Statistical Models and Social Science

1.1 Statistical Models and Social Reality

KEY:

1. complex society v.s statistical models
2. relationship,data,descriptive accuracy

With few exceptions, statistical data analysis describes the outcomes of real social processes
and not the processes themselves. It is therefore important to attend to the descriptive
accuracy of statistical models and to refrain from reifying them.

1.2 Observation and Experiment

KEY:

1. causal model
2. spurious v.s causal

Causal inferences are most certain—if not completely definitive—in randomized experiments,
but observational data can also be reasonably marshaled as evidence of causation.
Good experimental practice seeks to avoid confounding experimentally manipulated explanatory
variables with other variables that can influence the response variable. Sound analysis
of observational data seeks to control statistically for potentially confounding variables.

In analyzing observational data, it is important to distinguish between a variable that is a
common prior cause of an explanatory and response variable and a variable that intervenes
causally between the two.

It is overly restrictive to limit the notion of statistical causation to explanatory variables
that are manipulated experimentally, to explanatory variables that are manipulable in
principle, or to data that are collected over time.

1.3 Populations and Samples

KEY:

1. prototypical experiment

Randomization and good sampling design are desirable in social research, but they are
not prerequisites for drawing statistical inferences. Even when randomization or random
sampling is employed, we typically want to generalize beyond the strict bounds of statistical
inference.