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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

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

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

Prepare Data: Variable Types

Let's learn about variable types in R.

We'll cover the following

Variable types in R
- Factor variables in R

Preparing the imported data for analysis can be time-consuming and involves data manipulation and cleaning. Data management typically focuses on organizaing variables, observations, and datasets with the end goal of obtaining a clean dataset ready for analysis. Prior to data management, we must learn some prerequisite information, primarily regarding variable types and the indexing method for referring to observations and variables.

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