Factorial Validity of the Decisional Involvement Scale as a Measure of Content and Context of Nursing Practice

Nurses' Perception of Shared Decision-Making Processes: Quantifying a Shared Governance Culture

OBJECTIVE

The aim of this study is to describe how measuring the perceived and desired decision-making capacity of nurses in a model of shared governance (SG) can be beneficial.

BACKGROUND

Shared governance (SG) increases nurse's control over professional practice. Engagement in SG can be impacted by how much decision-making power nurses desire. This concept related to decision making has been termed decisional involvement (DI). Few studies exist that examine the concept of DI.

METHODS

Using the Decisional Involvement Scale, acute care nurses were sampled concerning desired and perceived decision making on 21 topics related to nursing practice.

RESULTS

Analysis of the data identified different governance priorities for several areas. Of particular interest was that those nurses on SG councils for more than 5 years did not report higher satisfaction with decision involvement.

CONCLUSIONS

A comprehensive evaluation of shared decision making was a valuable tool to establish a baseline of data and seek opportunities for improvement. A well-integrated model of SG requires continuous improvement and analysis to be sustained. Measuring and evaluating staff nurses desire to control varied aspects of DI can allow organizations to make focused efforts to strengthen SG.

Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques

With increasing access to population-based data and electronic health records for secondary analysis, missing data are common. In the social and behavioral sciences, missing data frequently are handled with multiple imputation methods or full information maximum likelihood (FIML) techniques, but healthcare researchers have not embraced these methodologies to the same extent and more often use either traditional imputation techniques or complete case analysis, which can compromise power and introduce unintended bias. This article is a review of options for handling missing data, concluding with a case study demonstrating the utility of multilevel structural equation modeling using full information maximum likelihood (MSEM with FIML) to handle large amounts of missing data. MSEM with FIML is a parsimonious and hypothesis-driven strategy to cope with large amounts of missing data without compromising power or introducing bias. This technique is relevant for nurse researchers faced with ever-increasing amounts of electronic data and decreasing research budgets. © 2016 Wiley Periodicals, Inc.