用切断点对等级分类是否会影响假设测试结果?
Ugurcan Sayili1,2, Esin Siddikoglu3, Deniz Turgut3
1Department of Biostatistics, Institute of Graduate Studies in Health Sciences, Istanbul University, Istanbul, Türkiye. ugurcan.sayili@iuc.edu.tr.
Discover mental health
|April 22, 2024
概括
这项研究发现,高度显著的抑郁症指标 (p < 0.001) 在各种分析组中保持一致. 然而,接近显著性值 (p ≈ 0.05) 的结果有所不同,这突显了在规模评估中需要进行二次测试的需要.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 统计分析 统计分析
- 心理测量 心理测量 心理测量
背景情况:
- 通过使用切断点对分类规模得分进行假设测试进行调查.
- 在使用子分析组来表示类别时评估结果的可靠性.
研究的目的:
- 为了评估假设测试结果,用切断点对尺度得分进行分类.
- 要确定在使用最能代表类别的子分析组时是否获得类似的结果.
主要方法:
- 使用贝克抑郁症目录II (BDI-II) 的横截面研究,共有1950名参与者.
- 将抑郁症分为四个组 (最小,轻度,中度,严重) 并根据BDI-II分数创建六个子分析组.
- 分析包括传统 (所有参与者) 和各种子分析组 (IQR,std,百分位数,随机样本).
主要成果:
- 收入和生活质量等具有高度意义的变量 (p < 0.001) 在所有分析组中都保持着意义.
- 对于p值接近0.05的变量,显著水平因子分析组而异.
- 人口统计学变量 (性别,年龄) 和药物使用在不同群体中具有不同的意义.
结论:
- 高度显著的发现 (p < 0.001) 在不同的分析方法中是稳定的.
- 接近p < 0.05值的发现的重要性与所选择的切线点和分析组有关.
- 这项研究支持了对二次测试的必要性,以进行可靠的规模评估和解释.
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