探索性比较和评估两种两步措施,以识别调查数据集中的跨性别者
Dylan Felt1, Lauren B Beach1, Florence Ashley2
1Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Transgender health
|May 1, 2025
概括
这项研究评估了在调查中识别跨性别个人的两种方法. 使用性别模式的替代方法显示出捕捉细微人口差异的希望,尽管需要进一步的研究.
科学领域:
- 社会科学 社会科学 社会科学
- 人口统计数据 人口统计数据
- 调查方法 调查方法
背景情况:
- 在调查中识别跨性别个人的传统两步方法依赖于出生时分配的性别 (SAB) 和当前的性别认同.
- 这些传统方法在充分捕捉人口中性别认同的多样性方面存在局限性.
- 性别模式,定义为SAB和当前性别认同之间的关系,提供了一个潜在的更细微的指标.
研究的目的:
- 为了比较和评估两种不同的两步方法来识别调查数据集中的跨性别者.
- 评估一种包含性别模式的替代两步方法与传统的基于SAB的方法相比的实用性.
- 分析这两种识别方法之间的趋同和分歧.
主要方法:
- 在美国,对952名性和性别少数群体成年人进行了一项在线横截面调查.
- 探索性分析采用了两种方法: (1) 修改传统的两步 (SAB + 当前的性别认同) 和 (2) 替代的两步 (当前的性别认同 + 模式).
- 分析了两种方法之间的分类分歧和融合.
主要成果:
- 在这两种方法之间观察到很高的收率 (95%).
- 差异主要发生在三个类别:被替代方法归类为"质疑"的个人,被一种方法唯一分类的个人,以及在两种方法之间具有不同性别模式的个人.
- 对调查问题的拒绝率一般较低,性别模式的拒绝率略高.
结论:
- 初步证据表明,采用另一种两步方法是有用的,特别是对于需要对跨性别人口子组进行详细分析的研究.
- 传统和替代方法都有局限性,需要进一步调查和认知测试.
- 未来的研究应该优先精炼替代方法,以利用其优势并解决已识别的局限性和不确定性领域.
相关概念视频
Multiple Comparison Tests
3.8K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.8K
Surveys
14.7K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
14.7K
Comparing the Survival Analysis of Two or More Groups
84
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
84
Test for Homogeneity
1.9K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
1.9K
One-Way ANOVA: Equal Sample Sizes
3.1K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.1K
Friedman Two-way Analysis of Variance by Ranks
93
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
93


