Nominal measurement scales

Nominal measurement scales involve names of characteristics. Characteristics frequently... 

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. 

Indicate which type of measurement scale (nominal, ordinal, interval, or ratio) is usually used for the following characteristics time, mass, library holdings, gender, type of heart attack, cholesterol level as measured by a clinical chemical laboratory, cholesterol level as reported by a doctor to a patient, pipet volume, and leaves on a plant. 

Regardless of whether stress is quoted as nominal values (scaled relative to the initial sample cross-section) or true values (scaled relative to the final cross-section), representative cross-sectional areas are needed for accurate characterisation of fibre strength and stiffness. Depending on the type of fibre being tested, the scale on which the test has to be performed, and the environment in which the fibre strength is being tested, it may or may not be possible to obtain such a measurement. 

Flavor Intensity. In most sensory tests, a person is asked to associate a name or a number with his perceptions of a substance he sniffed or tasted. The set from which these names or numbers are chosen is called a scale. The four general types of scales are nominal, ordinal, interval, and ratio (17). Each has different properties and allowable statistics (4,14). The measurement of flavor intensity, unlike the evaluation of quaUty, requires an ordered scale, the simplest of which is an ordinal scale. 

Ross (R2) measured liquid-phase holdup and residence-time distribution by a tracer-pulse technique. Experiments were carried out for cocurrent flow in model columns of 2- and 4-in. diameter with air and water as fluid media, as well as in pilot-scale and industrial-scale reactors of 2-in. and 6.5-ft diameters used for the catalytic hydrogenation of petroleum fractions. The columns were packed with commercial cylindrical catalyst pellets of -in. diameter and length. The liquid holdup was from 40 to 50% of total bed volume for nominal liquid velocities from 8 to 200 ft/hr in the model reactors, from 26 to 32% of volume for nominal liquid velocities from 6 to 10.5 ft/hr in the pilot unit, and from 20 to 27 % for nominal liquid velocities from 27.9 to 68.6 ft/hr in the industrial unit. In that work, a few sets of results of residence-time distribution experiments are reported in graphical form, as tracer-response curves. 

Contact ratio, a, is defined as the real contact area divided by the nominal contact zone, where the real contact area is referred to as the sum of all areas where film thickness is below a certain criterion in molecular scale. The contact area was measured by the technique of Relative Optical Interference Intensity (ROII) with a resolution of 0.5 nm in the vertical direction and 1 /xm in the horizontal direction [69]. 

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. 

In non-metric MDS the analysis takes into account the measurement level of the raw data (nominal, ordinal, interval or ratio scale see Section 2.1.2). This is most relevant for sensory testing where often the scale of scores is not well-defined and the differences derived may not represent Euclidean distances. For this reason one may rank-order the distances and analyze the rank numbers with, for example, the popular method and algorithm for non-metric MDS that is due to Kruskal [7]. Here one defines a non-linear loss function, called STRESS, which is to be minimized ... 

In order to obtain the desired quantitative measure of FRET (Fig. 7.3), an additional correction factor must scale the nominator to the denominator in Eq. (7.9) [1-3, 6], In other words, we must relate the FRET-induced sensitized emission in the S channel to the loss of donor emission in the D channel as in ... 

Full factorial designs have been especially useful for describing the effects of qualitative factors, factors that are measured on nominal or ordinal scales. This environment of qualitative factors is where factorial designs originated. Because all possible factor combinations are investigated in a full design, the results using qualitative factors are essentially historical and have little, if any, predictive ability. 

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

Ho vever this approach does not address inter-individual variability in CYP expression nor the apparent substrate specificity of RAFs. This may be overcome through the use of intersystem extrapolation factors (ISEFs) vhich compare the intrinsic activities of rCYP versus liver microsomes and provide CYP abundance scaling by mathematical means. This employs the RAF approach and adjusts for the actual amount of liver microsomes CYP present (measured by immunochemistry) rather than a theoretical amount (Equation 8.4). Such corrections can be made using nominal specific contents of individual CYP proteins in liver microsomes or more appropriately employ modeling and simulation software (e.g., SIMCYP www.simcyp.com) which takes into account population-based variability in CYP content. 

Quantitative methodology uses large or relatively large samples of subjects (as a rule students) and tests or questionnaires to which the subjects answer. Results are treated by statistical analysis, by means of a variety of parametric methods (when we have continuous data at the interval or at the ratio scale) or nonparametric methods (when we have categorical data at the nominal or at the ordinal scale) (30). Data are usually treated by standard commercial statistical packages. Tests and questionnaires have to satisfy the criteria for content and construct validity (this is analogous to lack of systematic errors in measurement), and for reliability (this controls for random errors) (31). 

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. 

In the frame of the EA-IRMM collaboration agreement, 61 laboratories that were nominated via their NABs, reported measurement results in IMEP-20 tuna Fsh. Furthermore, a number of IMEP-20 participants not nominated by EA indicated that they were accredited or/and authorized for this kind of measurements. Figure 7.21 shows the results for As in tuna Fsh according to self-declared accreditation or authorization status. It has to be mentioned that due to the overall large spread of As results in IMEP-20, the scale in this graph is +100 percent... 

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

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. 

Back in Chapter 1, data were described as interval (measurements on a regular scale), ordinal (measurements on a scale of undefined steps) and nominal (classifications). We have dealt extensively with two of these, but ordinal data have thus far been ignored. 

The pump pulse in time-resolved pump-probe absorption spectroscopy is often linearly polarized, so photoexcitation generally creates an anisotropic distribution of excited molecules. In essence, the polarized light photoselects those molecules whose transition moments are nominally aligned with respect to the pump polarization vector (12,13). If the anisotropy generated by the pump pulse is probed on a time scale that is fast compared to the rotational motion of the probed transition, the measured anisotropy can be used to determine the angle between the pumped and probed transitions. Therefore, time-resolved polarized absorption spectroscopy can be used to acquire information related to molecular structure and structural dynamics. 

As a prelude to the design of the tube reactor (10), a kinetic study of the phenolysis procedure as a function of temperature was carried out on a larger scale. The equipment used was a stainless steel pressure reactor (Model 4501, Parr Instrument Company, Moline, Illinois). This reactor is fitted with an internal stirrer, an external electric heater, and a continuous sampling device. A mixture of the commercial ammonium lignin sulfonate (668 g) and molten phenol (1000 mL) was sealed into the reactor and heated to the designated temperatures. Approximately 3 hours were needed to heat the reactor from room temperature to 200 °C. A similar period of time was required to cool the reactor and its contents back to 22 °C after completion of a run. After a reaction period nominally lasting 2 hours, the unreacted phenol was steam distilled from the reaction mixture and the amount measured by comparative UV spectroscopy. The results obtained and summarized in Table IV show that a substantial amount of phenol becomes chemically combined with the renewable resource feedstock. 

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