# Ordinal scale

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In statistics and quantitative research methodology, levels of measurement or scales of measure are types of data that arise in the theory of scale types developed by the psychologist Stanley Smith Stevens. The types are nominal, ordinal, interval, and ratio.

## Typology

Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement".[1] 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) stated:

 “ S. S. Stevens (1946, 1951, 1975) claimed that what counted was having an interval or ratio scale. Subsequent research has given meaning to this assertion, but given his attempts to invoke scale type ideas it is doubtful if he understood it himself… no measurement theorist I know accepts Stevens' broad definition of measurement… in our view, the only sensible meaning for 'rule' is empirically testable laws about the attribute. ”
 # Scale type Logical/math operations   allowed Examples:      Variable name                            (data values) Measure of central tendency Qualitative or Quantitative 1 Nominal =/≠ Dichotomous:     Gender                              (male vs. female) Non-dichotomous: Nationality                              (American/Chinese/etc) Mode Qualitative 2 Ordinal =/≠ ; Dichotomous:     Health                             (healthy vs.                             sick),                          Truth                             (true vs.                             false),                          Beauty                             (beautiful vs.                             ugly) Non-dichotomous: Opinion                             ('completely agree'/                              'mostly agree'/                              'mostly disagree'/                              'completely disagree') Median Qualitative 3 Interval =/≠ ;  ; +/− Date                             (from 9999 BC                              to 2013 AD)                           Latitude                             (from +90° to −90°) Arithmetic Mean Quantitative 4 Ratio =/≠ ;  ; +/− ; ×/÷ Age                             (from 0                              to 99 years) Geometric Mean Quantitative

## Nominal scale

The nominal type, sometimes also called the qualitative type, differentiates between items or subjects based only on their names and/or (meta-)categories and other qualitative classifications they belong to. Examples include gender, nationality, ethnicity, language, genre, style, biological species, visual pattern, and form (gestalt)....

### Central tendency

The mode, i.e. the most common item, is allowed as the measure of central tendency for the nominal type. On the other hand, the median, i.e. the middle-ranked item, makes no sense for the nominal type of data since ranking is not allowed for the nominal type.

## Ordinal scale

The ordinal type allows for rank order (1st, 2nd, 3rd, etc) by which data can be sorted, but still does not allow for relative degree of difference between them. Examples include, on one hand, dichotomous data with dichotomous (or dichotomized) values such as 'sick' vs. 'healthy' when measuring health, 'guilty' vs. 'innocent' when making judgments in courts, 'wrong/false' vs. 'right/true' when measuring truth value, and, on the other hand, non-dichotomous data consisting of a spectrum of values, such as 'completely agree', 'mostly agree', 'mostly disagree', 'completely disagree' when measuring opinion.

### Central tendency

The median, i.e. middle-ranked, item is allowed as the measure of central tendency; however, the mean (or average) as the measure of central tendency is not allowed. The mode is allowed.

In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales; thus means and standard deviations have no validity, but they can be used to get ideas for how to improve operationalization of variables used in questionnaires.

Most psychological data collected by psychometric instruments and tests, measuring cognitive and other abilities, are of the interval type, although some theoreticians have argued they can be treated as being of the ratio type (e.g. Lord & Novick, 1968; von Eye, 2005). However, there is little prima facie evidence to suggest that such attributes are anything more than ordinal (Cliff, 1996; Cliff & Keats, 2003; Michell, 2008). In particular,[2] IQ scores reflect an ordinal scale, in which all scores are meaningful for comparison only.[3][4][5] There is no absolute zero, and a 10-point difference may carry different meanings at different points of the scale.[6][7]

## Interval scale

The interval type allows for the degree of difference between items, but not the ratio between them. Examples include temperature with the Celsius scale, and date when measured from an arbitrary epoch (such as AD). Ratios are not allowed since 20°C cannot be said to be "twice as hot" as 10°C, nor can multiplication/division be carried out between any two dates directly. However, ratios of differences can be expressed; for example, one difference can be twice another. Interval type variables are sometimes also called "scaled variables", but the formal mathematical term is an affine space (in this case an affine line).

### Central tendency and statistical dispersion

The mode, median, and arithmetic mean are allowed to measure central tendency of interval variables, while measures of statistical dispersion include range and standard deviation. Since one cannot divide, one cannot define measures that require a ratio, such as the studentized range or the coefficient of variation. More subtly, while one can define moments about the origin, only central moments are meaningful, since the choice of origin is arbitrary. One can define standardized moments, since ratios of differences are meaningful, but one cannot define the coefficient of variation, since the mean is a moment about the origin, unlike the standard deviation, which is (the square root of) a central moment.

## Ratio scale

The ratio type takes its name from the fact that measurement is the estimation of the ratio between a magnitude of a continuous quantity and a unit magnitude of the same kind (Michell, 1997, 1999). Informally, the distinguishing feature of a ratio scale is the possession of a zero value. Most measurement in the physical sciences and engineering is done on ratio scales. Examples include mass, length, duration, plane angle, energy and electric charge. The Kelvin temperature scale has a non-arbitrary zero point of absolute zero, which is equal to −273.15 degrees Celsius.

### Central tendency and statistical dispersion

The geometric mean and the harmonic mean are allowed to measure the central tendency, in addition to the mode, median, and arithmetic mean. The studentized range and the coefficient of variation are allowed to measure statistical dispersion. All statistical measures are allowed because all necessary mathematical operations are defined for the ratio scale.

## Debate on typology

While Stevens' typology is widely adopted, it is still being challenged by other theoreticians, particularly in the cases of the nominal and ordinal types (Michell, 1986). .[8]

Duncan (1986) objected to the use of the word measurement in relation to the nominal type, but Stevens (1975) said of his own definition of measurement that "the assignment can be any consistent rule. The only rule not allowed would be random assignment, for randomness amounts in effect to a nonrule". However, so-called nominal measurement involves arbitrary assignment, and the "permissible transformation" is any number for any other. This is one of the points made in Lord's (1953) satirical paper On the Statistical Treatment of Football Numbers.

The use of the mean as a measure of the central tendency for the ordinal type is still debatable among those who accept Stevens' typology. Many behavioural scientists use the mean for ordinal data, anyway. This is often justified on the basis that the ordinal type in behavioural science is in fact somewhere between the true ordinal and interval types; although the interval difference between two ordinal ranks is not constant, it is often of the same order of magnitude. For example, applications of measurement models in educational contexts often indicate that total scores have a fairly linear relationship with measurements across the range of an assessment. Thus, some argue that so long as the unknown interval difference between ordinal scale ranks is not too variable, interval scale statistics such as means can meaningfully be used on ordinal scale variables. Statistical analysis software such as PSPP requires the user to select the appropriate measurement class for each variable. This ensures that subsequent user errors cannot inadvertently perform meaningless analyses (for example correlation analysis with a variable on a nominal level).

L. L. Thurstone made progress toward developing a justification for obtaining the interval type, based on the law of comparative judgment. A common application of the law is the analytic hierarchy process. Further progress was made by Georg Rasch (1960), who developed the probabilistic Rasch model that provides a theoretical basis and justification for obtaining interval-level measurements from counts of observations such as total scores on assessments.

Another issue is derived from Nicholas R. Chrisman's article "Rethinking Levels of Measurement for Cartography",[9] in which he introduces an expanded list of levels of measurement to account for various measurements that do not necessarily fit with the traditional notions of levels of measurement. Measurements bound to a range and repeating (like degrees in a circle, clock time, etc.), graded membership categories, and other types of measurement do not fit to Steven's original work, leading to the introduction of six new levels of measurement, for a total of ten: (1) Nominal, (2) Graded membership, (3) Ordinal, (4) Interval, (5) Log-Interval, (6) Extensive Ratio, (7) Cyclical Ratio, (8) Derived Ratio, (9) Counts and finally (10) Absolute. The extended levels of measurement are rarely used outside of academic geography.

### Scale types and Stevens' "operational theory of measurement"

The theory of scale types is the intellectual handmaiden to Stevens' "operational theory of measurement", which was to become definitive within psychology and the behavioral sciences, despite Michell's characterization as its being quite at odds with measurement in the natural sciences (Michell, 1999). Essentially, the operational theory of measurement was a reaction to the conclusions of a committee established in 1932 by the British Association for the Advancement of Science to investigate the possibility of genuine scientific measurement in the psychological and behavioral sciences. This committee, which became known as the Ferguson committee, published a Final Report (Ferguson, et al., 1940, p. 245) in which Stevens' sone scale (Stevens & Davis, 1938) was an object of criticism:

 “ …any law purporting to express a quantitative relation between sensation intensity and stimulus intensity is not merely false but is in fact meaningless unless and until a meaning can be given to the concept of addition as applied to sensation. ”

That is, if Stevens' sone scale genuinely measured the intensity of auditory sensations, then evidence for such sensations as being quantitative attributes needed to be produced. The evidence needed was the presence of additive structure – a concept comprehensively treated by the German mathematician Otto Hölder (Hölder, 1901). Given that the physicist and measurement theorist Norman Robert Campbell dominated the Ferguson committee's deliberations, the committee concluded that measurement in the social sciences was impossible due to the lack of concatenation operations. This conclusion was later rendered false by the discovery of the theory of conjoint measurement by Debreu (1960) and independently by Luce & Tukey (1964). However, Stevens' reaction was not to conduct experiments to test for the presence of additive structure in sensations, but instead to render the conclusions of the Ferguson committee null and void by proposing a new theory of measurement:

 “ Paraphrasing N.R. Campbell (Final Report, p.340), we may say that measurement, in the broadest sense, is defined as the assignment of numerals to objects and events according to rules (Stevens, 1946, p.677). ”

Stevens was greatly influenced by the ideas of another Harvard academic, the Nobel laureate physicist Percy Bridgman (1927), whose doctrine of operationism Stevens used to define measurement. In Stevens' definition, for example, it is the use of a tape measure that defines length (the object of measurement) as being measurable (and so by implication quantitative). Critics of operationism object that it confuses the relations between two objects or events for properties of one of those of objects or events (Hardcastle, 1995; Michell, 1999; Moyer, 1981a,b; Rogers, 1989).

The Canadian measurement theorist William Rozeboom (1966) was an early and trenchant critic of Stevens' theory of scale types.

## Notes

1. ^
2. ^ Sheskin, David J. (2007). Handbook of Parametric and Nonparametric Statistical Procedures (Fourth ed.). Boca Raton (FL): Chapman & Hall/CRC. p. 3. ISBN 978-1-58488-814-7 [Amazon-US | Amazon-UK]. Lay summary (27 July 2010). "Although in practice IQ and most other human characteristics measured by psychological tests (such as anxiety, introversion, self esteem, etc.) are treated as interval scales, many researchers would argue that they are more appropriately categorized as ordinal scales. Such arguments would be based on the fact that such measures do not really meet the requirements of an interval scale, because it cannot be demonstrated that equal numerical differences at different points on the scale are comparable."
3. ^ Mussen, Paul Henry (1973). Psychology: An Introduction. Lexington (MA): Heath. p. 363. ISBN 0-669-61382-7 [Amazon-US | Amazon-UK]. "The I.Q. is essentially a rank; there are no true "units" of intellectual ability."
4. ^ Truch, Steve (1993). The WISC-III Companion: A Guide to Interpretation and Educational Intervention. Austin (TX): Pro-Ed. p. 35. ISBN 0-89079-585-1 [Amazon-US | Amazon-UK]. "An IQ score is not an equal-interval score, as is evident in Table A.4 in the WISC-III manual."
5. ^ Bartholomew, David J. (2004). Measuring Intelligence: Facts and Fallacies. Cambridge: Cambridge University Press. p. 50. ISBN 978-0-521-54478-8 [Amazon-US | Amazon-UK]. Lay summary (27 July 2010). "When we come to quantities like IQ or g, as we are presently able to measure them, we shall see later that we have an even lower level of measurement—an ordinal level. This means that the numbers we assign to individuals can only be used to rank them—the number tells us where the individual comes in the rank order and nothing else."
6. ^ Eysenck, Hans (1998). Intelligence: A New Look. New Brunswick (NJ): Transaction Publishers. pp. 24–25. ISBN 1-56000-360-X [Amazon-US | Amazon-UK]. "Ideally, a scale of measurement should have a true zero-point and identical intervals. . . . Scales of hardness lack these advantages, and so does IQ. There is no absolute zero, and a 10-point difference may carry different meanings at different points of the scale."
7. ^ Mackintosh, N. J. (1998). IQ and Human Intelligence. Oxford: Oxford University Press. pp. 30–31. ISBN 0-19-852367-X [Amazon-US | Amazon-UK]. "In the jargon of psychological measurement theory, IQ is an ordinal scale, where we are simply rank-ordering people. . . . It is not even appropriate to claim that the 10-point difference between IQ scores of 110 and 100 is the same as the 10-point difference between IQs of 160 and 150"
8. ^ Velleman, Paul F.; Wilkinson,Leland (1993). "Nominal, Ordinal, Interval, and Ratio Typologies Are Misleading". The American Statistician (American Statistical Association) 47 (1): 65–72. doi:10.2307/2684788. JSTOR 2684788. Unknown parameter `|isue=` ignored (help)
9. ^ Chrisman, Nicholas R. (1998). Rethinking Levels of Measurement for Cartography. Cartography and Geographic Information Science, vol. 25 (4), pp. 231-242

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