  Finite population inference is a central goal in survey sampling. Probability
sampling is the main statistical approach to finite population inference.
Challenges arise due to high cost and increasing non-response rates. Data
integration provides a timely solution by leveraging multiple data sources to
provide more robust and efficient inference than using any single data source
alone. The technique for data integration varies depending on types of samples
and available information to be combined. This article provides a systematic
review of data integration techniques for combining probability samples,
probability and non-probability samples, and probability and big data samples.
We discuss a wide range of integration methods such as generalized least
squares, calibration weighting, inverse probability weighting, mass imputation
and doubly robust methods. Finally, we highlight important questions for future
research.
