Key terms:
Conceptualization: A researcher’s clarification of how they are defining a concept
Operationalization - Turning a conceptualization into a measure using specific, observable indicators
Valid - Measure has to actually measure the concept
Reliable - Measure has to work every time, even in different contexts
Explanatory variable: The supposed CAUSE
Outcome variable: The supposed EFFECT
Confounding variable
General notes:
Consider what questions are even being asked and by whom
Key parts of a project’s validity
Measurement (internal validity)
Operationalization methods
Ethnography
Surveys
Interviews
Four types of measurement validity
Face - measure seems reasonable/makes sense
Content - all possible answers are included in measure
Criterion - answers are comparable to answers that would be received from the ideal possible question (gold standard)
Construct - measure is supported by other measures for the same concept
Two ways to test reliability
Test-retest reliability - asks the same question at two different points in time
Alternate forms - Asks the same question two different ways
Generalizability (external validity)
Possible bias in sampling
Selection bias - not everyone has an equal chance of being selected for the sample
Non-response bias - cases that are selected in the sample are not actually included in the sample
Causality (Causal validity) - explanatory research questions
Are X and Y correlated?
Types of causal explanations
Idiographic
Focus on a single event and single set of people
Personal troubles
Nomothetic
Focus on patterns of events that happen to patterns of people
Public Issues
Time/temporal order - does X happen before Y
Non-spuriousness - there’s not something causing both X and Y to change at once
X doesn’t have to be the only cause of Y
Only spurious if X isn’t the real cause of Y
Mechanism - why could X cause Y?