Construct validity refers to the extent to which a study measures the underlying concept or construct that it is supposed to measure.
Internal validity refers to the extent to which observed changes in the dependent variable are caused by the manipulation of the independent variable rather than other factors, such as extraneous variables or research biases.
Construct validity vs. internal validity exampleYou’re studying the effect of exercise on happiness levels.
Construct validity would ask whether your measures of exercise and happiness levels accurately reflect the underlying concepts of physical activity and emotional state.
Internal validity would ask whether your study’s results are due to the exercise itself, or if some other factor (e.g., changes in diet or stress levels) might be causing changes in happiness levels.
Content validity and face validity are both types of measurement validity. Both aim to ensure that the instrument is measuring what it’s supposed to measure.
However, content validity focuses on how well the instrument covers the entire construct, whereas face validity focuses on the overall superficial appearance of the instrument.
The best way for a researcher to judge the face validity of items on a measure is by asking both other experts and test participants to evaluate the instrument.
The combination of experts with background knowledge and research experience, along with test participants who form the target audience of the instrument, provides a good idea of the instrument’s face validity.
Face validity refers to the extent to which a research instrument appears to measure what it’s supposed to measure. For example, a questionnaire created to measure customer loyalty has high face validity if the questions are strongly and clearly related to customer loyalty.
Construct validity refers to the extent to which a tool or instrument actually measures a construct, rather than just its surface-level appearance.
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.
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 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.
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).
