Using R for Data Analysis in Social Sciences

INTERACTIVE COURSE

Using R for Data Analysis in Social Sciences

Intermediate

121 Lessons

19h 45min

Certificate of Completion

AI Explanations

AI Explanations

Using R for Data Analysis in Social Sciences

224 Playgrounds

6 Quizzes

100 Illustrations

Takeaway Skills

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.

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

Course Content

1

Introduction to the course

Course Overview

2

Getting Started with R

Using R in a Research Project

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

3

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

4

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

5

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

6

Regression Analysis

12 Lessons

7

Regression Diagnostics and Sensitivity Analysis

15 Lessons

8

Replication of Findings in Published Analyses

7 Lessons

9

Conclusion

2 Lessons

10

Appendix-A

8 Lessons

11

Appendix-B

27 Lessons

COURSE AUTHOR

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