Attributions of Responsibility for Obesity: Narrative Communication Reduces Reactive Counterarguing Among Liberals

Informed by theory and research on attributions and narrative persuasion, we compared the effectiveness of narrative and nonnarrative messages in changing attributions of responsibility for causes and solutions related to obesity in the United States. We randomly assigned 500 adults to view one of three messages (narrative, evidence, and a hybrid of the two) emphasizing environmental causes of obesity, or a no‐exposure control condition. The narrative condition increased the belief that societal actors (government, employers) are responsible for addressing obesity, but only among liberals. This pattern of results was partially explained by the success of the narrative condition in reducing reactive counterarguing, relative to the evidence condition, among liberals. We conclude with theoretical and practical implications.     

The Struggle to Prevent and Evaluate: Application of Population Attributable Risk and Preventive Fraction to Suicide Prevention Researach

Population attributable risk (PAR) estimates have been used in suicide research to evaluate the impact of psychosocial and socioeconomic risk factors, including affective disorders, traumatic life events, and unemployment. A parallel concept of preventive fraction (PF), allowing for estimation of the impact of protective factors and effectiveness of preventive interventions, is practically unknown in suicidology. The study authors discuss the application of both concepts to suicide research and prevention, and review literature on the subject. Despite several methodological and conceptual limitations, both PAR and PF are valuable instruments to inform development and evaluation of suicide prevention programs.     

Functional measurement approach to self-estimation in multiattribute evaluation

Multiattribute analysis depends on measurement of values and weights. Unless these measures reflect the decision maker's true values and weights, the multiattribute formula may put a less-preferred alternative in first place. To avoid such disordinality requires stringent measurement conditions: First, the values and weights must be on linear (equal interval) or ratio (known zero) scales. Second, these scales must satisfy a condition of common unit across disparate attribute dimensions. Most methods of range adjustment beg both of these measurement questions. Functional measurement theory can solve both problems and so can be useful in multiattribute analysis. Past work has established the operation of a general cognitive algebra as an empirical reality. The averaging model, in particular, makes possible the definition and estimation of weights and values as distinct psychological parameters. It can also solve the problem of common unit. Cognitive algebra thus provides a grounded theoretical foundation on which to develop self-estimation methodology, in which decision makers provide direct estimates of their values and weights. The logic is straightforward. Functional measurement can analyze global judgments to obtain validated psychological scales. These scales may then be used as validational criteria for the self-estimates. Procedures to eliminate biases in the self-estimates can thus be tested and refined in well-learned multiattribute tasks, such as judgments of meals, in which global judgments are trustworthy. Once developed, such self-estimation procedures may be used with some confidence for general multiattribute analysis. A number of studies from 20-odd years of work on the theory of information integration are summarized to show good, although not unmixed promise for self-estimation.