Types of Data
Fundamental Classification of Data Responses
Data is primarily categorized by the nature of the responses collected and the mathematical utility of those responses.
Responses can be divided into three primary types:
Words or phrases.
Numbers with which calculation is NOT performed.
Numbers with which calculation IS performed.
Qualitative Data
Qualitative data consists of responses that are descriptive in nature, including words, phrases, and numbers that are not used for mathematical operations.
The classification of qualitative data depends on whether the responses possess a specific sense of order or ranking:
Nominal Data: These are words or phrases that do not have an inherent order or rank. They are used for naming or labeling variables without any quantitative value.
Ordinal Data: These are words or phrases that have a clear sense of order or ranking. In this category, the sequence or relative position of the data points is meaningful.
Quantitative Data
Quantitative data consists of numbers that are used for calculations and mathematical analysis.
Quantitative data is further divided into two specific types based on how the values are derived:
Discrete Data: This involves whole numbers and counts (e.g., counting individual items).
Continuous Data: This involves measurements (e.g., height, weight, temperature) that can take any value within a range.
Levels of Measurement and the Significance of Zero
When dealing with quantitative numbers, the meaning and implication of the value within the context of the data determines the level of measurement.
Interval Level:
At the interval level, the value acts as a placeholder or a random starting point rather than indicating the total absence of a quality.
Data at this level can include values that are negative.
Ratio Level:
At the ratio level, the value carries its usual meaning, implying "none" or "nothing."
This is considered the standard interpretation of zero in most mathematical and physical contexts.