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Several authors, according to different methodological approaches, have employed logistic Regression (LR), a multivariate statistical analysis adopted to assess the spatial probability of landslide, even though its fundamental principles have remained unaltered. <br><br> This study aims at assessing the influence of some of these methodological approaches on the performance of LR, through a series of sensitivity analyses developed over a test area of about 300 km<sup>2</sup> in Calabria (southern Italy). <br><br> In particular, four types of sampling (1 – the whole study area; 2 – transects running parallel to the general slope direction of the study area with a total surface of about 1/3 of the whole study area; 3 – buffers surrounding the phenomena with a 1/1 ratio between the stable and the unstable area; 4 – buffers surrounding the phenomena with a 1/2 ratio between the stable and the unstable area), two variable coding modes (1 – grouped variables; 2 – binary variables), and two types of elementary land (1 – cells units; 2 – slope units) units have been tested. The obtained results must be considered as statistically relevant in all cases (Aroc values > 70%), thus confirming the soundness of the LR analysis which maintains high predictive capacities notwithstanding the features of input data. <br><br> As for the area under investigation, the best performing methodological choices are the following: (i) transects produced the best results (0 < <i>P(y)</i> ≤ 93.4%; Aroc = 79.5%); (ii) as for sampling modalities, binary variables (0 < <i>P(y)</i> ≤ 98.3%; Aroc = 80.7%) provide better performance than ordinated variables; (iii) as for the choice of elementary land units, slope units (0 < <i>P(y)</i> ≤ 100%; Aroc = 84.2%) have obtained better results than cells matrix.