Developing a logistic regression model to measure project complexity

Bac Dao, Sharareh Kermanshachi, Jennifer Shane, Stuart Anderson, Ivan Damnjanovic

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The study develops a binary logistic regression model to assess and measure complexity levels of a project. The complexity measures were statistically verified to create a basis for the model. The variable reduction process called Principle Component Analysis was used to combine the significant complexity indicators into component variables. The study enriches the complexity theoretical basis in the field of project management by providing an innovative approach that aids scholars and practitioners in assessing complexity levels based on the applicability of identified complexity measures. The research results also help facilitate the management process and formulate an appropriate complexity management plan.

Original languageEnglish (US)
Pages (from-to)226-240
Number of pages15
JournalArchitectural Engineering and Design Management
Issue number3
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Architecture
  • Building and Construction
  • Business, Management and Accounting(all)

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