克朗巴赫阿尔法
怀孕
探索性因素分析
结构效度
比例(比率)
心理学
有效性
背景(考古学)
内容有效性
可靠性(半导体)
医学
临床心理学
心理测量学
生物
物理
古生物学
功率(物理)
量子力学
遗传学
作者
Meiqin Wu,Bingzheng Zhang,Junrui Ma,Qiuxing Chen,Yunpeng Nian,Jingfang Yu,Tiantian Zou,Qin Tian,Anqi He,Huiwen Wu,Yan Yu,Teng Fei,Tin Yu,Limin Huang,Yuliang Zou
出处
期刊:Chin J Reprod Contracep
日期:2019-01-25
卷期号:39 (1): 36-41
标识
DOI:10.3760/cma.j.issn.2096-2916.2019.01.008
摘要
Objective
To evaluate the reliability and validity of the pregnancy intention scale which can be used in different pregnancy status in the context of contemporary China.
Methods
On the basis of the conceptual model of pregnancy intention developed by qualitative research, the measurement of unplanned intention was preliminary established. A total of 438 samples including pregnant (continuing pregnancy and seeking for abortion) and postnatal women recruited from two maternal and child health hospitals in Hubei Province from August 20 to November 25, 2016 were surveyed. Internal consistency was assessed using the Cronbach’s α Statistic and item-total correlation coefficient, and construct validity was assessed using exploratory factor analysis and hypothesis testing.
Results
A twelve item measure of pregnancy intention was produced, women’s pregnancy intention/planning was represented by the range of scores (0–24). The Cronbach’α coefficient of the scale was 0.777, and all item-total correlations were >0.2. Three factors were extracted through exploratory factor analysis, the cumulative variance contribution rate was 51.86%, and all items’ factor loadings were >0.4. All hypotheses to test construct validity were met.
Conclusion
The pregnancy intention scale was reliable, valid, and acceptable for Chinese women. The scale can be used as an effective tool for improving our understanding, quantitative measurement and evaluation of women’s pregnancy intention, thus providing better contraception, family planning, and reproductive health service to women.
Key words:
Pregnancy intention; Scale; Reliability; Validity
科研通智能强力驱动
Strongly Powered by AbleSci AI