Level of measurement

Level of measurement or scale of measure is a classification that describes the nature of information within the numbers assigned to variables. Psychologist Stanley Smith Stevens developed the best known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. Other classifications include those by Chrisman and by Mosteller and Tukey. This framework of distinguishing levels of measurement originated in psychology and is widely criticized by scholars in other disciplines.

Stevens's typology

Overview

Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal," "ordinal," "interval," and "ratio," unifying both "qualitative" (which are described by his "nominal" type) and "quantitative" (to a different degree, all the rest of his scales). The concept of scale types later received the mathematical rigour that it lacked at its inception with the work of mathematical psychologists Theodore Alper (1985, 1987), Louis Narens (1981a, b), and R. Duncan Luce (1986, 1987, 2001). As Luce (1997, p. 395) wrote:

Data

Data (/ˈdeɪtə/ DAY-tə, /ˈdætə/ DA-tə, or /ˈdɑːtə/ DAH-tə) is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.

Raw data, i.e. unprocessed data, is a collection of numbers, characters; data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next. Field data is raw data that is collected in an uncontrolled in situ environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording.

The Latin word "data" is the plural of "datum", and still may be used as a plural noun in this sense. Nowadays, though, "data" is most commonly used in the singular, as a mass noun (like "information", "sand" or "rain").

Data analysis

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.