Part II Collecting and analysing data Photo by tanakawho, reproduced under a CC BY-NC 2.0 license. Other categories of data that you may encounter include: • Categorical data, which puts the item described into a category (e.g. “new”, “used”, “broken”) • Discrete data, which is numerical data that has gaps in it (e.g. test scores or shoe sizes) • Continuous data, which is numerical data where all values are possible, but with no gaps (e.g. your height or the size of your foot). 5.2. THE BENEFITS OF WORKING WITH QUANTITATIVE DATA Monitoring will always involve making a judgment about the adequacy or reasonableness of how a duty bearer is acting. This is necessarily a qualitative assessment. However, it is an assessment that will be stronger when it is supported by verifiable evidence. Basing such evidence on quantitative data is appealing for several reasons. WHAT IS AGGREGATED AND DISAGGREGATED DATA? Aggregated data is collected without making any distinctions. For example, the number of people without access to water in a community is 6,000. Disaggregated data is divided according to set criteria, e.g. by sex, age, area where people live to show where the needs are greatest. First, evidence gathered from victims, witnesses and even key informants is open to critique for being too anecdotal. Quantitative data is inherently well suited to mapping trends and patterns in a particular situation, in a way that more qualitative information on specific incidents cannot. A larger and wider number of facts can be captured, categorized and compared across groups and over time. For example, time-series data about the number of families living in informal settlements can tell us something about progressive realization of the right to housing, while budgetary data about how much the Government is investing in pensions can tell us something about whether it is dedicating the maximum available resources to the fulfilment of the right to social security. Significantly, as discussed in Chapter 4, quantification allows for disaggregation. This can help uncover patterns of disadvantage and inequality that might otherwise remain hidden. For example, disaggregated data on wages that shows a gender pay gap can tell us something about discrimination against women in employment. Chapter 5: The role of data in monitoring economic, social and cultural rights | 55