Content validity and face validity are both types of measurement validity.
- Content validity refers to the degree to which the items or questions on a measure accurately reflect all elements of the construct or concept that’s being measured. It assesses whether the items are accurate, relevant, and comprehensive in measuring the construct.
- Face validity refers to the degree to which a measure seems to be measuring what it claims to measure. It assesses whether the measure appears to be relevant.
Continue reading: What is the difference between content validity and face validity?
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.
Continue reading: Is age ordinal data?
Ordinal is the second level of measurement. It has two main properties:
- Ordinal data can be grouped into categories
- Ordinal data can be ranked in a logical order (e.g., low, medium, high)
Continue reading: What are properties of ordinal data?
Ordinal data and interval data are similar because they can both be ranked in a logical order. However, for interval data, the differences between adjacent scores are equal.
Continue reading: What is the difference between ordinal and interval data?
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).
Continue reading: Is ordinal data qualitative or quantitative?
You can’t use an ANOVA test if the nominal data is your dependent variable. The dependent variable needs to be continuous (interval or ratio data).
The independent variable for an ANOVA should be categorical (either nominal or ordinal data).
Continue reading: Can you use nominal data in an ANOVA test?
No, nominal data can only be assigned to categories that have no inherent order to them.
Categorical data with categories that can be ordered in a meaningful way is called ordinal data.
Continue reading: Does nominal data involve the use of variables that have been rank ordered?
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).
Continue reading: What is the difference between nominal and ordinal data?
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.
Continue reading: What type of information does data at the nominal level describe?
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.
Continue reading: Are data at the nominal level of measurement quantitative or qualitative?
