A Minimal Book Example
1
Workspace
1.1
Packages
1.2
Directory Path
1.3
Introduction
1.4
Codebook
1.5
Navigating This Tutorial
2
Data Cleaning
2.1
Berlin Aging Study (BASE-I)
2.1.1
Load Data
2.1.2
Recoding & Reverse Scoring
2.1.3
Covariates
2.1.4
Personality Variables
2.1.5
Outcome Variables
2.1.6
Combine Data
2.2
Einstein Aging Study (EAS)
2.2.1
Load Data
2.2.2
Recoding & Reverse Scoring
2.2.3
Covariates
2.2.4
Personality Variables
2.2.5
Outcome Variables
2.2.6
Combine Data
2.3
German Socioeconomic Panel (GSOEP)
2.3.1
Load Data
2.3.2
Recoding & Reverse Scoring
2.3.3
Covariates
2.3.4
Personality Variables
2.3.5
Outcome Variables
2.3.6
Combine Data
2.4
Household, Income, and Labour Dynamics in Australia (HILDA)
2.4.1
Load Data
2.4.2
Recoding & Reverse Scoring
2.4.3
Covariates
2.4.4
Personality Variables
2.4.5
Outcome Variables
2.4.6
Combine Data
2.5
Health and Retirement Study (HRS)
2.5.1
Load Data
2.5.2
Recoding & Reverse Scoring
2.5.3
Covariates
2.5.4
Personality Variables
2.5.5
Outcome Variables
2.5.6
Combine Data
2.6
Longitudinal Aging Study Amsterdam (LASA)
2.6.1
Load Data
2.6.2
Recoding & Reverse Scoring
2.6.3
Covariates
2.6.4
Personality Variables
2.6.5
Outcome Variables
2.6.6
Combine Data
2.7
MAP
2.7.1
Load Data
2.7.2
Recoding & Reverse Scoring
2.7.3
Covariates
2.7.4
Personality Variables
2.7.5
Outcome Variables
2.7.6
Combine Data
2.8
MARS
2.8.1
Recoding & Reverse Scoring
2.8.2
Covariates
2.8.3
Personality Variables
2.8.4
Outcome Variables
2.8.5
Combine Data
2.9
ROS
2.9.1
Load Data
2.9.2
Recoding & Reverse Scoring
2.9.3
Covariates
2.9.4
Personality Variables
2.9.5
Outcome Variables
2.9.6
Combine Data
2.10
Origins of the Variances of the Oldest-Old: Octogenarian Twins (OCTO-TWIN)
2.10.1
Load Data
2.10.2
Recoding & Reverse Scoring
2.10.3
Covariates
2.10.4
Personality Variables
2.10.5
Outcome Variables
2.10.6
Combine Data
2.11
Swedish Adoption Twin Study of Aging (SATSA)
2.11.1
Load Data
2.11.2
Recoding & Reverse Scoring
2.11.3
Covariates
2.11.4
Personality Variables
2.11.5
Outcome Variables
2.11.6
Combine Data
2.12
Seattle Longitudinal Study (SLS)
2.13
Descriptives of All Studies
3
Method 1: Pooled One Stage Models without Study-Specific Effects
3.1
Step 1: Combine Data
3.1.1
Study-Level Moderators
3.1.2
Harmonize Data
3.1.3
Save Data Files
3.2
Step 2: Run Models and Extract Results
3.2.1
Method 1A: Linear Regression
3.2.2
Part 1B: Pooled Linear Regression with Cluster Robust Standard Errors
4
Method 2: Pooled One Stage Models with Study-Specific Effects
4.1
Step 1: Combine Data
4.1.1
Study-Level Moderators
4.1.2
Harmonize Data
4.1.3
Save Data Files
4.2
Step 2: Run Models and Extract Results
4.2.1
Method 2A: Pooled One Stage Models with Dummy Codes
4.2.2
Method 2B: Pooled One Stage Models with Random Effects
5
Method 3: Two-Stage Individual Participant Meta-Analysis
5.1
Analytic Plan
5.1.1
1. Sample-Specific Statistical Modeling
5.1.2
2. Results Pooling Using Meta-Analysis
5.2
Step 1: Combine Data
5.2.1
Study-Level Moderators
5.2.2
Harmonize Data
5.2.3
Save Data Files
5.3
Step 2: Run Models for Each Study
5.3.1
Functions
5.3.2
Run Models
5.4
Step 3: Meta-Analyze Results
5.4.1
Functions
5.4.2
Run Meta-Analysis and Meta-Regression Models
5.4.3
Compile Results
5.4.4
Figures
5.4.5
Table Meta-Analytic Heterogeneity
5.5
Sample Results Section
6
Method 3: One-Stage Individual Participant Analyses Reported Together
6.1
Analytic Plan
6.2
Step 1: Combine Data
6.2.1
Harmonize Data
6.2.2
Save Data Files
6.3
Step 2: Run Models for Each Study
6.3.1
Functions
6.3.2
Run Models
6.3.3
Compile Results
6.3.4
Figures
6.4
Sample Results Section
7
Method 3: One-Stage Individual Participant Analyses Reported Together
7.1
Comparisons Across the Taxonomy
7.1.1
Tables
7.1.2
Figures
7.2
Comparisons Across Methods: Bayesian versus Frequentist
7.2.1
Tables
7.2.2
Figures
References
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A Taxonomy of Data Synthesis: A Tutorial
References