Levels of Measurement for Qualitative and Quantitative Data (Vocabulary Flashcards)

Vocabulary flashcards covering levels of measurement for qualitative and quantitative data, including nominal, ordinal, interval, and ratio scales, as well as discrete vs continuous data and examples.

Nominal level

Qualitative data with no inherent order; categories are named (e.g., political affiliation: Democrat, Republican, Independent).

Ordinal level

Qualitative data with a meaningful order but unequal intervals (e.g., heat levels: low, medium, high; movie ratings 1-5).

Qualitative data

Data describing categories or attributes; can be nominal or ordinal.

Level of pain (ordinal)

Pain intensity categories with order but not equal intervals (low, medium, high).

Survey scale (ordinal)

Likert-type responses (strongly disagree to strongly agree) that are ordered but not necessarily equally spaced.

Discrete data

Quantitative data that take only specific values (e.g., number of prerequisites).

Continuous data

Quantitative data that can take on any value within an interval (e.g., weight, height).

Interval level

Quantitative scale with equal intervals and no true zero; differences meaningful but ratios are not (e.g., Fahrenheit, calendar years).

Ratio level

Quantitative scale with equal intervals and a true zero; both differences and ratios are meaningful (e.g., mass, length, time).

Zero as absence (0 on interval scales)

On interval scales, zero does not represent absence of the attribute (e.g., 0° Fahrenheit does not mean no heat).

Difference vs. ratio on interval scales

On interval scales, differences have meaning; ratios do not (e.g., 20° - 10° is meaningful, but 20°/10° is not).

Calendar years as interval data

Year values have equal spacing but no true zero; ratios are not meaningful.

Shoe sizes as discrete data

Discrete quantitative values with steps (e.g., 7, 7.5, 8, 8.5).

Mass as ratio data

Mass has a true zero and both differences and ratios are meaningful (e.g., 4 g is twice 2 g).