中国社会科学院社会学研究所主持的中国社会综合状况调查（CSS）2019 年数据。与 CGSS 类似但侧重社会变迁、社会信任、阶层认同、社会公平感的追踪测量；是社会学、政治学量化研究的常用补充数据集。
Chinese Social Survey 2019 registers 18 variables — 4 commonly used as dependent variables, 8 as core regressors and 12 as controls. Common research directions for this dataset include: 主观阶层认同的客观决定因素, 社会信任与公平感的相互建构.
主观阶层认同的客观决定因素: DV self_class, IV income, education, controls age, gender, urban.
社会信任与公平感的相互建构: DV social_trust, IV fairness, self_class, controls age, gender, education.
| name | label | type/role | data file | description · keywords |
|---|---|---|---|---|
| pid | 个人ID Person ID | identifier / identifier | person | 唯一标识符 编号 |
| gender | 性别 Gender | binary / control | person | 性别 性别 |
| age | 年龄 Age | continuous / control | person | 受访者实足年龄 年龄 |
| education | 受教育程度 Education | ordinal / iv,control | person | 最高学历 教育学历 |
| urban | 城乡 Urban/rural | binary / control | person | 居住地 城乡 |
| hukou | 户口 Hukou | binary / iv,control | person | 户口性质 户口户籍 |
| marital | 婚姻状况 Marital status | categorical / control | person | 目前婚姻状况 婚姻 |
| income | 个人收入 Personal income | continuous / iv,control | person | 去年个人总收入 收入 |
| hh_income | 家庭收入 Household income | continuous / iv,control | person | 去年家庭总收入 家庭收入 |
| self_class | 主观阶层认同 Subjective class | ordinal / dv | person | 10 等阶梯自评所处社会阶层 阶层社会地位主观阶层 |
| social_trust | 社会信任 Generalized trust | ordinal / dv | person | 对一般陌生人的信任程度 信任社会信任 |
| fairness | 社会公平感 Perceived social fairness | ordinal / dv | person | 对社会整体公平程度的评价 公平公平感 |
| happiness | 主观幸福感 Subjective happiness | ordinal / dv | person | 5 级 Likert 自评幸福程度 幸福幸福感生活满意度 |
| internet_use | 互联网使用 Internet use | binary / iv | person | 是否使用互联网 互联网网络 |
| religion | 宗教信仰 Religion | categorical / iv,control | person | 宗教信仰类型 宗教 |
| migration | 流动状态 Migration status | binary / iv,control | person | 户口与居住地是否一致 流动迁移外来 |
| party | 党员身份 CCP membership | binary / iv,control | person | 是否中共党员 党员中共党员 |
| province | 省份 Province | categorical / control | person | 调查省份 省份 |
The codebook is an open lookup. The Wizard consumes the same codebook to drive code generation — upload your data and the system auto-recognises variables, applies cleaning rules, recommends research designs, runs regressions and produces a Word report.