RD - Large-N designs 1 (week 4)

Measurement

evaluation of cases with respect to variables

Case

spatially, temporally bounded object, phenomenon or event in the world

A case must be

1) bounded and separable
2) homogenous & stable

Variable

1) operationalised dimension of a concept
2) attribute of a case

Measurement requires consideration of

1) cases (observations)
2) variables of those cases

Levels of Measurement

1) Binary
2) Nominal/categorical
3) Ordinal
4) Interval
5) Ratio

Advantages of Large N Design

1) identify and estimate weak and heterogenous relationships
2) power of many observations to detect a systematic “signal” from the “noisy” data

Caution of Large N Design

1) statistical analysis alone insufficient for causal inference
2) must be coupled with appropriate research design

Pitfalls from Causation to Association

1) causal effect is heterogenous
2) Confounder conceals the association between two variables

Large N design can rule out

1) reversed causality
2) confounders
3) collider bias
WHEN appropriate research design

How does large-N research work?

comparative approach in which evidence across cases (cross-case) is used to evaluate a causal hypothesis