Abstract Temporal check-all-that-apply (TCATA) extends classical check-all-that-apply (CATA) by adding a temporal dimension to the evaluation. From a data analysis point of view, TCATA data are similar to Temporal Dominance of Sensations (TDS) data but differ in that more than one attribute can be selected at any time point. Procedures for analyzing TCATA data can hence be generalized from methods for CATA as well as for TDS. TCATA data can be organized in a matrix format with 0s and 1s similar to what has been described before for TDS, but with the relaxation that the column sums can exceed 1. Consequently, the same randomization tests as suggested for TDS are suitable for TCATA data. For TDS data, ad-hoc chance and significance limits are frequently used, a practice that we review critically. We also show that these do not generalize easily to TCATA data. Instead, we suggest comparing individual products to aggregate data across all other products in the test, with that aggregation being assumed to provide an estimate of what is to be expected in the respective product category. This approach does not rely on the number of attributes considered and is also recommended for TDS data. We further suggest a new way to visualize the respective results. The results are compared to the naive approach of applying standard CATA analyses to the data by time point. Results are reasonably close to warrant very similar interpretation, such that applying CATA analyses by time point to TCATA data seems a potential ad-hoc alternative in practice for preliminary data evaluation. Additionally, panel agreement for TCATA data is obtained over time using Gwet’s AC1 coefficient and its 95% confidence bands, along with raw agreement and mean citation rate. Results provide insight into the level of panel agreement across the various time segments. We exemplify the suggested methods by means of data from a study on Syrah wine finish.

中文翻译：

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时间检查所有应用 (TCATA) 数据的分析

Abstract Temporal check-all-that-apply (TCATA) 通过向评估添加时间维度来扩展经典的 check-all-that-apply (CATA)。从数据分析的角度来看，TCATA 数据类似于 Temporal Dominance of Sensations (TDS) 数据，但不同之处在于在任何时间点都可以选择多个属性。因此，可以从 CATA 和 TDS 的方法中推广用于分析 TCATA 数据的程序。TCATA 数据可以以矩阵格式组织，其中 0 和 1 类似于之前对 TDS 描述的内容，但列总和可以超过 1。因此，为 TDS 建议的相同随机化测试适用于 TCATA 数据. 对于 TDS 数据，经常使用临时机会和显着性限制，我们认真审查了这种做法。我们还表明，这些不容易推广到 TCATA 数据。相反，我们建议将单个产品与测试中所有其他产品的汇总数据进行比较，并假设该汇总提供了对相应产品类别中预期的估计。这种方法不依赖于所考虑的属性数量，也推荐用于 TDS 数据。我们进一步提出了一种新方法来可视化相应的结果。将结果与按时间点对数据应用标准 CATA 分析的简单方法进行比较。结果相当接近，可以保证非常相似的解释，因此按时间点对 TCATA 数据应用 CATA 分析似乎是实践中初步数据评估的潜在临时替代方案。此外，TCATA 数据的面板一致性是使用 Gwet 的 AC1 系数及其 95% 置信带以及原始一致性和平均引用率随时间获得的。结果提供了对跨不同时间段的小组一致程度的深入了解。我们通过对西拉葡萄酒完成研究的数据举例说明了建议的方法。