Interpreting data: Boxplots and tables
Interpreting data: Boxplots and tables

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Interpreting data: Boxplots and tables

3.1 Health personnel in Thailand

There are practically no new theories or new principles in this section. We shall work through some examples, and you will see how basic techniques and approaches that you have already learned can be combined to allow you to use tabular data efficiently.

Example 3.1 Health personnel in Thailand

The data shown in Table 3.1 are taken from the Thailand Mini Health Profile 1988, published by the Ministry of Public Health, Bangkok. They show the numbers of health care personnel at approximately five-year intervals.

Table 3.1 Health personnel in Thailand, 1966–1984


What do these data tell us about the change in health care personnel in Thailand over the period in question, and how can we work with the data in the table to make any pattern clearer?

First, notice that some features of the data are obvious. The total number of health care personnel increased hugely between 1966 and 1984, from under 15 000 to about 53 000. Also, throughout the period, the biggest category of staff was that of Nurses, and this category seems to have grown more rapidly than some of the others. (In 1966, there were very roughly twice as many nurses as there were doctors, for instance, but in 1984 there were almost four times as many nurses as there were doctors.)

How could the patterns that have already been identified be made clearer? The pattern of overall increase in numbers is already clear from the last row of the table. Perhaps it could be made even clearer by drawing an appropriate graph; we shall return to this idea later.

In Activity 6, the patterns of retirement reasons in different occupational groups were made easier to see by calculating how large each entry was as a percentage of the corresponding row total. In Activity 7, you are asked to consider whether similar approach would help here.


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