Statistical data type

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In statistics, data can have any of various types. Statistical data types include categorical (e.g. country), directional (angles or directions, e.g. wind measurements), count (a whole number of events), or real intervals (e.g. measures of temperature).

Contents

The data type is a fundamental concept in statistics and controls what sorts of probability distributions can logically be used to describe the variable, the permissible operations on the variable, the type of regression analysis used to predict the variable, etc. The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).

Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in degree Celsius or degree Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.

Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.

Other categorizations have been proposed. For example, Mosteller and Tukey (1977) [1] distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990) [2] described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman (1998), [3] van den Berg (1991). [4]

The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer" (Hand, 2004, p. 82). [5]

Simple data types

The following table classifies the various simple data types, associated distributions, permissible operations, etc. Regardless of the logical possible values, all of these data types are generally coded using real numbers, because the theory of random variables often explicitly assumes that they hold real numbers.

Data Type
Possible valuesExample usage
Level of
measurement
Common

Distributions

Scale of
relative
differences
Permissible statisticsCommon model
0, 1 (arbitrary labels)binary outcome ("yes/no", "true/false", "success/failure", etc.) Bernoulli mode, chi-squared logistic, probit
"name1", "name2", "name3", ... "nameK" (arbitrary labels)categorical outcome with names or places like "Rome", "Amsterdam", "Madrid", "London", "Washington" (specific blood type, political party, word, etc.) categorical multinomial logit, multinomial probit
ordering categories or integer or real number (arbitrary scale)Ordering adverbs like "Small", "Medium", "Large", relative score, significant only for creating a ranking categorical
relative
comparison
ordinal regression (ordered logit, ordered probit)
0, 1, ..., Nnumber of successes (e.g. yes votes) out of N possible binomial, beta-binomial
additive
mean, median, mode, standard deviation, correlation binomial regression (logistic, probit)
nonnegative integers (0, 1, ...)number of items (telephone calls, people, molecules, births, deaths, etc.) in given interval/area/volume Poisson, negative binomial
multiplicative
All statistics permitted for interval scales plus the following: geometric mean, harmonic mean, coefficient of variation Poisson, negative binomial regression
real-valued
additive
real number temperature in degree Celsius or degree Fahrenheit, relative distance, location parameter, etc. (or approximately, anything not varying over a large scale) normal, etc. (usually symmetric about the mean)
additive
mean, median, mode, standard deviation, correlation standard linear regression
real-valued
multiplicative
positive real number temperature in kelvin, price, income, size, scale parameter, etc. (especially when varying over a large scale) log-normal, gamma, exponential, etc. (usually a skewed distribution)
multiplicative
All statistics permitted for interval scales plus the following: geometric mean, harmonic mean, coefficient of variation generalized linear model with logarithmic link

Multivariate data types

Data that cannot be described using a single number are often shoehorned into random vectors of real-valued random variables, although there is an increasing tendency to treat them on their own. Some examples:

These concepts originate in various scientific fields and frequently overlap in usage. As a result, it is very often the case that multiple concepts could potentially be applied to the same problem.

Comparison to programming data types

Most data types in statistics have comparable types in computer programming, and vice versa, as shown in the following table:

StatisticsProgramming
real-valued (interval scale) floating-point
real-valued (ratio scale)
count data (usually non-negative) integer
binary data Boolean
categorical data enumerated type
random vector list or array
random matrix two-dimensional array
random tree tree

References

  1. Mosteller, F.; Tukey, J.W. (1977). Data analysis and regression. Addison-Wesley. ISBN   978-0-201-04854-4.
  2. Nelder, J.A. (1990). "The knowledge needed to computerise the analysis and interpretation of statistical information". Expert systems and artificial intelligence: the need for information about data. London: Library Association. OCLC   27042489.
  3. Chrisman, Nicholas R. (1998). "Rethinking Levels of Measurement for Cartography". Cartography and Geographic Information Science. 25 (4): 231–242. Bibcode:1998CGISy..25..231C. doi:10.1559/152304098782383043.
  4. van den Berg, G. (1991). Choosing an analysis method. Leiden: DSWO Press. ISBN   978-90-6695-062-7.
  5. Hand, D.J. (2004). Measurement theory and practice: The world through quantification. Wiley. p. 82. ISBN   978-0-470-68567-9.