conceptual variable + why operationalise
a conceptual variable is an unobservable construct (e.g., stress) that must be operationalised so it can be measured using observable indicators
strong vs weak operational definition
a strong operational definition is clear, replicable, and accurately represents the construct, while a weak one is vague or poorly reflects the concept
example of operationalising effort/stress
stress can be operationalised as a score on a validated stress questionnaire, as it provides a measureable and consistent indicator
nominal
categories with no order
ordinal
ordered categories
interval
equal intervals without true zero
ratio
equal intervals with a true zero
ordinal vs interval difference
ordinal scales show order without equal spacing, while interval scales have equal intervals between values
importance of true zero (ratio)
a true zero allows meaningful ratio comparisons
what does reliability tell us
reliability indicates the consistency or repeatability of a measure
test-retest reliability
measures consistency over time by repeating the same test
internal reliability (when relevant)
assesses consistency across items in a scale and is most relevant for multi-item questionnaires
why is cronbach’s alpha used
it measures internal consistency of multi-item scales, showing how well items assess the same construct
what does validity tell us
indicates whether a measure accurately measures the intended construct
reliability vs validity
reliability is about consistency while validity is about accuracy
reliable but not valid example
a scale that consistently measures weight 5kg too high is reliable but not valid
why construct validity is overarching
construct validity is overarching because it evaluates whether the measure truly represents the theoretical construct
when to use a bar graph
nominal or ordinal data to compare categories
histogram vs bar graph
continuous (interval/ratio) data while bar graphs are for categorical data
best graph for two continuous variables
a scatter plot
variable on x-axis
the explanatory (predictor) variable is placed on the x-axis because it is assumed to influence the outcome
