The literature on survey data fabrication is fairly thin, given the serious threat it poses to data quality. Recent contributions have focused on detecting interviewer fabrication, with an emphasis on statistical detection methods as a way to efficiently target reinterviews. We believe this focus to be too narrow. The paper looks at the problem of fabrication in a different way, exploring new data that shows the problem goes beyond interviewer curbstoning. A surprising amount of apparent fabrication is easily detected through comparatively rudimentary methods such as analysis of duplicate data. We then examine the motivations behind survey data fabrication and explore the utility of fraud investigation frameworks in detecting survey data fabrication. We finish with a brief discussion of the importance of additional research in this area and suggest questions worth exploring further. This paper is a synthesis of presentations given by the authors at an event sponsored by the Washington Statistical Society.

Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector-borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmission of malaria in several ways, affecting all stages of the disease's development. Using various weather-related factors that influence climate change, this study utilizes statistical analysis to determine the effect of climate change on reported malaria rates in an African region with endemic malaria. It examines the relationship between malaria prevalence and climate in western Africa using spatial regression modeling and tests for correlation. Our analysis suggests that minimal correlation exists between reported malaria rates and climate in western Africa. This analysis further contradicts the prevailing theory that climate and malaria prevalence are closely linked and negates the idea that climate change will increase malaria transmission in this region.

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