Proxies and Other External Raters: Methodological Considerations

Andrea Lynn Snow · Karon Frances Cook · Pay-Shin Lin · Robert O. Morgan · Jay S Magaziner

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Psychometric Methodologies and Testing Health Education and Validation Reliability and Agreement in Measurement

Abstract

OBJECTIVE: The purpose of this paper is to introduce researchers to the measurement and subsequent analysis considerations involved when using externally rated data. We will define and describe two categories of externally rated data, recommend methodological approaches for analyzing and interpreting data in these two categories, and explore factors affecting agreement between self-rated and externally rated reports. We conclude with a discussion of needs for future research. DATA SOURCES/STUDY SETTING: Data sources for this paper are previous published studies and reviews comparing self-rated with externally rated data. STUDY DESIGN/DATA COLLECTION/EXTRACTION METHODS: This is a psychometric conceptual paper. PRINCIPAL FINDINGS: We define two types of externally rated data: proxy data and other-rated data. Proxy data refer to those collected from someone who speaks for a patient who cannot, will not, or is unavailable to speak for him or herself, whereas we use the term other-rater data to refer to situations in which the researcher collects ratings from a person other than the patient to gain multiple perspectives on the assessed construct. These two types of data differ in the way the measurement model is defined, the definition of the gold standard against which the measurements are validated, the analysis strategies appropriately used, and how the analyses are interpreted. There are many factors affecting the discrepancies between self- and external ratings, including characteristics of the patient, the proxy, and of the rated construct. Several psychological theories can be helpful in predicting such discrepancies. CONCLUSIONS: Externally rated data have an important place in health services research, but use of such data requires careful consideration of the nature of the data and how it will be analyzed and interpreted.

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