Utne Inger, Miaskowski Christine, Bjordal Kristin, Cooper Bruce A, Valeberg Berit T, Rustøen Tone
Faculty of Nursing, Oslo University College, Oslo, Norway.
Clin J Pain. 2009 Jun;25(5):391-400. doi: 10.1097/AJP.0b013e318195ed9b.
The aims of this study of oncology outpatients with cancer pain were to perform an exploratory factor analysis (EFA) of the 48-item Coping Strategy Questionnaire (CSQ) and a confirmatory factor analysis of the 6-factor solution of the Coping Strategy Questionnaire-Revised (CSQ-R) suggested by Riley and Robinson in 1997. In addition, differences in latent factor means and in the CSQ-R subscale scores between inpatients and outpatients were evaluated.
Data from oncology outpatients (n=217) and inpatients (n=225) with pain were used. The Mplus program was used to perform both the EFA and confirmatory factor analysis treating the items as ordinal, and using robust maximum likelihood estimation. Quartimin oblique rotation was used for the EFA. Model fit was evaluated with the chi test, the comparative fit index, the root mean square error of approximation, and the standardized root mean square residual, as well as by substantive evaluation of the solutions.
The EFA of the original 48-item CSQ did not reproduce the factor structure defined by Rosentiel and Keefe. The 6-factor structure from the 27-item CSQ-R fit both the inpatient and outpatient data well with strong factorial invariance, as well as the combined data, allowing some correlated errors among items. Differences were found between the 2 samples for the ignoring, catastrophizing, and praying latent factor means, and for the catastrophizing and praying subscale means.
The 27-item CSQ-R is recommended for use as a clinical instrument. However, further research of the 6-factor structure is recommended to identify reasons for the correlated errors.
本研究针对患有癌痛的肿瘤门诊患者,旨在对包含48个条目的应对策略问卷(CSQ)进行探索性因子分析(EFA),并对莱利和罗宾逊于1997年提出的应对策略问卷修订版(CSQ-R)的六因素解决方案进行验证性因子分析。此外,还评估了住院患者和门诊患者在潜在因子均值以及CSQ-R分量表得分方面的差异。
使用了来自患有疼痛的肿瘤门诊患者(n = 217)和住院患者(n = 225)的数据。Mplus程序用于将条目视为有序变量进行EFA和验证性因子分析,并使用稳健最大似然估计。EFA采用四分位最小斜交旋转。通过卡方检验、比较拟合指数、近似均方根误差、标准化均方根残差以及对解决方案的实质性评估来评估模型拟合。
原始48条目CSQ的EFA未重现罗森蒂尔和基夫定义的因子结构。来自27条目CSQ-R的六因素结构与住院患者和门诊患者的数据拟合良好,具有很强的因子不变性,与合并数据也拟合良好,允许条目之间存在一些相关误差。在忽视、灾难化和祈祷潜在因子均值以及灾难化和祈祷分量表均值方面,发现两个样本之间存在差异。
推荐使用27条目CSQ-R作为临床工具。然而,建议对六因素结构进行进一步研究,以确定相关误差的原因。