The article discusses intercoder reliability (ICR) in qualitative research, highlighting its importance and the debates surrounding its use. ICR measures the agreement between coders in how data is coded, and while some argue it is unnecessary in qualitative analysis, others believe it enhances the systematicity, transparency, and credibility of the research. The authors provide practical guidelines for assessing ICR, emphasizing its role in improving the quality and reliability of qualitative studies. ICR is often confused with interrater reliability (IRR), but they differ in that ICR is used for nominal data (e.g., presence or absence of an emotion), while IRR is for ordinal or interval data (e.g., emotion intensity). ICR is distinct from intracoder reliability, which assesses consistency of a single coder over time. The article explains that ICR is more commonly used in content analysis than in grounded theory, where the recursive nature of the process makes ICR less relevant. The article reviews arguments for and against ICR. Proponents argue that ICR ensures consistency and transparency in coding, helps researchers refine their coding frames, and provides a basis for communicating findings to others. Critics, however, argue that ICR contradicts the interpretive nature of qualitative research and may lead to false precision. The article also discusses the practical aspects of performing ICR, including the number of coders needed, the proportion of data to be coded, and the methods for calculating ICR. The authors suggest that ICR should be calculated on a subset of the data, typically 10–25% of data units, to ensure representativeness. They also emphasize the importance of using appropriate statistical measures, such as Krippendorff's alpha, for ICR assessment. The article concludes that while ICR is not universally accepted, it can be a valuable tool in qualitative research when used appropriately, helping to ensure the reliability and credibility of the analysis.The article discusses intercoder reliability (ICR) in qualitative research, highlighting its importance and the debates surrounding its use. ICR measures the agreement between coders in how data is coded, and while some argue it is unnecessary in qualitative analysis, others believe it enhances the systematicity, transparency, and credibility of the research. The authors provide practical guidelines for assessing ICR, emphasizing its role in improving the quality and reliability of qualitative studies. ICR is often confused with interrater reliability (IRR), but they differ in that ICR is used for nominal data (e.g., presence or absence of an emotion), while IRR is for ordinal or interval data (e.g., emotion intensity). ICR is distinct from intracoder reliability, which assesses consistency of a single coder over time. The article explains that ICR is more commonly used in content analysis than in grounded theory, where the recursive nature of the process makes ICR less relevant. The article reviews arguments for and against ICR. Proponents argue that ICR ensures consistency and transparency in coding, helps researchers refine their coding frames, and provides a basis for communicating findings to others. Critics, however, argue that ICR contradicts the interpretive nature of qualitative research and may lead to false precision. The article also discusses the practical aspects of performing ICR, including the number of coders needed, the proportion of data to be coded, and the methods for calculating ICR. The authors suggest that ICR should be calculated on a subset of the data, typically 10–25% of data units, to ensure representativeness. They also emphasize the importance of using appropriate statistical measures, such as Krippendorff's alpha, for ICR assessment. The article concludes that while ICR is not universally accepted, it can be a valuable tool in qualitative research when used appropriately, helping to ensure the reliability and credibility of the analysis.