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Using R for Data Analysis in Social Sciences

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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

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

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

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

Analysis of Different Models

Quiz: Regression Diagnostics and Sensitivity Analysis

Challenge: Regression Diagnostics and Sensitivity Analysis

Solution: Regression Diagnostics and Sensitivity Analysis

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

Data Preparation

Learn how to prepare data for regression diagnostics and sensitivity analysis.

We'll cover the following

The broom package in R

For data preparation, we must first clean the workspace, set the working directory to our Project folder, and load the final.85.RData dataset. Then, we reproduce the table of descriptive statistics, rerun the OLS model from this lesson, and display the regression output.

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