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Create flyerMachine Learning for Social and Behavioral Research
Ross Jacobucci, Kevin J. Grimm, and Zhiyong Zhang
Request a free digital professor copy on VitalSource ?1. Introduction
- Why the Term Machine Learning?
- Why do We Need Machine Learning?
- How is this Book Different?
- Definitions
- Software
- Datasets
2. The Principles of Machine Learning Research
- Overview
- Principle #1: Machine Learning is Not Just Lazy Induction
- Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description
- Principle #3: Labeling a Study as Exploratory or Confirmatory is too Simplistic
- Principle #4: Report Everything
- Summary
3. The Practices of Machine Learning
- Comparing Algorithms and Models
- Model Fit
- Bias-Variance Tradeoff
- Resampling
- Classification
- Conclusion
II. Algorithms for Univariate Outcomes
4. Regularized Regression
- Linear Regression
- Logistic Regression
- Regularization
- Rationale for Regularization
- Alternative Forms of Regularization
- Bayesian Regression
- Summary
- Introduction
- Decision Tree Algorithms
- Miscellaneous Topics
6. Ensembles
- Bagging
- Random Forests
- Gradient Boosting
- Interpretation
- Empirical Example
- Important Notes
- Summary
III. Algorithms for Multivariate Outcomes
7. Machine Learning and Measurement
- Defining Measurement Error
- Impact of Measurement Error
- Assessing Measurement Error
- Weighting
- Alternative Methods
- Summary
8. Machine Learning and Structural Equation Modeling
- Latent Variables as Predictors
- Predicting Latent Variables
- Using Latent Variables as Outcomes and Predictors
- Can Regularization Improve Generalizability in SEM?
- Nonlinear Relationships and Latent Variables
- Summary
9. Machine Learning with Mixed-Effects Models
- Mixed-Effects Models
- Machine Learning with Clustered Data
- Regularization with Mixed-Effects Models
- Illustrative Example
- Additional Strategies for Mining Longitudinal Data
- Summary
10. Searching for Groups
- Finite Mixture Model
- Structural Equation Model Trees
- Summary
IV. Alternative Data Types
11. Introduction to Text Mining
- Key Terminology
- Data
- Basic Text Mining
- Text Data Preprocessing
- Basic Analysis of the Teaching Comment Data
- Sentiment Analysis
- Topic Models
- Summary
12. Introduction to Social Network Analysis
- Network Visualization
- Network Statistics
- Basic Network Analysis
- Network Modeling
- Summary
References
