  Selecting a suitable development method for a specific project context is one
of the most challenging activities in process design. Every project is unique
and, thus, many context factors have to be considered. Recent research took
some initial steps towards statistically constructing hybrid development
methods, yet, paid little attention to the peculiarities of context factors
influencing method and practice selection. In this paper, we utilize
exploratory factor analysis and logistic regression analysis to learn such
context factors and to identify methods that are correlated with these factors.
Our analysis is based on 829 data points from the HELENA dataset. We provide
five base clusters of methods consisting of up to 10 methods that lay the
foundation for devising hybrid development methods. The analysis of the five
clusters using trained models reveals only a few context factors, e.g.,
project/product size and target application domain, that seem to significantly
influence the selection of methods. An extended descriptive analysis of these
practices in the context of the identified method clusters also suggests a
consolidation of the relevant practice sets used in specific project contexts.
