Necessary Condition Analysis (NCA)

Overview

Welcome to Necessary Condition Analysis (NCA). NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed. Examples of necessary conditions are a student’s GMAT score for admission to a PhD program; a student will not be admitted to a PhD program when his GMAT score is too low. Intelligence for creativity, as creativity will not exist without intelligence, and management commitment for organizational change, as organizational change will not occur without management commitment. NCA can be used with existing or new data sets and can give novel insights for theory and practice. You can apply NCA as a stand-alone approach, or as part of a multi-method approach complementing multiple linear regression (MLR), structural equation modelling (SEM) or Qualitative Comparative Analysis (QCA). This course explains the basic elements of NCA and uses illustrative examples on how to perform NCA with R software. Topics include (i) Setting up an NCA study (ii) Run NCA and (iii) Present the results of NCA. We hope you enjoy the course!