The primary aim of this NINR-NIH–funded field test in 407 nursing homes in 3 states was to complete the development of and conduct psychometric testing for the Observable Indicators of Nursing Home Care Quality Instrument (Observable Indicators, OIQ). The development of the OIQ was based on extensive qualitative and iterative quantitative work that described nursing home care quality and did initial validity and reliability field testing of the instrument in 123 nursing homes in 1 state. The scale is meant for researchers, consumers, and regulators interested in directly observing and quickly evaluating (within 30 minutes of observation) the multiple dimensions of care quality in nursing homes. After extensive testing in this study, the Observable Indicators instrument has been reduced to 30 reliable and discriminating items that have a conceptually coherent hierarchical factor structure that describes nursing home care quality. Seven first-order factors group together into two second-order factors of Structure (includes Environment: Basics and Odors) and Process (includes Care Delivery, Grooming, Interpersonal Communication, Environment: Access, and Environment: Homelike) that are classic constructs of Quality, which was the third-order factor. Internal consistency reliability for the 7 first-order factors ranged from .77 to .93. Construct validity analyses revealed an association between survey citations and every subscale as well as the total score of the OIQ instrument. Known groups analysis revealed expected trends in the OIQ scores. The Observable Indicators instrument as a whole shows acceptable interrater and test-retest reliabilities, and strong internal consistency. Scale subscales show acceptable reliability as well. Generalizability Theory analyses revealed that dependability of scores can be improved by including a second site observer, or by revisiting a site. There is a small additional benefit from increasing observers or visits beyond two.
Nursing Home Care Quality: Insights From a Bayesian Network Approach