Escalas de mensuração

Hutcheson, G. D. (2011). Measurement Scales. In L. Moutinho and G. D. Hutcheson, The SAGE Dictionary of Quantitative Management Research. Pages 184-187.

Measurement Scales
Introduction
There are many different types of information and ways in which this information may be categorised and represented as data. Although a number of different schemes for representing data have been proposed that utilise a variety of categories and sub-divisions (see, for example, Agresti and Finlay, 1997; Barford, 1985; Harris, 1999; Lindsey, 1995; Sarle, 1995; Verzani, 2005), in this chapter I will distinguish between just a few distinct scales of measurement which will allow a wide range of graphical methods and statistical analyses to be applied. It is worth noting at this point that the process of representing information using particular scales of measurement is not always obvious, as some information may be legitimately classified in a variety of ways depending on the properties of the information, the coding scheme used to represent this, the number of observations recorded, the type of analysis to be used and the specific research questions being asked. The classification of data into different scales of measurement is not, therefore, an exact science. I will, however, concentrate on practical considerations by showing how a wide range of information may be profitably classified for analytical purposes.

Key Features
Measurement scales need to be accurately identified in order to...
  

appropriately code the data select appropriate analytical methods for the coded data (i.e., techniques designed for use on data recorded on a continuous scale are not used with data recorded on an ordered categorical scale) draw appropriate conclusions from the analyses (this requires one to distinguish between the scales of the attribute and the data).

Data can be broadly divided into two main categories; numeric data and categorical data, with each of these categories further