Nominal data

There is a hierarchy of usefulness of data, according to how well it can be statistically manipulated. The accepted order is continuous data > ordinal data > nominal data. 

Less commonly used is the mode, the most frequently occurring value in the dataset. The mode is the appropriate measure of central tendency for nominal data. Other measures of location (but not of central tendency) are percentiles or quartiles. The percentile of xis the percentage of the total cases that falls at or below xin value. Commonly used percentiles are the 25th and 75th percentiles (or 1st and 3rd quartiles). The median is the 50th percentile. The distance between the first and third quartile is the interquartile range (Figure 21.1). 

Before discussing how clinical data are described and analyzed, it is helpful to introduce several categories of data. Data are numerical representations of information, and different forms of numerical information have different characteristics that permit (or do not permit) certain analyses to be conducted on them. In clinical research, the term variable is often used when describing data for a particular characteristic of interest, since values for participants in a clinical trial will vary from one individual to another. Clinical data can fall within several categories, including numerical (continuous and discrete) data and categorical (ordinal and nominal) data. 

Nominal data for physical characteristics and dimensions of the Rhizon samplers are given in Table 10.2. 

With ordinal data we did at least know that a case scored as (say) +2 is going to be more similar to one scored +1 than to one scored 0 or — 1. However, with nominal data, we have no reason to expect Smith or Jones equipment to have any special... 

Figure 1. Atom % vanadium by ESCA versus nominal data for mixed V O and TiO or SnO powders, fused and unfused.

Dufresne et al. studied stress vs strain curves (nominal data) for the chitin whiskers/unvulcanized NR evaporated composites, shown in Figure 14.12."" The polymeric matrix is in the rubbery state and its elasticity from entropic origin is ascribed to the presence of numerous entanglements due to high molecular weight chains. They further observed that the incorporation of anhydride and isocyanate modified chitin whiskers into NR lead to composite materials with improved mechanical properties. The study of the morphology of these nanocomposites leads to the conclusion that the various chemical treatments improve the adhesion between the filler and the matrix (Figure 14.13). However in some cases there is loss of performance, which could be due to the partial or total destruction of the three-dimensional network of chitin whiskers assumed to be present in the unmodified composites. 

According to [24], this time is required to apply the reference points, mark the characteristic features, place the scale bars, record about fifty images and transfer them to a laptop, carry out an automatic evaluation (the definition of the marker lines, the alignment of the measurement data with the nominal data, the calculation of the deviations) and to prepare and print out a measurement report. 

What is the significance of these different scales of measurement As was mentioned in Section 1.5, many of the well-known statistical methods are parametric, that is, they rely on assumptions concerning the distribution of the data. The computation of parametric tests involves arithmetic manipulation such as addition, multiplication, and division, and this should only be carried out on data measured on interval or ratio scales. When these procedures are used on data measured on other scales they introduce distortions into the data and thus cast doubt on any conclusions which may be drawn from the tests. Non-parametric or distribution-free methods, on the other hand, concentrate on an order or ranking of data and thus can be used with ordinal data. Some of the non-parametric techniques are also designed to operate with classified (nominal) data. Since interval and ratio scales of measurement have all the properties of ordinal scales it is possible to use non-parametric methods for data measured on these scales. Thus, the distribution-free techniques are the safest to use since they can be applied to most types of data. If, however, the data does conform to the distributional assumptions of the parametric techniques, these methods may well extract more information from the data. 

One anomalous area which has already been identified is in the interpretation of what constitutes personal or nominative data. Many jurisdictions, including the UK, require a close hnk between the data and the individual for data protection law to apply. However, the definition of nominative data has been constraed rather wider in France, for example, than most other jurisdictions in the EU. This difference in scope may be sufficient to bring, for example, the information gathered by cookies within the ambit of data protection law in France, whereas this seems unlikely elsewhere because the coimection between the data and an identifiable individual in such a case may be tenuous... 

Stevens [4, 5] classified not just simple operations, but also statistical procedures according to the scales for which they are permissible . A scale that preserves meaning under some class of transformations shonld be restricted to statistics whose meaning would not change if any transformation is applied to the data. By this reasoning, analyses on nominal data, for example, should be limited to summary statistics such as the number of cases, the mode, and contingency correlation, which require only that the identity of the values be preserved. 

Nominal/categorical Chi-square Cochran s Q (dichotomous nominal data only)... 

Nominal Data n A type of categorical data in which the property of the population has only a finite number of more than two states. The states can be named but cannot be ordered by value for example, species of birds present in a given area. Also see Ordinal Data. 

In social science, we use number systems for essentially three purposes (1) to classify things, (2) to order things, and (3) to quantify things. Since data are measures of such variables, we refer to them as (1) nominal data, (2) ordinal data, (3) interval data, and (4) ratio data. 

Nominal data can be name, race, gender, or other data that does not have a numerical value. Interval data allows for varying values such as temperature, but not ratio values because they have scaled values. Ratio data is the value between data objects, such as height, weight, and age. Ordinal data refers to data with a ranking or ordering. Likert-scaled questions are an example of ordinal data. 

Nominal data are defined as such whenever we assign numbers to a set of categories without reference to the direction or magnitude of difference among the alternatives. Nominal pertains to, or consists of, a name or names. 

Three kinds of averages are defined from data that have been obtained for research questions. The mean is the point around which the values in the distribution balance it is the mathematical or arithmetic average. In order to calculate a mean, at least internal-level data exist. The median gives information about the value of the middle position in the distribntion. It is the point in the distribution of values at which 50 percent of the scores fall below and 50 percent of the scores fall above. In order to calculate a median, you must have at least an ordinally measured variable. Mode represents the most freqnent value in distribution. The mode is the simplest measure of averages and is, therefore, not viewed as an overly precise or informative measure of average. In addition, the mode is the only measure that is appropriate for nominal data. 

The effect matrix is key to estimating effects, and failure effects in particular. Especially the enable relation (<) is of importance for that. If a pair of transitions only occurs as ti < t2 in the state space, then ti and t2 can be considered as causally related. Typically, ti would be an error transition originating in the AADL error model and t2 would be a change to the nominal data ports or subcomponents as consequence to the error. This is demonstrated in the next section, where we report on our case study. 

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