Using R for Data Analysis in Social Sciences: A Research Project-Oriented Approach
by Quan Li  (Author)

About the Author
Dr. Quan Li is Professor of Political Science at Texas A&M University. His research has appeared in over thirty articles in numerous journals and two coauthored books, Democracy and Economic Openness in an Interconnected System: Complex Transformations and Politics and Foreign Direct Investment. He has served on the editorial boards of American Journal of Political Science, Journal of Politics, International Studies Quarterly, and International Interactions.

About this book
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings.
Using R for Data Analysis in Social Sciences 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, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

Brief contents
1. Learn about R and Write First Toy Programs 1
    WHEN TO USE R IN A RESEARCH PROJECT 2
    ESSENTIALS ABOUT R 3
    HOWTO START A PROJECT FOLDER ANDWRITEOUR FIRST R PROGRAM 4
    CREATE, DESCRIBE, AND GRAPH A VECTOR: A SIMPLE TOY EXAMPLE 7
    SIMPLE REAL-WORLD EXAMPLE: DATA FROM IVERSEN AND SOSKICE (2006) 23
    CHAPTER 1: R PROGRAM CODE 28
    TROUBLESHOOT AND GET HELP 32
    IMPORTANT REFERENCE INFORMATION: SYMBOLS, OPERATORS, AND FUNCTIONS 34
    SUMMARY 35
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 36
    EXERCISES 42
2. Get Data Ready: Import, Inspect, and Prepare Data 43
    PREPARATION 43
    IMPORT PENN WORLD TABLE 7.0 DATASET 45
    INSPECT IMPORTEDDATA 49
    PREPARE DATA I: VARIABLE TYPES AND INDEXING 55
    PREPARE DATA II: MANAGE DATASETS 59
    PREPARE DATA III: MANAGE OBSERVATIONS 65
    PREPARE DATA IV: MANAGE VARIABLES 68
    CHAPTER 2 PROGRAM CODE 78
    SUMMARY 85
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 86
    EXERCISES 93
3. One-Sample and Difference-of-Means Tests 94
    CONCEPTUAL PREPARATION 95
    DATA PREPARATION 101
    WHAT IS THE AVERAGE ECONOMIC GROWTH RATE IN THE WORLD ECONOMY? 104
    DID THEWORLDECONOMY GROWMORE QUICKLY IN 1990 THAN IN 1960? 115
    CHAPTER 3 PROGRAM CODE 128
    SUMMARY 133
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 133
    EXERCISES 142
4. Covariance and Correlation 143
    DATA AND SOFTWARE PREPARATIONS 143
    VISUALIZE THE RELATIONSHIP BETWEEN TRADE AND GROWTH USING SCATTER PLOT 146
    ARE TRADE OPENNESS AND ECONOMIC GROWTH CORRELATED? 149
    DOES THE CORRELATION BETWEEN TRADE AND GROWTH CHANGE OVER TIME? 154
    CHAPTER 4 PROGRAM CODE 160
    SUMMARY 163
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 164
    EXERCISES 168
5. Regression Analysis 170
    CONCEPTUAL PREPARATION: HOW TO UNDERSTAND REGRESSION ANALYSIS 171
    DATA PREPARATION 175
    VISUALIZE AND INSPECT DATA 182
    HOW TO ESTIMATE AND INTERPRET OLS MODEL COEFFICIENTS 185
    HOW TO ESTIMATE STANDARD ERROR OF COEFFICIENT 187
    HOW TO MAKE AN INFERENCE ABOUT THE POPULATION PARAMETER OF INTEREST 188
    HOWTO INTERPRET OVERALL MODEL FIT 190
    HOW TO PRESENT STATISTICAL RESULTS 193
    CHAPTER 5 PROGRAM CODE 194
    SUMMARY 198
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 199
    EXERCISES 204
6. Regression Diagnostics and Sensitivity Analysis 206
    WHY ARE OLS ASSUMPTIONS AND DIAGNOSTICS IMPORTANT? 206
    DATA PREPARATION 211
    LINEARITY AND MODEL SPECIFICATION 215
    PERFECT AND HIGH MULTICOLLINEARITY 221
    CONSTANT ERROR VARIANCE 223
    INDEPENDENCE OF ERROR TERM OBSERVATIONS 227
    INFLUENTIAL OBSERVATIONS 240
    NORMALITY TEST 245
    REPORT FINDINGS 247
    CHAPTER 6 PROGRAM CODE 251
    SUMMARY 259
    MISCELLANEOUS Q&AS FOR AMBITIOUS READERS 259
    EXERCISES 262
7. Replication of Findings in Published Analyses 263
    WHAT EXPLAINS THE GEOGRAPHIC SPREAD OF MILITARIZED INTERSTATE DISPUTES?
    REPLICATION AND DIAGNOSTICS OF BRAITHWAITE (2006) 264
    DOES RELIGIOSITY INFLUENCE INDIVIDUAL ATTITUDES TOWARD INNOVATION?
    REPLICATION OF BéNABOU ET AL. (2015) 284
    CHAPTER 7 PROGRAM CODE 295
    SUMMARY 301
8. Appendix: A Brief Introduction to Analyzing Categorical
    Data and Finding More Data 302
    OBJECTIVE 302
    GETTINGDATA READY 303
    DO MEN AND WOMEN DIFFER IN SELF-REPORTED HAPPINESS? 304
    DO BELIEVERS IN GOD AND NON-BELIEVERS DIFFER IN SELF-REPORTED HAPPINESS? 310
    SOURCES OF SELF-REPORTED HAPPINESS: LOGISTIC REGRESSION 313
    WHERE TO FIND MORE DATA 323
    References and Readings 327
Index 331

Pages: 368 pages
Publisher: Oxford University Press (June 6, 2018)
Language: English
ISBN-10: 9780190656225
ISBN-13: 978-0190656225