O'Malley A James, Landon Bruce E, Guadagnoli Edward
Department of Health Care Policy, 180 Longwood Ave., Boston, MA 02115-5899, USA.
Health Serv Res. 2007 Feb;42(1 Pt 1):146-64. doi: 10.1111/j.1475-6773.2006.00597.x.
To use multivariate regression methods to analyze simultaneously data obtained from multiple respondents or data sources (informants) at health centers.
Surveys of executive directors, medical directors, and providers from 65 community health centers (176 informants) who participated in an evaluation of the Health Disparities Collaboratives.
Cross-sectional survey of staff at the health centers during 2003-2004.
In order to illustrate this method, we analyze the association between informants' assessments of the culture of the center and participation in the collaborative, and the association between computer availability and the effort made by management to improve the quality of the care and services at their center. Multivariate regression models are used to pool information across informants while accounting for informant-specific effects and retaining informants in the analysis even if the data from some of them are missing. The results are compared with those obtained by traditional methods that use data from a single informant or average over informants' ratings.
In both the Collaborative participation and quality improvement efforts analyses, the multivariate regression multiple informants' analysis found significant effects and differences between informants that traditional methods failed to find. Participating centers emphasized developmental (entrepreneurship, innovation, risk-taking) and rational culture. The effect of hierarchical culture (stability and bureaucracy) on participation depended on the informant; executive directors and medical providers were the most discrepant. In centers that participated in the Collaborative, the availability of computers was positively associated with the effort that management made toward improving quality.
The multiple informants model provided the most precise estimates and alerts users to differential effects across informants. Because different informants may have different insights or experiences, it is important that differences among informants be measured and ultimately understood by health services researchers.
运用多元回归方法，同时分析从健康中心的多名受访者或多个数据源（信息提供者）获取的数据。
对参与健康差异协作评估的65家社区健康中心的执行董事、医疗主任和提供者进行的调查（176名信息提供者）。
2003 - 2004年期间对健康中心工作人员进行的横断面调查。
为了阐述此方法，我们分析信息提供者对中心文化的评估与参与协作之间的关联，以及计算机可用性与管理层为提高其中心护理和服务质量所做努力之间的关联。多元回归模型用于汇总信息提供者的信息，同时考虑信息提供者特定的影响，并在分析中保留信息提供者，即使其中一些人的数据缺失。将结果与使用单个信息提供者的数据或信息提供者评分平均值的传统方法所获得的结果进行比较。
在协作参与和质量改进努力分析中，多元回归多信息提供者分析发现了传统方法未发现的信息提供者之间的显著影响和差异。参与的中心强调发展型（创业、创新、冒险）和理性文化。层级文化（稳定性和官僚作风）对参与的影响取决于信息提供者；执行董事和医疗提供者的差异最大。在参与协作的中心，计算机的可用性与管理层为提高质量所做的努力呈正相关。
多信息提供者模型提供了最精确的估计，并提醒用户注意信息提供者之间的差异影响。由于不同的信息提供者可能有不同的见解或经历，健康服务研究人员测量并最终理解信息提供者之间的差异非常重要。
