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The training objective of these two weeks Training in Analysis of Complex Sample Survey Data will impact skills that are of very high demand in Data Management & Analysis. This course provides an introduction to specialized software procedures that have been developed for the analysis of complex sample survey data including testing for between-group differences in means and proportions, regression analysis, logistic regression and multilevel modelling.

Most standard statistical procedures in software packages commonly used for data analysis (e.g. SAS, SPSS, and STATA) do not allow the analyst to take into account characteristics often associated with survey data, including missing data, unequal probabilities of selection, stratified multistage sample designs, and measurement errors.

WHO SHOULD ATTEND
  • Statisticians & Researchers
  • Planners & Monitors and Evaluators
  • NGOS & Government Ministries
  • Project Managers
LEARNING OBJECTIVES
  1. The participant will understand both descriptive and inferential statistics
  2. The participant will learn various data collection techniques and data processing methods
  3. The participant will learn how to use mobile phones for data collection(Open data Kit) ODK
  4. The participant will learn the basic functions and navigation within STATA
  5. The participant will learn how to create and manipulate graphs and figures in STATA
  6. The participant will learn annotate data, set out command functions, create and execute Do files.
  7. The participant will learn how to use STATA effectively for manipulating and analysing data.
  8. The participant will learn how to keep records of your work and create reproducible analyses.
  9. The participant will learn how to export the results they analyse.

Outline

  • Sampling error calculation models; ultimate clusters
  • Sampling error estimation for descriptive statistics
  • Replication Methods for Variance Estimation
  • Estimation and inference for special statistics (percentiles, indices)
  • Methods for Categorical Data
  • Linear Regression Analysis
  • Logistic Regression Analysis
  • Multinomial, ordinal logistic regression
  • Poisson and negative binomial regression
  • Survival analysis and event history analysis
  • Multiple imputation inference for survey data
  • Multi-level models for complex sample survey data.

SURVEY ESTIMATION AND INFERENCE FOR COMPLEX DESIGNS

  • Complex sample designs, survey estimation and inference
  • Multi-stage designs, stratification, cluster sampling, weighting, item missing data, finite

population corrections

  • Models and assumptions for inference from complex sample survey data
  • Sampling distributions, confidence intervals
  • Design effects.
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