Making Sense of Methods and Measurement: The Need for Rigor in Simulation Research

Abstract

Few in health care education doubt the effectiveness of clinicalsimulationasapowerfulteachingelearningtechnique. As an instructional strategy, clinical simulation has been purported to teach a variety of content, including psychomotor skills, teamwork, leadership, and clinical reasoning, and to increase student confidence in clinical situations. The amount of research about clinical simulation has increased dramatically in the past 10 years. Much of this research has focused on the measurement of self-report data with locally developed instruments that have not been systematically evaluated or psychometrically tested. This reliance on untested instruments measuring indirect data provides little support for educators trying to make informed decisions about integrating simulation into a curriculum, and it prevents researchers from building a credible body of knowledge about simulation. We know from previous research that students and nursing staff enjoy clinical simulation and that each group reports significant learning from participation in simulation experiences. Also from previous research, we know that faculty members and administrators like clinical simulation and believe that students and staff learn from these experiences. We recognize that building a simulation lab and adequately preparing it with equipment and staff is expensive. It is now our collective responsibility to conduct rigorous research that produces valid and reliable data about the effectiveness of clinical simulation. This research should directly measure learning, help describe what characteristics of simulation experiences contribute most to learning, and identify what groups of learners benefit most from specific simulation strategies. The purpose of this ongoing column in Clinical Simulation in Nursing is to review a variety of topics directly related to research methodology and both qualitative and quantitative data analysis. In future columns, we will be exploring reliability and validity, statistical and practical significance, psychometric testing, levels of measurement, and a number of common qualitative and quantitative data analysis techniques.