Training on Analysis of Complex Sample Survey Data Using Stata

Module 1: Introduction to Complex Survey Design and Stata Setup

• Understanding survey sampling frameworks and design features

• Overview of Stata interface and data structures

• Importing and preparing survey datasets for analysis

• Case study: Preparing DHS data for survey analysis

Module 2: Data Cleaning and Management for Complex Surveys

• Managing missing values, outliers, and data inconsistencies

• Creating survey design objects (weights, strata, and PSU)

• Merging and appending multiple datasets

• Case study: Managing multi-round household data for consistency

Module 3: Descriptive Analysis and Cross-tabulation Using svy Commands

• Weighted frequency distributions and summary statistics

• Cross-tabulations and proportions with standard errors

• Domain and subgroup analysis

• Case study: Analyzing education indicators using household data

Module 4: Regression Modeling for Survey Data

• Linear, logistic, and multinomial regressions with survey weights

• Adjusting for clustering and stratification

• Interpreting and reporting model outputs

• Case study: Modeling determinants of health outcomes in a complex survey

Module 5: Advanced Applications and Report Generation

• Variance estimation and design effects

• Post-estimation commands and marginal effects

• Automating analysis workflows and visualizing results

• Case study: Producing policy-relevant outputs for a national survey report

Module 6: Survey Data Visualization and Policy Communication

• Creating tables, graphs, and dashboards for survey data

• Translating technical findings into policy messages

• Quality assurance and reproducibility best practices

• Case study: Visual presentation of poverty and inequality findings

Module 7: Multilevel and Longitudinal Analysis with Complex Surveys

• Introduction to multilevel models for nested data

• Panel survey data analysis concepts

• Case study: Tracking household welfare changes across survey waves

Module 8: Combining Qualitative and Quantitative Data for Mixed Methods

• Integrating qualitative data for richer interpretation

• Linking survey analysis with contextual insights

• Case study: Merging perception data with quantitative indicators

Module 9: Hands-on Project Work

• Participants apply learned skills on real datasets

• Data cleaning, weighting, and analysis of selected indicators

• Peer review and feedback on analytical outputs

Module 10: Presentation and Interpretation of Findings

• Structuring analytical reports and executive summaries

• Visualization best practices for policymakers and donors

• Final review and wrap-up session