The analysis of continuous hierarchical data such as repeated measures or data from meta-analyses can be carried out by means of the linear mixed-effects model. However, in some situations this model, in its standard form, does pose computational problems. For example, when dealing with crossed random-effects models, the estimation of the variance components becomes a non-trivial task if only one observation is available for each cross-classified level. Pseudolikelihood ideas have been used in the context of binary data with standard generalized linear multilevel models. However, even in this case the problem of the estimation of the variance remains non-trivial. In this paper, we first propose a method to fit a crossed random-effects model with two levels and continuous outcomes, borrowing ideas from conditional linear mixed-effects model theory. We also propose a crossed random-effects model for binary data combining ideas of conditional logistic regression with pseudolikelihood estimation. We apply this method to a case study with data coming from the field of psychometrics and study a series of items (responses) crossed with participants. A simulation study assesses the operational characteristics of the method.

中文翻译：

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具有交叉随机效应的条件混合模型。

可以通过线性混合效应模型对连续的分层数据（例如重复测量或来自荟萃分析的数据）进行分析。但是，在某些情况下，该模型以其标准形式确实会带来计算问题。例如，当处理交叉随机效应模型时，如果每个交叉分类级别只有一个观察值可用，则方差分量的估计就变得不容易了。伪似然性思想已在具有标准广义线性多级模型的二进制数据的上下文中使用。然而，即使在这种情况下，方差估计的问题仍然是不平凡的。在本文中，我们首先借鉴条件线性混合效应模型理论的思想，提出了一种适用于具有两个水平和连续结果的交叉随机效应模型的方法。我们还提出了一种将条件对数回归与伪似然估计相结合的二进制数据交叉随机效应模型。我们将此方法应用于来自心理计量学领域的数据的案例研究，并研究与参与者交叉的一系列项目（响应）。仿真研究评估了该方法的操作特性。