Data Management and Analysis with Stata.

Name: Data Management and Analysis with Stata.
Rating: 4.6 (468 reviews)

2 in 1: Learn Stata and Statistics. A Comprehensive and Intuitive Guide for a Beginner.

Highest Rated

Created byIhsan Ullah

Last updated 7/2023

English

What you'll learn

Get familiar with environment of Stata
Understand the syntax structure and the five fundamental commands
Create new variables, replace and recode values in existing variables
Handle missing data, apply variable and value labels, and work with string variables
Compute and interpret descriptive statistics including mean, standard deviation, range, skewness, kurtosis and percentiles
Compute and interpret correlations, one and two-sample t tests, construct multivariate means graphs
Analysis of variance (ANOVA)
Scatter plots, simple linear regression, multiple linear regression, regression with dummy variables
Interpreting regression output and hypothesis testing

Course content

8 sections • 67 lectures • 7h 18m total length

The Course Overview12:15
Obtaining Stata6:48
Learn how to obtain Stata through institutional licenses, short-term access, or purchasing options, and compare IC, SE, and MP versions, durations of 6 months, 1 year, or perpetual.
Section One Resources0:01
Stata's Main Screen and Multiple Windows5:32
Stata’s main screen organizes five windows; explore opening data files, counting observations, and using the review, variable, properties, and command windows to run and rerun commands.
Browse, Edit, Enter, and Import Data9:07
Learn to browse and edit data in stata, enter new data, replace values, rename variables, and import excel files, then save your work.
Types of Variables3:27
Changing Working Directory5:47

The Section Two Resources0:01
The Basic Command Structure7:00
Practice: Stata's Command Syntax9:47
Practice Stata command syntax by loading a dataset, using summarize with detail, and computing descriptive statistics for wage and education, including gender and experience subgroups and the first 500 observations.
The Tabulate Command8:10
Explore tabulate in Stata to create frequency distribution tables for employment status, interpret percentages and cumulative percentages, and perform cross-tabulations with gender.
Practice: The Tabulate Command8:39
The Summary Command4:49
The Generate Command6:17
The Replace Command (1)7:35
The Replace Command (2)5:04
The Recode Command5:41
The Rename, Drop, Keep, and Display Commands4:08

The Section Four Resources0:01
Descriptive Statistics for Nominal and Ordinal Variables10:22
Explore descriptive statistics in Stata by analyzing a data file with nominal and ordinal variables to create frequency distributions, compare gender groups, and handle missing observations.
Producing Histogram7:21
Learn to produce histograms in Stata using the point-and-click interface, apply value labels, customize axis titles and colors, and save or copy the underlying commands for reuse.
Summary Statistics for Interval and Ratio Variables6:45
Use frequency distributions for nominal and ordinal data, and compute mean and standard deviation for interval or ratio variables like bmi with Stata's sum command.
Variance, Skewness, Kurtosis, Range and Percentiles7:18
Explore how to obtain detailed summary statistics in Stata with the sum command and detail option, including variance, standard deviation, skewness, kurtosis, range, mean, median, and percentiles.
Tabstat Command to control Quantity and Output5:03

The Section Five Resources0:03
Necessary Background for Hypothesis Testing and Types of Variables7:29
Getting Data Ready for Hypothesis Testing (1)8:52
Getting Data Ready for Hypothesis Testing (2)6:50
Chi-Square Test for Hypothesis Testing5:26
Use a chi-square test in Stata to examine whether college education influences risk taking. Compare expected versus observed frequencies and interpret the p-value against the null hypothesis.
Measures of Association / Relationship between Nominal / Ordinal Variables5:12
Elaboration (To analyse the effect of an additional variable)9:20

The Section Six Resources0:04
Please download the attached files for the section six.
Differentiate Variables and the Main Research Question6:20
distinguishes nominal and ordinal from interval and ratio variables, explains that ratio data like numvolunteer has real-world meaning, and outlines the main question linking denomination to volunteering.
Handling Legitimately Skipped Cases8:06
Constructing and Interpreting a Confidence Interval8:30
Construct and interpret confidence interval for the population mean using sample data, understand sampling error, and conduct a one sample t test in stata.
One Sample t Test6:52
Two Samples t Test (1)5:44
Analyze whether religious denomination affects volunteering using a two-sample t test in Stata, with descriptive statistics and mean differences (5.8 vs 3.57) guiding hypothesis testing.
Two Samples t Test (2)7:11
Perform a two-sample t test to compare mean volunteering between religious and nonreligious groups, and conclude the difference is not zero, with religious volunteers higher (about 5.79 vs 3.57).
Analysis of Variance (ANOVA)9:24

The Section Seven Resources0:02
Please download the attached resources for this section.
Scatterplot9:11
Correlation (1)8:46
Learn to measure the strength and direction of relationships with the corr command in Stata, using workhours and totalactivities, while noting how missing data affects correlations.
Correlation (2)7:34
Understanding Regression5:59
Simple (Bivariate) Linear Regression (1)7:35
Simple (Bivariate) Linear Regression (2)5:26
Multiple Linear Regression7:32
Explains multiple linear regression with more than one independent variable, using least squares to estimate coefficients like marrymin and totalactivities on workhours, and assess statistical significance via p-values and r-squared.
Predicted/Fitted Values and Residuals7:15
Learn to generate fitted values and residuals from a least-squares regression in Stata using predict with xb, store them as new variables, and use them to assess model accuracy.
A Dichotomous Variable in Multiple Linear Regression9:50
Learn to use a dichotomous variable in a Stata regression, coding dating as 0/1, handling missing values, and interpreting beta, p-values, and changes in explanatory power.

Section Eight Resources0:01
List, Summarize and Scatter Graph6:45
Fitted Values, Residuals and Regression Line7:52
A Quadratic Regression Model (1)7:54
Learn to fit a quadratic regression in Stata, transforming square feet to square feet squared, generate fitted values, and plot a curve that shows price grows faster for larger houses.
A Quadratic Regression Model (2)11:38
A Log-Linear Regression Model9:27
Explore log-linear regression in stata by transforming price with the natural log, diagnosing skew with histograms, and interpreting how square footage affects log price and predicted price.
A Regression using Indicator Variables (1)11:40
A Regression using Indicator Variables (2)10:30

Requirements

To install Stata on your machine.

Description

Note: The course is COMPLETE now.

This course, extended over seven sections, provides a comprehensive introduction to Stata and Statistics. The aim of the course is to teach all the variables, and the relevant Stata commands, used in Statistics. These variables are nominal, ordinal, interval, and ratio variables.

There are two alternative ways to undertake the course.

1. If you have a basic understanding of Stata, you can directly start from section 3, which teaches Data Management. You should then proceed to section 4 on Descriptive Statistics, which is common to all types of research. Section 5 analyses a relationship and interprets it between Nominal/Ordinal variables. Examples of these types of variables are gender, race, employment status, ethnicity, levels of satisfaction, customer service quality, hair color, and religion among others. Section 6 investigates a relationship and interprets it between the Nominal/Ordinal variable and the Interval/Ratio variable. Section 7 finds an effect of one Interval/Ratio variable on another Interval/Ratio variable. Examples of these types of variables are age, income, prices, exam scores, temperature, distance, and area among others. Note: If you adopt this strategy, you may need to go back to the second section, if you have any trouble understanding a particular Stata command in sections 3, 4, 5, 6, and 7. The advantage of this strategy is you will study the more important content first.

2. Alternatively, you can follow the exact order of the course, starting from section 1 and then proceeding to the next section until you reach the section 7. If you follow this strategy, make sure you do not give up in the middle of the course. The research shows that, and this course is not an exception, some students do not complete the entire course. In this course, the first 3 sections are meant to prepare you for the next 4 sections. Therefore, quitting in the first half of the course will deprive you of the intended benefits.

Whichever alternative you choose, you must download the resources and practice with me during the lectures. In addition, you must attempt all exercises given at the end of each section.

Captions: Each video/lecture is accompanied by accurate captions to enhance your comprehension of the course contents.

Resources: You will be provided with a separate data set for each section to practice with me during the lectures. You will also be given a separate data set to attempt the exercises at the end of each section. You will obtain five do-files, one on data management, and the remaining four on data analysis. The only prerequisites for the course are to install Stata on your computer and remain committed.

Good Luck!

Who this course is for:

Anyone involved in Data Management and Data Analysis

Data Management and Analysis with Stata.

What you'll learn

Explore related topics

Course content

Knowing Stata7 lectures • 43min

The Fundamental Commands11 lectures • 1hr 7min

Data Management and Do-File10 lectures • 1hr 1min

Descriptive Statistics6 lectures • 37min

Analyzing a Relationship between Nominal/Ordinal Variables7 lectures • 43min

Analyzing a Relationship between Nominal/Ordinal and Interval/Ratio Variables8 lectures • 52min

Regression Analysis (Analyzing a Relationship between Interval/Ratio Variables)10 lectures • 1hr 9min

Bonus Videos8 lectures • 1hr 6min

Requirements

Description

Who this course is for: