Ex Post Survey Harmonization with retroharmonize

Surveys, i.e., systematic primary observation and data collections are important data sources of both social and natural sciences. They are in most cases the primary data sources of scientific research.  Drawing information from several surveys, conducted in different locations or in different time can greatly enhance the inferential capacity of the surveys, but it requires significant data processing and statistical processing work. 

Survey data harmonization refers to procedures that improve the data comparability or the inferential capacity of multiple surveys conducted in different periods of time, or in different geographical locations, using different languages. Retrospective harmonization, or “survey recycling” is a practice for integrating information from two or more data sources, and to create a from several survey documentations, several codebooks, and several data tables, single, consolidated documents, and tables. 

Survey harmonization is closely related to the concept of statistical matching, also known as data fusion or data matching, the practice of “drawing information piecewise from different independent sample surveys”, particularly the bottom-up approaches to these problems. Statistical matching takes survey recycling a step further, aiming to improve the statistical inference capacities of the joined dataset, for example, with creating a unified weighting for the variables.  In our software package we drew the line where the joined datasets, joined codebooks, and a general description is delivered: that is where our retroharmonize package aims to help.