Open dataset for: "Reflecting on existential threats elicits self-reported negative affect but no physiological arousal"

Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Scheepers, DT, Mühlberger, C., & Jonas, E. (2019). Reflecting on existential threats elicits self-reported negative affect but no physiological arousal." A dataset of 171 undergraduate students were randomly allocated to one of four existential threat conditions: mortality salience, freedom restriction, uncontrollability, and uncertainty; or to the non-existential threat condition: social-evaluative threat; or to a control condition (TV salience). Three facets of arousal were measured: positive and negative affect before and after reflection, subjective arousal during baseline and reflection, and physiological activation during baseline and reflection (electrodermal, cardiovascular, and respiratory), as well as personality traits (e.g. trait avoidance and approach, self-esteem). Description of files: - File 'README.txt' contains the description of the files (metadata). - File '20191024_IJMData_brief.sav' contains the raw data. - Files 'EXI.outl.del.RData' contains the complete dataset with missing values, with extra variables calculated, and with outliers deleted. - File 'Codebook_EXI.outl.del.csv' contains a description of all variables in the 'EXI.outl.del.RData' file (metadata). - Files 'EXI.outl.del.imp.RData' and 'EXI.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper. - File '01_CalculationOfData.R' is an R analysis script that imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file. - File '02_AnalysisOfImputedData.R' is an R analysis script that calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R'.本数据集配套已投稿论文《Poppelaars, E. S., Klackl, J., Scheepers, DT, Mühlberger, C., & Jonas, E. (2019). 反思存在主义威胁会引发自我报告的负面情绪，但无生理唤醒》的开放数据与R分析脚本。 本数据集纳入171名本科生，被试被随机分配至四组存在主义威胁条件：死亡凸显（mortality salience）、自由限制（freedom restriction）、不可控性（uncontrollability）与不确定性（uncertainty）威胁；或非存在主义威胁条件：社会评价威胁（social-evaluative threat）；亦或控制条件（电视凸显，TV salience）。研究共测量三类唤醒指标：反思前后的正负性情绪、基线与反思阶段的主观唤醒水平，以及基线与反思阶段的生理激活水平（涵盖皮肤电、心血管与呼吸指标）；同时还收集了人格特质数据（如回避与趋近特质、自尊水平）。 ### 文件说明 - 文件'README.txt'包含各文件的元数据说明。 - 文件'20191024_IJMData_brief.sav'包含原始实验数据。 - 文件'EXI.outl.del.RData'存储完整数据集，包含缺失值、已计算的额外变量且已剔除异常值。 - 文件'Codebook_EXI.outl.del.csv'包含'EXI.outl.del.RData'中所有变量的元数据说明。 - 文件'EXI.outl.del.imp.RData'与'EXI.outl.del.imp.extra.RData'包含经多重插补（multiple imputation）处理后的完整数据集（无缺失值），可用于复现论文中的研究结果。 - 文件'01_CalculationOfData.R'为R分析脚本，用于导入原始数据、计算新变量，并通过'predictorMatrixAdj.xlsx'文件采用多重插补法处理缺失值。 - 文件'02_AnalysisOfImputedData.R'为R分析脚本，用于计算描述性统计量、生成可视化图表，并通过t检验、贝叶斯统计（Bayesian statistics）与多元线性回归（multiple linear regression）检验研究假设。该脚本同时使用了以下自定义函数：'BF.evidence.R'、'cohen.d.magnitude.R'与'p.value.sig.R'。