A Guide on Data Analysis
Preface
- How to cite these books
1 Introduction
- 1.1 General Recommendations
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
- 12.1 Continuous Variables
- 12.2 Categorical Variables
13 Imputation (Missing Data)
14 Model Specification Tests
15 Variable Selection
16 Hypothesis Testing
17 Marginal Effects
18 Moderation
19 Mediation
- 19.1 Traditional Approach
- 19.2 Causal Inference Approach to Mediation
  - 19.2.1 Example: Traditional Mediation Analysis
  - 19.2.2 Two Approaches in Causal Mediation Analysis
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
- 29.1 Understanding
- 29.2 Application
  - 29.2.1 Block Treatment
  - 29.2.2 Staggered Adoption
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
- 36.1 Endogenous Treatment
- 36.2 Endogenous Sample Selection
37 Other Biases
38 Directed Acyclic Graphs
39 Controls
V. MISCELLANEOUS
40 Reporting Your Analysis
41 Exploratory Data Analysis
42 Sensitivity Analysis and Robustness Checks
43 Replication and Synthetic Data
44 High-Performance Computing
APPENDIX
A Appendix
B Bookdown cheat sheet
References
Published with bookdown

A Guide on Data Analysis

9.7 Summary

A flowchart showing the hierarchy of regression models. At the top is 'Generalized Linear Mixed Model,' which branches into 'Non-linear Mixed Model,' 'Linear Mixed Model,' and 'Generalized Linear Model.' These in turn connect to simpler models: 'Non-linear Regression' and 'Linear Regression.' Arrows indicate derivation or conceptual specialization.

Figure 9.10: Taxonomy of Regression Models