UK dataset of self-referential valence, imageability and subjective frequency ratings of 300 adjectives for use in cognitive-emotional tasks

The present dataset provides subjective ratings of valence, imageability and frequency for 150 positive and 150 negative adjectives describing personality characteristics. The words in this dataset can be used for the development or validation of existing or novel experimental tasks used in a wide range of cognition research.

In Phase 1 of data collection, an initial sample of 100 participants provided self-referential valence ratings for a list of 482 adjectives depicting personality characteristics. These ratings were averaged across the sample to facilitate the exclusion of ambiguous words rated neither negative nor positive and produce a final list of 300 words (150 negative and 150 positive). In Phase 2 of data collection, we sought to further characterise these 300 words with three separate online surveys collecting ratings of self-referential valence, imageability and subjective frequency. A further 102 participants provided self-referential valence ratings, 200 participants provided imageability ratings and 202 participants provided subjective frequency ratings. Basic demographics and data on depressive symptoms and state anxiety were collected from all participants; see Tables 1a and 1b. The raw ratings collected in each of the four surveys are provided in the "Raw Datasets" folder, and the exact surveys used are provided in Supplementary file 1.

We computed a series of statistics (mean, standard deviation, standard error, number of ratings received, median, minimum rating, maximum rating, range, skew, kurtosis) for each type of rating for each of the 300 personality descriptors. The statistics for self-referential valence, imageability, subjective frequency and word length were merged into a final dataset (see Positive and negative personality descriptor words dataset). We pooled scores from all participants for the reported statistical analyses, based on exploratory analyses showing age, gender and depression/anxiety symptoms had little effect on participant ratings (see Figures 2-8). However, if greater stratification is desired, specific population statistics can be re-calculated from the raw datasets. The R script we used for data cleaning and analysis is provided in Supplementary file 2.

We also explored the relationship between the initial self-referential valence ratings collected in Phase 1 (first Qualtrics survey) and those collected during Phase 2 (second Qualtrics survey) for our final list of 300 words. We found the mean ratings for each word to be highly correlated between the two surveys (Spearman’s rho = 0.97, p < .01; see Figure 9). Additionally, we conducted a mixed effects analysis of variance to statistically assess the effects of data collection phase on the self-referential valence ratings acquired for each personality descriptor (see Self-referential valence reliability dataset).

Only fully anonymised data is provided – all pseudonymous variables have been removed by the research team prior to sharing.

本数据集提供了150个正面和150个负面描述人格特征的形容词的主观评价，包括情感价值、形象化和频率。数据集中的词汇可用于开发或验证现有或新实验任务，这些任务广泛应用于认知研究领域。

在数据收集的第一阶段，100名参与者对482个描述人格特征的形容词进行了自我参照的情感价值评价。通过对样本的平均值进行计算，以排除既不被视为负面也不被视为正面的模糊词汇，从而产生了包含300个词汇的最终列表（150个负面和150个正面）。在数据收集的第二阶段，我们通过三次独立的在线调查进一步描述这300个词汇，收集自我参照的情感价值、形象化和主观频率的评价。另外102名参与者提供了自我参照的情感价值评价，200名参与者提供了形象化评价，202名参与者提供了主观频率评价。从所有参与者中收集了基本人口统计数据和抑郁症状以及状态焦虑的数据；详见表格1a和1b。

在四个调查中收集的原始评分均提供在“原始数据集”文件夹中，具体的调查内容详见补充文件1。

我们对每个300个人格描述词的每种评分类型计算了一系列统计数据（平均值、标准差、标准误、收到的评分数量、中位数、最小评分、最大评分、范围、偏度和峰度）。自我参照的情感价值、形象化、主观频率和词汇长度的统计数据合并为一个最终数据集（参见正面和负面人格描述词词汇数据集）。基于探索性分析显示年龄、性别以及抑郁/焦虑症状对参与者评分的影响甚微，我们将所有参与者的评分合并进行报告的统计分析。

然而，如果需要更细致的分层，可以从原始数据集中重新计算特定人群的统计数据。我们用于数据清洗和分析的R脚本提供在补充文件2中。

我们还探讨了第一阶段（第一次Qualtrics调查）收集的初始自我参照情感价值评价与第二阶段（第二次Qualtrics调查）收集的评价之间对我们最终300个词汇的关系。我们发现每个词汇的平均评分在这两个调查中高度相关（Spearman's rho = 0.97，p < .01；详见图9）。此外，我们还进行了混合效应方差分析，以统计评估数据收集阶段对每个人格描述词的自我参照情感价值评价的影响（详见自我参照情感价值可靠性数据集）。

我们提供的是完全匿名化的数据——研究团队在分享之前已移除所有化名变量。