Suppr超能文献

基于 Rasch 的抑郁筛查在大规模德国一般人群样本中的验证。

Validation of the Rasch-based Depression Screening in a large scale German general population sample.

机构信息

Institute of Medical Psychology and Medical Sociology, University Hospital of RWTH Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.

出版信息

Health Qual Life Outcomes. 2010 Sep 21;8:105. doi: 10.1186/1477-7525-8-105.

Abstract

BACKGROUND

The study aimed at presenting normative data for both parallel forms of the "Rasch-based Depression Screening (DESC)", to examine its Rasch model conformity and convergent and divergent validity based on a representative sample of the German general population.

METHODS

The sample was selected with the assistance of a demographic consulting company applying a face to face interview (N = 2509; mean age = 49.4, SD = 18.2; 55.8% women). Adherence to Rasch model assumptions was determined with analysis of Rasch model fit (infit and outfit), unidimensionality, local independence (principal component factor analysis of the residuals, PCFAR) and differential item functioning (DIF) with regard to participants' age and gender. Norm values were calculated. Convergent and divergent validity was determined through intercorrelations with the depression and anxiety subscales of the Hospital Anxiety and Depression Scale (HADS-D and HADS-A).

RESULTS

Fit statistics were below critical values (< 1.3). There were no signs of DIF. The PCFAR revealed that the Rasch dimension "depression" explained 68.5% (DESC-I) and 69.3% (DESC-II) of the variance, respectively which suggests unidimensionality and local independence of the DESC. Correlations with HADS-D were rDESC-I = .61 and rDESC-II = .60, whereas correlations with HADS-A were rDESC-I = .62 and rDESC-II = .60.

CONCLUSIONS

This study provided further support for the psychometric quality of the DESC. Both forms of the DESC adhered to Rasch model assumptions and showed intercorrelations with HADS subscales that are in line with the literature. The presented normative data offer important advancements for the interpretation of the questionnaire scores and enhance its usefulness for clinical and research applications.

摘要

背景

本研究旨在为“基于 Rasch 的抑郁筛查(DESC)”的两种平行形式提供规范数据,基于德国普通人群的代表性样本,检验其 Rasch 模型拟合度以及聚合和发散效度。

方法

本研究样本由一家人口统计咨询公司协助采用面对面访谈方式选取(N = 2509;平均年龄 = 49.4,标准差 = 18.2;55.8%为女性)。采用 Rasch 模型拟合分析(infit 和 outfit)、单维性、局部独立性(残差的主成分因子分析,PCFAR)和与参与者年龄和性别有关的项目区分度(DIF)来确定 Rasch 模型假设的符合程度。计算规范值。通过与医院焦虑和抑郁量表(HADS-D 和 HADS-A)的抑郁和焦虑分量表的相关性来确定聚合和发散效度。

结果

拟合统计量低于临界值(< 1.3)。没有 DIF 的迹象。PCFAR 表明,Rasch 维度“抑郁”分别解释了 DESC-I(68.5%)和 DESC-II(69.3%)的方差,这表明 DESC 的单维性和局部独立性。与 HADS-D 的相关性分别为 rDESC-I =.61 和 rDESC-II =.60,而与 HADS-A 的相关性分别为 rDESC-I =.62 和 rDESC-II =.60。

结论

本研究进一步支持了 DESC 的心理测量质量。DESC 的两种形式均符合 Rasch 模型假设,并与文献中 HADS 分量表的相关性一致。所提供的规范数据为解释问卷分数提供了重要进展,并增强了其在临床和研究应用中的有用性。

相似文献

1
Validation of the Rasch-based Depression Screening in a large scale German general population sample.
Health Qual Life Outcomes. 2010 Sep 21;8:105. doi: 10.1186/1477-7525-8-105.
2
Cross-sectional validation of the Rasch-based Depression Screening (DESC) in a mixed sample of patients with mental and somatic diseases.
Compr Psychiatry. 2013 Oct;54(7):1082-9. doi: 10.1016/j.comppsych.2013.05.001. Epub 2013 Jun 14.
3
Psychometric evaluation of the Rasch-based depression screening in patients with neurologic disorders.
Arch Phys Med Rehabil. 2010 Aug;91(8):1188-93. doi: 10.1016/j.apmr.2010.04.021.
5
Rasch analysis of the hospital anxiety and depression scale (HADS) for use in motor neurone disease.
Health Qual Life Outcomes. 2011 Sep 29;9:82. doi: 10.1186/1477-7525-9-82.
7
8
10
Rasch analysis of the hospital anxiety and depression scale among Chinese cataract patients.
PLoS One. 2017 Sep 26;12(9):e0185287. doi: 10.1371/journal.pone.0185287. eCollection 2017.

引用本文的文献

2
Forgetting is comparable between healthy young and old people.
Sci Rep. 2024 Dec 28;14(1):31176. doi: 10.1038/s41598-024-82570-w.
7
Ambivalent heroism? - Psychological burden and suicidal ideation among nurses during the Covid-19 pandemic.
Nurs Open. 2022 Jan;9(1):785-800. doi: 10.1002/nop2.1130. Epub 2021 Nov 18.
9
A Rasch analysis of the self-administered Foot Health Assessment Instrument (S-FHAI).
BMC Nurs. 2021 Jun 15;20(1):98. doi: 10.1186/s12912-021-00625-z.
10
Interpersonal theory of suicide: prospective examination.
BJPsych Open. 2020 Sep 22;6(5):e113. doi: 10.1192/bjo.2020.93.

本文引用的文献

1
Psychometric evaluation of the Rasch-based depression screening in patients with neurologic disorders.
Arch Phys Med Rehabil. 2010 Aug;91(8):1188-93. doi: 10.1016/j.apmr.2010.04.021.
2
Rasch analysis of the Beck Depression Inventory-II in a neurological rehabilitation sample.
Disabil Rehabil. 2010;32(1):8-17. doi: 10.3109/09638280902971398.
3
Screening for depression: Rasch analysis of the dimensional structure of the PHQ-9 and the HADS-D.
J Affect Disord. 2010 May;122(3):241-6. doi: 10.1016/j.jad.2009.07.004. Epub 2009 Aug 7.
4
Development and validation of the Rasch-based Depression Screening (DESC) using Rasch analysis and structural equation modelling.
J Behav Ther Exp Psychiatry. 2009 Sep;40(3):468-78. doi: 10.1016/j.jbtep.2009.06.003. Epub 2009 Jun 23.
6
Rasch model analysis of the Depression, Anxiety and Stress Scales (DASS).
BMC Psychiatry. 2009 May 9;9:21. doi: 10.1186/1471-244X-9-21.
7
Rasch analysis of the hospital anxiety and depression scale in Parkinson's disease.
Mov Disord. 2009 Mar 15;24(4):526-32. doi: 10.1002/mds.22409.
8
Comparing depression diagnostic symptoms across younger and older adults.
Aging Ment Health. 2008 Nov;12(6):800-6. doi: 10.1080/13607860802428000.
9
[Measurement of change with the Hospital Anxiety and Depression Scale (HADS): sensitivity and reliability of change].
Psychother Psychosom Med Psychol. 2009 Nov;59(11):394-400. doi: 10.1055/s-2008-1067578. Epub 2008 Nov 4.
10
Psychometric comparison of PHQ-9 and HADS for measuring depression severity in primary care.
Br J Gen Pract. 2008 Jan;58(546):32-6. doi: 10.3399/bjgp08X263794.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验