Developing a Satisfactory Input for Project Complexity Model Using Principal Component Analysis (PCA)

A methodology for assessing and measuring project complexity can help project teams increase the likelihood of success and predictable project outcomes. This research used the Principal Component Analysis (PCA) technique to create a satisfactory input for a potential project complexity predictive model that helps identify the levels of project complexity. In this research, 37 project complexity measures were statistically verified based on the data collected from 44 historical projects. However, the number of independent variables (37 complexity measures) was too large to generate a statistically stable and reliable model. Thus, the variable reduction process PCA was used to combine those significant complexity measures to create a required input for the project complexity predictive model. This PCA process resulted in a significantly smaller number of principal components functioning as the moderating variables. The process helped in generating a valid input for a numerically stable model while the subject observations were limited. The research helps enrich the theoretical basis in the field of project management by providing usable input for a potential project complexity model that can help scholars and practitioners assess a project's complexity levels based on the applicably identified complexity measures.