. Invoking insights from postcolonial psychology, we posit that populist polarization dynamics in the Global South are structurally shaped by colonial histories. Turning to the Philippines under populist President Rodrigo Duterte, we examine polarized online constructions of his controversial war on drugs. Employing a discourse‐historical approach, we uncover a structurally bifurcated view of populist polarization. We specifically unpack sociolinguistic rifts across vernacular discourses of community security uttered in Filipino versus cosmopolitan discourses of democratic integrity uttered in English. Our findings expand prevailing scholarship by illustrating postcolonial discursive possibilities of populism from above and resistance from below. We conclude with insights for studying populist polarization at the nexus of local and global inequalities.     

A Note on Comparing the Estimates of Models for Cluster-Correlated or Longitudinal Data with Binary or Ordinal Outcomes

When using linear models for cluster-correlated or longitudinal data, a common modeling practice is to begin by fitting a relatively simple model and then to increase the model complexity in steps. New predictors might be added to the model, or a more complex covariance structure might be specified for the observations. When fitting models for binary or ordered-categorical outcomes, however, comparisons between such models are impeded by the implicit rescaling of the model estimates that takes place with the inclusion of new predictors and/or random effects. This paper presents an approach for putting the estimates on a common scale to facilitate relative comparisons between models fit to binary or ordinal outcomes. The approach is developed for both population-average and unit-specific models.