A zero would mean that there was no agreement between the two groups, and 1 would indicate total agreement. The resultant coefficient takes a value in the range 0 to 1. The researcher might wish to measure similarities and differences in the rankings of pesticide brands according to whether the respondents' farm enterprises were classified as "arable" or "mixed" (a combination of crops and livestock). Consider again the ranking of pesticides example in figure 3.2. ![]() Using either procedure one can, for example, ascertain the degree to which two or more survey respondents agree in their ranking of a set of items. The two main methods are Spearman's Ranked Correlation Coefficient and Kendall's Coefficient of Concordance. It is possible to test for order correlation with ranked data. In addition, positional statistics such as the median, quartile and percentile can be determined. All of the information a nominal scale would have given is available from an ordinal scale. For example, if a researcher asked farmers to rank 5 brands of pesticide in order of preference he/she might obtain responses like those in table 3.2 below.įigure 3.2 An example of an ordinal scale used to determine farmers' preferences among 5 brands of pesticide.įrom such a table the researcher knows the order of preference but nothing about how much more one brand is preferred to another, that is there is no information about the interval between any two brands. Ordinal scales involve the ranking of individuals, attitudes or items along the continuum of the characteristic being scaled. it is not capable of establishing cause and effect. It can tell nothing about the form of that relationship, where it exists, i.e. However, it should be noted that the Chi-square is a test to determine whether two or more variables are associated and the strength of that relationship. ![]() The most likely would be the Chi-square test. Hypothesis tests can be carried out on data collected in the nominal form. The only measure of average which can be used is the mode because this is simply a set of frequency counts. The numbers have no arithmetic properties and act only as labels. Which of the following food items do you tend to buy at least once per month? (Please tick) It involves a simply count of the frequency of the cases assigned to the various categories, and if desired numbers can be nominally assigned to label each category as in the example below: It is a system of classification and does not place the entity along a continuum. Indeed it is often referred to as a categorical scale. This, the crudest of measurement scales, classifies individuals, companies, products, brands or other entities into categories where no order is implied. However, it is an important topic since the type of scale used in taking measurements directly impinges on the statistical techniques which can legitimately be used in the analysis. Most texts on marketing research explain the four levels of measurement: nominal, ordinal, interval and ratio and so the treatment given to them here will be brief. The marketing researcher who is familiar with the complete tool kit of scaling measurements is better equipped to understand markets. ![]() ![]() Some of these scales are numeric, others are semantic and yet others take a graphical form. After the properties of the four categories of scale have been explained, various forms of comparative and non-comparative scales are illustrated. Structure Of The ChapterĪll measurements must take one of four forms and these are described in the opening section of the chapter.
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