The amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media and smartphones, just to name a few. Such data are often referred to as "big data", and can be used to create value in different areas such as health and crime prevention, commerce and fraud detection.  An emerging practice in many areas is to append or link big data sources with more specific and smaller scale sources that often contain much more limited information.  This practice has been used for some time by survey researchers in constructing frames by appending auxiliary information that is often not directly available on the frame, but can be obtained from an external source.   Using Big Data has the potential to go beyond the sampling phase for survey researchers and in fact has the potential to influence the social sciences in general.  Big Data is of interest for public opinion researchers and agencies that produce statistics to find alternative data sources either to reduce costs, to improve estimates or to produce estimates in a more timely fashion. However, Big Data pose several interesting and new challenges to survey researchers and others who want to extract information from data. As Robert Groves (2012) pointedly commented, the era is “appropriately called Big Data and not Big Information”, because there is a lot of work for analysts before information can be gained from “auxiliary traces of some process that is going on in society.”  
In this course we explore how Big Data concepts, processes and methods can be used within the context of Survey Research.  Throughout this course we will illustrate key concepts using specific survey research examples including tailored survey designs and nonresponse adjustments and evaluation.