Introduction to Data Analysis using STATA – Health Action and Research Courses

About Course

Instructor:

Dr. Richa Shah

Global Health Researcher | Epidemiologist & Gerontologist | Consultant, IARC (WHO) | Research Associate, Health Action and Research

Dr. Richa Shah is a medical doctor and global health professional specializing in epidemiology and gerontology. She earned her Master’s in Global Health (2017) and PhD in Global Health and Gerontology (2020) from The University of Tokyo, Japan.

Dr. Shah has worked as a postdoctoral researcher and is currently serving as a consultant at the International Agency for Research on Cancer (IARC), part of the World Health Organization, where she contributes to high-impact studies on global cancer trends. As a research associate at Health Action and Research (HAR), she has led numerous training programs on research methodology and data analysis.

Expertise & Teaching Approach
Dr. Shah combines strong academic rigor with practical experience in data analysis and managing large health datasets. She empowers learners to build the analytical skills and confidence needed to excel in global health research.

Course Details:

The course consists of 8 modules designed to teach the fundamentals of data analysis using Stata, a widely used statistical software in health research.
Beginner-friendly: Introduces core concepts on data cleaning, descriptive statistics, data visualization, and basic inferential methods. However, some foundational knowledge on basic data analysis could be beneficial.
Gain hands-on experience with real-world datasets and develop practical skills to perform meaningful analyses and interpret results confidently.
Suitable for students, researchers, and professionals, providing a strong foundation to start your journey with Stata.

Course Structure:

Each module contains specific lessons; some modules have one lesson, while others include more lessons.
Combines theoretical concepts with practical application using real-world datasets.
Exercise files and resources for each lesson are provided in the Exercise Files section.
For best results, follow the instructions using Stata commands demonstrated in the videos and provided in the exercise files.

Questions and Support

Post any queries in the Q&A section.
Alternatively, you can email us at healthactionandresearch@gmail.com

Certification:

Learners who complete all modules and pass all quizzes will receive a certificate of completion.
Learners are allowed to attempt quizzes multiple times to achieve a passing score.

Requirements:

A computer ready for installation.
Stata is installed on your computer (you may use your existing version if it is already installed).
If you do not have Stata installed, download the trial version from the following link:
https://www.stata.com/customer-service/stata-evaluation/
Refer to the video tutorial in Module 0 Introduction for guidance on installing Stata. (Enrollment is required for access to the video.)

Note:

It takes 2-3 business days to have access to the trial version of Stata.
The trial version is valid for approximately two weeks to one month. If you are using the trial version, please plan and manage your time accordingly.

Understand the fundamentals of Stata and its interface
Import, clean, and manage datasets
Perform descriptive and inferential statistical analyses
Apply bivariate analysis techniques such as t-tests, chi-square tests, and correlations
Apply multivariable analysis techniques such as linear regression and logistic regression
Visualize data effectively using Stata
Interpret statistical results and present findings with confidence
Use shortcut commands, loops, and other techniques to improve efficiency in your workflow

Course Content

Module 0: Introduction to Stata

0.1 Introduction to Stata and overview of the course

06:18

Module 1: Stata Interface

Module 2: Importing data, types of variables, grammar of STATA commands, setting a working directory
This module covers importing datasets, understanding variable types and labels, browsing and exploring data, and saving datasets. You will also learn how to import a dataset into Stata. For practice, please download the dataset (Stata_practice_dataset_2023.xlsx) provided in the Exercise Files.

Module 3: Data browsing, cleaning, filtering, labeling, renaming, dropping

3.1.1 Basic Stata commands – browse and edit

02:18
3.1.2 Basic Stata commands -ds, describe, and codebook

01:53
3.1.3 Basic Stata commands: “set more on” and “set more off”

01:25
3.1.4 Basic Stata commands: count, inspect, list, summarize

02:02
3.1.5 Basic Stata commands: combining variables, summarize, detail, sort

03:04
3.2 Assess and drop variables and cases

05:24
3.3.1 Filtering and conditional listing

01:11
3.3.2 Renaming variables

01:30
3.4.1 Renaming Multiple Variables at Once Using a Do-file in Stata

03:42
3.4.2 Label variables

02:10
3.5.1 tabulate (or tab) command in Stata and assigning value labels to categorical variables

02:30
3.5.2 Practice labelling from Do file and modifying/correcting mistakes

04:43
Quiz 3

Module 4: Generating and modifying variables
This module is divided into six sections which cover how to create new variables, recode existing ones, group or categorize data, convert string variables to numeric and finally, compute internal consistency using Cronbach’s alpha.

4.0 Introduction to generating and modifying variables

00:30
4.1.1 Generating New Variables Using “generate” and “replace” commands

05:13
4.1.2 Grouping Categories of a Variable and Transforming It into a New Variable

03:16
4.1.3 Recoding variables

05:24
4.2 Converting string variables to numeric

04:46
4.3.1 Reverse coding and computing variables: Overview of scale for quality of life (WHO-QOL BREF)

01:39
4.3.2 Reverse coding and computing variables: Recoding variables

02:24
4.3.3 Reverse coding and computing variables: Calculating (computing) scores of a scale

04:02
4.3.4 Reverse coding and computing variables: transforming continuous to categorical variable

03:25
4.3.5 Reverse coding and computing variables -dichotomizing variables

02:11
4.4 Cronbach’s alpha

02:33
4.5 Practice Exercise Table 1

01:14
Quiz 4

Module 5: Data Visualization in STATA and presenting descriptive stats

Module 6: Bivariate Analysis

6.1.1 Introduction to Bivariate analysis: basic concept about variables

03:17
6.1.2 Introduction to different types of bivariate analysis

04:24
6.2.1 Stata commands for bivariate analysis: t-test

01:31
6.2.2 Stata commands for bivariate analysis: ANOVA test

01:27
6.2.3 Stata commands for bivariate analysis: Mann Whitney U test (Wilcoxon ranksum) test

01:41
6.2.4 Stata commands for bivariate analysis: chi-square test

00:52
6.2.5 Stata commands for bivariate analysis: Pearson correlation

01:59
6.2.6 Stata commands for bivariate analysis: spearman correlation

01:07
6.3 Practice exercise

00:30
Quiz 6

Module 7: Multi variable analysis

7.1 Introduction to Multivariable Analysis

05:09
7.2 Understanding basics of linear regression

06:09
7.3.1 Linear regression: for continuous independent variable

01:58
7.3.2 Linear regression: for categorical independent variable

01:10
7.3.3 Linear regression: exporting regression table to different files using outreg2 command

02:49
7.3.4 Linear regression: applying multiple predictor variables to regression model

02:43
7.3.5 Linear regression: changing the reference group of predictor categorical variable

01:48
7.4 Practice Exercise Table 3

00:42
7.5 Variation Inflation Factor

01:51
7.6.1 Understanding basic concepts of logistic regression: OR and AOR

04:03
7.6.2 Interpretation of logistic regression table

02:26
7.7.1 logistic regression: simple command and interpretation

03:15
7.7.2 Multivariable logistic regression command

01:20
7.8 Practice Exercise Table 4

00:56
Quiz 7

About Course

What Will You Learn?

Course Content

Module 0: Introduction to Stata

0.1 Introduction to Stata and overview of the course

Module 1: Stata Interface

1: Stata Interface

Quiz 1

2.1 Importing data to Stata from an excel file

2.2 Variables in Stata and syntax grammar

2.3 Entering variable name to command box

(OPTIONAL LESSON) Setting the working directory

Quiz 2

Module 3: Data browsing, cleaning, filtering, labeling, renaming, dropping

3.1.1 Basic Stata commands – browse and edit

3.1.2 Basic Stata commands -ds, describe, and codebook

3.1.3 Basic Stata commands: “set more on” and “set more off”

3.1.4 Basic Stata commands: count, inspect, list, summarize

3.1.5 Basic Stata commands: combining variables, summarize, detail, sort

3.2 Assess and drop variables and cases

3.3.1 Filtering and conditional listing

3.3.2 Renaming variables

3.4.1 Renaming Multiple Variables at Once Using a Do-file in Stata

3.4.2 Label variables

3.5.1 tabulate (or tab) command in Stata and assigning value labels to categorical variables

3.5.2 Practice labelling from Do file and modifying/correcting mistakes

Quiz 3

Module 4: Generating and modifying variables This module is divided into six sections which cover how to create new variables, recode existing ones, group or categorize data, convert string variables to numeric and finally, compute internal consistency using Cronbach’s alpha.

4.0 Introduction to generating and modifying variables

4.1.1 Generating New Variables Using “generate” and “replace” commands

4.1.2 Grouping Categories of a Variable and Transforming It into a New Variable

4.1.3 Recoding variables

4.2 Converting string variables to numeric

4.3.1 Reverse coding and computing variables: Overview of scale for quality of life (WHO-QOL BREF)

4.3.2 Reverse coding and computing variables: Recoding variables

4.3.3 Reverse coding and computing variables: Calculating (computing) scores of a scale

4.3.4 Reverse coding and computing variables: transforming continuous to categorical variable

4.3.5 Reverse coding and computing variables -dichotomizing variables

4.4 Cronbach’s alpha

4.5 Practice Exercise Table 1

Quiz 4

Module 5: Data Visualization in STATA and presenting descriptive stats

5.1 Overview of histogram and bar charts

5.2 Data visualization: generating histogram with frequency and percentage

5.3 Data visualization: generating bar charts

5.4 Data visualization: generating box plot

5.5 Data visualization: generating scatter plots and customizing graphs

5.6 Summary table using “tabstat”

5.7 Using tabout command

Quiz 5

Module 6: Bivariate Analysis

6.1.1 Introduction to Bivariate analysis: basic concept about variables

6.1.2 Introduction to different types of bivariate analysis

6.2.1 Stata commands for bivariate analysis: t-test

6.2.2 Stata commands for bivariate analysis: ANOVA test

6.2.3 Stata commands for bivariate analysis: Mann Whitney U test (Wilcoxon ranksum) test

6.2.4 Stata commands for bivariate analysis: chi-square test

6.2.5 Stata commands for bivariate analysis: Pearson correlation

6.2.6 Stata commands for bivariate analysis: spearman correlation

6.3 Practice exercise

Quiz 6

Module 7: Multi variable analysis

7.1 Introduction to Multivariable Analysis

7.2 Understanding basics of linear regression

7.3.1 Linear regression: for continuous independent variable

7.3.2 Linear regression: for categorical independent variable

7.3.3 Linear regression: exporting regression table to different files using outreg2 command

7.3.4 Linear regression: applying multiple predictor variables to regression model

7.3.5 Linear regression: changing the reference group of predictor categorical variable

7.4 Practice Exercise Table 3

7.5 Variation Inflation Factor

7.6.1 Understanding basic concepts of logistic regression: OR and AOR

7.6.2 Interpretation of logistic regression table

7.7.1 logistic regression: simple command and interpretation

7.7.2 Multivariable logistic regression command

7.8 Practice Exercise Table 4

Quiz 7

Module 8: Addtional commands

8.1 Additional commands – Short form commands

8.2 Additional commands: using loop commands

Module 4: Generating and modifying variables
This module is divided into six sections which cover how to create new variables, recode existing ones, group or categorize data, convert string variables to numeric and finally, compute internal consistency using Cronbach’s alpha.