Strong Collapsibility of Association Measures in Linear Models
研究了线性模型中关联测度在离散背景变量上的强可合并性，给出了其成立的必要和充分条件，帮助研究者判断背景变量的分类或记录是否影响关联测度。
SUMMARY A measure of association in linear models is strongly collapsible over a discrete background variable if it remains unchanged no matter how the background variable is partially pooled and if it also coincides with the corresponding marginal measure of association. Strong collapsibility implies that the measure of association can be studied no matter how the background variable is categorized and no matter whether or not it is recorded. In this paper, necessary and sufficient conditions are given for strong collapsibility of the measure of association in linear models.