Using Google Maps to Generate Organizational Sampling Frames

Abstract

Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating organizational sampling frames that is cost-effective, uses publicly available data, and can produce sampling frames for many geographic areas in the U.S. The Python-based program we developed systematically scans the Google Maps platform to identify organizations of interest and retrieve their contact information. We demonstrate the program's viability and utility by generating a sampling frame of religious congregations in the U.S. To assess Google Maps’ coverage and representativeness of such congregations, we examined two nationally representative samples of congregations and censuses of congregations in a small, medium, and large city. We found that Google Maps contains approximately 98% of those congregations––extensive coverage that ensures a high degree of representativeness. This study provides evidence that using Google Maps to generate sampling frames can improve the process for obtaining representative samples for organizational studies by reducing costs, increasing efficiency, and providing greater coverage and representativeness.