Interval data is usually considered continuous data because it can take on a wide range of numerical values, including decimals. The values are measured on a scale where the distance between each point is meaningful and consistent.
For example, temperature measured in Celsius is interval data and can take values such as 20°C, 20.5°C, or 20.75°C. Each value represents a meaningful point on the scale.
However, not all interval data is continuous. Some interval variables are recorded as whole numbers only. For example, standardized test scores are measured on an interval scale but are often reported as discrete values because only certain scores are possible.
In short, interval data describes the measurement scale, while continuous data describes how values can occur. Many types of interval data are continuous, but the two terms are not interchangeable.
Structured interviews: Follow a predefined set of questions, with both the topics and order of questions determined in advance.
Semi-structured interviews: Use a set of planned questions or themes while allowing the interviewer to introduce additional questions as the conversation develops.
Unstructured interviews: Do not follow a predetermined list of questions, allowing the discussion to develop naturally based on participants’ responses.
Focus group interviews: Involve asking questions to a group of participants rather than an individual, with the goal of exploring shared perspectives, discussions, and group dynamics.
Some variables have fixed levels. For example, gender and ethnicity are always nominal level data because they cannot be ranked.
However, for other variables, you can choose the level of measurement. For example, income is a variable that can be recorded on an ordinal or a ratio scale:
At an ordinal level, you could create 5 income groupings and code the incomes that fall within them from 1–5.
At a ratio level, you would record exact numbers for income.
If you have a choice, the ratio level is always preferable because you can analyze data in more ways. The higher the level of measurement, the more precise your data is.
The level at which you measure a variable determines how you can analyze your data.
Depending on the level of measurement, you can perform different descriptive statistics to get an overall summary of your data and inferential statistics to see if your results support or refute your hypothesis.
