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Improvement of hospital unit work environments is key to quality patient care, productivity, nurse retention, and job satisfaction. Accurate measurement of such environments is necessary prior to introduction and evaluation of improvement structures and strategies. Characteristics and attributes of work environments are group level phenomena. Accurate assessment of these phenomena requires survey response rates of sufficient size to ensure sample representativeness and data that can reliably be aggregated to group level. What is the sufficient response rate? This question was answered through psychometric testing of five random samples from the population of 23 M.D. Anderson Cancer Center clinical units that had 100% response rates on an environmental survey. Response rates of 40% or more had acceptable psychometric properties for unit-specific scales. © 2009 Wiley Periodicals, Inc. Res Nurs Health 32:229–240, 2009 [ABSTRACT FROM AUTHOR]

Provides information on cross-lagged panel correlation (CLPC) analysis, a technique used in longitudinal, nonexperimental designs to draw inferences about the relationship between two variables. Characteristics of CLPC; Assumptions that underlie the technique.

Discusses the application of exploratory data analysis in nursing research. Comparison of groups and variables; Oncology knowledge test of baccalaureate nursing students; Measurement of attitudes toward caring for the oncology patient.

Describes exploratory data analysis procedures that are used to examine single variable distributions. Stem-and-leaf display technique; Usefulness of the quantile or Q-plot on the initial examination of the data; Median hinge number summary; Box plots technique.

Explores the technique of residual analysis which may be used by nurse researchers to examine linear equation for violations of statistical assumption. Robustness of regression analysis; Basis for the acceptance of assumption violations; List of regression assumptions surrounding a regression analysis.

Presents a brief review of the assumptions underlying the causal modeling technique in nursing research. Benefits of causal modeling to nurse researchers; Use of graphical residual analysis to assess for violations of the assumptions underlying the modeling process; Example of residual examination.

Patient classification according to nursing care requirements is routine in inpatient settings. When a monograph from the Division of Nursing was published, little information was included about patient classification in community health settings, such as ambulatory care. Since 1978, some work on classification for outpatient areas has been done, but not to the extent of the work in inpatient settings that established standards for classification instruments. Although the terms patient classification instrument and patient classification system appear similar, they are not meant to be used interchangeably. A patient classification instrument is a tool to identify nursing care requirements of patients in a specific setting. A patient classification system is the full program for identifying the number and type of staff needed to meet the care requirements of clients.

Among the many symptoms associated with Alzheimer's disease, disrupted nighttime sleep has been identified as one of the most troublesome. Frequently disrupted sleep, as well as nighttime wandering, determines whether or not the person is able to remain within his/her home or must be institutionalized. The first step toward altering disrupted sleep is to gain an understanding of the associated behavior patterns. The purposes of this research were to:• Describe - based on family care provider reports, the evening and nighttime behavior patterns of persons diagnosed with Alzheimer's disease; and• Develop a taxonomy that characterized the identified patterns.Data were collected using a guided interview format. Eleven care providers were interviewed at length and asked to respond to open-ended questions. Themes and categories were identified including two major, four minor and 10 sub-categories. In addition, precipitating events that triggered, intensified or accelerated the behavior patterns were identified. These data will be used to develop an instrument designed to quantify the behaviors associated with sleep disruption. This will allow for evaluation of care provider burden as well as the effect of interventions designed to enhance the sleep/rest of persons with Alzheimer's disease. [ABSTRACT FROM PUBLISHER]

Comments on Susan E. Pollock's article on adaptive responses to diabetes mellitus. Enhancement of stress resulting from illness; Importance of diabetic identification; Explanatory theory for adaptive responses to chronic disease.

Describes the philosophy and value of exploratory data analysis (EDA) for nursing research. Requirement for a successful use of the EDA; Purpose of EDA; Use of visual displays and graphical approaches.

Presents responses of authors to several commentaries on the use of data in testing multivariate theoretical models. Usefulness of graphic residual analysis for reassessing the underlying model assumptions; Ways to avoid the increasing experiment-wise error rate; Benefits of causal modeling.

The article discusses various reports published within the issue, including one on the effects of hospital restructuring and nursing leadership and another on information styles of nursing staff.

Discusses the use of residual analysis to examine for violation of assumptions specific to causal modeling. Analysis of the empirical results of Young's Professional Commitment Model for three groups of subjects; Assessment of the causal model assumptions of correct model specification; Valuable methodology in the secondary analysis of Young's Professional Commitment model.

Discusses the use of residual analysis to test the statistical assumptions of a causal model. Review of the graphical and numerical analysis involved in the examination of the empirical results of Young's Professional Commitment model; Background of subjects involved in the study; Variables assessed through the use of graphic residual analysis and supplementary statistical tests.