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Insufficient resources are associated with negative consequences including decreased valuation of future reinforcers. To determine if these effects result from scarcity, we examined the consequences of acute, abrupt changes in resource availability on delay discounting-the subjective devaluation of rewards as delay to receipt increases. In the current study, 599 individuals recruited from Amazon Mechanical Turk read a narrative of a sudden change (positive, neutral, or negative) to one's hypothetical future income and completed a delay discounting task examining future and past monetary gains and losses. The effects of the explicit zero procedure, a framing manipulation, was also examined. Negative income shock significantly increased discounting rates for gains and loses occurring both in the future and the past. Positive income windfalls significantly decreased discounting to a lesser extent. The framing procedure significantly reduced discounting under all conditions. Negative income shocks may result in short-term choices.

The goal of this exploratory study was to compare sternum drop—the decrease in sternum height during an attempt to recover balance after tripping—between lab-induced trips and naturally occurring real-world trips. Twenty community-dwelling adults 71.8 (4.6) years old used three inertial measurement units (IMUs) and a wrist-worn voice recorder daily for three weeks to capture sternum drop during any naturally-occurring real-world trips. Participants then completed a single laboratory testing session during which they were intentionally exposed to two lab-induced trips while wearing the same IMUs to also evaluate sternum drop. All real-world trips resulted in recoveries while only 12 of the 22 lab-induced trips resulted in recoveries (the remaining 10 were falls). When including all lab-induced trips, sternum drop after real-world trips was 8.8 cm smaller ( p  < 0.001), exhibited less variance ( p  < 0.001), and was not associated with lab-induced trips ( R 2  = 0.005; p  = 0.757). When only including lab-induced trips that resulted in recoveries, sternum drop after real-world trips did not differ from the lab ( p  = 0.163), exhibited less variance ( p  < 0.001) and was not associated with lab-induced trips ( R 2  = 0.006; p  = 0.766). These results were likely dependent upon 1) our lab protocol that required participants to walk at a gait speed that was likely faster than typical gait speed in the real-world, and 2) the aggressive lab tripping obstacle height of 8.6 cm was likely taller than at least some real-world trips. While reducing gait speed and obstacle height in future laboratory studies may improve agreement with real-world trips, this would lower the physical demands during recovery and may not be as effective at revealing factors contributing to falls. Nevertheless, additional research appears warranted to clarify the linkage between lab and real-world trips. To our knowledge, this is the first study comparing tripping kinematics between the lab and real world.

Modification and prevention of risky sexual behavior is important to individuals' health and public health policy. This study employed a novel sexual discounting task to elucidate the effects of social learning and regret expression on delay to sexual gratification in a behavioral task. Amazon Mechanical Turk Workers were assigned to hear one of three scenarios about a friend who engages in similar sexual behavior. The scenarios included a positive health consequence, a negative health consequence or a negative health consequence with the expression of regret. After reading one scenario, participants were asked to select from 60 images, those with whom they would have casual sex. Of the selected images, participants chose one image each for the person they most and least want to have sex with and person most and least likely to have a sexually transmitted infection. They then answered questions about engaging in unprotected sex now or waiting some delay for condom-protected sex in each partner condition. Results indicate that the negative health outcome scenario with regret expression resulted in delayed sexual gratification in the most attractive and least STI partner conditions, whereas in the least attractive and most STI partner conditions the negative health outcome with and without regret resulted in delayed sexual gratification. Results suggest that the sexual discounting task is a relevant laboratory measure and the framing of information to include regret expression may be relevant for prevention of risky sexual behavior.

The two-way unreplicated layout remains a popular study design in the physical sciences. However, detection of statistical interaction and subsequent inference has been problematic in this class of designs. First, lack of replication precludes inclusion of standard interaction parameters. Second, while several restricted forms of interaction have been considered, existing approaches focus primarily on accept/reject decisions with respect to the presence of interaction. Approaches to estimate cell means and error variance are lacking when the possibility of interaction exists. For these reasons, we propose model selection and averaging-based approaches to facilitate statistical inference when the presence of interaction is uncertain. Hidden additivity, a recently proposed and intuitive form of interaction, is used to accommodate latent group-based nonadditive effects. The approaches are fully Bayesian and use the Zellner-Siow formulation of the mixture g-prior. The method is illustrated on three empirical datasets and simulated data. The estimates from the model averaging approach are compared with a customized regularization approach which shrinks interaction effects toward the additive model. The study concludes that Bayesian model selection is a fruitful approach to detect hidden additivity, and model averaging allows for inference on quantities of interest under model uncertainty with respect to interaction effects within the two-way unreplicated design.

Hypothetical rewards are commonly used in studies of laboratory-based tobacco demand. However, behavioral economic demand procedures require confirmation that the behavior elicited from real and hypothetical reward types are equivalent, and that results attained from these procedures are comparable to other accepted tasks, such as the hypothetical purchase task. Nineteen smokers were asked to purchase 1 week's worth of cigarettes that they would consume over the following week either at one price that incrementally increased across four weekly sessions (\"real\" sessions) or four prices in a single session (\"potentially real\" session), one of which was randomly chosen to be actualized. At each session, participants also completed a hypothetical cigarette purchase task. After each week, participants reported the number of cigarettes they actually smoked. Demand was found to be equivalent under both the real and potentially real reward conditions but statistically different from the demand captured in the hypothetical purchase task. However, the amounts purchased at specific prices in the hypothetical purchase task were significantly correlated with the amount purchased at comparable prices in the other two tasks (except for the highest price examined in both tasks of $1.00 per cigarette). Number of cigarettes consumed that were obtained outside of the study was correlated with study cigarette price. Combined, these results suggest that purchasing behavior during potentially real sessions (1) was not functionally different from real sessions, (2) imposes fewer costs to the experimenter, and (3) has high levels of both internal and external validity.

Frequently, biomedical researchers need to choose between multiple candidate statistical models. Several techniques exist to facilitate statistical model selection including adjusted R2, hypothesis testing and p-values, and information criteria among others. One particularly useful approach that has been slow to permeate the biomedical literature is the notion of posterior model probabilities. A major advantage of posterior model probabilities is that they quantify uncertainty in model selection by providing a direct, probabilistic comparison among competing models as to which is the “true” model that generated the observed data. Additionally, posterior model probabilities can be used to compute posterior inclusion probabilities which quantify the probability that individual predictors in a model are associated with the outcome in the context of all models considered given the observed data. Posterior model probabilities are typically derived from Bayesian statistical approaches which require specialized training to implement, but in this paper we describe an easy-to-compute version of posterior model probabilities and inclusion probabilities that rely on the readily-available Bayesian information criterion. We illustrate the utility of posterior model probabilities and inclusion probabilities by re-analyzing data from a published gait study investigating factors that predict required coefficient of friction between the shoe sole and floor while walking.

Background Total ankle arthroplasty (TAA) is an alternative to arthrodesis, but no randomized trial has examined whether a fixed bearing or mobile bearing implant provides improved gait mechanics. Questions/purposes We wished to determine if fixed- or mobile-bearing TAA results in a larger improvement in pain scores and gait mechanics from before surgery to 1 year after surgery, and to quantify differences in outcomes using statistical analysis and report the standardized effect sizes for such comparisons. Methods Patients with end-stage ankle arthritis who were scheduled for TAA between November 2011 and June 2013 (n = 40; 16 men, 24 women; average age, 63 years; age range, 35–81 years) were prospectively recruited for this study from a single foot and ankle orthopaedic clinic. During this period, 185 patients underwent TAA, with 144 being eligible to participate in this study. Patients were eligible to participate if they were able to meet all study inclusion criteria, which were: no previous diagnosis of rheumatoid arthritis, a contralateral TAA, bilateral ankle arthritis, previous revision TAA, an ankle fusion revision, or able to walk without the use of an assistive device, weight less than 250 pounds (114 kg), a sagittal or coronal plane deformity less than 15°, no presence of avascular necrosis of the distal tibia, no current neuropathy, age older than 35 years, no history of a talar neck fracture, or an avascular talus. Of the 144 eligible patients, 40 consented to participate in our randomized trial. These 40 patients were randomly assigned to either the fixed (n = 20) or mobile bearing implant group (n = 20). Walking speed, bilateral peak dorsiflexion angle, peak plantar flexion angle, sagittal plane ankle ROM, peak ankle inversion angle, peak plantar flexion moment, peak plantar flexion power during stance, peak weight acceptance, and propulsive vertical ground reaction force were analyzed during seven self-selected speed level walking trials for 33 participants using an eight-camera motion analysis system and four force plates. Seven patients were not included in the analysis owing to cancelled surgery (one from each group) and five were lost to followup (four with fixed bearing and one with mobile bearing implants). A series of effect-size calculations and two-sample t-tests comparing postoperative and preoperative increases in outcome variables between implant types were used to determine the differences in the magnitude of improvement between the two patient cohorts from before surgery to 1 year after surgery. The sample size in this study enabled us to detect a standardized shift of 1.01 SDs between group means with 80% power and a type I error rate of 5% for all outcome variables in the study. Results This randomized trial did not reveal any differences in outcomes between the two implant types under study at the sample size collected. In addition to these results, effect size analysis suggests that changes in outcome differ between implant types by less than 1 SD. Detection of the largest change score or observed effect (propulsive vertical ground reaction force [Fixed: 0.1 ± 0.1; 0.0–1.0; Mobile: 0.0 ± 0.1; 0.0–0.0; p = 0.0.051]) in this study would require a future trial to enroll 66 patients. However, the smallest change score or observed effect (walking speed [Fixed: 0.2 ± 0.3; 0.1–0.4; Mobile: 0.2 ± 0.3; 0.0–0.3; p = 0.742]) requires a sample size of 2336 to detect a significant difference with 80% power at the observed effect sizes. Conclusions To our knowledge, this is the first randomized study to report the observed effect size comparing improvements in outcome measures between fixed and mobile bearing implant types. This study was statistically powered to detect large effects and descriptively analyze observed effect sizes. Based on our results there were no statistically or clinically meaningful differences between the fixed and mobile bearing implants when examining gait mechanics and pain 1 year after TAA. Level of Evidence Level II, therapeutic study.

Tripping is a common mechanism for inducing falls. The purpose of this study was to present a method that determines the probability of tripping over an unseen obstacle while avoiding the ambiguous situation wherein median minimum foot clearance (MFC) and MFC interquartile range concurrently increase or decrease, and determines how the probability of tripping varies with potential obstacle height. The method was used to investigate the effects of age, obesity, gender, and gait speed on the probability of tripping. MFC was measured while 80 participants walked along a 10-m walkway at self-selected and hurried gait speeds. The method was able to characterize the probability of tripping as a function of obstacle height, and identify effects of age, obesity, gender, and gait speed. More specifically, the probability of tripping was higher among older adults, higher among obese adults, higher among females, and higher at the slower self-selected speed. Many of these results were not found, or clear, from the more common approach on characterizing likelihood of tripping based on MFC measures of central tendency and variability.

Predicting the outcome of elections, sporting events, entertainment awards and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-driven journalism intended for a general audience as the availability of historical information rapidly balloons. Providing statistical methodology to probabilistically predict competition outcomes faces two main challenges. First, a suitably general modeling approach is necessary to assign probabilities to competitors. Second, the modeling framework must be able to accommodate expert opinion which is usually available but difficult to fully encapsulate in typical data sets. We overcome these challenges with a combined conditional logistic regression/subjective Bayes approach. To illustrate the method, we reanalyze data from a recent Time.com piece in which the authors attempted to predict the 2019 Best Picture Academy Award winner using standard logistic regression. Toward engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.

This article describes foundational processes of a larger project examining U.S. home builders' choices to adopt innovative housing technologies that improve the environmental performance of new single-family homes. Home builders sit at a critical juncture in the housing creation decision chain and can influence how new housing units change related to energy consumption, and the units they produce can also reflect shifting technology, demography, and policy landscapes. With some exceptions, U.S. home builders have been characterized as being slow to adopt or resistant to the adoption of product and process innovations, largely because of path-dependent and risk-averse behavior. This article focuses on home builder choices by analyzing a summary of innovation adoption literature and that literature's relationship to homebuilding. Researchers then describe analytical approaches for studying home builders' choices and markets at a Core Based Statistical Area level, the data and statistical methodologies used in the study, and the policy implications for promoting energy efficiency in housing. Future work will draw on the foundation presented in this article to specify versions of this generic model and report results using improved quantitative analyses.