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Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as '''qualitative''' and '''quantitative''', as shown below, although through manipulation, these types can be changed. Within each of these groups, data types can be classified further.
Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as '''qualitative''' and '''quantitative''', and within each of these groups, data can be further categorised as shown below. Although different grouping systems are available, it is important to consider the type of data being dealt with prior to any analysis. If desired, data can often be changed into different types through manipulation (for example, the quantitative variable weight can be converted to qualitative variables such as low/medium/high or low/not low).<br>
==Qualitative data==
==Qualitative data==
Qualitative data are 'categorical' (or binary) data, and as such are often not expressed numerically, meaning that they are best summarised using percentages or proportions.These types of data can be classified as '''nominal''' and '''ordinal''':
Qualitative data are 'categorical' (or binary) data, and as such are often not expressed numerically, meaning that they are best summarised using percentages or proportions. These types of data can be further classified as '''nominal''' and '''ordinal''':
===Nominal===
===Nominal===
Nominal data differ from all other data types described here by lacking any order between the different categories, and can be described further as either binary ('yes/no') or categorical in nature. Examples of binary data are disease status (positive/negative), sex (male/female) and presence/absence of a factor of interest; whereas examples of categorical data are breed, coat colour, location and feed type.
Nominal data differ from all other data types described here by lacking any order between the different categories, and can be described further as either binary ('yes/no') or categorical (containing more than two categories) in nature. Examples of binary data are disease status (positive/negative), sex (male/female) and presence/absence of a factor of interest; whereas examples of categorical data may be breed, coat colour, location and feed type.
===Ordinal===
===Ordinal===
==Quantitative data==
==Quantitative data==
Quantitative data are numerical in nature, with a set, meaningful interval between different measurements. Depending on the shape of the distribution, they may be described using the mean and standard deviation (for normally distributed data), or the median and the range/interquartile range (for non-normally distributed data). Quantitative data can be further classified as '''discrete''' or '''continuous''':
Quantitative data are numerical in nature, with a set, meaningful interval between different measurements. Depending on the shape of the distribution, they may be described using the mean and standard deviation (for normally distributed data), or the median and the range/interquartile range (for non-normally distributed data). Quantitative data can be further classified as '''discrete''' or '''continuous''':
===Discrete===
===Discrete===
Discrete data only include integer values, with decimal places having little or no meaning. 'Count' data, derived by counting the number of events or animals of interest, are a type of discrete data. Examples of discrete data are the number of infected animals within a group, the number of episodes of pathogen shedding following initial infection, the number of piglets born per year, and the number of lactations which the animal has been through.
Discrete data only include integer values, with decimal places having little or no meaning. 'Count' data, derived by counting the number of events or animals of interest, are a type of discrete data. Examples of discrete data are the number of infected animals within a group, the number of episodes of pathogen shedding following initial infection, the number of piglets born per year, and the number of lactations which the animal has been through.