Review of Causal Analysis  Guidelines for Theory Building  The Structure of a Causal Argument (And Key Assumptions)  Common Problems with Operationalization.

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Presentation transcript:

Review of Causal Analysis  Guidelines for Theory Building  The Structure of a Causal Argument (And Key Assumptions)  Common Problems with Operationalization and the Gathering of Evidence

Guidelines for Building Theory  Falsifiability  Replicability  Significance (the “so what?” test)  Plausibility  Operationalizability  Parsimony / Communicability

The Structure of a Causal Argument Explanatory Variables Dependent Variable Causal Directionality

The Structure of a Causal Argument Explanatory Variables Dependent Variable Causal Directionality

The Structure of a Causal Argument Explanatory Variables Dependent Variable Causal Directionality

The Structure of a Causal Argument Explanatory Variables Dependent Variable Causal Directionality Rules for Setting Up an Independent Variable:  Unit homogeneity – units of the explanatory variable across time and space are homogenous.  Conditional independence – values on the explanatory variables are independent of the values taken by the dependent variable.

The Structure of a Causal Argument Explanatory Variables Dependent Variable Causal Directionality Rules for Setting Up a Dependent Variable:  Dependent variable must be dependent (explanatory variable is exogenous).  Dependent variables must vary. (Case selection must allow DV to vary).

Operationalization What are you going to look at to measure the variable in question? Indicators are the components of change on a given variable, they take different values across cases (observations) and/or over time. Indicators must be valid for the variable that they are measuring. A valid indicator for temperature: degrees on a thermostat. An invalid indicator for lying: what a polygraph measures.

Common Problems with Case Selection  Selection on the dependent variable.  Omitted variable bias.  Case selection that does not allow variables to vary.  Selection on outliers.  Data gathering bias.  Measurement error.

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