Address correspondence to David W. Harless, Ph.D., Department of Economics, Virginia Commonwealth University, Box 844000, Richmond, VA 23284. Barbara A. Mark, R.N., Ph.D., F.A.A.N, is with the School of Nursing, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Addressing Measurement Error Bias in Nurse Staffing Research
Article first published online: 9 JUN 2006
Health Services Research
Volume 41, Issue 5, pages 2006–2024, October 2006
How to Cite
Harless, D. W. and Mark, B. A. (2006), Addressing Measurement Error Bias in Nurse Staffing Research. Health Services Research, 41: 2006–2024. doi: 10.1111/j.1475-6773.2006.00578.x
- Issue published online: 9 JUN 2006
- Article first published online: 9 JUN 2006
- Nurse staffing;
- research methodologies;
- measurement error
Objective. To assess the extent of measurement error bias due to methods used to allocate nursing staff to the acute care inpatient setting and to recommend estimation methods designed to overcome this bias.
Data Sources/Study Setting. Secondary data obtained from the California Office of Statewide Health Planning and Development (OSHPD) and the Centers for Medicare and Medicaid Services' Healthcare Cost Report Information System for 279 general acute care hospitals from 1996 to 2001.
Study Design. California OSHPD provides detailed nurse staffing data for acute care inpatients. We estimate the measurement error and the resulting bias from applying different staffing allocation methods. Estimates of the measurement errors also allow insights into the best choices for alternate estimation strategies.
Principal Findings. The bias induced by the adjusted patient days method (and its modification) is smaller than for other methods, but the bias is still substantial: in the benchmark simple regression model, the estimated coefficient for staffing level on quality of care is expected to be one-third smaller than its true value (and the bias is larger in a multiple regression model). Instrumental variable estimation, using one staffing allocation measure as an instrument for another, addresses this bias, but only particular choices of staffing allocation measures and instruments are suitable.
Conclusions. Staffing allocation methods induce substantial attenuation bias, but there are easily implemented estimation methods that overcome this bias.