Ipseity Daily Data

At what rate do individuals say they are X? Begining on 2025-07-08, I surveyed a small random sample of American adults on a daily cadence. They responded to many items of the form: Does <signifier> describe you today? The file ipseity-daily-aggregated.csv in this repository contains the aggregated results through 2025-07-28. The columns are as follows: signifier - a word, phrase or emoji yes_count - the number of respondents who answered yes (aggregated all-time). no_count - the number of respondents who answered no (aggregated all-time). prevalence_per_10k - an estimate of the prevalence per 10,000. If we were to survey 10,000 American adults, how many would we expect to respond yes to this signifier? The number is calculated as yes_count / (yes_count + no_count) * 10,000. earliest_obs_date - this date represents the first time this particular signifier was ever displayed to a respondent. Signifiers are chosen with stochasticity from a long list; new signifiers are sometimes added, old signifiers are sometimes deleted, and a signifier may be moved higher or lower in priority for sampling. latest_obs_date - the date closest to the present date on which signifier was most recently presented to a respondent. Version created on 2025-11-18 — cumulative data.个体自述符合X特质的比例如何？ 本研究自2025年7月8日起，以每日调研频次对小规模美国成年人随机样本开展调查。受访者需回应一系列形如「<signifier（标识语）>是否可描述今日的你？」的问题。 本仓库内的ipseity-daily-aggregated.csv文件收录了截至2025年7月28日的汇总调研结果，各字段说明如下： 1. signifier（标识语）：单词、短语或表情符号 2. yes_count（肯定应答数）：全累计周期内选择“是”的受访者总人数 3. no_count（否定应答数）：全累计周期内选择“否”的受访者总人数 4. prevalence_per_10k（每万人应答率估算值）：针对该标识语，若调研10000名美国成年人，预计会给出肯定应答的人数。计算公式为：yes_count / (yes_count + no_count) × 10000 5. earliest_obs_date（首次观测日期）：该标识语首次向受访者展示的日期。标识语从长列表中随机抽取，抽样过程存在随机性；研究期间有时会新增标识语、删除旧标识语，或调整标识语的抽样优先级 6. latest_obs_date（末次观测日期）：该标识语最近一次向受访者展示的、距当前日期最近的日期 本数据集版本创建于2025年11月18日，数据为累计汇总版本。