Department of Psychometrics and Statistics, University of Groningen, Groningen, the Netherlands.
Psychol Methods. 2013 Jun;18(2):165-85. doi: 10.1037/a0031037. Epub 2013 Mar 4.
Researchers using single-subject designs are typically interested in score differences between intervention phases, such as differences in means or trends. If intervention effects are suspected in data, it is desirable to determine how much evidence the data show for an intervention effect. In Bayesian statistics, Bayes factors quantify the evidence in the data for competing hypotheses. We introduce new Bayes factor tests for single-subject data with 2 phases, taking serial dependency into account: a time-series extension of Rouder, Speckman, Sun, Morey, and Iverson's (2009) Jeffreys-Zellner-Siow Bayes factor for mean differences, and a time-series Bayes factor for testing differences in intercepts and slopes. The models we describe are closely related to interrupted time-series models (McDowall, McCleary, Meidinger, & Hay, 1980).
研究人员通常使用单被试设计，对干预阶段的分数差异（如均值或趋势差异）感兴趣。如果数据中怀疑存在干预效应，那么就需要确定数据显示干预效应的证据有多少。在贝叶斯统计中，贝叶斯因子量化了数据中对竞争假设的证据。我们介绍了新的考虑序列相关性的两阶段单被试数据的贝叶斯因子检验：一种是 Rouder、Speckman、Sun、Morey 和 Iverson（2009）对均值差异的 Jeffreys-Zellner-Siow 贝叶斯因子的时间序列扩展，另一种是检验截距和斜率差异的时间序列贝叶斯因子。我们描述的模型与中断时间序列模型（McDowall、McCleary、Meidinger 和 Hay，1980）密切相关。
