False confessions are the most difficult type of confession to detect. Because the Reid interview and interrogation technique is the global gold standard for interviews, interrogations, and confessions, it is used to obtain confessions from suspects. However, the Reid method has been untested in regard to if it can detect false confessions to potentially eliminate wrongful convictions. The purpose of this qualitative study was to perform a content analysis of videos of confessions using several models that make up the Reid interview and interrogation technique. Utilizing attribution theory as a framework, these models were qualitatively assessed for their ability to detect false confessions in comparison with the legal casebook analysis and linguistic analysis. The key research questions addressed how interviewers attribute identification of false confessions through the assessment of the various models and the complete Reid interview and interrogation technique. An additional research question concerned how interviewers identify attribution error in false confessions through the assessment of the various models and the complete Reid interview and interrogation technique. Data were collected from 6 videos and subjected to content analysis, triangulated with discourse analysis and conversation analysis. The results of this study showed that the models applied to the confessions could distinguish between true and false confessions. A social change could occur if some or all of these models are applied to all interrogations to detect false confessions, which would provide law enforcement and the intelligence professions the tools to assess confessions in order to potentially stop wrongful convictions and intelligence failures in interviews and interrogations.