MY APPROACH TO RESEARCH AND M&E: BLENDING QUALITATIVE DEPTH WITH QUANTITATIVE PRECISION

In my research and M&E work, I blend qualitative and quantitative approaches to deeply understand social issues. While I have recently focused on qualitative data for its rich and nuanced insights (which demands strong interpersonal skills throughout collection, analysis, and reporting for reliability and clear communication), I also equally value quantitative data. I believe it is essential for validating findings and continuously sharpening my analytical abilities to better reveal the stories hidden in the numbers. This week, therefore, I want to briefly share my quantitative data analysis toolkit.

My Quantitative Data Analysis Toolkit: From Data Collection to Communication

I rely on specific tools to effectively collect, analyze, and share data, turning raw information into clear and impactful insights.

  • Excel: This is my go-to for quick data cleaning, sorting, and initial entry, especially for smaller datasets. I use it for early summaries, visualizations, and dashboards since many stakeholders prefer Excel's accessibility. Excel dashboards are often my first choice for internal team use or when sharing preliminary findings in a familiar format. Pivot Tables help me efficiently analyze data by demographics, activities, or timeframes.
  • SPSS: This is my primary statistical workbench for deeper analysis without coding. I heavily rely on it for both descriptive and inferential statistics, including t-tests, ANOVA, chi-square, and logistic regression, which are vital for measuring impact and validating findings.
  • SQL: This is helpful for navigating the vast data landscapes where program beneficiaries, outcomes, and progress indicators often reside. Beyond just extraction, I sometimes use SQL as a data cleaning step. It allows me to precisely filter, transform, and ensure data quality right at the source, giving me accurate and relevant information for analysis.
  • R: I leverage R for advanced statistical analysis and data visualization when projects require more flexibility and sophisticated techniques. Its powerful packages allow me to explore complex data patterns and automate workflows. However, I have rarely needed R for my M&E projects so far, as their quantitative data is typically straightforward.
  • Looker Studio (formerly Google Data Studio): I occasionally use this for creating interactive dashboards. I choose it over BI tools like Power BI and Tableau, despite its limitations, because it is free and makes data insights accessible without any cost for individual or small team use.

Why These Tools Matter

Together, these tools form my trusted quantitative data analysis toolkit. They empower me to transform raw data into actionable insights by highlighting successes, identifying gaps, and supporting evidence-based decisions that drive meaningful change. With these tools, I confidently track progress, foster learning, and create meaningful, real-world impact.


Hnin Ei Lwin

#Monitoring #Evaluation #Reporting #Research #MEARL

#social #development #humanitarian #publichealth 

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