Comorbidity Child (ASD-CC) in 136 children and adolescents with ASD. Eighty-four percent had food selectivity, followed by food refusal (78.7%), rapid eating (76.5%), chewing problems (60.3%), food stealing (49.3%) and vomiting (19.1%). Higher rates of GI symptoms, challenging behavior, and sensory issues were found in those who presented with rapid eating, food refusal and food stealing than those without these problems. Comorbid psychopathology predicted rapid eating, food selectivity and food refusal.
beezdemand: Behavioral Economic Easy Demand, a novel package for performing behavioral economic analyses, is introduced and evaluated. beezdemand extends the statistical program to facilitate many of the analyses performed in studies of behavioral economic demand. The package supports commonly used options for modeling operant demand and performs data screening, fits models of demand, and calculates numerous measures relevant to applied behavioral economists. The free and open source beezdemand package is compared to commercially available software (i.e., GraphPad Prism™) using peerreviewed and simulated data. The results of this study indicated that beezdemand provides results consistent with commonly used commercial software but provides a wider range of methods and functionality desirable to behavioral economic researchers. A brief overview of the package is presented, its functionality is demonstrated, and considerations for its use are discussed.
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 P max , in the exponential model of demand; however, these methods vary in their potential for error. Current methods for determining P max are presented here and a novel exact solution for P max in the exponential model of demand is introduced. This solution provides an exact calculation of P max using the omega function, as algebraic solutions are not possible. This novel approach is introduced, discussed, and systematically compared to earlier methods for determining P max using computer simulations and reanalyses of published study data. Systematic comparison indicated that this new approach, an exact analytic solution for P max , provides results that are identical to computationally intensive P max methods that directly evaluate the slope of the demand function. The exact analytic P max approach is reviewed, its calculations explained, and an easy-to-use web tool is provided to assist researchers in easily performing this calculation of P max . Implications for reducing potential sources of error are reviewed and future directions are also discussed.
Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches "fit to group" and "two stage" with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of "nonsystematic" response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons.
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