Stata Software Course

Lesson 1 • Command Syntax Fundamentals

Teaches the standard command-varlist-if-in-options syntax structure. Correct syntax is the prerequisite for every subsequent operation in Stata.

Lesson 2 • Getting Help and Documentation

Demonstrates built-in help, search, and findit commands for self-directed learning. Equips students to resolve errors independently throughout the course.

Lesson 3 • Stata File Types and Formats

Introduces .dta, .do, .log, and .gph file types and their roles. Connects file management habits to reproducible, organized workflows.

Lesson 1 • Importing Data from Multiple Sources

Covers import delimited, import excel, and use commands for common formats. Builds the entry point for all subsequent data manipulation tasks.

Lesson 2 • Saving and Exporting Data

Demonstrates save, export delimited, and export excel for output workflows. Students learn version control habits that protect data integrity.

Lesson 3 • Variable Types and Storage Formats

Explains numeric, string, and date storage types and their memory implications. Correct typing prevents errors in calculations and merges downstream.

Lesson 4 • Labeling Variables and Values

Teaches variable labels, value labels, and notes for self-documenting datasets. Labeled data reduces misinterpretation during analysis and reporting.

Lesson 1 • Identifying and Handling Missing Values

Covers missing value codes, mvdecode, mvencode, and missingness patterns. Accurate missing-value handling prevents biased results in every analysis.

Lesson 2 • Merging and Appending Datasets

Covers merge 1:1, 1:m, m:1, and append for combining datasets. Correct merge type selection prevents duplicate or lost observations.

Lesson 3 • Generating New Variables

Covers generate, egen, and string functions for creating derived variables. New variables encode analytical constructs directly into the dataset.

Lesson 4 • Recoding and Replacing Variables

Teaches recode, replace with conditions, and clonevar for variable transformation. These tools reshape raw codes into analytically meaningful categories.

Lesson 1 • Exploratory Data Visualisation

Introduces histogram, box plot, scatter plot, and kdensity for visual exploration. Graphs reveal outliers and distributional features that statistics alone miss.

Lesson 2 • Cross-Tabulations and Frequency Tables

Teaches two-way tabulate, chi-square tests, and row/column percentages. Cross-tabs reveal associations between categorical variables before modelling.

Lesson 3 • Univariate Summary Statistics

Covers summarise, tabulate, and codebook for single-variable description. These commands form the first analytical step for any new dataset.

Lesson 4 • By-Group Analysis

Covers the by prefix, bysort, and table command for stratified summaries. Group-level statistics expose heterogeneity masked by aggregate measures.

Lesson 1 • Looping with foreach and forvalues

Demonstrates foreach over varlists and string lists, and forvalues over numeric ranges. Loops reduce code repetition across variables, groups, or datasets.

Lesson 2 • Conditional Logic with if and while

Covers if-else blocks and while loops for branching program flow. Conditional logic enables scripts to adapt to data conditions automatically.

Lesson 3 • Writing and Running Do-Files

Covers the do-file editor, run vs. do commands, and script organisation. Do-files are the foundation of reproducible research in Stata.

Lesson 4 • Log Files and Output Management

Teaches log using, log close, and translate for capturing and converting output. Logs create an auditable record of every analytical session.

Lesson 1 • Regression Diagnostics

Covers heteroscedasticity, multicollinearity, and influential observation tests. Diagnostics validate OLS assumptions and guide model corrections.

Lesson 2 • Post-Estimation Commands

Demonstrates predict, margins, and test for inference after regress. Post-estimation commands extract quantities of interest beyond raw coefficients.

Lesson 3 • Simple and Multiple OLS Regression

Covers regress syntax, coefficient interpretation, and model fit statistics. OLS is the baseline technique from which all advanced models extend.

Lesson 4 • Categorical Predictors and Interactions

Teaches factor variable notation (i. and c.) and interaction terms (##). Factor variables automate dummy coding and interaction specification.

Lesson 1 • Binary Outcome Models

Covers logit and probit estimation, marginal effects, and model fit. Binary models are required when the outcome is a zero-one indicator variable.

Lesson 2 • Ordered and Multinomial Models

Teaches ologit, oprobit, and mlogit for ordinal and nominal outcomes. These models handle outcomes with more than two unordered or ordered categories.

Lesson 3 • Survival and Duration Analysis

Introduces stset, sts graph, and streg for time-to-event data. Survival models account for censoring that standard regression ignores.

Lesson 4 • Count Data Models

Demonstrates poisson, nbreg, and zero-inflated variants for count outcomes. Count models respect the non-negative integer nature of count data.

Lesson 1 • Declaring Panel and Time-Series Data

Covers xtset and tsset for declaring panel and time-series structure. Correct declaration activates all xt and ts prefix commands and operators.

Lesson 2 • Time-Series Models: AR, MA, and ARIMA

Covers arima, ac, and pac commands for univariate time-series modelling. ARIMA models capture autocorrelation structure for forecasting and inference.

Lesson 3 • Dynamic Panel Models

Demonstrates xtabond2 and xtdpd for Arellano-Bond GMM estimation. Dynamic models include lagged outcomes and address endogeneity in panels.

Lesson 4 • Vector Autoregression and Cointegration

Introduces var, irf, and vecm for multivariate time-series systems. VAR and VECM capture dynamic interdependencies among multiple time series.