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A Guide on Data Analysis
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Table of contents
Preface
1
Introduction
2
Prerequisites
I. BASIC
3
Descriptive Statistics
4
Basic Statistical Inference
II. REGRESSION
5
Linear Regression
6
Non-Linear Regression
7
Generalized Linear Models
8
Linear Mixed Models
9
Nonlinear and Generalized Linear Mixed Models
10
Nonparametric Regression
III. RAMIFICATIONS
11
Data
12
Variable Transformation
13
Imputation (Missing Data)
14
Model Specification Tests
15
Variable Selection
16
Hypothesis Testing
17
Marginal Effects
18
Moderation
19
Mediation
20
Prediction and Estimation
IV. CAUSAL INFERENCE
21
Causal Inference
A. EXPERIMENTAL DESIGN
22
Experimental Design
23
Sampling
24
Analysis of Variance
25
Multivariate Methods
B. QUASI-EXPERIMENTAL DESIGN
26
Quasi-Experimental Methods
27
Regression Discontinuity
28
Temporal Discontinuity Designs
29
Synthetic Difference-in-Differences
30
Difference-in-Differences
31
Changes-in-Changes
32
Synthetic Control
33
Event Studies
34
Instrumental Variables
35
Matching Methods
C. OTHER CONCERNS
36
Endogeneity
37
Other Biases
38
Directed Acyclic Graphs
39
Controls
V. MISCELLANEOUS
40
Report
41
Exploratory Data Analysis
42
Sensitivity Analysis/ Robustness Check
43
Replication and Synthetic Data
44
High-Performance Computing
APPENDIX
References
A
Appendix
B
Bookdown cheat sheet
References
C
Chapter: Cluster Randomization and Interference Bias
View book source
References
44
High-Performance Computing
A
Appendix
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