introduction to statistics

Data

Variables

Nominal, ordinal, numerical, inteval, ration, discrete, continious

Validity & Reliability

Reliability: is it replicatible? Consisteant coding rule that produce the same results when done by other researchers.

Measurement bias: over/under reporting the atual values of a given measure for some or all cases. Random error is fine, but when systematic = problem. If high-income respondents are shy about reporting their true income, this will create a false representation of reality.

Validity: a measure that accurately represent the concept. Face validity = common sense. Are test scores a good proxy for intelligence.

Construct/external validity: is our measure related to other measures with which we would expect a strong relationship?

Relationship between valid and reliable results. Results can be reliable but not valid. We may have controlled for all biases and ensured complete robustness but our measure for democracy might be density of politicians (who can be autocratic elites or elected representatives).

Causal inference

Operationalization

How do we measure democracy? Concepts in social science can often be vague and abstract. However, somehow we have to define it in a way that can be measured.

Think about concept clairity. Are elections a good proxy for democracy?

Testing hypotheses

Rejecting the null-hypothesis should be tough. Although we might be sure about the theory that supports our hypothesis, we must accept the possiblity that we can be wrong. Therefore, we must beware the desire to interpret a result that confirms our prejudice.

Importance of robustness

Garbage in, garbage out. If we are not diligent about the data, the methodology, and robustness testing we use then we cannot claim that our results are valid or reliable.

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