Online Training

Stata NetCourse and NetCourseNow ™ web-based courses

Stata now offers on-line training which can be done from the comfort of your own home.

NETCOURSE Stata NetCourses are web-based training courses all about using Stata. They vary in length from six to seven weeks. You must have an email address and a web browser to participate. Stata NetCourses are not conducted in real time, so you are not required to be online participating at any specific time. Instead, lessons are posted every Friday, and participants can view/work on course materials at their convenience anytime thereafter. After reading the lesson, participants can post questions and comments to the course discussion area. Course leaders respond to the questions and comments on Tuesday and Thursday. The other participants are encouraged to respond to the questions or comments, as well. After the last lesson, discussion continues for a few weeks until the course concludes.

NETCOURSENOW Would you like to choose the time and set the pace of a NetCourse? Would you like to have a personal NetCourse instructor? Then, enrol in a NetCourseNow. All lessons are posted at once, and you will be given the email address of your personal NetCourse instructor to whom you can email questions about the lessons. Enroll now, and begin when you're ready.

Prerequisites for courses:

  • Stata 14 installed and working.
  • Basic knowledge of using Stata interactively.
  • Internet web browser, installed and working.

NOTE: The courses are platform and Stata flavour independent and are suitable for Windows, Mac and Linux users of Stata.

Available Stata NetCourses and NetCourseNow ™ Courses

An introductory, six-week (4 lessons) interactive course that teaches you how to use Stata. 

Through a combination of lessons, example applications, and carefully chosen problems, the course covers the basic commands necessary to be most productive in the Stata environment.

Learn how to use all of Stata's tools and become a sophisticated Stata user. You will understand the Stata environment, how to import and export data from different formats, how Stata's intuitive syntax works, data management in Stata, matching and merging, how to analyze subgroups of data, how to reproduce your work and document it for publication and review, how to interact with the Stata community online, and more.

Videos, lessons, examples, and exercises combine to develop your Stata skills.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form)

Prerequisites:

  • Stata 14 installed and working.
  • Knowledge of your computer.

Note: Course is platform independent.

Lesson 1
  • Sample session
  • Main Stata window
  • Menus and dialogs
  • Command and Results windows
  • Review window
  • Variables window
  • Data Editor
  • Renaming and formatting variables
  • Hide, show, and order your data
  • The Viewer
  • Installing user-written commands
  • Do-file Editor
  • Graph window and Editor
Lesson 2
  • Current working directory
  • Loading and saving data
  • Working with data
  • Stata’s command syntax
  • The data in memory
  • Value labels
  • Formats
  • Notes
  • Other kinds of labels
  • Keeping logs
  • Safe computing
  • Basic data reporting
  • Data manipulation
  • Making strings into numerical categorical variables
  • Doing things the long way
  • The second solution
  • The best solution
  • Observation subscripts _n and _N
  • Memory management
Lesson 3
  • Obtaining data
  • Import and export Excel data
  • import excel
  • export excel
  • Import and export text data
  • import delimited
  • export delimited
  • Other commands to import and export data
  • Import and export data from and to a database (ODBC)
  • Undelimited text data
  • Import and export SAS XPORT files
  • Import Haver databases
  • Transfer programs
  • Reading dates and times
Lesson 4
  • Appending data
  • Merging data
  • Notes about merge
  • Merge with repeated variables
  • An example of append and merge
  • Wide versus long data
  • How to think about variables and their contents

Note: There is an one-week break between lessons 2 and 3 in this course for extra discussion.


Enrol:

Course Price in $AUD (incl GST)

Learn how to communicate your data with Stata's powerful graphics features. This course will introduce different kinds of graphs and demonstrate how to use them for exploratory data analysis.

Topics include how to use graphs to check model assumptions, how to format, save, and export your graphs for publication using the Graph Editor, how to create custom graph schemes, how to create complex graphs by layering and combining multiple graphs, how to use margins and marginsplot, and more.

Bonus material includes information on user-written graph commands and useful data management tools.

Learn how to communicate your data with Stata's powerful graphics features. This course will introduce different kinds of graphs and demonstrate how to use them for exploratory data analysis. Topics include how to use graphs to check model assumptions, how to format, save, and export your graphs for publication using the Graph Editor, how to create custom graph schemes, how to create complex graphs by layering and combining multiple graphs, how to use margins and marginsplot, and more. Bonus material includes information on user-written graph commands and useful data management tools.

Requirements: Must have access to Stata 14 or higher (licence number to be quoted on order form)

Prerequisites:

  • Stata 14 installed and working.
  • Knowledge of your computer.

Note: Course is platform independent.

Lesson 1: Getting to know your data using graphs

Introduction

  • Why graphs are an important tool for exploratory data analysis.
  • Data management tools for graphing data.
  • How to create and edit basic graphs using Stata
  • How to create and edit graphs with dialog boxes
  • How to edit graphs with the Graph Editor
  • How to create and edit graphs with commands
  • Some basic graphs
  • Graphs for one continuous variable
  • Graphs for one categorical variable
  • Graphs for two continuous variables
  • Graphs for two categorical variables
  • Graphs for one continuous and one categorical variable
  • Graphs for many variables
  • Storing, saving, and exporting graphs
  • Storing graphs in memory
  • Saving graphs to disk
  • Exporting graphs in .png format
  • Automating the process: Looping and saving
Lesson 2: Understanding your results using graphs
  • Model checking using graphs
  • Using the predict command
  • Checking model assumptions
  • Checking the normality assumption
  • Checking the linearity assumption
  • Checking the homoskedasticity assumption
  • Identifying outliers and influential observations
  • Visualizing the results of your models
  • Using the margins and marginsplot commands
  • A brief review of factor variables
  • Categorical independent variables
  • Multiple categorical independent variables
  • Continuous independent variables
  • Continuous and categorical independent variables
  • Average response versus response at average: The atmeans option
  • Contrasts of margins
  • Marginal effects: Margins of derivatives of responses
  • Using contour plots to visualize continuous-by-continuous interactions
Lesson 3: Formatting graphs for publication
  • Formatting titles, legends, and text boxes
  • Formatting titles
  • Formatting legends
  • Adding text boxes
  • Using italics, bold, superscripts, and subscripts
  • Using specialty characters
  • Using different fonts
  • Formatting numbers
  • Formatting axes, axis labels, ticks, gridlines, graph, and plot regions
  • Formatting categorical axis labels
  • Formatting the x and y axes
  • Formatting the x- and y-axis labels
  • Formatting major and minor ticks and gridlines
  • Adding reference lines
  • Formatting the graph and plot regions
  • Controlling the aspect ratio and size of graphs
  • Using schemes to change the overall look of graphs
  • Using built-in schemes
  • Defining your own schemes
  • Recording and saving edits in the Graph Editor
Lesson 4: Advanced graphs: How to layer and combine multiple graphs
  • Layering multiple graphs with the graph twoway command
  • Basic layered graphs with one y axis
  • Advanced layered graphs with one y axis
  • Basic layered graphs with two y axes
  • Advanced layered graphs with two y axes
  • Layering multiple graphs with the addplot() option
  • Creating multiple graphs with the by() option
  • Combining different graphs with the graph combine command
  • Making a table of separate graphs
  • Making a single complex graph from separate graphs
  • Exporting graphs for publication
  • Exporting graphs in pixel-based formats
  • Exporting graphs in vector-based formats

Note: The previous four lessons constitute the core material of the course. The following material is optional and introduces user-written graphic commands and useful data management tools.

  • User-written graph commands
  • The Statistical Software Components (SSC) archive
  • The coefplot package by Ben Jann
  • How to write a simple graphics wrapper command
  • How to create animated graphs
  • Some fun graphs
  • How to show scatterplots with regression lines and residuals
  • How to add normal curves to regression lines
  • How to graph a histogram with a box plot
  • The destring and encode commands
  • The recode command
  • The tabulate command with the generate() option
  • The egen command
  • The contract and collapse commands
  • The statsby command
  • The snapshot command
  • The reshape command
  • Macros and loops
  • Extracting value labels to local macros

Note: There is a one-week break between lessons 2 and 3 in this course for extra discussion.

Enrol:

Course Price in $AUD (incl GST)

An introductory, six-week interactive course that teaches Stata data-analysis programming to those who have a basic knowledge of how to use Stata.

This course is intended for Stata users who wish to learn about key programming topics such as macro processing, program flow of control, using ado-files, programming ado-files, Monte-Carlo simulation and bootstrapped standard errors.

Become an expert in organizing your work in Stata. Make the most of Stata's scripting language to improve your workflow and create concretely reproducible analyses. Learn how branching, looping, flow of control, and accessing saved estimation results can speed up your work and lead to more complete analyses. Learn about bootstrapping and Monte Carlo simulations, too.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form) and a basic knowledge of how to use Stata.

Prerequisites:

  • Stata 14 installed and working
  • Knowledge of your computer

Note: Course is platform independent.

Lesson 1: Organization of analysis
  • Entering and executing a program
  • The do-file
  • The interactive program command
  • A program in a do-file
  • Combination do-files
  • Ado-files
  • Organizing do-files
  • An individual do-file
  • A do-file to perform verification
  • Infiling data
  • Reproducibility
  • Indexing
  • assert as an alternative to branching
  • Consuming calculated results
Lesson 2: Macros, arguments, and looping
  • Macros
  • How macros might be used
  • Macro names
  • The related-persons example
  • Another example (plant data)
  • Potential problem—variable scope
  • More on arguments
  • Branching and looping
  • Physical program style
  • foreach
  • Looping across observations
  • if
Lesson 3: Examples and applications
  • Data management example
  • Handling time and date variables
  • Checking assumptions
  • Returned values and saving results
  • What can be returned in r()?
  • Referring to returned results in other programs
  • Referring to returned results in the program that sets them
  • Bootstrapped standard errors
  • Aside: reading a trace
  • A warning on bootstrapping
  • Speeding up bootstrapping
  • Bootstrapping, how to
  • Monte Carlo simulations
  • postfile and post
  • Using quietly
  • Speeding up simulations
Lesson 4: Ado-files
  • A first real ado-file
  • discard
  • More improvements to doanl
  • capture
  • The exit command
  • Making doanl a general tool
  • Writing a help file for doanl
  • Do-files, programs, and ado-files: when to use which
  • Temporary variables
  • Temporarily destroying data
  • Temporary files
  • An analysis-specific ado-file
  • General-purpose (GP) ado-files
  • A GP ado-file
  • Fine tuning display output
  • Stata syntax
  • syntax
  • varlist macro
  • syntax’s other specifiers
  • Whether to use syntax
  • A note on quotes
  • Version control

Note: There is a one-week break between lessons 2 and 3 in this course for extra discussion.

Enrol:

Course Price in $AUD (incl GST)

An advanced, seven-week course that teaches how to add new commands to Stata, for those who understand the basics of Stata programming.

This course covers advanced issues of programming in the Stata language focusing on writing commands for general use.

Learn how to create and debug your own commands that are indistinguishable from the commands in Stata. You will be able to parse both standard and nonstandard Stata syntax using the intuitive syntax command, to manage and process saved results, to post your own saved results, to process by-groups, to create data management commands, to program your own maximum-likelihood estimator, and more. In short, learn to create commands that act just like the commands that ship with Stata.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form) and a basic knowledge of how to use Stata.

Prerequisites:

  • Course content of NetCourse 151 or equivalent knowledge
  • Stata 14 installed and working
  • Knowledge of your computer

Note: Course is platform independent.

Lesson 1: Parsing Stata syntax/Stata programming basics
  • Review of Stata’s programming features
  • Parsing
  • Parsing options
  • Parsing complicated syntax
  • Aside on subprograms
Lesson 2: Parsing Stata syntax, continued/Quotes, returned results, and subsamples
  • Quotes
  • Development continues
  • Temporary variables
  • Development continues
  • An aside concerning r()
  • Programming the formulas
  • Putting it togeter
Lesson 3: Using scalars and macros and introduction to low-level parsing
  • What you must learn
  • Scalars
  • Binary accuracy
  • Accuracy of macros versus scalars
  • Converting a program from macros to scalars
  • Handling by() options
  • Sorting
  • Low-level parsing
  • Programming immediate commands
  • Rewriting mytt in terms of mytti
Lesson 4: Returning results and writing estimation commands
  • Where are we?
  • Stored results
  • What can be returned in r()?
  • Referring to returned results in other programs
  • Referring to returned results in the program that sets them
  • Other types of returned values: s() and e()
  • S-class returned values
  • E-class returned results
  • Writing postestimation commands
  • Writing an estimation (e-class) command
  • An alternative estimation command outline
  • Writing estimation commands from first principles
  • Writing estimation commands via maximum likelihood
Lesson 5: List processing, controlling program output, and naming conventions
  • Restricting commands to the relevant subsample
  • Which is better: marksample or mark?
  • Programming by varlist
  • Lists
  • Creating lists
  • Stepping through list elements one by one
  • Deleting elements from lists
  • Adding elements to lists
  • Macro vectors
  • Parsing revisited: gettoken
  • Quietly blocks
  • The relation between capture and quietly
  • Capture blocks
  • Naming conventions
  • Program naming convention
  • Calling convention
  • Version control

Note: There is a week break between lessons 3 and 4 to allow for extra discussion.

Enrol:

Course Price in $AUD (incl GST)

This seven week course will appeal to a broad range of users, including economists, forecasters, business analysts and anyone who encounters time-series data.

This course covers topics such as using time-series data in Stata, moving averages and exponential smoothers, ARMA processes, autocorrelation and partial autocorrelation functions, ARIMA and ARMAX models, regression analysis, unit roots and ARCH models, as well as Stata's multivariate time-series features.

Learn about univariate time-series analysis with an emphasis on the practical aspects most needed by practitioners and applied researchers. Written for a broad array of users, including economists, forecasters, financial analysts, managers, and anyone who wants to analyze time-series data. Become expert in handling date and date-time data; time-series operators; time-series graphics, basic forecasting methods; ARIMA, ARMAX, and seasonal models.

We provide lesson material, detailed answers to the questions posted at the end of each lesson, and access to a discussion board on which you can post questions for other students and the course leader to answer.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form) and a basic knowledge of how to use Stata.

Prerequisites:

  • Course content of NetCourse 101 or equivalent knowledge.
  • Familiarity with basic cross-sectional summary statistics and linear regression.
  • Stata 14 installed and working.
  • Knowledge of your computer.

Note: Course is platform independent.

Lesson 1: Introduction
  • Course outline
  • Follow along
  • What is so special about time-series analysis?
  • Time-series data in Stata
  • The basics
  • Clocktime data
  • Time-series operators
  • The lag operator
  • The difference operator
  • The seasonal difference operator
  • Combining time-series operators
  • Working with time-series operators
  • Parentheses in time-series expressions
  • Percentage changes
  • Drawing graphs
  • Basic smoothing and forecasting techniques
  • Four components of a time series
  • Moving averages
  • Exponential smoothing
  • Holt–Winters forecasting
Lesson 2: Descriptive analysis of time series
  • The nature of time series
  • Stationarity
  • Autoregressive and moving-average processes
  • Moving-average processes
  • Autoregressive processes
  • Stationarity of AR processes
  • Invertibility of MA processes
  • Mixed autoregressive moving-average processes
  • The sample autocorrelation and partial autocorrelation functions
  • A detour
  • The sample partial autocorrelation function
  • A brief introduction to spectral analysis—The periodogram
Lesson 3: Forecasting II: ARIMA and ARMAX models
  • Basic ideas
  • Forecasting
  • Two goodness-of-fit criteria
  • More on choosing the number of AR and MA terms
  • Seasonal ARIMA models
  • Additive seasonality
  • Multiplicative seasonality
  • ARMAX models
  • Intervention analysis and outliers
  • Final remarks on ARIMA models
Lesson 4: Regression analysis of time-series data
  • Basic regression analysis
  • Autocorrelation
  • The Durbin–Watson test
  • Other tests for autocorrelation
  • Estimation with autocorrelated errors
  • The Newey-West covariance matrix estimator
  • ARMAX estimation
  • Cochrane-Orcutt and Prais-Winsten methods
  • Lagged dependent variables as regressors
  • Dummy variables and additive seasonal effects
  • Nonstationary series and OLS regression
  • Unit-root processes
  • ARCH
  • A simple ARCH model
  • Testing for ARCH
  • GARCH models
  • Extensions

Note: The previous four lessons constitute the core material of the course. The following lesson is optional and introduces Stata’s multivariate time-series capabilities.

  • VARs
  • he VAR(p) model
  • Lag order selection
  • Diagnostics
  • Granger causality
  • Forecasting
  • Impulse-response functions
  • Orthogonalized IRFs
  • VARX models
  • VECMs
  • A basic VECM
  • Fitting a VECM in Stata
  • Impulse-response analysis

Enrol:

Course Price in $AUD (incl GST)

Learn the analysis and implementation of linear, nonlinear and dynamic panel-data estimators in Stata.

This six week course focuses on the interpretation of panel-data estimates and the assumptions underlying the models that give rise to them. The course is geared for researchers and practitioners in all fields.

Become an expert in the analysis and implementation of linear, nonlinear, and dynamic panel-data estimators using Stata. This course focuses on the interpretation of panel-data estimates and the assumptions underlying the models that give rise to them. The course is geared for researchers and practitioners in all fields. The breadth of the lessons will be helpful if you want to learn about panel-data analysis or if you are familiar with the subjects.

The concepts presented are reinforced with practical exercises at the end of each section. We also provide additional exercises at the end of each lecture and access to a discussion board on which you can post questions for other students and the course leaders to answer.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form) and a basic knowledge of how to use Stata.

Prerequisites:

  • Course content of NetCourse 101 or equivalent knowledge
  • Familiarity with basic time-series, cross-sectional summary statistics and linear regression
  • Stata 14 installed and working
  • Knowledge of your computer

Note: Course is platform independent.

Lesson 1
  • An introduction to panel data and its features
  • Getting started with panel data
  • Summary statistics and dynamics
  • Overview of basic concepts
  • Data generation
  • The regression model
  • Variance-covariance estimators
  • Margins and marginal effects
  • Basic panel-data estimation concepts
  • Moment-based estimation
  • Panel data, regression, and efficiency
Lesson 2
  • Random-effects model
  • Fixed-effects models
  • Deciding between random and fixed effects
  • Hausman test
  • Mundlak test
  • Population-averaged models
Lesson 3
  • Probit model
  • Probit models for panel data: Random effects
  • Probit models for panel data: Population averaged
  • Probit models for panel data: Remarks
  • Logit model
  • Logit models for panel data: Random effects
  • Logit models for panel data: Fixed effects
  • Logit models for panel data: Population averaged
  • Poisson model
  • Poisson models for panel data
Lesson 4
  • Endogeneity
  • Cross-sectional estimation under endogeneity
  • Panel-data estimation under endogeneity
  • Dynamic models
  • Building your own dynamic models
  • A more complex dynamic structure
  • Concluding remarks

Note: There is a week-long break between lessons 2 and 3 for extra discussion.

Enrol:

Course Price in $AUD (incl GST)

Written for everyone who uses Stata, whether health researchers or social scientists and anyone who encounters Survival Analysis.

Learn how to effectively analyse survival data using Stata. The course covers censoring, truncation, hazard rates and survival functions. Topics include data preparation, descriptive statistics, life tables, Kaplan–Meier curves, and semiparametric (Cox) regression and parametric regression. Discover how to set the survival-time characteristics of your dataset just once then apply any of Stata's many estimators and statistics to that data.

Become an expert in the analysis and implementation of linear, nonlinear, and dynamic panel-data estimators using Stata. This course focuses on the interpretation of panel-data estimates and the assumptions underlying the models that give rise to them. The course is geared for researchers and practitioners in all fields. The breadth of the lessons will be helpful if you want to learn about panel-data analysis or if you are familiar with the subjects.

The concepts presented are reinforced with practical exercises at the end of each section. The course also provides additional exercises at the end of each lesson and access to a discussion board on which you can post questions for other students and the course leaders to answer.

Requirements: Must have access to Stata 14 (licence number to be quoted on order form) and a basic knowledge of how to use Stata.

Prerequisites:

  • Course content of NetCourse 101 or equivalent knowledge
  • Stata 14 installed and working
  • Knowledge of your computer

Note: Course is platform independent.

Lesson 1
  • An introduction to panel data and its features
  • Getting started with panel data
  • Summary statistics and dynamics
  • Overview of basic concepts
  • Data generation
  • The regression model
  • Variance-covariance estimators
  • Margins and marginal effects
  • Basic panel-data estimation concepts
  • Moment-based estimation
  • Panel data, regression, and efficiency
  • Closing remarks
Lesson 2
  • Random-effects model
  • The model
  • Fixed-effects model
  • Within estimator
  • Comparing within and random-effects estimates
  • First-differenced estimator
  • Deciding between random and fixed effects
  • Hausman test
  • Mundlak test
  • Population-averaged model
Lesson 3
  • Probit model
  • Probit models for panel data: Random effects
  • Probit models for panel data: Population averaged
  • Probit models for panel data: Remarks
  • Logit model
  • Logit models for panel data: Random effects
  • Logit models for panel data: Fixed effects
  • Logit models for panel data: Population averaged
  • Poisson model
  • Poisson models for panel data
Lesson 4
  • Endogeneity
  • Cross-sectional estimation under endogeneity
  • Panel-data estimation under endogeneity
  • Dynamic models
  • Building your own dynamic models
  • A more complex dynamic structure

Note: There is a week-long break between lessons 2 and 3 for extra discussion.

Enrol:

Course Price in $AUD (incl GST)

NetCourseNow is a flexible self-paced web-based training. In NetCourseNow you receive a personal instructor to answer questions by email. You have six months to complete the training following registration.


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