在因素中使用随机拦截或Z标准化：两种相同的方法来解决因素之间的差异？
我有以下有关统计信息的（简单？）问题：我有一个数据集，其中我在其中寻找变量之间的相关性，并希望控制因子水平之间的差异。对于可视化，请考虑以下示例：n = 100人执行20个任务，每个任务的时间都用作绩效度量。现在，我希望将这些任务的性能与人民智商相关联，这也是已知的。在这里，以某种方式说明任务之间的差异似乎是合理的，而做到这一点的两种方法：
- 我首先z-标准化每个任务中的性能度量，然后计算与智商的简单相关性（可能是在跨任务汇总数据之后）
- 我允许在预测智商的线性混合模型中对每个任务进行随机拦截，
这些方法本质上是相同的，还是在任何方式上有所不同？
I have the following (simple?) question about statistics: I have a dataset where I look for correlations between variables and would like to control for differences between factor levels. For visualization, consider this example: N = 100 people perform 20 tasks and the time taken for each task serves as performance measure. I now wish to correlate the performance in these tasks with the people's IQ, which is also known. Here it seems reasonable to somehow account for differences between the tasks, and two ways to do this come to mind:
- I first z-standardize the performance measures in each task, then compute a simple correlation with IQ (possibly after aggregating data across tasks)
- I allow for a random intercept for each task in a linear mixed model predicting IQ
Are these approaches essentially identical or do they differ in any way?
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