<b>Survey Data on BSCS, DOSPERT, PSSS, SCL-90, and Family APGAR in Chinese Undergraduates</b>

This dataset contains the fully preprocessed and reverse‑coded survey data used in “Interplay of Internal and External Factors in Adolescent Risk‑Taking: Insights from Latent Profile and Network Analyses,” submitted to <i>Journal of Social and Personal Relationships</i>. It includes responses from 1,159 Chinese first‑year undergraduates (M_age = 18.2, 62.3% female) collected between May and October 2023 at a university in Guangzhou.<b>Contents</b><b>Demographics:</b>Participant IDAge, sex, only‑child status, urban/rural originHousehold monthly income bracketSubjective social and school status (MacArthur Scale scores)<b>Psychosocial Measures:</b><b>Brief Self‑Control Scale (BSCS):</b> 7 items (reverse‑coded; higher = poorer self‑control)<b>Perceived Social Support Scale (PSSS):</b> 12 items (family, friends, significant others)<b>Family APGAR:</b> 5 items measuring family function<b>Interpersonal Sensitivity (SCL‑90 subscale):</b> 9 items (reverse‑coded; higher = greater sensitivity)<b>DOSPERT Risk‑Taking (RT) Subscale:</b> 30 items across five domains (Ethical, Financial, Health/Safety, Recreational, Social)<b>Data Structure &amp; Processing:</b>All scales scored according to original manuals; two reverse‑coding steps applied (BSCS, SCL‑90 interpersonal sensitivity)Cases with aberrantly fast or patterned responding were excluded (final N = 1,159)No missing data remain; raw item scores and composite subscale scores are providedVariables are labeled consistently for latent profile analysis (LPA) and subsequent network analysis<b>Usage Notes</b>Intended for replication of latent profile analyses (identifying four interpersonal profiles: SI, IH, IL, SC) and network models (bridge node detection)Each row represents one participant; columns include both item‑level and aggregated subscale scoresRecommended software: R (packages <b>tidyverse</b>, <b>poLCA</b> or <b>mclust</b>, <b>qgraph</b>/<b>bootnet</b>)本数据集包含已完成全预处理并经过反向编码的调研数据，该数据用于已提交至《社会与人际关系期刊》（Journal of Social and Personal Relationships）的论文《青少年冒险行为的内外因交互作用：潜在剖面与网络分析视角》的相关研究。数据集涵盖2023年5月至10月间，在广州某高校收集的1159名中国一年级本科生的有效作答数据（年龄均值为18.2岁，女性占比62.3%）。 【内容构成】 【人口统计学变量】： - 被试ID、年龄、性别、独生子女身份、城乡户籍来源 - 家庭月收入档位 - 主观社会地位与学校地位（麦克阿瑟量表得分） 【社会心理测量工具】： - 简版自我控制量表（Brief Self-Control Scale, BSCS）：共7个条目，已完成反向编码，得分越高表示自我控制能力越差 - 领悟社会支持量表（Perceived Social Support Scale, PSSS）：共12个条目，涵盖家庭、朋友、重要他人三个维度 - 家庭APGAR量表：共5个条目，用于评估家庭功能 - 人际敏感性量表（SCL-90子量表）：共9个条目，已完成反向编码，得分越高表示人际敏感性越强 - DOSPERT冒险行为（Risk-Taking, RT）子量表：共30个条目，涵盖五大领域：伦理、财务、健康/安全、娱乐、社交 【数据结构与预处理流程】： - 所有量表均严格按照原始手册计分；共执行两次反向编码操作（分别针对BSCS与SCL-90人际敏感量子量表） - 剔除作答时长异常或作答模式存在偏差的被试，最终有效样本量N=1159 - 无缺失数据留存；同时提供原始条目得分与合成子量表得分 - 变量命名格式适配潜在剖面分析（Latent Profile Analysis, LPA）与后续网络分析的需求 【使用须知】： - 本数据集旨在支持潜在剖面分析（已识别出四种人际类型：SI、IH、IL、SC）与网络模型（桥接节点检测）的复现研究 - 每一行代表一名被试，列字段同时包含条目级得分与聚合后的子量表得分 - 推荐使用R语言进行分析，所需依赖包包括：tidyverse、poLCA或mclust、qgraph/bootnet