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一个可靠性概括自我报告的元分析,统计数据的可靠性,焦虑和焦虑
Emine Ören Özdemir1,2, Ibrahim Yildirim1
1Department of Educational Sciences, Gaziantep University, Gaziantep, Türkiye.
Frontiers in psychology
|February 9, 2026
概括
这项研究使用可靠性概括元分析分析了统计焦虑量表的84个可靠性系数. 平均克伦巴赫的时间.
科学领域:
- 心理测量 心理测量 心理测量
- 教育心理学教育心理学
- 量化研究方法 量化研究方法
背景情况:
- 评估统计焦虑量表的可靠性对于教育和心理学研究中的准确测量至关重要.
- 之前的研究报告了不同统计焦虑量表的各种克朗巴赫α系数,导致解释中的潜在不一致性.
- 需要对可靠性数据进行全面的综合分析,以建立这些仪器的一般可靠性估计.
研究的目的:
- 通过元分析来确定统计焦虑量表的一般可靠性系数.
- 调查报告可靠性系数异质性的来源.
- 提供广泛使用的统计焦虑指标的内部一致性的可靠估计.
主要方法:
- 在Web of Science,ERIC,Scopus和谷歌学者中进行了系统的文献搜索.
- 提取了84个克朗巴赫对统计焦虑量表的α系数.
- 使用了可靠性概括元分析,包括随机效应建模和ANOVA类型.
主要成果:
- 在所有尺度上,Cronbach的α系数的整体平均值为0.927,表明其可靠性很高.
- 具体的平均阿尔法值报告了每个尺度的具体平均值:STARS (.931),SAS (.917),SAS-10 (.918) 和WAESTA (.951).
- 尺度类型是影响可靠性的重要因素 (类似于ANOVA),平均尺度得分及其标准偏差预测了平均α.
结论:
- 统计焦虑量表通常显示出高可靠性,支持其在研究中的使用.
- 尺度的类型及其心理测量属性 (平均分数,标准偏差) 导致可靠性的变化.
- 这一元分析为统计焦虑测量的内部一致性提供了整合和可靠的估计.
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