简单(哲学)
统计
回归
变量(数学)
回归分析
标准差
零(语言学)
简单线性回归
线性回归
魔术(望远镜)
计量经济学
数学
计算机科学
心理学
考试(生物学)
医学物理学
医学
认识论
物理
哲学
数学分析
古生物学
生物
量子力学
语言学
作者
Stephen A. Spiller,Gavan J. Fitzsimons,John Lynch,Gary H. McClelland
摘要
It is common for researchers discovering a significant interaction of a measured variable X with a manipulated variable Z to examine simple effects of Z at different levels of X. These “spotlight” tests are often misunderstood even in the simplest cases, and it appears that consumer researchers are unsure how to extend them to more complex designs. The authors explain the general principles of spotlight tests, show that they rely on familiar regression techniques, and provide a tutorial demonstrating how to apply these tests across an array of experimental designs. Rather than following the common practice of reporting spotlight tests at one standard deviation above and below the mean of X, it is recommended that when X has focal values, researchers should report spotlight tests at those focal values. When X does not have focal values, it is recommended that researchers report ranges of significance using a version of Johnson and Neyman's test the authors term a “floodlight.”
科研通智能强力驱动
Strongly Powered by AbleSci AI