Data Analysis Using Stata Training Course

Participants can import common data formats and assess dataset structure before analysis.

• Importing Excel, CSV, delimited text, and native Stata datasets
• Variable types, storage formats, display formats, and value labels
• Codebook, describe, summarize, and inspect commands for initial data review
• Identifying identifier fields, duplicate records, and unexpected record counts
• Missing-value patterns examined with misstable summarize and tabulate commands
• Date strings converted into Stata daily, monthly, quarterly, and yearly dates

Participants can clean inconsistent variables and create analysis-ready derived fields.

• String functions used to standardise names, codes, categories, and text fields
• Destring and tostring conversions handled without losing meaningful leading zeroes
• Replace, recode, and generate commands for transparent variable transformation
• Missing values distinguished from valid zeroes, refusals, and not-applicable responses
• Outliers investigated using percentiles, box plots, and domain-specific thresholds
• Assertions and data checks used to detect impossible values early

Participants can combine related datasets and restructure data for analysis.

• Merge operations matched through unique identifiers and verified with _merge
• Append commands used to stack monthly, regional, and survey-wave files
• Collapse and contract commands used for aggregate indicator datasets
• Reshape wide and reshape long applied to repeated-measures data
• Preserve and restore used when testing alternative data transformations
• Unique identifiers checked before joining customer, transaction, and reference files

Participants can construct transparent indicators, categories and derived analytical measures.

• Binary indicators created from eligibility rules, thresholds, and event conditions
• Categorical bands built with recode, egen cut, and labelled values
• Egen functions used for group totals, ranks, means, and counts
• Ratios and rates calculated with explicit denominators and zero checks
• Standardised scores constructed after reviewing variable direction and scale
• Indicator definitions documented through labels, notes, and code comments

Participants can describe distributions and select appropriate summary measures.

• Mean, median, standard deviation, and interquartile range compared appropriately
• Percentile distributions examined using summarize detail and centile commands
• Skewness and kurtosis assessed before selecting statistical modelling approaches
• Grouped summaries produced with bysort, egen, collapse, and table
• Distributional differences examined across regions, products, demographic groups, and periods
• Confidence intervals reported alongside estimates where sampling uncertainty matters

Participants can produce clear one-way, two-way and comparative statistical tables.

• One-way frequency tables produced with tabulate, missing values, and labels
• Two-way cross-tabulations examined with row, column, and cell percentages
• Table command used to build multiway descriptive statistical outputs
• Collect framework used to format, label, and export statistical tables
• Subgroup comparisons presented with denominators, bases, and suppressed small cells
• Excel and Word tables exported through putexcel, putdocx, and collect export

Participants can select chart forms that accurately communicate analytical findings.

• Chart selection matched to comparison, composition, distribution, and relationship questions
• Bar charts designed with ordered categories, baselines, and readable labels
• Histograms and kernel densities used to examine continuous variable distributions
• Box plots used to compare spread, median, outliers, and group distributions
• Scatterplots used to inspect associations, clusters, nonlinearity, and extreme observations
• Graph schemes, titles, notes, legends, and colour choices applied consistently

Participants can build layered Stata graphics and export them for professional reporting.

• Twoway graphics combine scatterplots, fitted lines, confidence bands, and annotations
• Connected line charts display trends across dates, categories, and comparison groups
• Marginsplot visualises adjusted predictions after regression and interaction models
• By() panels compare distributions and trends without misleading scale changes
• Graph combine assembles related figures into report-ready analytical panels
• Vector and raster formats selected for Word, PowerPoint, web, and print output

Participants can conduct common hypothesis tests and state results accurately.

• Sampling distributions, standard errors, confidence intervals, and p-values interpreted correctly
• One-sample and paired t-tests applied to mean comparison questions
• Two-sample t-tests conducted with equal-variance and unequal-variance assumptions
• Proportion tests used for rates, uptake, defaults, and service outcomes
• Chi-square tests assess association between categorical variables in contingency tables
• Statistical significance distinguished from practical importance and decision relevance

Participants can estimate, interpret and report multiple linear regression models.

• Ordinary least squares regression specified from analytical questions and theory
• Dependent variables, predictors, controls, and reference categories defined explicitly
• Factor-variable notation handles categorical predictors and interaction terms correctly
• Coefficient estimates interpreted as conditional associations with stated units
• Predicted values and marginal effects calculated after fitted regression models
• Regression tables exported with coefficients, confidence intervals, observations, and fit statistics

Participants can diagnose common linear regression problems and choose corrective actions.

• Residual plots assessed for linearity, normality, variance, and unusual patterns
• Heteroskedasticity tested using Breusch-Pagan and White test procedures
• Variance inflation factors used to assess multicollinearity among predictors
• Influential observations examined through leverage, Cook's distance, and dfbeta measures
• Functional form checked using residual-versus-fitted plots and added-variable plots
• Robust standard errors selected when heteroskedasticity affects inferential estimates

Participants can model binary outcomes and communicate probability-based findings.

• Logistic regression used for default, dropout, adoption, and event outcomes
• Odds ratios distinguished from probabilities, risks, and marginal effects
• Linktest and classification tables used to assess model specification
• Receiver operating characteristic curves assess discriminatory model performance
• Predicted probabilities calculated across meaningful values of key predictors
• Interaction effects interpreted using margins and marginsplot rather than coefficients alone

Participants can select and interpret models for non-continuous outcome variables.

• Poisson regression modelled for counts of claims, visits, incidents, and cases
• Negative binomial models used when count data show overdispersion
• Ordered logistic regression applied to ranked satisfaction and risk categories
• Multinomial logistic regression used for unordered outcome classification
• Model choice guided by outcome measurement scale and distributional evidence
• Marginal effects translated into predicted changes for nontechnical audiences

Participants can analyse complex survey data using weights, strata and clusters.

• Survey designs defined through weights, primary sampling units, and strata
• Svyset declarations prepared for complex household and institutional surveys
• Weighted means, proportions, and totals estimated with svy commands
• Design effects and standard errors interpreted under complex sample designs
• Subpopulation analysis conducted without incorrectly dropping non-target observations
• Survey tabulations reported with weighted bases, confidence intervals, and caveats

Participants can prepare and estimate models using repeated observations over units and time.

• Panel identifiers and time variables declared using xtset correctly
• Within-unit change distinguished from cross-sectional differences between units
• Fixed-effects models estimate associations after controlling for time-invariant characteristics
• Random-effects models assessed alongside fixed-effects alternatives using Hausman tests
• Lagged variables created carefully after verifying panel completeness and ordering
• Clustered standard errors applied where repeated observations violate independence

Participants can prepare time-series data and conduct introductory trend and forecast analysis.

• Time indexes declared with tsset after checking gaps and duplicates
• Trend, seasonality, and irregular variation examined through line graphs
• Lag and difference operators used to model temporal relationships
• Stationarity assessed using plots, autocorrelation functions, and unit-root tests
• Autoregressive models estimated for short operational forecasting applications
• Forecast accuracy compared using residuals, mean absolute error, and holdout periods

Participants can assess missing data mechanisms and document defensible treatment decisions.

• Item nonresponse patterns profiled by variable, subgroup, period, and source
• Missing completely at random, MAR, and MNAR assumptions distinguished
• Complete-case analysis assessed for sample loss and potential selection bias
• Multiple imputation introduced using chained equations and diagnostic comparisons
• Sensitivity analyses compare findings under alternative missing-data treatment assumptions
• Data-quality decisions documented for review, replication, and future updates

Participants can automate recurring outputs and implement checks before publication.

• Loops automate repeated tables, graphs, models, and subgroup analyses
• Foreach and forvalues syntax applied to variable lists and sequences
• Stored estimates compared across model specifications using estimates store
• Return codes and capture commands used for controlled error handling
• Automated checks verify row counts, ranges, identifiers, and output completeness
• Reporting scripts separate raw data, cleaned data, outputs, and final documents

Participants can communicate statistical results accurately to technical and nontechnical audiences.

• Analytical questions linked explicitly to indicators, models, findings, and decisions
• Tables and charts selected to support a single stated message
• Effect sizes, uncertainty, limitations, and assumptions reported without overclaiming
• Technical appendices document sources, transformations, model specifications, and diagnostics
• Executive summaries distinguish descriptive patterns from causal claims clearly
• Peer review criteria applied to code, outputs, interpretation, and presentation

Participants can build, review and present a complete reproducible Stata analysis addressing a defined business, policy or programme question.

• Analytical question framed around a defined decision, population, and outcome
• Raw data imported, cleaned, documented, and transformed through reproducible do-files
• Descriptive tables and graphics developed to establish key patterns and data limitations
• Statistical model selected, estimated, diagnosed, and interpreted against stated assumptions
• Findings presented through report-ready outputs, conclusions, limitations, and recommendations
• Peer review evaluates code quality, analytical validity, visual clarity, and presentation