在跨数据记录方法的条件歧视培训中预测响应模式.
Delanie F Platt1, Tom Cariveau1, Paige Ellington1
1Department of Psychology, University of North Carolina-Wilmington, Wilmington, NC 28403 USA.
Behavior analysis in practice
|December 11, 2023
概括
增强数据表帮助行为分析师比标准数据表更有效地识别客户端错误模式. 这种改进的数据收集有助于个性化干预,以获得更好的治疗结果.
科学领域:
- 行为分析行为分析.
- 应用行为分析应用行为分析.
- 干预的有效性 干预的有效性
背景情况:
- 干预的有效性取决于准确的实施和特定于客户的个性化.
- 增强的数据表,代表先例和响应,与标准表相比,提高程序完整性.
- 加强数据收集可以揭示干预修改的错误模式.
研究的目的:
- 用标准与增强数据表来比较识别客户端性能错误的准确性.
- 评估增强数据表是否提高了天真参与者预测客户绩效的能力.
主要方法:
- 素朴的参与者审查了在标准和增强数据表上呈现的客户绩效数据.
- 评估了参与者预测客户绩效和识别错误模式的准确性.
主要成果:
- 当数据在增强数据表上呈现时,参与者始终识别出错误模式.
- 在标准和增强数据表之间没有观察到性能预测准确度的显著差异,当响应准确或不可预测时.
结论:
- 增强的测量工具可以显著帮助行为分析师检测指令错误模式.
- 这有助于更精确的个性化行为干预策略.
- 进一步的研究可以探索对临床决策的影响.
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