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When measurement of a variable is conducted, it is common for estimates of a sample to have a substantial amount of bias. Some factors responsible of this outcome are asymmetry and outliers. In psychology and the social sciences, it is usual to find that most of the commonly used statistics, such as the arithmetic mean and its associated standard error are, at best, imprecise in the task of data summary and inference. To overcome this situation, researchers can make use of robust statistics. Robust statistics provide a series of alternative estimates resistant to the influence of outliers, resulting in more precise information and inferences. The aim of this paper is to describe a group of procedures to calculate measures of location and scale with robust methods using R and IBM SPSS software. First, the different visual and quantitative methods for detecting outliers are reviewed. Then, different alternatives of location measures are reviewed such as the trimmed mean, the winsorized mean and M estimators. Each location measure will be presented with its associated standard error. Last, some scale measures are presented, such as the interquartile range and a modification, called ideal fourths. Conclusions emphasize the thoughtful use of the procedures in relation to the reader possibilities, interests and the theoretical and methodological implications.

Estadística robusta aplicada a las medidas de localización y escala: Nota Técnica