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Using R for Data Analysis in Social Sciences

Intermediate

121 Lessons

19h 45min

Certificate of Completion

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224 Playgrounds

6 Quizzes

Course Overview

What You'll Learn

Course Content

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Course Overview

With the rapid progress in statistical computing, proficiency in using statistical software such as R, SPSS, and SAS has become almost a universal requirement. The highly extensible R programming language offers a wide range of analytical and graphical capabilities ideal for manipulating large datasets. This course integrates R programming, the logic and steps of statistical inference, and the process of empirical social science research in a highly accessible and structured fashion. It emphasizes learning...Show More

What You'll Learn

Learn to use R to import and inspect data, identify dataset attributes, and manage observations, variables, and datasets.

Learn to use R to graph simple histograms, box plots, scatter plots, and research findings.

Learn to use R to summarize data, conduct a one-sample t-test, and test the difference of means between groups.

Learn how to replicate research findings in published journal articles.

Learn to use R to compute covariance and correlation, estimate and interpret ordinary least square (OLS) regression, and diagnose and correct regression assumption violations.

Learn to use R to import and inspect data, identify dataset attributes, and manage observations, variables, and datasets.

Course Content

Introduction to the course

1 Lessons

Course Overview

Getting Started with R

11 Lessons

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

15 Lessons

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

13 Lessons

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

10 Lessons

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

12 Lessons

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

15 Lessons

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

7 Lessons

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

2 Lessons

Final Words

10.

Appendix-A

8 Lessons

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

11.

Appendix-B

27 Lessons

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

Course Author

Oxford University Press

Project

Analyzing Cardiovascular Risk Factors with R

intermediate

1 hour

Project

Analyzing Cardiovascular Risk Factors with R

This project analyze cardiovascular risk factors with machine learning to identify key influences on cardiac diseases

intermediate

1 hour

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