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This new edition offers a beginner-friendly introduction to Stata, designed for both students and non-technical users. Through straightforward examples and practical exercises, it covers essential topics like data management, statistical analysis, and visualization, empowering readers to use Stata with confidence and ease.
This book takes a hands-on approach to mastering Stata, focusing on data cleaning, transformation, and statistical analysis. Filled with practical examples from health and social sciences, it’s an excellent resource for students and researchers working with real-world datasets and seeking actionable insights.
This updated edition is ideal for those aiming to improve their data analysis skills. It provides comprehensive coverage of descriptive statistics, hypothesis testing, regression models, and data visualization, making it a versatile and valuable resource for applied research across disciplines.
This comprehensive guide to advanced econometrics explores complex topics such as panel data, instrumental variables, and limited dependent variables. Widely regarded by graduate students and professionals in economics and finance, it’s an essential resource for mastering sophisticated analytical techniques.
This book is an excellent choice for understanding econometric modeling in Stata. It breaks down concepts like time series, multicollinearity, and heteroskedasticity with step-by-step tutorials and detailed command explanations.
With a focus on practical application, this book walks readers through quantitative research methods using Stata. The updated edition adds new insights on advanced statistical techniques and includes social science examples for better clarity.
This handbook combines statistical theory with hands-on Stata tutorials, making it an essential resource for applied researchers. It’s especially useful for those analyzing clinical or survey data with limited statistical backgrounds.
Designed for behavioral science students and researchers, this book simplifies complex statistical methods like ANOVA and multivariate analysis. It includes annotated output and examples that help readers connect theory with practice.
This hands-on guide emphasizes efficient workflows for managing, cleaning, and analyzing data in Stata. With a focus on reproducibility and detailed documentation, it’s an essential resource for graduate students and research teams aiming to streamline their data processes effectively.
This latest edition covers the full range of Stata’s statistical capabilities, from basic to advanced methods. With updated examples and clear instructions, it’s perfect for learners seeking comprehensive knowledge of Stata’s tools.
Command |
Description |
---|---|
generate |
Creates new variables. |
replace |
Modifies the values of an existing variable. |
drop |
Removes variables or observations. |
keep |
Retains specified variables or observations. |
rename |
Changes a variable's name. |
sort |
Arranges data in ascending order by variable(s). |
merge |
Combines datasets by matching key variables. |
append |
Adds new observations from another dataset. |
Statistical Analysis Methods
Command |
Description |
---|---|
summarize |
Provides descriptive statistics for variables. |
tabulate |
Creates frequency or cross-tabulation tables. |
regress |
Runs linear regression analysis. |
logit |
Estimates logistic regression models. |
anova |
Performs analysis of variance (ANOVA). |
probit |
Runs probit regression models. |
xtreg |
Runs panel data regression models. |
Graphing and Visualization Methods
Command |
Description |
---|---|
graph |
Creates customizable plots (bar, scatter, etc.). |
histogram |
Plots histograms for a variable. |
kdensity |
Plots the kernel density estimate. |
twoway |
Combines scatterplots, lines, and other plots. |
boxplot |
Creates box-and-whisker plots for comparison. |
Data Reshaping Methods
Command |
Description |
---|---|
reshape wide |
Converts data from long format to wide format. |
reshape long |
Converts data from wide format to long format. |
collapse |
Aggregates data by groups, summarizing variables. |
xtset |
Declares panel data structure for time-series. |
tsset |
Prepares data for time-series analysis. |
Labeling and Formatting Methods
Command |
Description |
---|---|
label variable |
Assigns a descriptive label to a variable. |
label define |
Creates a value label for a categorical variable. |
label values |
Links a value label to a variable. |
encode |
Converts string variables into labeled numeric ones. |
decode |
Converts numeric variables with labels to strings. |
Programming and Automation Methods
Command |
Description |
---|---|
foreach |
Loops over a list of items to run commands. |
forvalues |
Loops over numeric ranges to apply commands. |
capture |
Suppresses errors from a command if it fails. |
macro define |
Creates macros for automating repetitive tasks. |
do |
Executes commands from a .do script file. |
Git integration within Stata allows users to run Git commands directly from the Stata command prompt, making it easier to manage project versions without leaving the Stata environment.
Key Features:
Use Case: For instance, to initialize a Git repository in your Stata project, simply use
stata
shell git init .
In summary, this integration allows you to handle version control directly from within Stata, providing a smooth, efficient, and consistent workflow for data analysis projects. This is especially useful when managing complex analyses that require frequent updates or collaboration.
Overview: This repository provides an organized framework for creating tables in Stata, with an emphasis on streamlined workflows for reproducible research. It includes example code, outputs, and a demo with LaTeX code for generating publication-ready tables.
Steps
Adapting the Code
Creating Nice Tables in Stata without Wasting Time:
To avoid the hassle of manually copying and formatting tables, this repository focuses on automating table creation within Stata, allowing easy adjustments and exports, especially useful for research papers and reports.
Stata Version Required: 14.2+
Overview
This Stata package is designed for Structural Equation Modeling (SEM) with Partial Least Squares (PLS-SEM), providing tools for path modeling and estimation of complex relationships between observed and latent variables.
Installation Steps:
Notes:
The provided mlib file was compiled for Stata 15. If you are using Stata 14, make sure to copy the mlib file from the mlib/Stata 14 directory included in the package.
This package streamlines the process of conducting PLS-SEM in Stata, making it easier to apply advanced statistical techniques to your data analysis.
MarkDoc is a powerful, easy-to-use literate programming package for Stata that allows users to create dynamic documents in various formats, including sthlp, pdf, docx, tex, html, odt, epub, and markdown. It's designed to be beginner-friendly, making it suitable for teaching statistical reporting and code documentation in introductory courses.
Installation
kaksongen is a Stata package that computes the decomposition of the impact of taxation on social welfare, based on the generalization of Kakwani and Son (2021) by Ledić, Rubil, and Urban (2022).
Installation
To install, run the following command in Stata:
net install kaksongen, replace from(https://raw.githubusercontent.com/irubil/kaksongen/main)
Latest Version
Stata-TeX enables the creation of highly customizable LaTeX tables for complex regression results. It separates LaTeX table templates from data, allowing for automatic population of placeholders with estimation results.
Advantages
Limitations
Installation:
Ensure table_from_tpl.py is accessible via the $PYTHONPATH for Python (preferably 2.7).
Scalarsave is a Stata ado file that helps economists and researchers save numeric scalar values to delimited files, allowing automatic updates of these values in LaTeX documents. This simplifies the process of keeping numbers consistent in research papers, especially when values change over time.
Usage:
scss
scalarsave using "example.csv", scalar(100) id("Example scalar")
Installation:
LaTeX Integration:
yaml
observations, 100
mean_income, 2000
In LaTeX, use:
kotlin
There are \var{observations} observations in my data and the mean income is \var{mean_income} dollars.
This will display the numbers dynamically based on the values in the CSV file.
Testing:
This tool automates updates for numbers in research papers, making document management much more efficient.
Objective:
Estimate the Cobb-Douglas production function for formal firms using the Annual Survey of Industries (2016-2017) data.
Data Set:
Data Preparation:
Inference:
This analysis demonstrates how changes in capital, labour, and R&D investment contribute to the overall output of a firm, supporting the relevance of these factors in the Cobb-Douglas production function.
Stata is used for data management, statistical analysis, and producing graphics, often utilized in fields like economics, sociology, and public health.
Check if the variable name exceeds 32 characters or contains invalid characters. Use describe to verify all variables in your dataset.
Yes, use the if qualifier, e.g., regress depvar indepvar if age > 30 to perform commands only for certain observations.
Ensure the file is in your working directory or specify the full file path. Check for typos in the filename or extension.
Macros are placeholders for text or values, helping to automate repetitive tasks. Use global or local to define them, e.g., local myvar age.
Use descriptive labels for variables and values. For example, label variable income "Annual Income" makes the dataset easier to understand.
Check your variable's scale or missing values. Use graph display to redraw graphs or adjust options like axis limits.
Yes, but performance depends on your computer’s RAM. Use commands like compress to reduce memory usage for large datasets.
Install and use the vif command after regression. Variables with a Variance Inflation Factor (VIF) above 10 may indicate multicollinearity.
A do-file allows you to save and execute a series of commands, ensuring reproducibility, while the command line is used for quick, one-off commands.