Research Methods- Levels Of Measurement Flashcards
Nominal data:
Often referred to as categorical data, the frequency count of a particular variable is recorded at this level of measurement. These variables are discrete (they don’t overlap), and the categories have no natural order.
What can psychologists not discuss?
Nominal data
When using nominal data other than frequency, psychologists can’t discuss differences between each category.
Examples of nominal data
Examples of nominal data: Country of birth, career choice, and taste in music.
Ordinal data:
it has the same properties as nominal data (also a form of categorical datal: however, the categories have a natural order. The difference between each point in an ordinal scale is not consistent.
Examples of ordinal data:
Examples of ordinal data: include positions in a competition (1st, 2nd, 3rd).
Choice on a Likert scale (e g. how happy do you feel 1-7) and relative height among a group of people (tall, medium, small / tallest to smallest)
Interval data
Interval scales are precise due to having the same distance (equal intervals) between each adjacent point in a standardised scale. Interval data is continuous; the value recorded could be any point on the scale used (e.g. 9.69872375 seconds 24.5604315 grams), not limited to a small set of discrete categories.
Examples of interval data
Examples of interval data: Weight in grams, length in millimetres, temperature in Celsius, and time in seconds.
Ratio data
is interval data with an absolute zero point but is treated as interval data in this course. e.g. Temperature in Kelvin as it cannot go below 0 degrees.
Converting between types of data:
it is possible to convert from a higher level of measurement to a lower level of measurement (but not the other way around). Interval can be converted into ordinal, and ordinal can be converted into nominal.
Converting interval to ordinal
Start with participant interval scores, for example, biological measures like galvanic skin response, reaction times or psychometric scores (e.g. IQ, personality, depression, aggression) on a standardised test.
Each participant is assigned a rank score to turn the interval measure into an ordinal measure. This is done by listing each participant from the highest scoring to the lowest scoring (using the interval measurement to place each participant). Any participants with the same interval score share the same rank position.
Converting ordinal to nominal
To convert ordinal data to nominal data, separate categories are created.
E.g. fast reaction/slow reaction, intelligent/unintelligent, extravert/introvert, depressed/happy, aggressive/passive. The highest-ranked half of the participants are assigned to one category, and the other half to the other category.
