Predicting students' academic and licensure success: A review of methods for high-stakes faculty decision making
Brandon Kyle Johnson, PhD, RN, CHSE, Texas Tech University Health Sciences Center School of Nursing, Assistant Professor/Clinical & Simulation Director, Lubbock, Texas, USA
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Do you ever wish you had a clear-cut method for evaluating the predictive validity and classification accuracy of standardized exams when establishing admission, progression, and high-stakes testing policy for your nursing students? If you or your faculty colleagues have struggled to establish evidence-based admissions criteria or to evaluate what standardized test score threshold is most predictive of NCLEX-RN outcomes within your program, this session is for you. The purpose of this session is to explore powerful but accessible methods for evaluating the accuracy of binary (e.g., yes/no, pass/fail) decisions including diagnostic/classification accuracy and ROC (receiver operating characteristics) curves. In addition to a detailed discussion and demonstration of various software tools, attendees will be provided access to a detailed spreadsheet application for use in their own programs.
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|Review Type||Abstract Review Only: Reviewed by Event Host|
|Name||Nursing Education Research Conference 2020: Transforming Nursing Education Through Evidence Generation and Translation|
|Host||Sigma Theta Tau International;
National League for Nursing
|Location||Washington, DC, USA|
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