Skip to Content

Quantitative Research


We provide comprehensive support in quantitative research design, data management, and statistical analysis tailored for academic, public health, and implementation research projects. Our approach combines methodological rigor with practical application to ensure data-driven, evidence-based insights.

Basic Statistics​


We teach and apply foundational statistical concepts essential for understanding data and making informed decisions. Through interactive sessions and practical exercises, participants learn about types of data, measures of central tendency, dispersion, probability, and distributions, building a strong base for advanced analysis.

Statistical Analysis​


Descriptive Statistics

  • Summarize and visualize data using tables, charts, and summary measures.
  • Teach effective ways to communicate trends and key insights from data.


Parametric Analysis

  • Guide on applying tests such as t-test, ANOVA, and correlation analysis.
  • Explain assumptions, interpretation, and reporting for real-world use.


Non-Parametric Analysis

  • Teach methods like Mann-Whitney U, Kruskal-Wallis, Chi-square, and Wilcoxon tests.
  • Emphasize choosing the right test when data assumptions are violated.


Inferential Analysis

  • Focus on hypothesis testing, confidence intervals, and significance levels.
  • Develop skills to draw valid conclusions and support data-driven decisions.


Regression Analysis

  • Cover linear, logistic, and multiple regression modeling.
  • Train on identifying predictors, interpreting coefficients, and checking model fit.


Multivariate Analysis

  • Introduce advanced methods like factor analysis, PCA, and cluster analysis.
  • Support in identifying patterns and underlying data structures.


 

Tools & Programming Language


IBM SPSS : User-friendly statistical analysis and data visualization for all research levels.

STATA : Advanced data management and regression modeling for health system research.

R Programming : Open-source, flexible, and powerful environment for data science and visualization.

Python : Versatile programming language for data cleaning, statistical modeling, and machine learning applications with strong integration for research automation and visualization.