Back To Course Home

Using R for Data Analysis in Social Sciences

0% completed

Introduction to the course

Course Overview

Getting Started with R

Introduction Using R in a Research Project Vectors in R Descriptive Statistics for a Vector Handle Missing Values in Vectors Convert a Vector into a Data Frame Format Output into a Table Graph the Distribution of a Variable Simple Real-world Example: Data from Iversen and Soskice (2006)Quiz: Getting Started Summary

Get Data Ready: Import, Inspect, and Prepare Data

Introduction Preparations Import Penn World Table 7.0 Dataset Inspect Imported Data Prepare Data: Variable Types Prepare Data: Indexing Prepare Data: Manage Datasets Manage Datasets: Reshaping Data Structure Prepare Data: Manage Observations Prepare Data: Manage Variables Create Group-Specific Variables Quiz: Getting Data Ready Challenge: Importing a Dataset Solution: Importing Dataset Summary

One-sample and Difference-of-Means Tests

Introduction Conceptual Preparation Conceptual Preparation: Methods of Statistical Inference Conceptual Preparation: Statistical Inference Data Preparation Case Study: Average Economic Growth Rate in the World Economy Hypothesis Test of Hypothetical Population Mean Confidence Interval Inferring Population Difference Two-sample t-Tests Alternate Difference of Mean Tests Quiz: One-sample and Difference of Means Tests Summary

Covariance and Correlation

Introduction Data and Software Preparations Visualizing Relationship Between Variables Trade Openness and Economic Growth Pearson Correlation Coefficient of Population and Sample Change Between Trade and Growth Quiz: Covariance and Correlation Challenge: Covariance and Correlation Solution: Covariance and Correlation Summary

Regression Analysis

Introduction Conceptual Preparation Regression Analysis Data Preparation Visualize and Inspect Data OLS Model Coefficients Hypothesis Testing Interpret Overall Model Fit Quiz: Regression Analysis Challenge: Regression Analysis Solution: Regression Analysis Summary

Regression Diagnostics and Sensitivity Analysis

Introduction OLS Assumptions and Diagnostics Data Preparation Linearity and Model Specification Perfect and High Multicollinearity Constant Error Variance Independence of Error Term Observations Clarification of Interaction Model Influential Observations Normality Test Analysis of Different Models Quiz: Regression Diagnostics and Sensitivity Analysis Challenge: Regression Diagnostics and Sensitivity Analysis Solution: Regression Diagnostics and Sensitivity Analysis Summary

Replication of Findings in Published Analyses

Introduction to Replication Population Regression Model Data Preparation Replicate the Braithwaite Study Results Diagnostic Tests and Robustness Checks Report and Discuss Estimation Results Summary

Conclusion

Final Words Copyright Information

Appendix-A

Objectives Gender-based Self-reported Happiness Two-way Contingency Table Statistical Inference Belief-based Self-reported Happiness Sources of Self-reported Happiness: Logistic Regression Interpret Size of Effect Where to Find More Data

Appendix-B

Installation and Setting Up

Writing First Program in R

How to Install and Load an Add-on Package

Troubleshoot and Get Help

Important Reference Information:Symbols, Operators, and Functions

How to Use R on Mac

How to Export R Output to a File

Testing Populations for Equal Variances

Statistical Power

Differences between Two-Tailed and One-Tailed Tests

Anscombe’s Quartet and Failures of the Correlation Coefficient

Import Datasets of Other Formats into R

Observation’s Values in the Spreadsheet Viewer

Indexing Method to Indicate Variables

White Space for Character Fields

Another Way to Get a Subset of a Dataset

Another Way to Recode Variable Values

Save Duplicate Observations Into a Different Dataset

Another Way to Rename Variables

Alternative Source of Penn World Table Data

Information from lm() Model Output

Matrix Algebra to Solve for OLS Coefficient Estimat

Partial Regression

Pairwise Scatter Plot Among Multiple Variables

Show Multiple Diagnostic Plots Simultaneously

Test Spatial Correlation Using Moran’s I

Panel Data Models

Important Reference Information:Symbols, Operators, and Functions

Learn about symbols, operators, and functions in R.

Let’s look at important reference information, including symbols frequently used in R programs, common arithmetic and logical operators, and common mathematical and statistical functions. The following tables provide:

List of symbols frequently used in R
List of arithmetic operators
List of logical operators
List of common statistical and mathematical functions

Get hands-on with 1400+ tech skills courses.