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The Essential Guide to Stata

F. Buscha

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3).

The Essential Guide to Data Analytics with Stata

Learning and applying new statistical techniques can be daunting experience.

Read more

Make sure to check out my twitter feed for monthly promo codes and other updates (@easystats3).

The Essential Guide to Data Analytics with Stata

Learning and applying new statistical techniques can be daunting experience.

This is especially true once one engages with “real life” data sets that do not allow for easy “click-and-go” analysis, but require a deeper level of understanding of programme coding, data manipulation, output interpretation, output formatting and selecting the right kind of analytical methodology.

In this course you will receive a comprehensive introduction to Stata and its various uses in modern data analysis. You will learn to understand the many options that Stata gives you in manipulating, exploring, visualizing and modelling complex types of data. By the end of the course you will feel confident in your ability to engage with Stata and handle complex data analytics. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques without resorting to deep statistical theory or equations.

This course will focus on providing an overview of data analytics using Stata.

No prior engagement with is Stata needed. Some prior statistics knowledge will help but is not necessary.

The course is aimed at anyone interested in data analytics using Stata.

Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.

Some basic quantitative/statistical knowledge will be required; this is not an introduction to statistics course but rather the application and interpretation of such using Stata.

Topics covered include:

  1. Getting started with Stata

  2. Viewing and exploring data

  3. Manipulating data

  4. Visualising data

  5. Correlation and ANOVA

  6. Regression including diagnostics (Ordinary Least Squares)

  7. Regression model building

  8. Hypothesis testing

  9. Binary outcome models (Logit and Probit)

  10. Fractional response models (Fractional Logit and Beta Regression)

  11. Categorical choice models (Ordered Logit and Multinomial Logit)

  12. Simulation techniques (Random Numbers and Simulation)

  13. Count data models (Poisson and Negative Binomial Regression)

  14. Survival data analysis (Parametric, Cox-Proportional Hazard and Parametric Survival Regression)

  15. Panel data analysis (Long Form Data, Lags and Leads, Random and Fixed Effects, Hausman Test and Non-Linear Panel Regression)

  16. Difference-in-differences analysis (Difference-in-Difference and Parallel Trends)

  17. Instrumental variable regression (Endogenous Variables, Sample Selection, Non-Linear Endogenous Models)

  18. Epidemiological tables (Cohort Studies, Case-Control Studies and Matched Case-Control Studies)

  19. Power analysis (Sample Size, Power Size and Effect Size)

  20. Matrix operations (Matrix operators, Matrix functions, Matrix subscripting)

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What's inside

Learning objectives

  • An essential introduction to stata
  • Data manipulation in stata
  • Data visualisation in stata
  • Data analysis in stata
  • Regression modelling in stata
  • Simulation in stata
  • Count data modelling
  • Categorical data modelling
  • Survival analysis
  • Panel data analysis
  • Epidemiology
  • Instrumental variables
  • Power analysis
  • Difference-in-differences
  • Show more
  • Show less

Syllabus

Introduction
Getting Started

Learn about the basic Stata interface. What does each window do and how do you interact with them?

If there's ONE thing you need to know how to use it's how to use help. Once you can use help you can "help" yourself. Learn about the different ways you can find help inside and outside Stata.

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This course is taught via the command syntax (code). Professionals do not use 'point-and-click' to do their work but write all their analysis in code. Learn about the basic Stata syntax and how it works.

Stata is modular and uses something called .do and .ado files to operate. Learn how these are related to Stata code and how you can interact with them.

Log files save all your work to a continuously updated log file. This keeps a record of your results and code in case something goes wrong. Learn how to start, end and view a log file in Stata.

Not all data comes in Stata's .dta format. Learn how to import other data types in Stata such as excel spreadsheets.

Exploring Data

Unlikes Excel, Stata does not display the underlying raw data by default. Learn how to access and view the raw data and how to modify it.

Basic data analysis often requires summary statistics such as means, standard deviation and min/max values. Learn how to get a overview of your data and produce basic summary statistics.

Not all data is suited for means and standard deviation analysis. Some data requires tabulation or tables. Learn more about how to tabulate data and how to create custom tables in Stata.

Not all data is 100% perfect. Sometimes there are missing values. Learn how Stata handles missing values and how to detect missing data.

Analysing distributions is an important part of statistical analysis. Learn what commands are available to analyse data distributions numerical in Stata.

Survey data often contains weights that give some observations more or less significance. Learn how to use weights in statistical analysis in Stata.

The New Table Command (Stata 17)
Manipulating Data

Learn how to recode an existing variables with the recode command. This command is fast and easy to use and allows you to recode numerical values quickly.

A limitation of the recode command is that is has trouble using "if" conditions in data manipulation. Learn how to use the generate and replace command to generate new and replace existing variable using "if" conditions.

New data often requires renaming and labelling to avoid confusion. Learn how to attach labels and rename variables.

If you are looking for a very complex data manipulation then the EGEN command may help. This command offers more complex extensions to generate and allows you to perform complex data manipulations.

Indicator/Categorical data is an important data type. Learn how to construct and recode categorical variables.

Not all data needs to be retained. Learn how to remove (or keep) selected observations and variables in your data.

Learn how to set the file path and save your data in Stata. Learn how to export your data in other formats.

String data (data that contains non-numeric characters) can be challenging to deal with. Get a quick introduction on how you can format such data to be useable in data analysis.

Some datasets are split into multiple files. Learn how to merge and append different datasets together to create larger and more complex datasets.

Advanced users use custom macro's to speed up their workflow. Get an introduction to macro's and learn the basics of looping code over your data.

Accessing Stored Information
Multiple Loops
Date Variables
Subscripting over Groups
Visualising Data

Learn the basics of graphing in Stata. How to load, save and use graphs and how to change their colour scheme.

Bar charts and dot charts are useful to plot summary statistics over categorical data. Learn how to create bar and dot charts in Stata and what options are most relevant.

Examining distributions visually is an important part of data analysis. Learn how to use histograms and kernel density plots in Stata.

Pie charts are used to display numerical proportions. Learn how to create and customise pie charts in Stata.

Scatterplots allow users to analysis bivariate relationships. Learn how to create scatterplots and overlay lines of best fit to analyse relationships between two variables visually.

Remember that old graphical calculator you used in school that could could create custom functions? Learn how to draw custom functions (e.g. y = 2x^2) in Stata. Learn how this might be useful in a regression scenario.

Contour plots allow 3-dimensional visualisation in 2-dimensions via contours. Learn how to use and apply contour plots to Statistical analysis in Stata. These can be useful to visualise complicated interaction effects.

Sometimes you need to fake a scatterplot, but retain the original structure. Learn how to use jitter to randomly distribute actual datapoint and why this might be useful

Sunflower Plots

Learn how to combine multiple graphs into one graph. This can be a powerful way to increase the visual effectiveness of your work. Learn what options you need to know about when combining graphs in Stata.

Changing Graph Sizes
Graphing by Groups
Changing Graph Colours
Adding Text to Graphs
Scatterplots with Categories
Testing Means, Correlations and ANOVA

Learn how to test the association (relationship) between two categorical variables in Stata using the tabulate command.

Learn how to perform basic means test across two variables. Is the mean of one variable really different to the mean of another variables? Learn how to perform group mean tests.

Learn how to perform bivariate correlation (Pearson's correlation) on two more variables.

A short introduction to basic ANOVA analysis in Stata.

Linear Regression

A brief introduction and example of basic OLS regression in Stata.

Learn how to integrate categorical variables into your regression analysis in Stata.

Learn how to perform common diagnostic analysis and tests after a regression in Stata.

Learn how to run and interpret a log transformed regression. Learn how to use interaction terms in a regression.

Learn how to use the test command to perform hypothesis testing after a regression.

Learn how to output the results from a regression to other programmes such as Word or Excel and how to make your results look professional.

Standardizing Regression Estimates
Graphing Regression Estimates
Oaxaca Decomposition Analysis
Mixed Models: Random Intercepts and Random Coefficients
Constrained Linear Regression
Categorical Choice Models

When a dependent variable is binary it requires a logit/probit regression model for analysis. Learn how to apply such models in Stata.

Learn basic diagnostic tools after a login/probit regression model including goodness-of-fit statistics.

Ranked or unranked categorical dependent variables require more complex non-linear models. Learn the basics of the ordered logit and multinomial legit regression models in Stata.

Fractional/Proportional Variable Models
Fractional Logit, Beta Regression and Zero-inflated Beta Regression
Random Numbers and Simulation

Learn about simulation and how to create basic random numbers in Stata.

Learn how we can use random variables to create fake data with known properties.

Learn how we can test the assumptions of a regression estimator with fake data that has properties that do not match the requirements of the estimator. What happens when our data misbehaves?

Learn about Monte Carlo Simulation and how professionals test the statistical properties of estimation procedures.

Count Data Models
Features of Count Data
Poisson Regression
Negative Binomial Regression
Truncated and Censored Count Regression
Hurdle Count Regression
Survival Analysis
What is Survival Analysis?
Setting up Survival Data
Descriptive Statistics in Survival Data
Non-parametric Survival Analysis
Cox Proportional Hazard's Model
Diagnostics for Cox Models
Parametric Survival Analysis
Panel Data Analysis
Setting up Panel Data
Panel Data Descriptives
Lags and Leads
Linear Panel Estimators
The Hausman Test
Non-Linear Panel Estimators
Difference-in-Differences Analysis
Difference-in-Differences Estimation
Parallel Trend Assumption

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Builds a solid foundation for beginners and extends an existing foundation for intermediate learners in data analytics using Stata
Taught by experienced instructors with a strong reputation in the field
Covers a broad range of essential topics to perform various types of statistical analyses and data visualization using Stata, including data manipulation, statistical modeling, and simulation techniques
Provides a comprehensive overview of the software and its application in data analytics, making it suitable for learners with different levels of experience
Can be taken alone or as part of a series for a more comprehensive understanding
Requires some prior statistical knowledge, which may not be suitable for complete beginners

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Career center

Learners who complete The Essential Guide to Stata will develop knowledge and skills that may be useful to these careers:
Data Analyst
As a Data Analyst, you will use data to solve business problems. You will need to be able to collect, clean, and analyze data, as well as interpret the results of your analysis. This course will provide you with the skills you need to do all of these things. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques.
Data Scientist
As a Data Scientist, you will use data to develop new products and improve existing ones. You will need to be able to understand the business problems that you are trying to solve, as well as the data that is available to you. This course will provide you with the skills you need to do both of these things. The course is aimed at anyone interested in data analytics using Stata. Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.
Statistician
As a Statistician, you will use statistical methods to collect, analyze, and interpret data. You will use this data to solve a variety of problems, such as improving the quality of products and services, increasing sales, and reducing costs. This course will provide you with the statistical skills you need to do all of these things. This course covers many statistical techniques that are commonly used by statisticians, such as regression analysis, time series analysis, and multivariate analysis.
Epidemiologist
As an Epidemiologist, you will study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Your work may involve designing and conducting studies to investigate the causes of disease outbreaks, evaluating the effectiveness of public health interventions, or developing new ways to prevent and treat diseases. This course will provide you with the statistical skills you need to do all of these things by covering topics including hypothesis testing, binary outcome models, fractional response models, and epidemiological tables.
Data Visualization Specialist
As a Data Visualization Specialist, you will use data visualization techniques to communicate data to others. You will need to be able to understand the data that you are working with, as well as the audience that you are trying to reach. This course will provide you with the skills you need to do all of these things. Visualising Data is a central part of the course, covering topics such as graphing in Stata, bar and dot charts, scatterplots, and histograms.
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze financial data. You will use this data to make investment decisions. This course will provide you with the statistical skills you need to do all of these things. The course covers many statistical techniques that are commonly used in quantitative finance, such as regression analysis, time series analysis, and risk management.
Biostatistician
As a Biostatistician, you will design and analyze statistical experiments to test hypotheses about biological or medical data. You will need to be familiar with a variety of statistical techniques, including regression analysis, survival analysis, and clinical trial design. This course will provide you with a solid foundation in these techniques, as well as the opportunity to apply them to real-world data. This course covers many statistical techniques that are commonly used in biostatistics, such as correlation and ANOVA, regression including diagnostics, hypothesis testing, and survival data analysis.
Public Health Analyst
As a Public Health Analyst, you will use data to improve the health of populations. You will need to be able to collect, clean, and analyze data, as well as interpret the results of your analysis. This course will provide you with the skills you need to do all of these things. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques.
Survey Researcher
As a Survey Researcher, you will design and conduct surveys to collect data about populations. You will need to be able to understand the business problems that you are trying to solve, as well as the data that is available to you. This course will provide you with the skills you need to do both of these things. The focus of each session will consistently be on creating a “good practice” and emphasising the practical application – and interpretation – of commonly used statistical techniques.
UX Researcher
As a UX Researcher, you will conduct research on user experience. You will use this research to improve the design and usability of products and services. This course will provide you with the skills you need to do all of these things. This course will focus on providing an overview of data analytics using Stata.
Market Researcher
As a Market Researcher, you will collect and analyze data about consumers and markets. You will use this data to help businesses make better decisions about their products and services. This course will provide you with the skills you need to do all of these things. This course will focus on providing an overview of data analytics using Stata.
Software Engineer
As a Software Engineer, you will design, develop, and maintain software applications. You will need to be able to understand the business problems that you are trying to solve, as well as the data that is available to you. This course will provide you with the skills you need to do both of these things. The course is aimed at anyone interested in data analytics using Stata. Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.
Web Analyst
As a Web Analyst, you will collect and analyze data about website traffic. You will use this data to improve the design and performance of websites. This course will provide you with the skills you need to do all of these things. This course will focus on providing an overview of data analytics using Stata.
Research Analyst
As a Research Analyst, you will conduct research on a variety of topics. You will need to be able to collect, clean, and analyze data, as well as interpret the results of your analysis. This course will provide you with the skills you need to do all of these things. This course will focus on providing an overview of data analytics using Stata.
Operations Research Analyst
As an Operations Research Analyst, you will use mathematical and analytical techniques to solve business problems. You will need to be able to understand the business problems that you are trying to solve, as well as the data that is available to you. This course will provide you with the skills you need to do both of these things. The course is aimed at anyone interested in data analytics using Stata. Like for other professional statistical packages the course focuses on the proper application - and interpretation - of code.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in The Essential Guide to Stata.
This user's guide provides comprehensive documentation for Stata. It covers topics such as data management, statistical analysis, and graphical presentation.
This short textbook is specifically designed to complement a Principles of Econometrics textbook and shows how to use Stata to perform the techniques that are explained in such a textbook. The book is useful for those that have some background in statistics or econometrics and want to learn how to perform the analysis in Stata.
Is specifically geared towards those who are using Stata for the behavioral sciences. It assumes some knowledge of the basics of Stata.
While not focused on Stata, this book generally useful reference for econometric techniques. It provides a practical approach to econometrics and is useful for those who want to deepen their understanding of the topics in the course in a more general setting. It focuses primarily on causal inference.
This textbook is designed to introduce students to modern econometrics using Stata. It covers a variety of topics, such as regression analysis, time series analysis, and panel data analysis.
A classic textbook on econometrics that covers the more advanced topics covered in the course. It is well-regarded for its clear explanations and thorough treatment of the topics. It is particularly useful for those who want to pursue advanced econometric studies.
This multi-volume handbook provides a comprehensive and detailed reference to econometric techniques. It is aimed at advanced researchers and covers a wide range of topics, including some of the more specialized topics covered in the course.
This journal publishes articles on the use of Stata. It covers a wide range of topics, such as data management, statistical analysis, and graphical presentation.
This technical bulletin provides information on new features and bug fixes in Stata. It also contains articles on the use of Stata.

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