Promoting climate actions: A cognitive-constraints approach.

The present paper reports an experiment with a two-year-delayed (M = 695 days) follow-up that tests an approach to raising willingness to take political and personal climate actions. Many Americans still do not view climate change as a threat requiring urgent action. Moreover, among American conservatives, higher science literacy is paradoxically associated with higher anthropogenic climate-change skepticism. Our experimental materials were designed to harness the power of two central cognitive constraints - coherence and causal invariance, which map onto two narrative proclivities that anthropologists have identified as universal - to promote climate action across the political spectrum. Towards that goal, the essential role of these constraints in the causal-belief-formation process predicts that climate-change information would be more persuasive when it is embedded in a personal climate-action narrative, the evocation of which can benefit from exposure to parsimonious scientific explanations of indisputable everyday observations, juxtaposed with reasoners' own, typically less coherent explanations, occurring in a context that engages their moral stance. Our brief one-time intervention, conducted in ten U.S. states with the highest level of climate skepticism, showed that across the political spectrum, our materials raised appreciation of science, openness to alternative views, and willingness to take climate actions in the immediate assessment. It also raised how likely were reports two years later of having taken those actions or would have taken them had the opportunity existed, suggesting a long-lasting effect. Our approach adopts the framework that conceptions of reality are representations, and adaptive solutions in that infinite space of representations require cognitive constraints to narrow the search.

Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model.

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 (not at all true) and 1 (a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 (very true) versus 2 (mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.

Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression.

Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is "a better fit" or more "valid" than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

Cognitive and motivational factors driving sharing of internet memes.

As naturally occurring examples of folk culture and creativity, internet memes provide a rich testbed to examine the interrelationships among cognitive and motivational factors that influence their impact. In two studies with participants recruited over the internet, we measured a variety of appraisals of both apolitical and political memes with a focus on the role of metaphorical aptness and personal relatability as predictors of comprehensibility and humor. Structural equation modeling was used to analyze interconnections among appraisals. A major network path connects relatability to aptness, which in turn is linked to appraisals of comprehensibility, humor, and propensity to share. For political memes, the congruity of the meme with the person's political position (liberal or conservative) has a powerful but indirect impact on the propensity to share it. These findings indicate that appraisals of memes are based on cognitive and motivational processes that also underlie metaphor comprehension and appreciation of humor.