Chief Executive Officers' Perceptions of Collective Organizational Engagement and Patient Experience in Acute Care Hospitals.

The concept of employee engagement has garnered considerable attention in acute care hospitals because of the many positive benefits that research has found when clinicians are individually engaged. However, limited, if any, research has examined the effects of engaging all hospital employees (including housekeeping, cafeteria, and admissions staff) in a collective manner and how this may impact patient experience, an important measure of hospital performance. Therefore, this quantitative online survey-based study examines the association between 60 chief executive officers' (CEOs') perceptions of the collective organizational engagement (COE) of all hospital employees and patient experience. A summary measure of the US Hospital Consumer Assessment of Healthcare Providers and Systems survey scores was used to assess patient experience at each of the 60 hospitals represented in the study. A multiple linear regression model was tested using structural equation modeling. The findings of the research suggest that CEOs' perceptions of COE explain a significant amount of variability in patient experience at acute care hospitals. Practical implications for CEOs and other hospital leaders are provided that discuss how COE can be used as an organizational capability to influence organizational performance.

Investigating bias in squared regression structure coefficients.

The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients.

Statistical assumptions of substantive analyses across the general linear model: a mini-review.

The validity of inferences drawn from statistical test results depends on how well data meet associated assumptions. Yet, research (e.g., Hoekstra et al., 2012) indicates that such assumptions are rarely reported in literature and that some researchers might be unfamiliar with the techniques and remedies that are pertinent to the statistical tests they conduct. This article seeks to support researchers by concisely reviewing key statistical assumptions associated with substantive statistical tests across the general linear model. Additionally, the article reviews techniques to check for statistical assumptions and identifies remedies and problems if data do not meet the necessary assumptions.