This paper presents a comprehensive review of methodologies for eliciting, monitoring, and measuring self-disclosure, a critical component of human interaction. It examines both experimental and naturalistic techniques, ranging from interview-based approaches to digital communication analysis, and discusses their advantages and limitations in various contexts. Additionally, it explores various self-disclosure measurement tools, including self-reports, observer ratings, and automated content analysis. The study highlights the complexity of self-disclosure, shaped by individual, relational, and situational factors, and underscores the importance of employing diverse, interdisciplinary methods to accurately capture its dynamics and effects on relationship establishment and maintenance, well-being, and social connections.