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Quantitative data analysis Toolbox

2.1 Starting with definitions


To begin, there are a few essential definitions that must be understood for full application of the analysis toolbox. A more extensive list of program data related definitions can be found in CartONG’s Information Management Beginner’s Glossary.

2.1.1 Data

Data is a standalone element that has not been interpreted or put into context. Bringing data together through processing can lead to information. Therefore, data must be interpreted to be informative, and if this information is useful, then it becomes knowledge.

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There are many types of data that can be processed into information in the humanitarian and development sphere.

Quantitative data expresses a specific ‘quantity’, usually numerical, and has units.

For example, ‘the number of people in a household’ is a piece of quantitative data (where the unit is persons), that can be useful to determine the level of support needed in some types of humanitarian programming.

Qualitative data is instead focused on the characteristics of something, and cannot be expressed in numbers.

For example, ‘the principal source of drinking water’ is a piece of qualitative data, (not expressed in number), that can be useful to determine the type of support needed in some types of humanitarian programming.

Further data essentials are described by the Global Disaster Preparedness Center here.

2.1.2 Dataset

A dataset is a structured collection of data, usually based on one instance of data collection. Generally, datasets in the humanitarian sector are structured in a table, where each column of the table represents a variable and each row represents one unit of analysis.

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The unit of analysis can change depending on the research itself, but in the humanitarian sector it is usually 1 instance of completing a survey, either on behalf of one individual, household, or community.

2.1.3 Database

A database is an organized collection of data stored as multiple datasets. Those datasets are generally stored and accessed electronically from a computer system that allows the data to be easily accessed, manipulated, and updated.

A database is “a tool that stores data, and let’s you create, read, update, and delete the data in some manner” (ACAPS, How to approach a dataset, 2013, pp. 3).

Often, the database is the Excel file that teams use to follow their program data, but a database can take many forms; paper, an Excel sheet, or more complex purpose-built software.

Databases are necessary for program data and information management in order to provide a storage location for project data that can then easily be retrieved, updated, and analyzed to make evidence-based decisions.