Search

A primary advantage of IRT-based patient-reported outcome measures such as PROMIS short forms and computer-adaptive tests is that each estimate of the latent trait comes with a standard error. Such measurement error needs to be acknowledged, in particular when monitoring individual patients over time. In this study, we use plausible values to account for measurement error and analyze the probability of true within-individual change.We use a longitudinal, observational study of stable and exacerbated COPD patients (N = 185), providing PROMIS Physical Function and Fatigue T-scores over 3 months. At each measurement, we imputed 1000 plausible values from the scores' posterior distribution. These were then used to calculate probability of true change using a pre-specified threshold such as minimally important difference supported by the literature, or [Formula: see text]0. We demonstrate assessment of change in individuals and in groups, across different measures (Short Forms and CATs), and at various levels of confidence.Using plausible value imputation and with 95% certainty, 47.5% of participants in the exacerbated group reported less fatigue, compared with 26.5% of participants in the stable group. Comparison of Short Forms and CATs suggests that CATs have better ability to detect change compared to short forms. We also illustrate this method using an individual's probability of change at different time points.Plausible values offer a flexible way to include measurement error in analysis of individuals and on sample level. Assessment of probability of true change can complement existing distribution-based approaches and facilitates interpretation of improvement or decline.

Introduction Early detection of cognitive decline in older adults is a public health priority. Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA), a multisite study, is validating cognition, emotion, motor, and sensory modules of the National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT). Methods Participants 65 to 85 years old, in demographic groups racially proportional to the general US population, are recruited in one of three groups to validate the NIHTB: cognitively normal, amnestic mild cognitive impairment (aMCI), or mild DAT. Additional special emphasis cohorts include (1) Blacks in the three clinical groups; (2) Spanish-speakers in the three clinical groups; (3) cognitively normal, population-proportional, over age 85. Discussion Longitudinal study will determine whether NIHTB can predict cognitive decline and is associated with Alzheimer's disease biomarkers. Here, we detail the methods for the ARMADA study.

A carbon tax is widely accepted as the most effective policy for curbing carbon emissions but is controversial because it imposes costs on consumers. An alternative, ‘nudge,’ approach promises smaller benefits but with much lower costs. However, nudges aimed at reducing carbon emissions could have a pernicious indirect effect if they offer the promise of a ‘quick fix’ and thereby undermine support for policies of greater impact. Across six experiments, including one conducted with individuals involved in policymaking, we show that introducing a green energy default nudge diminishes support for a carbon tax. We propose that nudges decrease support for substantive policies by providing false hope that problems can be tackled without imposing considerable costs. Consistent with this account, we show that by minimizing the perceived economic cost of the tax and disclosing the small impact of the nudge, eliminates crowding-out without diminishing support for the nudge. Behavioural interventions aimed at curbing carbon emissions are inexpensive and easy to implement but can offer the false promise of a quick fix. Across six experiments, the authors show that exposure to a green energy nudge diminishes support for carbon taxes.

The consequences of global warming will be dire, but the full extent of these effects on society is unknown and includes uncertainties. Research now suggests that how scientists communicate about the uncertainty over such climate change impacts can influence the public’s trust and acceptance of this information.

Advances in medical testing and widespread access to the internet have made it easier than ever to obtain information. Yet, when it comes to some of the most important decisions in life, people often choose to remain ignorant for a variety of psychological and economical reasons. We design and validate an information preference scale to measure an individual’s desire to obtain or avoid information that may be unpleasant, but could improve their future decisions. The scale measures information preferences in three domains that are psychologically and materially consequential: consumer finance, personal characteristics, and health. We present tests of the scale’s reliability and validity and show that the scale predicts a real decision to obtain (or avoid) information in each of the three domains, as well as in the domain of politics, which is not explicitly measured in the scale. We find that across settings, many respondents prefer to remain in a state of active ignorance even when information is freely available, and that information preferences are a stable trait but can differ across domains. (Under R&R at Management Science)

Public policymakers routinely receive and communicate information characterized by uncertainty. Decisions based on such information can have important consequences, so it is imperative that uncertainties are communicated effectively. Many organizations have developed dictionaries, or lexicons , that contain specific language (e.g., very likely , almost certain ) to express uncertainty. But these lexicons vary greatly and only a few have been empirically tested. We have developed evidence-based methods to standardize the language of uncertainty so that it has clear meaning understood by all parties in a given communication. We tested these methods in two policy-relevant domains: climate science and intelligence analysis. In both, evidence-based lexicons were better understood than those now used by the Intergovernmental Panel on Climate Change, the U.S. National Intelligence Council, and the U.K. Defence Intelligence. A well-established behavioral science method for eliciting the terms’ full meaning was especially effective for deriving such lexicons.

In large-scale assessments, multiple forms are often administered to minimize test security concerns. To ensure that scores across different forms are comparable, a statistical procedure called equ...

Despite the central role of posttraumatic stress disorder (PTSD) in international humanitarian aid work, there has been little examination of the measurement invariance of PTSD measures across culturally defined refugee subgroups. This leaves mental health workers in disaster settings with little to support inferences made using the results of standard clinical assessment tools, such as the severity of symptoms and prevalence rates. We examined measurement invariance in scores from the most widely used PTSD measure in refugee populations, the Harvard Trauma Questionnaire (HTQ; Mollica et al., 1992), in a multinational and multilingual sample of asylum seekers from 81 countries of origin in 11 global regions. Clustering HTQ responses to justify grouping regional groups by response patterns resulted in three groups for testing measurement invariance: West Africans, Himalayans, and all others. Comparing log-likelihood ratios showed that while configural invariance seemed to hold, metric and scalar invariance did not. These findings call into question the common practice of using standard cut-off scores on PTSD measures across culturally dissimilar refugee populations. In addition, high correlation between factors suggests that the construct validity of scores from North American and European measures of PTSD may not hold globally.