Data Analysis for Social Science

Chapter 1 How do we examine our interests with data?: Distribution and mean
ㆍ Understanding our world with data
ㆍ Mapping what we want to study into numbers
ㆍ Less likely or more likely? Think about the probabilities of events
ㆍ Which group of subjects do we want to study?: The population of interest and the random sample
ㆍ Random sample assumption and sampling methods
ㆍ What useful information can we have from a sample?: sample mean and sample variance
ㆍ Normal distribution and its application: One of the most popular and useful distributions
ㆍ Alternative measures to mean: median and mode
ㆍ Chapter Summary
ㆍ Exercises

Chapter 2 Do more with the sample mean: Inference
ㆍ Sampling distribution of the sample mean and the Central Limit Theorem
ㆍ The confidence interval (CI) for the population mean μ
ㆍ Hypothesis test for the population mean μ
ㆍ How to choose an appropriate sample size in the survey for inference
ㆍ Chapter Summary
ㆍ Exercises

Chapter 3 Examining the relationship between the two quantitative variables I: Correlation coefficient and introduction to the OLS regression analysis
ㆍ Covarience and correlation coefficent
ㆍ Introduction to the OLS regression analysis
ㆍ Chapter Summary
ㆍ Exercises

Chapter 4 Examining the relationship between the two continuous variables II: Inference in the OLS regression analysis
ㆍ The normally of the error term and the sampling distribution of the OLS estimator
ㆍ The linear regression model when the sample size becomes larger
ㆍ The Confidence Interval (CI) for the regression parameter β1
ㆍ Hypothesis test for the regression parameter β1
ㆍ Chapter Summary
ㆍ Exercises

Chapter 5 Handling two or more explanatory variables in OLS regression analysis I: Multivariate Regression Analysis
ㆍ Partialling out and multicollinearity in multivariate regression analysis
ㆍ Omitted variable bias in the linear regression model
ㆍ Adding an explanatory variable and the efficiency of OLS estimators
ㆍ Chapter Summary
ㆍ Exercises

Chapter 6 Handling two or more explanatory variables in OLS regression analysis II: Hypothesis tests and more in Multivariate Regression Analysis
ㆍ Hypothesis tests in multivariable regression analysis
ㆍ Adjusted R-squared
ㆍ Chapter Summary
ㆍ Exercises

Chapter 7 The OLS regression analysis when comparing the outcomes of the two or more groups: Use of binary explanatory variables
ㆍ Estimating group differences in an outcome variable
ㆍ Estimating group differences in an outcome variable without the constant
ㆍ Estimating group differences using an interval variable
ㆍ Estimating group differences in a slope coefficient
ㆍ Estimating group differences in all explanatory variables
ㆍ Estimating the nonlinear relationship between an explanatory variable and an outcome variable
ㆍ Subsample analysis based on exogenous explanatory variables
ㆍ Chapter Summary
ㆍ Exercises

Chapter 8 Developing and completing the OLS regression analysis by using rescaling and functional specifications
ㆍ Rescaling of the outcome and explanatory variables
ㆍ Linearity in the OLS analysis
ㆍ Linear and nonlinear specifications in the OLS analysis
ㆍ Choosing specifications by considering three different types of causal paths
ㆍ General rules for including additional variables and making specifications in multivariate regression analysis
ㆍ Chapter Summary
ㆍ Exercises

Chapter 9 The OLS regression analysis when the variance of the error term depends on the explanatory variables: Heteroscedasticity
ㆍ Chapter Summary
ㆍ Exercises

Chapter 10 The regression analysis when the outcome variable is binary: LPM, Logit, and Probit
ㆍ Linear Probability Model (LPM): Using OLS when the outcome variable is binary
ㆍ The estimation of logit and probit models
ㆍ Statistical inference and goodness of it for probit and logit models
ㆍ Chapter Summary
ㆍ Exercises

Appendix
A. Software programs for data analysis: SPSS, SAS, Stata, R
B. How to do a reliable empirical study
C. z distribution table: standard normal curve tail probabilities
D. t distribution table: critical values of the t distribution
E. Chi-square distribution table: critical values of the Chi-square distribution
F. F distribution table: critical values of the F distribution

Haeil Jung Haeil Jung is a professor in the Department of Public Administration at Korea University in Seoul, South Korea. He earned his PhD degree in Public Policy from the University of Chicago, Chicago, USA. Before assuming his current role, he was an assistant professor in the Paul H. O'Neill School of Public and Environmental Affairs at Indiana University, Bloomington, IN, USA, from 2009 to 2015. Additionally, from 2012 to 2020, he served as a consultant for the World Bank, where he played a key role in the evaluation of the early childhood education program in Indonesia. His research expertise lies in policy analysis and program evaluation, particularly focusing on poverty, inequality, and related social policy interventions. He has authored numerous peer-reviewed research articles on diverse topics such as early childhood education, college education, labor market participation, immigration, fertility, obesity, incarceration, COVID-19, and empirical methods, making significant contributions to these fields. Along with his research, he has a comprehensive teaching background. He has taught introductory, intermediate, and advanced data analysis courses, as well as social policy courses, at the University of Chicago, Indiana University, and Korea University.