FASUS: Fertility Awareness Survey of 750 Chinese University Students
by Changmin Niu·Updated 17d ago
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Description
750 Chinese university students were surveyed using the 22-item Fertility Awareness Scale for University Students (FASUS). The scale, developed through expert consultation and psychometric testing, measures knowledge, attitudes, and skills across three dimensions. The dataset, authored by Changmin Niu and last updated in May 2026, reveals an average fertility awareness score of 78.01 ± 16.44, indicating a low to moderate level.
Use Cases
Analyze correlations between demographic factors and fertility awareness based on variables like gender, ethnicity, and only-child status.
Validate psychometric properties of a multi-dimensional scale based on the reported Cronbach's α of 0.97 and factor structure.
Identify target groups for reproductive health education based on regression analysis of influencing factors like prior fertility knowledge education.
Benchmark fertility awareness levels in a specific demographic based on the reported average score and standard deviation.
Strengths
Scale demonstrates strong reported reliability with a Cronbach's α of 0.97 and test-retest reliability of 0.981.
Dataset is based on a survey of 750 students, providing a substantive sample size for analysis.
The FASUS scale was developed through a structured process including literature review, interviews, and two rounds of Delphi expert consultation.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment for certain modeling tasks.
Data reflects a specific geographic and demographic bias, focusing solely on Chinese university students.
Provenance
Source
Changmin Niu via figshare.
Collection Method
Cross-sectional survey using a self-developed scale, with data gathered from university students.
Freshness
Last updated 2026-05-20 05:47:29; freshness should be verified.
Geography
China
Primary data file is a 178.1 KB DOCX document; users may need to extract or convert tabular data for analysis.