2022
DOI: 10.1177/10883576221081076
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Training Individuals to Implement Discrete Trials with Fidelity: A Meta-Analysis

Abstract: Discrete trial training is a popular teaching method for individuals with autism, but is not easily implemented with fidelity due to its complexity. This is the first meta-analysis of single-case experimental design studies to quantify the impact of behavioral skills training on individuals’ ability to implement discrete trials with fidelity. Furthermore, this meta-analysis examines the four training methods that make up behavioral skills training—feedback, instruction, modeling, and rehearsal—to determine the… Show more

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Cited by 4 publications
(3 citation statements)
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References 78 publications
(67 reference statements)
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“…Given that only 19% of the total studies we reviewed required a mastery criterion of 100% and even fewer required this criterion across multiple consecutive sessions, we suggest researchers replicate and extend the effects of different mastery criteria on the skill maintenance of DTT procedures. This is especially important given that a recent meta‐analysis identified a statistically significant difference in procedural fidelity of DTT implementation between an active‐BST phase and post‐BST phase (or maintenance phase), suggesting participants’ procedural fidelity declined after completion of training (i.e., performance did not maintain; Fingerhut & Moeyaert, 2022). Given this anticipated decline, future research should also examine the relation between different levels of mastery criteria (i.e., procedural fidelity) and how it may influence the performance maintenance of the trainee, as well as how this may ultimately affect the learner's skill acquisition (Bergmann et al, 2021).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Given that only 19% of the total studies we reviewed required a mastery criterion of 100% and even fewer required this criterion across multiple consecutive sessions, we suggest researchers replicate and extend the effects of different mastery criteria on the skill maintenance of DTT procedures. This is especially important given that a recent meta‐analysis identified a statistically significant difference in procedural fidelity of DTT implementation between an active‐BST phase and post‐BST phase (or maintenance phase), suggesting participants’ procedural fidelity declined after completion of training (i.e., performance did not maintain; Fingerhut & Moeyaert, 2022). Given this anticipated decline, future research should also examine the relation between different levels of mastery criteria (i.e., procedural fidelity) and how it may influence the performance maintenance of the trainee, as well as how this may ultimately affect the learner's skill acquisition (Bergmann et al, 2021).…”
Section: Resultsmentioning
confidence: 99%
“…This training combines the use of instructions, modeling, rehearsal, and feedback to target the correct implementation of the skill(s) being taught, and these components are typically implemented until a preset performance criterion (i.e., mastery criterion) is met (e.g., at least 80% accuracy across multiple sessions). Despite research suggesting that BST is an effective method for training individuals to implement DTT (Fingerhut & Moeyaert, 2022; Parsons et al, 2012), results from a survey on staff training and performance management practices indicated that evidence‐based training approaches were used in practice about half of the time (DiGennaro Reed & Henley, 2015). These findings are particularly concerning because behavior analysts are responsible for providing evidence‐based training to supervisees and trainees.…”
mentioning
confidence: 99%
“…Ever since Van den Noortgate and Onghena (2003a,b) proposed the usage of HLM for meta-analysis of SCED data, its statistical properties have been intensively investigated and validated through Monte Carlo simulation studies (e.g., Ugille et al, 2012;Moeyaert et al, 2013aMoeyaert et al, ,b, 2014. It also has been applied in many SCED meta-analyses (e.g., Asaro-Saddler et al, 2021;Fingerhut and Moeyaert, 2022).…”
Section: Multilevel Modelingmentioning
confidence: 99%