write a response with recent peer reviewed articles for this discussion post: Yin's (2018) Four Principles of Data Collection provide a systematic framework for addressing my research question on evidence-based strategies to improve employee morale and reduce voluntary turnover in higher education. The principle of using multiple sources of evidence allows me to capture diverse perspectives from administrators, faculty, and staff, increasing the credibility of findings. Establishing a case study database ensures that I organize interview transcripts, institutional reports, and survey results in a way that enhances transparency and reliability. Yin also emphasized maintaining a chain of evidence, which helps trace each conclusion back to the original data and strengthens validity. His call for care when using electronic sources safeguards against errors when analyzing HR data and digital institutional reports. These principles equip me with a structured and rigorous approach to gathering data that supports the development of actionable, evidence-based strategies to address my applied business problem.

The Role of Triangulation

The use of triangulation plays a critical role in ensuring both the quality and trustworthiness of data. Triangulation enhances construct validity by corroborating findings across sources, investigators, theories, and methods (Bans-Akutey & Tiimub, 2021; Yin, 2018). Data triangulation could involve comparing faculty climate survey results with administrator interviews, while investigator triangulation might draw on insights from multiple researchers to reduce bias. Theoretical triangulation allows for analysis through different conceptual lenses, and methodological triangulation combines qualitative interviews with quantitative HR metrics to strengthen interpretation. In the context of my study, this process ensures that conclusions about employee morale and turnover are not based on isolated accounts but reflect patterns across multiple perspectives and methods. Hollweck (2015) reinforced Yin's point that triangulation strengthens construct validity, making findings more credible and actionable.

Implications for Higher Education Research

The importance of triangulation becomes especially clear when examining complex organizational challenges such as turnover in higher education. Sharma and Syal (2022) found that turnover intentions among faculty are influenced by multifaceted factors, including workload, institutional culture, and leadership practices. Such complexity demands multiple forms of data to uncover underlying causes and inform effective interventions. By integrating administrator interviews, faculty surveys, and turnover reports, I can generate a more refined understanding of the problem while identifying strategies grounded in strong evidence. This process directly supports my leadership goal of equipping higher education administrators with actionable, evidence-based practices that strengthen employee morale and reduce voluntary turnover. In doing so, I not only apply Yin's (2018) principles but also position my research to contribute meaningfully to the broader scholarly and professional conversation on organizational improvement in higher education.

Conclusion

In summary, Yin's (2018) Four Principles of Data Collection and the use of triangulation provide the methodological rigor needed to address my research problem on employee morale and turnover in higher education. By applying multiple sources of evidence, maintaining transparency, and validating findings through triangulation, I can strengthen the credibility and applicability of my study. This approach not only aligns with best practices in case study research (Hollweck, 2015) but also responds to the complex realities of turnover in higher education settings (Sharma & Syal, 2022). Using these principles allows me to generate actionable strategies that higher education administrators can implement to create supportive environments, improve morale, and reduce voluntary turnover.