University of Waikato, School of Psychology, Hamilton, New Zealand.
University of North Texas, Department of Educational Psychology, United States of America.
J Affect Disord. 2022 Jul 1;308:391-397. doi: 10.1016/j.jad.2022.04.009. Epub 2022 Apr 7.
The 10-item Edinburgh Postnatal Depression Scale (EPDS) is a widely used depression measure with acceptable psychometric properties, but it uses ordinal scaling that has limited precision for assessment of outcomes in clinical and research settings. This study aimed to apply Rasch methodology to examine and enhance psychometric properties of the EPDS by developing ordinal-to-interval conversion algorithm.
The Partial Credit Rasch model was implemented using a sample of 621 mothers of infants (birth to 2 years old) who completed the EPDS as a part of a larger online survey.
Initial analysis indicated misfit to the Rasch model attributed to local dependency between individual EPDS items. The best model fit was achieved after combining six locally dependent items into three super-items resulting in non-significant item-trait interaction (χ(49) = 46.61, p < 0.57), strong reliability (PSI = 0.86), unidimensionality and item invariance across personal factors such as age and mothers' education. This permitted generation of ordinal-to-interval conversion algorithms derived from person estimates of the Rasch model.
Ordinal-to-interval conversion cannot be applied for individuals with missing data.
The EPDS met expectations of the unidimensional Rasch model after internal modifications, and its precision can be enhanced by using ordinal-to-interval conversion tables published in this article without the need to alter the original scale format. Scores derived from these conversion tables should decrease error and improve conformity with statistical assumptions in both clinical and research use of the EPDS, making monitoring of clinical status and outcomes more accurate.
10 项爱丁堡产后抑郁量表（EPDS）是一种广泛使用的抑郁测量工具，具有可接受的心理测量学特性，但它使用有序标度，在临床和研究环境中评估结果的精度有限。本研究旨在应用 Rasch 方法通过开发有序到区间的转换算法来检查和增强 EPDS 的心理测量特性。
使用 621 名婴儿（出生至 2 岁）母亲的样本实施部分信用 Rasch 模型，这些母亲完成了作为更大规模在线调查一部分的 EPDS。
初步分析表明，与个别 EPDS 项目之间的局部依赖性不符合 Rasch 模型。通过将六个局部依赖项目合并为三个超级项目，最佳模型拟合度得到实现，导致项目特征相互作用不显著（χ（49）= 46.61，p < 0.57），可靠性强（PSI = 0.86），维度不变性和个人因素（如年龄和母亲教育）的项目不变性。这允许从 Rasch 模型的个人估计生成有序到区间的转换算法。
对于缺失数据的个体，不能应用有序到区间的转换。
EPDS 在进行内部修改后符合单维 Rasch 模型的预期，并且可以通过使用本文中发布的有序到区间转换表来增强其精度，而无需改变原始量表格式。从这些转换表中得出的分数应减少错误，并提高在 EPDS 的临床和研究使用中符合统计假设的程度，从而更准确地监测临床状况和结果。
