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Learn about the four levels of measurement: nominal, ordinal, interval, and ratio. Find out how they differ in terms of data types, operations, and examples.
Learn about the different types of statistical data, such as categorical, quantitative, binary, ordinal, and multivariate, and how they affect the choice of distributions, operations, and regression analysis. See examples, tables, and references for each data type.
Ordinal data are categorical variables with natural, ordered categories and unknown distances between them. Learn how to identify, measure, and analyse ordinal data with examples, methods, and models.
Normalization is a term used in statistics to adjust values or distributions to a common scale or form. Learn about different types of normalization, such as standardization, quantile normalization, and feature scaling, and their applications and formulas.
A Likert scale is a psychometric scale that measures respondents' level of agreement or disagreement on a series of statements. It is widely used in research questionnaires and can be analyzed as interval-level or ordinal data, depending on the scale's design and purpose.
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data. It can be used for descriptive statistics or statistical inference, and it can be more robust and general than parametric methods.
A categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category. Learn about the types, examples, notation, and regression analysis of categorical variables and data.
Measurement is the quantification of attributes of an object or event, which can be used to compare with other objects or events. Learn about the history, methodology and standards of measurement, as well as the International System of Units (SI) and other systems of measurement.