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The course introduces student to data management and analysis skills through usage of STATA software. Researchers in public health and health services research rely on STATA because of its breadth, reproducible, and ease of use. Whether you study interventions to address obesity, investigate small-area variations in care, or conduct program evaluation, STATA provides support for a wide variety of study designs. It also gives you data management tools specifically designed for health research and the ability to make publication-quality graphics for presentations.

This hands-on and learner-centered training is intended for learners who have basic computer knowledge and wish to learn data management, research and statistical techniques using popular statistical packages. Among other skills, the training intends to introduce

  • Understanding variables and measurement
  • Setup design & data entry
  • Data manipulation & cleaning techniques
  • Descriptive statistical analyses.
  • Inferential/Predictive analysis

Course Outline


Module 1: Getting Started

  • Tour of the Stata 15 interface
  • Copy/paste data from Excel into Stata
  • Import Excel data into Stata
  • Saving estimation results to Excel
  • Importing delimited data

Module 2: Data Management

  • Creating variable
  • Changing and renaming variables
  • Convert a string variable to a numeric variable
  • Convert categorical string variables to labeled numeric variables
  • Create a categorical variable from a continuous variable
  • Convert missing value codes to missing values
  • Combining data
  • Merging files into a single dataset
  • Append files into a single dataset
  • Creating and dropping variables
  • Create a new variable that is calculated from other variables
  • Identify and replace unusual data values
  • Create a date variable from a date stored as a string
  • Optimize the storage of variables
  • Round a continuous variable
  • Stata’s Expression Builder
  • Examining data
  • Identify and remove duplicate observations
  • Labeling, display formats, and notes
  • Label variables
  • Add notes to a variable
  • Reshaping datasets

Module 3: Graph

  • Bar graphs
  • Box plots
  • Histograms
  • Pie charts
  • Basic scatterplots
  • Modifying graphs using the Graph Editor

Module 4: Descriptive Data Analysis

  • Measures of Central Tendency
  • Measures of Dispersion
  • Kurtosis
  • Skewness

Module 5: Probabilities & Comparing Means

  • Hypothesis Testing
  • One-sample t test
  • t test for two paired samples
  • t test for two independent samples
  • One-way ANOVA
  • Pearson’s chi2 and Fisher’s exact test
  • Pearson’s correlation coefficient

Module 6: Case–Control Studies

  • Odds ratios for case–control data
  • Stratified analysis of case–control data

Module 7: Regression

  • Simple Linear Regression
  • Multiple Regression
  • Logistic Regression

Module 8: Survival Analysis

  • Interval-censored survival models
  • Learn how to set up your data for survival analysis
  • Describe and summarize survival data
  • Constructing life tables
  • Calculating incidence rates and incidence-rate ratios
  • Calculating the Kaplan-Meier survivor and Nelson-Aalen cumulative hazard functions
  • Plotting graph survival curves
  • Testing the equality of survivor functions using nonparametric tests
  • Fit a Cox proportional hazards model and check proportional-hazards assumption