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11. The department has exceeded its __________ level of spending.
12. The survey got more than 1,000 ________________ .
13. Sixty-four per cent of ______ reported side effects from the drug.
PRACTICE ACTIVITIES
10. Analyze information on data types and fill in the spaces in Figure 6.9. below. Illustrate each type of data
with your own examples.
TYPES OF QUANTITATIVE DATA
According to many statistics textbooks quantitative data are classified into data types using a hierarchy of meas-
urement, often in ascending order of numerical precision. These different levels of numerical measurement dictate the
range of techniques available to you for the presentation, summary and analysis of your data.
Quantitative data can be divided into two distinct groups: categorical and quantifiable.
Categorical data refer to data whose values cannot be measured numerically but can be either classified into sets
(categories) according to the characteristics in which you are interested or placed in rank order. They can be further
subdivided into descriptive and ranked. A car manufacturer might categorise the cars it produces as hatchback, saloon
and estate. These are known as descriptive (or nominal) data as it is impossible to measure the category numerically or
rank it. For virtually all analyses the categories should be unambiguous and not overlap. Although these data are purely
descriptive, you can count them to establish which category has the most and whether cases are spread evenly between
categories. Ranked (or ordinal) data are more precise. In such instances you know the definite position of each case
within your data set, although the actual numerical measures on which the position is based are not recorded.
Quantifiable data are those whose values you actually measure numerically as quantities. This means that quanti-
fiable data are more precise than categorical as you can assign each data value a position on a numerical scale. Within
this group there is, again, a subdivision: continuous and discrete. Continuous data are those whose values can theoreti-
cally take any value provided that you can measure them accurately enough. Data such as furnace temperature, delivery
distance and length of service are therefore continuous data. Discrete data can, by contrast, be measured precisely. Each
case takes one of a finite number of values from a scale that measures changes in discrete units. These data are often
whole numbers (integers) such as number of mobile phones manufactured or customers served.
Figure 6.9. Types of data and levels of numerical measurement
The more precise he level of measurement the greater the range of analytical techniques available to you. Data that
have been collected and coded using precise numerical measurements can also be regrouped to a less precise level
where they can also be analysed. For example, a student’s score in a test could be recorded as the actual mark (discrete
data) or as the position in their class (ranked data). By contrast, less precise data cannot be made more precise. There-
fore, if you are not sure about the level of precision you require, it is usually better to collect data at the highest level
possible and regroup them if necessary.
Quantitative
Data
Categorical
Ranked
Discrete
Increasin
g
p
recision
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