Hnin Ei Lwin

  This post builds upon my article from last week about creating an inclusive workplace and the importance of establishing a feedback mechanism. This week, I dive into how to initiate such a mechanism, which is crucial for fostering open communication, mutual respect, and continuous improvement within any organization.  By developing an accessible, anonymous, and engaging feedback system, every voice has the opportunity to be heard and valued. From defining its scope to monitoring, evaluation, learning, and refining the system, each step contributes to creating an environment where feedback leads to actionable changes, driving the organization forward. The recommendations provided offer a practical approach to building a feedback mechanism that evolves with the organization’s changing needs.   #Feedback #Inclusive_Feedback_Mechanism    Hnin Ei Lwin #Monitoring   #Evaluation   #Reporting   #Research   #MEARL #social   #development   ...

True inclusion requires a fundamental shift from 'working for' to 'working with.' This shift is essential not only for inclusion projects but also for cultivating a workplace where collaboration and shared decision-making are the cornerstones of a truly cooperative work culture. These two phrases may be close to each other, but they manifest in completely different ways. The goal is to 'work with,' not 'work for,' across a wide range of inclusion efforts.  Moreover, this distinction extends beyond superficial approaches to inclusion, highlighting how we approach genuine collaboration in all aspects of our work.   Why is this important?  Having a diverse workforce is a helpful start, but it alone does not suffice. Without inclusive methods, people from different backgrounds may feel excluded, unappreciated, or ignored. "Working for" usually leads to a top-down approach, where leaders make choices with little input from others. This can limit ope...

Happy New Year 2025! As my very first post of this new year, I would like to share two excellent resources for colleagues interested in self-learning R programming. While I’m not a programmer myself, I’ve realized that data analysis using R is something we simply cannot avoid, especially in social science subjects, and will become even more essential in the future. Whether you are completely new to R or have some experience with other data analysis tools, self-studying R is entirely possible and rewarding! One of the best things about R is that it is free, versatile, and actually quite enjoyable once you get the hang of it. These resources below are particularly helpful for those transitioning from tools like Excel or SPSS to more advanced data analysis with R. 1.  R for Data Science by Hadley Wickham & Garrett Grolemund:  This book provides a beginner-friendly introduction to R, with a focus on practical applications like data cleaning, visualization, and modeling. It is ...