Traditional content analysis has already faced the issues of reliability and validity. It evaluates results by considering the reliability of a model and the validity of the components of the model. Stability refers to the temporal stability of preparing text for machine parsing. When the same coder prepares input text at different times, the resulting text model may reflect several different coding practices within the processing model. Stability should encompass classificational stability , or the effects on classification of variations in coding practices.
Instability can arise as coders make texts literal and explicit.
Allowing multiple word senses within part of speech reduces the changes to the text, and therefore, should enhance stability. Although stability measures the consistency of private understandings, intercoder reliability, or reproducibility, measures the similarities and differences between different coders. Ambiguous or inconsistent coding rules for rendering text explicit can diminish reproducibility. Similarly, cognitive factors, such as situation framing or abstraction mismatches, may yield different codings. As in stability, the concern here is with the impact of coding on the possible classifications.