Concluded: Training on Introduction to Quantitative Data Analysis Techniques Part I of the "From Data to Discovery" Four-Part Series
TRAINING SUCCESSFULLY CONCLUDED
Training on Introduction to Quantitative Data Analysis Techniques
Part I of the "From Data to Discovery" Four-Part Series
Organized by:
Institute for Social and Environmental Research-Nepal (ISER-N), Fulbari, Chitwan, Nepal
Population Studies Center, University of Michigan, Ann Arbor, USA
I. Training Overview
We are pleased to announce an intensive, 40-hour (5-day) in-person training workshop designed to equip researchers with essential knowledge and practical skills required for professional quantitative data analysis. This is the foundational installment of a four-part series aimed at elevating analytical research capacity—progressing toward advanced topics such as Longitudinal and Correlated Data Analysis in future sessions.
Dates: 2nd Week of March 2026
Location: Fulbari, Chitwan, Nepal
Primary Tool: STATA (Instruction will focus on Stata; users of other software like R are welcome but must be self-sufficient.)
II. Key Training Pillars
Rather than a theoretical overview, this workshop focuses on the following core competencies:
Research Foundations & Survey Architecture: Defining scientific research questions and understanding the structure of large-scale surveys such as NDHS, NLSS, MICS, and CVFS.
Advanced Data Management: Technical mastery of the Stata system, including data importation, cleaning raw files, coding variables, and utilizing transformation functions.
Inferential Statistics & Bivariate Analysis: Hypothesis testing logic (Null vs. Alternative), p-values, t-tests, ANOVA, and Chi-square associations.
Multivariate Modeling & Diagnostics: Application of OLS and Logistic Regression with focus on model assumptions and diagnostics.
Scientific Writing & Publication Strategy: Interpreting coefficients, formatting professional results tables, and navigating the publication process.
III. The "Capstone" Research Requirement
This workshop is strictly output-driven. Participants are researchers, not just students:
Selection: Every participant must identify a research topic and dataset prior to the training.
Application: Throughout the five days, you will use your own data for all hands-on exercises.
Output: By the conclusion of the workshop, you are expected to have a draft results section or a technical report ready for professional review.
IV. Requirements & Readiness
Because this program is intensive and moves toward advanced series with University of Michigan faculty, we are looking for candidates who are keen to strengthen their applied statistical tools, demonstrate clear research intent, and commit significant time to this endeavor.
Academic: Minimum of a Master’s degree with a background in basic statistics.
Technical: Prior computing knowledge is mandatory; background in social science research is an asset.
Commitment: Participants must complete the attached Application Form acknowledging the workshop's rigor.
V. Training Successfully Completed
The training workshop was successfully conducted with active participation from researchers and scholars. We sincerely thank all participants and collaborating institutions for contributing to the success of this program.
This event has now concluded. Information regarding future training opportunities under the "From Data to Discovery" series will be announced separately.
