1/10
Looks like no tags are added yet.
Name | Mastery | Learn | Test | Matching | Spaced |
---|
No study sessions yet.
choosing a statistical test
levels of measurement
helps decide which statistical test should be used
nominal, ordinal or interval
nominal data
categorical data
e.g. if a researcher was interested to know if more students doing A-Level Psychology went to a school or college, the data would be categorised as either ‘school’ or ‘college’: two distinct categories
if the data is nominal, then each participant will only appear in one category (discrete data)
ordinal data
ordered or ranked data
e.g. 1st, 2nd, 3rd
is subjective
does not allow for differences in response values to be measured
Likert scales can be used to collect data
usually qualitative
interval data
when there are equal intervals on a measurement scale
e.g. temperature
uses recognised methods of measurement
e.g. seconds, metres, kg
difference between values can be measured
objective and scientific
strength of nominal data
easily generated from closed questions on a questionnaire or interview
limitation of nominal data
has no scale of difference so cannot express the complexity of the data and can be seen as too simple
strength of ordinal data
gives more detail than nominal data as the scores are ordered in a linear fashion e.g. from highest to lowest
limitation of ordinal data
intervals are not of equal value so an average cannot be used
strength of interval data
most informative as the intervals are of equal value so more reliable
limitation of interval data
You can’t say “twice as much” because interval data has no true zero.
Example: 100°C is not twice as hot as 50°C because 0 is not the starting point
ratios are not meaningful