......................................................................................................................................................................................................................................................................................................................... doi:10.1017/S1049096519001161 © American Political Science Association, 2019 PS • 2019 1 Politics Improving Data Quality in Face-to-Face Survey Research Carolyn Logan, Michigan State University Pablo Parás, Data OPM Michael Robbins, Princeton University Elizabeth J. Zechmeister, Vanderbilt University ABSTRACT Data quality in survey research remains a paramount concern for those studying mass political behavior. Because surveys are conducted in increasingly diverse contexts around the world, ensuring that best practices are followed becomes ever more important to the field of political science. Bringing together insights from surveys conducted in more than 80 countries worldwide, this article highlights common challenges faced in survey research and outlines steps that researchers can take to improve the quality of survey data. Importantly, the article demonstrates that with the investment of the necessary time and resources, it is possible to carry out high-quality survey research even in challenging envi- ronments in which survey research is not well established. E nsuring data quality in survey research remains a paramount—if often under-discussed—issue for research into mass political behavior. During the past two decades, this has been especially true as sur- veys became a more common method of research in countries around the world. Best practices, often developed in the United States, have been imported to other contexts despite dif- ferences in survey modes, techniques, and challenges. Data quality encompasses a wide range of issues, including implementing sam- ple plans correctly and ensuring that field teams have the capacity to carry out the design. Meanwhile, in recent years, the issue of data fabrication—that is, the intentional departure from specified pro- tocols by a member of the data-collection team—also has become a more prominent concern (Bredl, Winker, and Kötschau 2012; Kuriakose and Robbins 2016; Spagat 2016). Afrobarometer, AmericasBarometer, and Arab Barometer have all confronted the challenges of ensuring data quality and prevent- ing fabrication in the work they have done in more than 80 coun- tries worldwide. Evidence from these projects makes clear that it is possible to conduct high-quality, nationally representative, and reliable public-opinion surveys even in challenging condi- tions. However, this process is time and resource intensive, and investments in data quality must be made throughout the life of the project. Achieving high-quality data is not only a question of monitoring and policing by principal investigators (PIs) but also of partner commitment, capacity, and correctly structured incentives. Following is a summary of best practices developed by these organizations during each stage of the research process. When these methods are followed, the likelihood of obtaining valid and reliable data and reducing fabrication is high. Although our own focus is on projects in settings that are relatively less developed with relatively more challenges, many of these practices are rel- evant to researchers working in other contexts as well, whether comparative survey projects, single-country surveys, or surveys of a specific population within a country. STAGE 1: PRE-FIELDWORK Investments in data integrity must start before fieldwork begins, when designing the survey instrument, identifying suitable partners, selecting and training fieldworkers, and planning fieldwork logistics. A well-designed questionnaire not only ensures conceptual equivalence but also helps interviews to flow quickly and smoothly, reducing the likelihood of unau- thorized shortcuts. Partner organizations that demonstrate real commitment and buy-in to the methodology and that have adequate resources and the necessary capacity to do the work well will be on the frontlines in producing high-quality data. Effectively recruited fieldworkers who are adequately trained, resourced, fairly compensated, and motivated are more likely to commit to collecting good quality data and foregoing fabrica- tion. Finally, adequately planning, resourcing, and supervising Carolyn Logan is associate professor of political science at Michigan State University and deputy director of the Afrobarometer. She can be reached at clogan@msu.edu. Pablo Parás is president and founder of Data OPM in Mexico City. He can be reached at pp@dataopm.net. Michael Robbins is director of the Arab Barometer based at Princeton University. He can be reached at mdr7@princeton.edu. Elizabeth J. Zechmeister is Cornelius Vanderbilt Professor of political science at Vanderbilt University and director of the Latin America Public Opinion Project (LAPOP). She can be reached at liz.zechmeister@vanderbilt.edu. This is an updated version of the original article. For details please see the notice at https://doi.org/10.1017/S1049096519001689