The Effects of Artificial Intelligence on Implementors' Fidelity of Instructional Strategies During Handwashing Acquisition in Children with Autism.

Handwashing is a vital skill for maintaining health and hygiene. For individuals with intellectual and developmental disabilities (IDD), such as autism spectrum disorder, evidence-based strategies, such as prompting and task analysis, may be effective in teaching these skills. Due to the shortage of experts who teach individuals with IDD skills such as handwashing, staff working with children need a means of ensuring these instructional strategies are implemented with fidelity. This study examined the effects of a tablet-based application that used artificial intelligence (GAINS®) on four behavior technicians' implementation of least-to-most prompting, total task chaining, and time delay during an acquisition of handwashing program with young children with autism. All four technicians increased fidelity immediately upon using GAINS and all four technicians reached mastery criteria within the shortest number of sessions possible. One child participant met mastery criteria, two showed some gains, and one demonstrated a high degree of variability across sessions. Limitations of the least-to-most prompting procedure, user design, considerations and directions for future research and practice are discussed.

Exact solutions for area-based delay discounting analyses.

Novel methods are provided for calculating a model-based area under curve (MB-AUC) using exact solution. These model-specific calculations produce an AUC ratio that does not need numerical approximation nor access to the source discounting data to perform. This approach supports a calculation of MB-AUC that is useful in summarizing current and retrospective discounting analyses using fitted models (e.g., k and s) and corresponding study parameters (i.e., range of delays). Solutions were compared against numerical methods for various discounting models and results indicated that each approach provided identical results. This newer, simpler, and more efficient method is reviewed and demonstrated to show how comparisons can be drawn between the fitted discounting models and empirical methods, such as the empirical point-based area under curve. Reanalyses of published findings revealed that reconstituting published findings using a common scale (i.e., area) yielded similar AUC ratios, despite varying approaches, suggesting new avenues for research synthesis (e.g., reducing sources of measurement error). The MB-AUC measure is discussed as one means of addressing the challenges encountered when research synthesis includes metrics on varying scales and differing domains. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A cluster analysis of mothers of reproductive age based on their attitude and risk of alcohol use.

The rates of alcohol use and binge drinking are increasing among women. To examine factors that can differentiate women with low-risk alcohol use from those with high-risk alcohol use, the present study explored whether there would be distinct subgroups of mothers who differed in their attitudes and risk of alcohol use. A sample of 141 women aged between 18 and 50 years old who had given birth within 3 years was recruited on Amazon Mechanical Turk. A hierarchical cluster analysis was conducted to categorize the mothers based on the similarities between their attitudes and risk of alcohol use, which resulted in the identification of the following distinct subgroups: (a) mothers with negative attitudes toward alcohol use and low risk for problematic alcohol use, (b) mothers with positive attitudes and low risk, and (c) mothers with positive attitudes and high risk. These subgroups of mothers were then compared on the extent to which they differed in trait impulsivity and impulsive decision-making toward instant gratification. The results showed that the subgroups significantly differed in trait impulsivity but not in impulsive decision-making toward instant gratification. The present study demonstrated the usefulness of cluster analysis for profiling distinct, practically meaningful subgroups of mothers of reproductive age based on their attitudes and risk of alcohol use, which has important implications for developing intervention strategies for problematic alcohol use in this population. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A behavioral economic demand analysis of mothers' decision to exclusively breastfeed in the workplace.

The present study determined whether behavioral economic demand analysis could characterize mothers' decision to exclusively breastfeed in the workplace. Females, aged between 18 and 50 who have given birth in the past three years, completed a novel demand task with hypothetical scenarios, in which they returned to work with a 2-month-old baby. Participants rated their likelihood of breastfeeding their baby at a workplace lactation room versus formula-feeding their baby at their desk. The distance to the lactation room ranged from 10 s to 60 min. This assessment was conducted with and without hypothetical financial incentives for 6-month exclusive breastfeeding. Primary dependent measures were demand intensity and change in demand elasticity, which could conceptually represent initiation and continuation of breastfeeding, respectively. Demand for breastfeeding was more intense and less elastic (i.e., more likely to initiate and continue breastfeeding) among mothers with an experience of 6-month exclusive breastfeeding and under the condition with the financial incentives. The novel demand task can potentially provide a useful behavioral marker for quantifying mothers' decision to initiate and continue exclusive breastfeeding in the workplace, informing workplace policy regarding lactation rooms, identifying risk for early cessation, and developing and individualizing an intervention to assist mothers to exclusively breastfeed in the workplace.

Meta-Analytic Methods to Detect Publication Bias in Behavior Science Research.

Publication bias is an issue of great concern across a range of scientific fields. Although less documented in the behavior science fields, there is a need to explore viable methods for evaluating publication bias, in particular for studies based on single-case experimental design logic. Although publication bias is often detected by examining differences between meta-analytic effect sizes for published and grey studies, difficulties identifying the extent of grey studies within a particular research corpus present several challenges. We describe in this article several meta-analytic techniques for examining publication bias when published and grey literature are available as well as alternative meta-analytic techniques when grey literature is inaccessible. Although the majority of these methods have primarily been applied to meta-analyses of group design studies, our aim is to provide preliminary guidance for behavior scientists who might use or adapt these techniques for evaluating publication bias. We provide sample data sets and R scripts to follow along with the statistical analysis in hope that an increased understanding of publication bias and respective techniques will help researchers understand the extent to which it is a problem in behavior science research.

Predicting Intention to Take a COVID-19 Vaccine in the United States: Application and Extension of Theory of Planned Behavior.

PURPOSE: This study aims to apply and extend the theory of planned behavior (TPB) to predict intention to take a COVID-19 vaccine. DESIGN: Cross-sectional. SETTING: Online. SAMPLE: Adult US residents recruited from Amazon Mechanical Turk (n = 172). MEASURES: Intention to take a COVID-19 vaccine (outcome variable), demographic variables (predictors), standard TPB variables (perceived behavioral control, attitude, and subjective norm; predictors), and non-TPB variables (anticipated regret, health locus of control, and perceived community benefit; predictors). ANALYSIS: Hierarchical linear regression predicting intention to take a COVID-19 vaccine, with demographic, standard TPB, and non-TPB variables entered in regression models 1, 2, and 3, respectively. RESULTS: The extended TPB model accounted for 72.5% of the variance in vaccination intention (p < .001), with perceived behavioral control (ß = .29, p < .001), attitude (ß = .23, p = .043), and perceived community benefit (ß = .23, p = .020) being significant unique predictors. CONCLUSION: Despite the relatively small and non-representative sample, this study, conducted after COVID-19 vaccines were widely available in the USA, demonstrated that perceived behavioral control was the most robust predictor of intention to take a COVID-19 vaccine, suggesting that the TPB is a useful theoretical framework that can inform effective strategies to promote vaccine acceptance.

Meta-analyses and effect sizes in applied behavior analysis: A review and discussion.

For more than four decades, researchers have used meta-analyses to synthesize data from multiple experimental studies often to draw conclusions that are not supported by individual studies. More recently, single-case experimental design (SCED) researchers have adopted meta-analysis techniques to answer research questions with data gleaned from SCED experiments. Meta-analyses enable researchers to answer questions regarding intervention efficacy, generality, and condition boundaries. Here we discuss meta-analysis techniques, the rationale for their adaptation with SCED studies, and current indices used to quantify the effect of SCED data in applied behavior analysis.

An exact solution for unit elasticity in the exponential model of operant demand.

Research applying the behavioral economic demand framework is increasingly conducted across disciplines. With respect to psychopharmacology and substance abuse, real and hypothetical purchase tasks are regularly used to evaluate the demand for various substances and reinforcers, such as alcohol. At present, a variety of methods has been introduced to solve for the point of unit elasticity, or Pmax, in the exponential model of demand; however, these methods vary in their potential for error. Current methods for determining Pmax are presented here and a novel exact solution for Pmax in the exponential model of demand is introduced. This solution provides an exact calculation of Pmax using the omega function, as algebraic solutions are not possible. This novel approach is introduced, discussed, and systematically compared to earlier methods for determining Pmax using computer simulations and reanalyses of published study data. Systematic comparison indicated that this new approach, an exact analytic solution for Pmax, provides results that are identical to computationally intensive Pmax methods that directly evaluate the slope of the demand function. The exact analytic Pmax approach is reviewed, its calculations explained, and an easy-to-use web tool is provided to assist researchers in easily performing this calculation of Pmax. Implications for reducing potential sources of error are reviewed and future directions are also discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Demand Curve Analyzer: Behavioral economic software for applied research.

Free and open-source software for applying models of operant demand called the Demand Curve Analyzer (DCA) was developed and systematically evaluated for use in research. The software was constructed to streamline the use of recommended screening measures, prepare suitable scaling parameters, fit one of several models of operant demand, and provide publication-quality figures. The DCA allows users to easily import price and consumption data into spreadsheet-based controls and to perform statistical modeling with the aid of a graphical user interface. The results from computer simulations and reanalyses of published study data indicated that the DCA provides results consistent with commercially available software that has been traditionally used to apply these analyses (i.e., GraphPadTM Prism). Further, the DCA provides additional functionality that other statistical packages do not include. Practical issues and future directions related to the determination of scaling parameter k, screening for nonsystematic data, and the incorporation of more advanced behavioral economic methods are also discussed.

Discounting model selection with area-based measures: A case for numerical integration.

A novel method for analyzing delay discounting data is proposed. This newer metric, a model-based Area Under Curve (AUC) combining approximate Bayesian model selection and numerical integration, was compared to the point-based AUC methods developed by Myerson, Green, and Warusawitharana (2001) and extended by Borges, Kuang, Milhorn, and Yi (2016). Using data from computer simulation and a published study, comparisons of these methods indicated that a model-based form of AUC offered a more consistent and statistically robust measurement of area than provided by using point-based methods alone. Beyond providing a form of AUC directly from a discounting model, numerical integration methods permitted a general calculation in cases when the Effective Delay 50 (ED50) measure could not be calculated. This allowed discounting model selection to proceed in conditions where data are traditionally more challenging to model and measure, a situation where point-based AUC methods are often enlisted. Results from simulation and existing data indicated that numerical integration methods extended both the area-based interpretation of delay discounting as well as the discounting model selection approach. Limitations of point-based AUC as a first-line analysis of discounting and additional extensions of discounting model selection were also discussed.

The discounting model selector: Statistical software for delay discounting applications.

Original, open-source computer software was developed and validated against established delay discounting methods in the literature. The software executed approximate Bayesian model selection methods from user-supplied temporal discounting data and computed the effective delay 50 (ED50) from the best performing model. Software was custom-designed to enable behavior analysts to conveniently apply recent statistical methods to temporal discounting data with the aid of a graphical user interface (GUI). The results of independent validation of the approximate Bayesian model selection methods indicated that the program provided results identical to that of the original source paper and its methods. Monte Carlo simulation (n = 50,000) confirmed that true model was selected most often in each setting. Simulation code and data for this study were posted to an online repository for use by other researchers. The model selection approach was applied to three existing delay discounting data sets from the literature in addition to the data from the source paper. Comparisons of model selected ED50 were consistent with traditional indices of discounting. Conceptual issues related to the development and use of computer software by behavior analysts and the opportunities afforded by free and open-sourced software are discussed and a review of possible expansions of this software are provided.