The purpose of this study was to demonstrate how examining the bivariate correlations between items in self-report measures can assist in differentiating between possible common method variance vs. model specification errors. Specifically, social desirability was viewed as either a possible source of common method variance or as a theoretically meaningful construct that should be included in the model of interest (i.e., a specification error). In the first instance, LISREL was used, and the level of correlation between measures of social desirability and measures of the five constructs of interest was manipulated. These results provided some insight as to when one needs to be concerned about the possible "common variance effects" on the structural model. In the second instance, the correlations between measures of social desirability and the measures of only two constructs of interest were again manipulated. These analyses illustrated the point at which the omission of social desirability as a theoretically relevant variable began to result in a poor fit of the structural model.

Needlestick injuries (NSIs) are the result of multiple factors which interact in a complex manner. In an attempt to understand the causes of NSIs, a systems approach was adopted. An extensive literature review identified behaviors, equipment, safety and interpersonal environments, and administrative policies and procedures as potential contributors to NSIs. Two phases in the development of an instrument that measures the contributions of the individual, organization, and environment are reported. In phase one, a questionnaire was developed using the critical incident technique and tested on nursing and paramedic students and nursing professors. In phase two, a revised survey was administered to 205 nurses. The following factors representing different levels of a health care system emerged: safety environment, stressors, conflicting or conflicting procedures, critical behaviors, and knowledge. The discussion addresses how the questionnaire can be used to test the relative influences of these factors on the occurrence of NSIs and generate recommendations for system-wide interventions.