1.2.1 Basic descriptive statistics

Descriptive analysis is the process of summarising data and generally involves characterising and visualising your dataset. Depending on the nature of the data, you may only need to undertake a descriptive analysis before reporting your findings. The approach to descriptive analysis varies depending on whether the data is categorical or numerical.

1.2.1.1 Categorical variables

Descriptive statistics for categorical variables include counts (frequencies) and proportions (relative frequencies). Percentages are obtained by multiplying proportions by 100.

AMR data is often presented as basic descriptive statistics in sentences in a report, in a frequency table, or in a visual format using a pie chart or a bar chart. An example of using text is in reporting the percentage of isolates that were resistant:

‘A large number of MRSA isolates showed resistance to levofloxacin (83.9%), ciprofloxacin (83%), erythromycin (77.7%), and clindamycin (72.3%).’

(Kot et al., 2020)

Activity 3: Approaches to presenting information

Timing: Allow about 10 minutes

Figure 1 and Table 3 display data from the WHO GLASS dashboard showing AMC data for Daily Defined Dose (DDD) of antibiotics per 1000 inhabitants in African and Asian countries (WHO, n.d.). Table 3 is a frequency table showing the number of DDDs and Figure 1 shows the same data in a bar chart.

Table 3 AMC data for antibiotics expressed as Defined Daily Dose (DDD) per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) in African and Asian countries (WHO, n.d.).
CountryDefined daily dose
Benin21.0
Cote d’Ivoire16.7
Gabon19.9
Kenya19.0
Mali23.4
Uganda25.3
Brunei Darussalam4.1
Lao People’s Democratic Republic20.6
Bhutan9.6
Maldives19.6
Nepal78.9
Described image
Figure 1 AMC data for antibiotics expressed as DDD per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial Resistance and Use Surveillance System (GLASS) in African and Asian countries (WHO, n.d.).
Show description|Hide description

An ordered list of the DDDs of antibiotics per 1000 inhabitants in selected countries in Africa and Asia.

Figure 1 AMC data for antibiotics expressed as DDD per 1,000 inhabitants per day, as collected by the WHO’s Global Antimicrobial ...

Which type of data display (Table 3 or Figure 1) do you think most effectively conveys information about the country with the highest and lowest DDD of antibiotics per 1000 inhabitants?

To use this interactive functionality a free OU account is required. Sign in or register.
Interactive feature not available in single page view (see it in standard view).

Answer

While tables can summarise and display data to great effect, in this example, the bar chart (Figure 1) demonstrates a superior approach to presenting the data. When looking at the bar chart, it can be rapidly determined that there is a pronounced difference between the highest DDD and lowest DDD by country.

Do you agree that your brain needs to work harder to get the same information from the table?

1.2.1.2 Quantitative variables

Descriptive statistics for quantitative (numerical) variables (continuous or discrete) include measures of central tendency and measures of spread. Summary statistics include the mean, standard deviation, median, minimum and maximum. Summary statistics can be calculated to summarise quantitative variables in a dataset.

Descriptive statistics for quantitative variables can be reported as text, as a summary table, or using a histogram or box-and-whisker plot. We’ll be discussing histograms and box-and-whisker plots later in this course.

1.2 Review of different approaches to data analysis

1.2.2 Plotting variability in data