Ensuring the validity of private forest owner typologies by controlling for response style bias and the robustness of statistical methods

Abstract

In survey-based segmentation of forest owners, two threats to the validity of results have largely been ignored: (1) response style bias and (2) the robustness of the statistical methods. This study demonstrates response style bias detection, presents an approach for correcting for acquiescence – the systematic tendency to agree with survey items, and explores the sensitivity of a probabilistic clustering algorithm to requirements for the validity of the typology. Structural equation modeling and Monte Carlo data generation techniques were employed to detect acquiescence and estimate its effect on construct validity. A survey of the relevance of management information for private forest owners (N = 364) was used as an example. Although acquiescence was confirmed, it had minor effect on the results and no effect on the substantive construct. Uncertainty about the number of forest owner types and membership can be reduced by using probabilistic clustering and observing the number of clusters while changing the requirements for the validity of clusters. The expectation maximization algorithm proved to be robust even to stringent requirements for the validity of clusters. By controlling for response style and the robustness of statistical methods, the validity of private forest owner typologies can be better ensured.