Uncovering surprises in home health services' effects post-hospitalization. Patients seen at home by a nurse, occupational therapist, or physical therapist tend to readmit to hospital at a high rate, but this might be expected based on the higher-than-average severity of their conditions. With this accounted for (statistically controlled) one would expect a reversal of the readmission effect. Not true for any of the seven hospital populations nationwide that colleagues and I examined. A conference presentation and a journal submission explored the reasons why and went on to specify the conditions under which such a reversal could occur in the context of the common analytic technique of logistic regression.
Explaining the incidence of surgical errors. A researcher working with a national nursing organization forged the opportunity to collect a wealth of data on the occurrence of wrong-site surgeries (e.g., removal of the wrong kidney) as well as on staffing, staff education, and climate in several hundred US hospitals. She sought to assemble these many types of objective and subjective data into a model that could help predict incidence of these catastrophic errors. Because such errors are rare given the vast number of surgeries performed, standard linear regression would have been ineffectual. We helped her devise more general linear models -- using logistic and Poisson regression -- that were more sensitive to distinctions between the rare and the "extremely rare." We also advised on scale development (combining survey responses into scale scores for greatest validity and reliability) and on model specification (maximizing the model's stability and sensitivity by choosing the most promising variables and the best number to include).
Understanding bullying among nurses and its implications for staff turnover. For a Ph.D. dissertation, a nurse-researcher had surveyed 500 nurses to learn about ways in which they might have been mistreated by fellow nurses; how these harmful behaviors might have related to one another; and how such mistreatment, combined with demographic factors, might explain some nurses' intention to leave the profession. Item distributions were heavily skewed, and so we searched for effective power transformations to make the variables more amenable to modeling. With this accomplished, we guided her through a series of factor analyses that helped sort two dozen behaviors into four underlying factors (roughly: punishment, belittling, exclusion, and undermining). We used these factors in multiple regression and analysis of covariance models to explain the outcome of intention to leave the profession, after generating and thoroughly checking diagnostic statistics and graphs to see whether the strength or direction of any relationships hinged on gender, ethnicity, experience, or nursing specialty. We advised and supported her as she wrote up her findings, interpreted their larger significance, and underwent a successful Ph.D. defense.
The research led to publication of three articles in peer-reviewed journals, the latter article exploring the most valid and efficient ways of determining the extent to which bullying affects an organization. A new instrument comprised of just four survey questions was found to be superior to a popular one that required 22. In response to this paper we have fielded many requests by other researchers asking for help in replicating our approach.