Survey data on forest owner attitudes can be used to divide forest owners into types, each type forming a group of similar owners with attitudes differing markedly from those observed for other types. Such an empirical typology can be developed using a range of different statistical methods, including cluster analyses and latent class analysis. In this paper we examine the sensitivity of a forest owner typology to the choice of statistical model. Based on a latent class analysis of data from a survey among private forest owners in Denmark, we identify different types of owners. The resulting typology is compared with previous results based on k-means clustering of the same data. It emerges that the two statistical methods lead to almost identical forest owner types, but also that the final typology is influences by various choices made on the way through the analysis. The most significant of these choices is the number of owner types.


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