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首页> 外文期刊>Behavior Research Methods, Instruments & Computers >Making Treatment Effect Inferences Frommultiple-baseline Data: The Utility Ofrnmultilevel Modeling Approaches
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Making Treatment Effect Inferences Frommultiple-baseline Data: The Utility Ofrnmultilevel Modeling Approaches

机译：从多基线数据中得出治疗效果推断：多级建模方法的实用性

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Multiple-baseline studies are prevalent in behavioral research, but questions remain about how to best analyze the resulting data. Monte Carlo methods were used to examine the utility of multilevel models for multiple-baseline data under conditions that varied in the number of participants, number of repeated observations per participant, variance in baseline levels, variance in treatment effects, and amount of autocorrelation in the Level 1 errors. Interval estimates of the average treatment effect were examined for two specifications of the Level 1 error structure (σ~2I and first-order autoregressive) and for five different methods of estimating the degrees of freedom (containment, residual, between-within, Satterthwaite, and Kenward-Roger). When the Satterthwaite or Kenward-Roger method was used and an autoregressive Level 1 error structure was specified, the interval estimates of the average treatment effect were relatively accurate. Conversely, the interval estimates of the treatment effect variance were inaccurate, and the corresponding point estimates were biased.
机译：多基线研究在行为研究中很普遍，但是仍然存在有关如何最好地分析结果数据的问题。在参与者数量，每个参与者重复观察的数量，基线水平的变化，治疗效果的变化以及自相关量的变化的条件下，使用蒙特卡罗方法检查多基线数据的多级模型的效用。 1级错误。针对1级误差结构的两个规范（σ〜2I和一阶自回归）和五个估计自由度的方法（包含，残差，内部之间，Satterthwaite，和Kenward-Roger）。当使用Satterthwaite或Kenward-Roger方法并指定了自回归1级误差结构时，平均治疗效果的区间估计值相对准确。相反，治疗效果方差的区间估计不准确，相应的点估计存在偏差。

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