Models in Statistical Social Research provides a comprehensive insight of models used in statistical social research based on statistical data and methods. While traditionally understood statistical models relate to data generating processes which presuppose facts, this book focuses on analytical models which relate to substantial processes generating social facts. It formally develops individual-level, population-level, and multilevel versions of such models and uses these models as frameworks for the definition of notions of functional causality.
The book further develops a distinction between the representation of states and events, which is then used to formally distinguish between comparative and dynamic notions of causality. It is shown that, due to the involvement of human actors in substantial processes considered in social research, the conceptual framework of randomized experiments is of only limited use. Instead, modelling selection processes should become an explicit task of social research.
1. Variables and Relations 1.1 Variables and Distributions 1.2 Relations 2. Notions of Structure 2.1 Statistical Notions of Structure 2.2 Taking Relations into Account 3. Processes and Process Frames 3.1 Historical and Repeatable Processes 3.2 Time Series and Statistical Processes 3.3 Stochastic Process Frames 4. Functional Models 4.1 Deterministic Models 4.2 Models with Stochastic Variables 4.3 Exogenous and Unobserved Variables 5. Functional Causality 5.1 Functional Causes and Conditions 5.2 Ambiguous References to Individuals 5.3 Isolating Functional Causes 6. Models and Statistical Data 6.1 Functional Models and Data 6.2 Experimental and Observational Data 6.3 Interventions and Reference Problems 7. Models with Event Variables 7.1 Situations and Events 7.2 Event Models with Time Axes 7.3 Dynamic Causality 8. Multilevel and Population-level Models 8.1 Conceptual Frameworks 8.2 Models of Statistical Processes 8.3 Functional Causality and Levels