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Research into understanding individual vote choice in U.S. presidential elections begins anew every 4 years. The 2016 U.S. presidential election raised the question of why large numbers of White women voted for Donald Trump. My research case study examines the sources of support (testing competing theories) for Donald Trump and other Republican candidates among White women using 2016 American National Election Studies Pilot Study data. In this case study, I discuss how to use secondary survey data to operationalize concepts that are contained in causal theories that explain support for Trump among White women.

Documentation of research results is an essential process within the research lifecycle, which includes the steps of study planning and developing the survey instruments, data collection and preparation, data analysis, and data archiving. Primary researchers have to ensure that the collected data and all accompanying materials are properly documented and archived. This enables the scientific community to understand and reproduce the results of a scientific project. The purpose of this survey guideline is to provide a brief introduction and an overview about data preparation and data documentation in order to help primary researchers to make their data and other study-related materials long-term accessible. This overview will therefore help researchers to comply with the principles of reproducibility as a crucial aspect of good scientific practice. This guideline will be useful for researchers who are in the stages of planning a study as well as for those who have already collected data and would like to prepare it for archiving.

AbstractRecent research linking administrative to survey data has laid the groundwork for improvements in survey data products. However, the opportunities have not been fully realized yet. In this article, our main objective is to use administrative-survey linked microdata to demonstrate the potential of data linkage to reduce survey error through model-based blended imputation methods. We use parametric models based on the linked data to create imputed values of Medicaid enrollment and food stamp (SNAP) receipt. This approach to blending data from surveys and administrative data through models is less likely to compromise confidentiality or violate the terms of the data sharing agreements among the agencies than releasing the linked microdata, and we demonstrate that it can yield substantial improvements of estimate accuracy. Using the blended imputation approach reduces root mean squared error (RMSE) of estimates by 81 percent for state-level Medicaid enrollment and by 93 percent for substate area SNAP receipt compared with estimates based on the survey data alone. Given the high level of measurement error associated with these important programs in the United States, data producers should consider blended imputation methods like the ones we describe in this article to create improved estimates for policy research.

AbstractIn this chapter we discuss how to download, clean, and transform online survey results in preparation for data analysis.

This review focuses on recent methodological and technological developments in survey data collection. Surveys are facing unprecedented challenges from both societal and technological changes. Against this backdrop, I review the survey profession's response to these challenges and developments to enhance and extend the survey tool. I discuss the decline in random digit dialing and the rise of address-based sampling, along with the corresponding shift from telephone surveys to self-administered (mail and/or Web) modes. I discuss the rise in nonprobability sampling approaches, especially those associated with online data collection. I also review so-called big data alternatives to surveys. Finally, I discuss a number of recent methodological and technological trends designed to modernize the survey method. I conclude that although they face a number of major challenges, surveys remain a robust and flexible method for collecting data on, and making inference to, populations.

'How to Analyze Survey Data' by Arlene Fink is reviewed.