The variable age can be measured at the ordinal or ratio level.
If you ask participants to provide you with their exact age (e.g., 28), the data is ratio level.
If you ask participants to select the bracket that contains their age (e.g., 26–35), the data is ordinal.
Ordinal data and ratio data are similar because they can both be ranked in a logical order. However, for ratio data, the differences between adjacent scores are equal and there’s a true, meaningful zero.
Ordinal data andinterval data are similar because they can both be ranked in a logical order. However, for interval data, the differences between adjacent scores are equal.
Ordinal data is usually considered qualitative in nature. The data can be numerical, but the differences between categories are not equal or meaningful. This means you can’t use them to calculate measures of central tendency (e.g., mean) or variability (e.g., standard deviation).
Nominal data and ordinal data are similar because they can both be grouped into categories. However, ordinal data can be ranked in a logical order (e.g., low, medium high), whereas nominal data can’t (e.g., male, female, nonbinary).
Data at the nominal level of measurement typically describes categorical or qualitative descriptive information, such as gender, religion, or ethnicity.
Contrary to ordinal data, nominal data doesn’t have an inherent order to it, so you can’t rank the categories in a meaningful order.
Data at the nominal level of measurement is qualitative.
Nominal data is used to identify or classify individuals, objects, or phenomena into distinct categories or groups, but it does not have any inherent numerical value or order.
You can use numerical labels to replace textual labels (e.g., 1 = male, 2 = female, 3 = nonbinary), but these numerical labels are random and are not meaningful. You could rank the labels in any order (e.g., 1 = female, 2 = nonbinary, 3 = male). This means you can’t use these numerical labels for calculations.
A pre-experimental design is a simple research process that happens before the actual experimental design takes place. The goal is to obtain preliminary results to gauge whether the financial and time investment of a true experiment will be worth it.
Pre-experimental design exampleA researcher wants to investigate the effect of a new type of meditation on stress levels in college students. They decide to conduct a small pre-experiment with 10 college students who are already interested in meditation.
The students are asked to participate in a 30-minute meditation session once a week for 4 weeks. The students’ stress levels are measured before and after the meditation sessions with a standardized questionnaire.
The researcher compares the outcomes and notices significant differences in stress scores. They decide to move forward with a more costly and time-consuming experiment where they take into account all criteria for an experimental design (e.g., random assignment of participants, control group, controlling for extraneous variables).
