Best practices in data acquisition and entry are the central theme of this chapter.Correct entry of variables and data in spreadsheets like Excel is discussed along with common problems of data entry that may prevent software from reading and analyzing data correctly. Typical practices of entering data for nominal, ordinal, and interval variables give the student information on how to enter data in Excel for these variables.The purpose of codebooks and composing them to match data are discussed.Different types of data including cross-sectional, time-series, and panel are presented to the student.Finally, common sources of public administration data are listed and described.

In working with network data, data acquisition is often the most basic yet the most important and challenging step. The availability of data and norms around data vary drastically across different areas and types of research. A team of biologists may spend more than a decade running assays to gather a cells interactome; another team of biologists may only analyze publicly available data. A social scientist may spend years conducting surveys of underrepresented groups. A computational social scientist may examine the entire network of Facebook. An economist may comb through large financial documents to gather tables of data on stakes in corporate holdings. In this chapter, we move one step along the network study life-cycle. Key to data gathering is good record-keeping and data provenance. Good data gathering sets us up for future success—otherwise, garbage in, garbage out—making it critical to ensure the best quality and most appropriate data is used to power your investigation.