Regression Analysis: A Practical Introduction

Cover
Half Title
Title
Copyright
Dedication
Contents
List of figures
List of tables
About the author
Preface
Acknowledgments
List of abbreviations
1 Introduction
	1.1 The problem
	1.2 The purpose of research
	1.3 What causes problems in the research process?
	1.4 About this book
	1.5 The most important sections in this book
	1.6 Quantitative vs. qualitative research
	1.7 Stata and R code
	1.8 Chapter summary
2 Regression analysis basics
	2.1 What is a regression?
	2.2 The four main objectives for regression analysis
	2.3 The Simple Regression Model
	2.4 How are regression lines determined?
	2.5 The explanatory power of the regression
	2.6 What contributes to slopes of regression lines?
	2.7 Using residuals to gauge relative performance
	2.8 Correlation vs. causation
	2.9 The Multiple Regression Model
	2.10 Assumptions of regression models
	2.11 Calculating standardized effects to compare estimates
	2.12 Causal effects are “average effects”
	2.13 Causal effects can change over time
	2.14 A quick word on terminology for regression equations
	2.15 Definitions and key concepts
	2.16 Chapter summary
3 Essential tools for regression analysis
	3.1 Using binary variables (how to make use of dummies)
	3.2 Non-linear functional forms using OLS
	3.3 Weighted regression models
	3.4 Chapter summary
4 What does “holding other factors constant” mean?
	4.1 Case studies to understand “holding other factors constant”
	4.2 Using behind-the-curtains scenes to understand “holding other factors constant”
	4.3 Using dummy variables to understand “holding other factors constant”
	4.4 Using Venn diagrams to understand “holding other factors constant”
	4.5 Could controlling for other factors take you further from the true causal effect?
	4.6 Application of “holding other factors constant” to the story of oat bran and cholesterol
	4.7 Chapter summary
5 Standard errors, hypothesis tests, p-values, and aliens
	5.1 Setting up the problem for hypothesis tests
	5.2 Hypothesis testing in regression analysis
	5.3 The drawbacks of p-values and statistical significance
	5.4 What the research on the hot hand in basketball tells us about the existence of other life in the universe
	5.5 What does an insignificant estimate tell you?
	5.6 Statistical significance is not the goal
	5.7 Chapter summary
6 What could go wrong when estimating causal effects?
	6.1 How to judge a research study
	6.2 Exogenous (good) variation vs. endogenous (bad) variation
	6.3 Setting up the problem for estimating a causal effect
	6.4 The big questions for what could bias the coefficient estimate
	6.5 How to choose the best set of control variables (model selection)
	6.6 What could bias the standard errors and how do you fix it?
	6.7 What could affect the validity of the sample?
	6.8 What model diagnostics should you do?
	6.9 Make sure your regression analyses/interpretations do no harm
	6.10 Applying the big questions to studies on estimating divorce effects on children
	6.11 Applying the big questions to nutritional studies
	6.12 Chapter summary: a review of the big questions
7 Strategies for other regression objectives
	7.1 Strategies for forecasting/predicting an outcome
	7.2 Strategies for determining predictors of an outcome
	7.3 Strategies for adjusting outcomes for various factors
	7.4 Summary of the strategies for each regression objective
8 Methods to address biases
	8.1 Fixed-effects
	8.2 A thorough example of fixed effects
	8.3 An alternative to the fixed-effects estimator
	8.4 Random effects
	8.5 First-differences
	8.6 Difference-in-differences
	8.7 Two-stage Least Squares (instrumental-variables)
	8.8 Regression discontinuities
	8.9 Case study: research on how divorce affects children
	8.10 Knowing when to punt
	8.11 Chapter summary
9 Other methods besides Ordinary Least Squares
	9.1 Types of outcome variables
	9.2 Dichotomous outcomes
	9.3 Ordinal outcomes – ordered models
	9.4 Categorical outcomes – Multinomial Logit Model
	9.5 Censored outcomes – Tobit models
	9.6 Count variables – Negative Binomial and Poisson models
	9.7 Duration models
	9.8 Chapter summary
10 Time-series models
	10.1 The components of a time-series variable
	10.2 Autocorrelation
	10.3 Autoregressive models
	10.4 Distributed-lag models
	10.5 Consequences of and tests for autocorrelation
	10.6 Stationarity
	10.7 Vector Autoregression
	10.8 Forecasting with time series
	10.9 Chapter summary
11 Some really interesting research
	11.1 Can discrimination be a self-fulfilling prophecy?
	11.2 Does Medicaid participation improve health outcomes?
	11.3 Estimating peer effects for academic outcomes
	11.4 How much does a GED improve labor-market outcomes?
12 How to conduct a research project
	12.1 Choosing a topic
	12.2 Conducting the empirical part of the study
	12.3 Writing the report
13 Summarizing thoughts
	13.1 Be aware of your cognitive biases
	13.2 What betrays trust in published studies
	13.3 How to do a referee report responsibly
	13.4 Summary of the most important points and interpretations
	13.5 Final words of wisdom
Appendix of background statistical tools
	A.1 Random variables and probability distributions
	A.2 The normal distribution and other important distributions
	A.3 Sampling distributions
	A.4 Desired properties of estimators
Glossary
Index