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VOLUME 42 | NUMBER 1 | FEBRUARY 2007


Analyzing Multiple Informant Data from an Evaluation of the Health Disparities Collaboratives

Objective.
To use multivariate regression methods to analyze simultaneously data obtained from multiple respondents or data sources (informants) at health centers.

Data Source.
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.

Study Design.
Cross-sectional survey of staff at the health centers during 2003–2004.

Statistical Methods.
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.

Findings.
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.

Conclusions.
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.

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