Abstract Throughout the social sciences, there has been growing pressure to present effect sizes when publishing empirical data (see American Psychological Association, 2001; Parsons & Nelson, 2004). While it seems indisputable that for the majority of quantitative research foci, effect size is an essential element of statistical analysis, this paper argues that specifically for key word analysis in corpus linguistics, the means of reporting effect size must depend on the level of the unit of study of each investigation (single text, collection or large corpus). After exploring some main criticisms of the log-likelihood measure, this paper unpacks the parameters of different measures for keyness and how they might address underlying concerns. It maintains that for the exploration of foregrounded/deviant/salient/marked features in text, the use of log-likelihood scores to rank the results is still fit for purpose and coupled with Bayes Factors is a solid approach for key word analyses.

中文翻译：

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文本构成学习单元时的关键词

摘要 在整个社会科学领域，在发布经验数据时呈现效应大小的压力越来越大（参见美国心理学会，2001 年；帕森斯和纳尔逊，2004 年）。虽然对于大多数定量研究焦点来说，效应大小是统计分析的一个基本要素似乎是无可争辩的，但本文认为，特别是对于语料库语言学中的关键词分析，报告效应大小的手段必须取决于单位的水平每个调查的研究（单个文本、集合或大型语料库）。在探讨了对数似然度量的一些主要批评之后，本文解开了关键性的不同度量的参数以及它们如何解决潜在问题。它认为对于文本中前景/异常/突出/标记特征的探索，