Data Map

A data map is a template designed by DIME for organizing the 3 main aspects of data work: data analysis, data cleaning, and data management. The data map template consists of three components: a data linkage table, a master dataset, and data flow charts. DIME Analytics recommends using data maps to organize the various components of your data work in order to increase the quality of data, as well as of research.

Read First

• The best time to start creating a data map is before starting with data collection.
• A data map template has three components: a data linkage table, one or more master datasets, and one or more data flow charts.
• The research team should keep updating the data map as the project moves forward.
• The data map template is meant to act as a starting point for data management within a research team.
• It is important to understand the underlying best practices for each component of a data map before discussing which components do not apply in a given situation.

Overview

Most of the details required for preparing a data map are not complex. For example, it is easy for the field coordinator (FC) to remember what the respondent ID is when data collection is still ongoing. However, it is harder to ensure that everyone in the research team has the same level of understanding. Further, as time passes, the field coordinator (FC) themselves can forget what exactly a particular variable measures, or why it was included in the dataset. Research teams often do not spend enough time planning and organizing data work because small details like the purpose of a variable might seem obvious. However, this tendency is exactly what makes lack of, or inadequate planning a common source of error. Fortunately, the solution - a data map - is quick and easy to implement.

DIME Analytics has prepared a data map template, which has the following three components:

• A data linkage table: The data linkage table lists all the datasets in a particular project, and explains how they are linked to each other. For example, a data linkage table can describe how a dataset containing information about students can be merged with a dataset containing information about various schools. It also specifies which ID variable can be used to perform the merging. Finally, the data linkage table should also include meta-information, that is, information about the datasets, where the original version of these data sets are backed-up, and so on.

• One or more master datasets: Master datasets allow the research team to keep track of units for each level of observation. For example, master datasets are useful for keeping track of each household if the unit of observation is individual households, each company if the unit of observation is individual companies, and so on. Most importantly, the master dataset should specify the uniquely and fully identifying ID variable for each unit of observation. Include variables related to the research design in the master dataset, such as treatment assignment variables in the form of dummy variables. The master dataset is therefore the authoritative source of all information in a particular project.

• One or more data flow charts: A data flow chart specifies which datasets are needed to create the analysis dataset, and how they may be combined by either appending or merging datasets. This means that there should be one data flow chart per analysis dataset. Make sure that every original dataset that is mentioned in a data flow chart should be listed in the data linkage table. For example, in a particular data flow chart, information about which variables to use as the basis for merging datasets should correspond to the information in the data linkage table.

Note - Please keep the following points in mind regarding data maps:

• A good data map can save a lot of time. If you are in the middle or towards the end of your project and you spend more time linking your datasets than doing other data work, you should step back and create a data plan.

• Modify the data map based on the context. As with all templates, you might need to add items to our Data Plan Template or you may find that some items do not apply.

• There can be multiple master datasets, but only one data linkage table. Many projects have multiple units of observation, in which case there should be one master dataset for each unit of observation that is considered central to the project. However, there should only be one data linkage table per project.