five

Beta Regression on Causal Ratings Predicted by Experimental Factors and WMC.

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://figshare.com/articles/dataset/_Beta_Regression_on_Causal_Ratings_Predicted_by_Experimental_Factors_and_WMC_/1576615
下载链接
链接失效反馈
官方服务:
资源简介:
The dummy variable C2 was coded 1 for the zero contingency condition, -1 for the positive condition and 0 for the negative condition. C3 was coded 1 for the negative contingency condition, -1 for the positive condition and 0 for the zero condition. Ambiguity was coded 1 for the AU condition and -1 for the unambiguous conditions. Cognitive Demand was coded 1 for the high cognitive demand condition and -1 for the low conditions. aOspan is standardized. Beta Regression on Causal Ratings Predicted by Experimental Factors and WMC.

虚拟变量(dummy variable)C2的编码规则为:零因果依存条件组编码为1,正因果依存条件组编码为-1,负因果依存条件组编码为0。虚拟变量C3的编码规则为:负因果依存条件组编码为1,正因果依存条件组编码为-1,零因果依存条件组编码为0。歧义性(Ambiguity)的编码规则为:AU条件组编码为1,无歧义条件组编码为-1。认知需求(Cognitive Demand)的编码规则为:高认知需求条件组编码为1,低认知需求条件组编码为-1。 aOspan已完成标准化处理。 基于实验因素与工作记忆容量(Working Memory Capacity, WMC)对因果评分开展贝塔回归(Beta Regression)分析。
创建时间:
2015-10-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作