Nominal scales

In general, there are four scales that provide the qualitative appreciation of any object. These are nominal, ordinal, interval and ratio scales. The information content of each one inaeases from nominal towards ratio scale. During the mind sensing process the qualitative scale appreciations are gained, which is stored in the memory after the right comparison [1,2]. 

A Tropical moist climates all months have average temperatures above 18 °C 

B Dry climates with deficient precipitation during most of the year 

C Moist mid-latitude climates with mild winters 

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Flavor Description. TypicaHy, a sensory analyst determines if two samples differ, and attempts to explain their differences so that changes can be made. The Arthur D. Litde flavor profile (FP), quantitative descriptive analysis (QDA), and spectmm method are three of the most popular methods designed to answer these and more compHcated questions (30—33). AH three methods involve the training of people in the nominal scaling of the flavor quaHties present in the food being studied, but they differ in their method for quantitation. 

Table 32.1 describes 30 persons who have been observed to use one of four available therapeutic compounds for the treatment of one of three possible disorders. The four compounds in this measurement table are the benzodiazepine tranquillizers Clonazepam (C), Diazepam (D), Lorazepam (L) and Triazolam (T). The three disorders are anxiety (A), epilepsy (E) and sleep disturbance (S). In this example, both measurements (compounds and disorders) are defined on nominal scales. Measurements can also be defined on ordinal scales, or on interval and ratio scales in which case they need to be subdivided in discrete and non-overlapping categories. 

Four types of measurement scale can be used for assigning values to varying amounts of a property associated with a system input or system output [Summers, Peters, and Armstrong (1977)]. In order of increasing informing power, they are nominal, ordinal, interval, and ratio scales. The characteristics at determine a measurement scale s level of sophistication are name, order, distance, and origin. The characteristics of the four types of measurement scale are shown in Table 1.3. The nominal scale possesses only one of these characteristics the ratio scale possess all four characteristics. 

When numbers are used simply to classify an observational unit, the nominal scale is used. For example, red wines might be described by the number 1, white wines might be assigned the number 2, and pink wines might be designated by the number 3. A key aspect of the nominal (or named ) scale is that numbers are chosen arbitrarily to describe the categorical property. Any other numbers could be used to describe the property just as well. For example, red wines could be assigned the... 

The student collected one data pair for each business day for the past nine-months. Each data pair consisted of (1) the amount of money issued by her department in computer-generated checks on that day, and (2) the amount of money in checks that cleared the banks on that day. Table 10.1 is a four-column list of the 177 pairs of data she collected. Each entry gives (1) the sequence, or acquisition number ( Seq ), starting with Thursday, August 8, and increasing by one each business day, five business days a week (2) a nominal scale (that can also be used as an ordinal or interval scale) for the day of the week ( D ), where 1 = Monday, 2 = Tuesday, 3 = Wednesday, 4 = Thursday, and 5 = Friday (3) the amount of money issued in checks ( Iss ) and (4) the amount of money in checks that cleared ( Clr ). 

Cohen, J. A. (1960) A coefficient of agreement for nominal scales. Educ. Psychol. Measure. 20, 37-46. 

Cohen, J. A. (1968) Weighted kappa nominal scale agreement with provision for scale disagreement or partial credit. Psychol. Bull. 70, 213-220. 

Another important feature of mathematical modeling techniques is the nature of the response data that they are capable of handling. Some methods are designed to work with data that are measured on a nominal or ordinal scale this means the results are divided into two or more classes that may bear some relation to one another. Male and female, dead and alive, and aromatic and nonaromatic, are all classifications (dichotomous in this case) based on a nominal scale. Toxic, slightly toxic, and non-toxic are classifications based on an ordinal scale since they can be written as toxic > slightly toxic > non-toxic. The rest of this section is divided into three parts methods that deal with classified responses, methods that handle continuous data, and artificial neural networks that can be used for both. 

Nominal scale - classifications that form no natural sequence. 

Show how we describe nominal scale data (data that consist of categorizations rather than measurements)... 

Emphasise the inefficient nature of nominal scale data... 

In this chapter we will start to look at data where no measurements are made on individuals. Instead, each individual is placed in a category and the numbers in each category are then counted. A classic example is where we look at a medical treatment and declare each patient s outcome as successful or unsuccessful . We then count the number of successes and failures. This type of data was introduced in Chapter 1 as nominal scale data. 

The inefficiency of nominal scale data is further exacerbated when one of the categories is rare. For example, a drug may produce a side-effect in a small proportion of users. In the example that follows, we will assume a 4 per cent occurrence rate. If we studied 500 patients that might sound like a perfectly adequate number. 

Analysis will then probably progress to hypothesis testing — looking to see whether the answer provided to one question influences the pattern of answers to another question. As so many questionnaire data are nominal scale, contingency chi-tests tend to dominate. 

Another division of the factors can be made into mixture-related, quantitative (continuous), or qualitative (discrete) factors (4,5,16,18,24). A mixture-related factor in CE is usually related to a mixture of solvents, for example, the composition of the background electrolyte solution. A quantitative factor can vary on a continuous scale, for example, the buffer pH, the electrolyte concentration, the additive concentration, the capillary temperature, or the voltage. A qualitative factor, on the other hand, varies on a discrete nominal scale, for example, batch or manufacturer of a reagent, solvent, or capillary. 

Examples A nominal scale for temperature is not feasible, since the relevant descriptive terms can be ranked in order of magnitude. 

A nominal scale puts people into boxes, without specifying the relationship between the boxes. Gender is a common example of a nominal scale with two groups, male and female. Anytime you can say, It s either this or that, you are dealing with a nominal scale. Other examples cities, drug vs control group. 

Discrete (nominal scaled) quantities, for example, the number of crystallizations or extractions... 

Feature variables may be defined on different scales. The nominal scale characterizes qualitative equivalence, for example, male and female. The ordinal scale describes ordering or ranking. The interval scale measures distances between values of the features. The ratio scale also enables quotients between feature values to be evaluated. 

The outcomes of test and inspection can thus be classified by the level of measurement upon which they are based and the type of decision required. These outcomes in turn define the typical statistical methods used, such as prototype testing for decision 1 above. For in-process quaUty control (decision 3 above), nominal scale decisions lead to attributes control charts, while interval or ratio scales lead to variables control charts (Vardeman and Jobe 1998). The second type of decision above would lead to either attributes of variables sampling plans, although the whole concept of sampling a batch to determine its quality has largely been abandoned in favor of in-process quality control. 

Nominal scale Data are simply descriptive a series of categories, which are identified and labeled using a name or a number, is listed samples are classified into groups on basis of the frequencies of each category. They contain very little information because no quantitative relationships are established. 

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