Explanation Plus Prediction-The Logical Focus of Project Management Research

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R (2) metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model's predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models' predictive power.

Automated summary

Most project management research focuses on explanatory analyses, but it is important to consider predictive power as well. Evaluating predictive power using metrics such as mean absolute error or root mean square error can help researchers quantify the accuracy of their statistical models.