3.2 - Levels & Types of Data
Levels of data measurement
Data can be measured at different levels, each with distinct characteristics that determine how it can be collected, analysed, and interpreted.
Categories of data measurement levels
- Nominal level data - This level involves recording data in named categories without any inherent order or numerical value.
- Ordinal level data - Data at this level is recorded as points along a scale where the order matters, but the gaps between points are not necessarily equal.
- Interval level data - This level records data on a scale where the gaps between points are equal.
Types of data: quantitative and qualitative
Data can also be classified based on its nature, distinguishing between numerical and descriptive forms.
Distinguishing between data types
- Quantitative data - This refers to numerical data.
- Qualitative data - This consists of descriptive data.
Sources of data: primary and secondary
Data can be obtained from different sources, depending on whether it is collected directly by the researcher or derived from existing research.
Origins of data collection
- Primary data - This is data collected first-hand directly from the sample.
- Secondary data - This refers to data obtained from research conducted by others.
Common misconceptions about data types
Misunderstandings about data types can lead to errors in research design and analysis.
Key clarification on data types
- Confusing quantitative with qualitative data - A frequent error is mixing up these two types. Quantitative data deals with measurable quantities (how much/how many), while qualitative data focuses on descriptive qualities. Although qualitative data can sometimes be quantified using tools like rating scales—for example, assigning numbers to mood states—it remains fundamentally descriptive in nature.
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