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Using R for data analysis in social sciences : a research project-oriented approach / Quan Li.

Author
Li, Quan, 1966-
Published
New York, NY : Oxford University Press, 2019.
Physical Description
1 online resource : illustrations (black and white).
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Contents
Machine generated contents note: 1.Learn about R and Write First Toy Programs -- When To User In A Research Project -- Essentials About R -- How To Start A Project Folder And Write Our First R Program -- Create, Describe, And Graph A Vector: A Simple Toy Example -- Simple Real-World Example: Data From Iversen And Soskice (2006) -- ch. 1 R PROGRAM CODE -- Troubleshoot And Get Help -- Important Reference Information: Symbols, Operators, And Functions -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 2.Get Data Ready: Import, Inspect, and Prepare Data -- Preparation -- Import Penn World Table 7.0 Dataset -- Inspect Imported Data -- Prepare Data I Variable Types And Indexing -- Prepare Data II Manage Datasets -- Prepare Data III Manage Observations -- Prepare Data IV Manage Variables -- ch. 2 Program Code -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 3.One-Sample and Difference-of-Means Tests -- Conceptual Preparation -- Data Preparation -- What Is The Average Economic Growth Rate In The World Economy? -- Did The World Economy Grow More Quickly In 1990 Than In 1960? -- ch. 3 Program Code -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 4.Covariance and Correlation -- Data And Software Preparations -- Visualize The Relationship Between Trade And Growth Using Scatter Plot -- Are Trade Openness And Economic Growth Correlated? -- Does The Correlation Between Trade And Growth Change Over Time? -- ch. 4 Program Code -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 5.Regression Analysis -- Conceptual Preparation: How To Understand Regression Analysis -- Data Preparation -- Visualize And Inspect Data -- How To Estimate And Interpret OLS Model Coefficients -- How To Estimate Standard Error Of Coefficient -- How To Make An Inference About The Population Parameter Of Interest -- How To Interpret Overall Model Fit -- How To Present Statistical Results -- ch. 5 Program Code -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 6.Regression Diagnostics and Sensitivity Analysis -- Why Are Ols Assumptions And Diagnostics Important? -- Data Preparation -- Linearity And Model Specification -- Perfect And High Multicollinearity -- Constant Error Variance -- Independence Of Error Term Observations -- Influential Observations -- Normality Test -- Report Findings -- ch. 6 Program Code -- Summary -- Miscellaneous Q&As For Ambitious Readers -- Exercises -- 7.Replication of Findings in Published Analyses -- What Explains The Geographic Spread Of Militarized Interstate Disputes? Replication And Diagnostics Of Braithwaite (2006) -- Does Religiosity Influence Individual Attitudes Toward Innovation? Replication Of Benabou Et Al. (2015) -- ch. 7 Program Code -- Summary -- 8.Appendix: A Brief Introduction to Analyzing Categorical Data and Finding More Data -- Objective -- Getting Data Ready -- Do Men And Women Differ In Self-Reported Happiness? -- Do Believers In God And Non-Believers Differ In Self-Reported Happiness? -- Sources Of Self-Reported Happiness: Logistic Regression -- Where To Find More Data -- References and Readings.
Summary
Statistical analysis is common in the social sciences, and among the more popular programs is R. This text provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualise, and analyse data. The focus is on how to address substantive questions with data analysis and replicate published findings. The work adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualise data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyse data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares regression, and model assumption diagnostics.
Subject(s)
ISBN
9780190656256 (ebook)
Audience Notes
Specialized.
Note
Previously issued in print: 2018.
Bibliography Note
Includes bibliographical references and index.
View MARC record | catkey: 28939990