ABSTRACT
The use of computers in content analysis can often improve efficiency by allowing researchers to study more complex data sets reliably. We challenge the notion that human and algorithmic coders necessarily should produce equivalent, much less identical, data, and that if they fail to produce equivalent data, it is necessarily a “fault” of the algorithmic coder. We distinguish the use of algorithmic coders as a different method from human coding by assigning a different name: algorithmic text analysis (ATA). Currently, the algorithmic coder is best suited to studying the manifest meaning of text. Computers, though, can also be used to enhance the efficiency and reliability of human coders by gathering, sorting, and filtering data, and by organizing the coding task. Computers should be used in content analysis to improve efficiency and reliability, as long as their use to perform tasks does not significantly threaten the validity of data. Validity is often enhanced by using a hybrid approach that combines ATA and human coding. This chapter discusses how content analysis can be “scaled up,” particularly with an eye toward developing tools that make content analysis more efficient.
