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dc.contributor.advisorJuanico, Jessica F
dc.contributor.authorBenson, Morgan E
dc.date.accessioned2024-06-16T18:51:02Z
dc.date.available2024-06-16T18:51:02Z
dc.date.issued2021-05-31
dc.date.submitted2021
dc.identifier.otherhttp://dissertations.umi.com/ku:17779
dc.identifier.urihttps://hdl.handle.net/1808/35148
dc.description.abstractFunctional analyses are the most precise method for determining behavioral function (Iwata et al., 2000); however, there are many behavioral and environmental constraints that may prevent clinicians from conducting a standard functional analysis (Oliver et al., 2015). There are functional analysis variations that address constraints such as time (e.g., pairwise, Tarbox et al., 2004; brief, Northup et al., 1991), high-risk behavior (e.g., precursor, Fritz et al., 2013; Smith & Churchill, 2002), and setting (e.g., trial-based; Bloom et al., 2013). The purposes of the current study were to create and validate a functional analysis decision-making model for selecting an initial functional analysis variation (Study 1) and to evaluate the decision-making model with and without training for selecting an initial functional analysis variation given a scenario (Study 2). Results of Study 1 suggest the validity of the decision-making model due to the partial and exact agreement with experts. Results of Study 2 suggest the decision-making model in the absence of training was effective for three participants, whereas additional training components (i.e., computer-based training, rehearsal and feedback) were needed for five of eight participants. Half of the participants met mastery criteria.  
dc.format.extent51 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectBehavioral sciences
dc.subjectcomputer-based training
dc.subjectdecision-making model
dc.subjectfunctional analysis
dc.titleEvaluation of a Functional Analysis Decision-Making Model
dc.typeThesis
dc.contributor.cmtememberNeidert, Pamela L
dc.contributor.cmtememberPenrod, Becky
dc.thesis.degreeDisciplineApplied Behavioral Science
dc.thesis.degreeLevelM.A.
dc.identifier.orcid


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