Measurement Invariance for MGA not necessary?!
Posted: Wed Dec 06, 2023 1:46 pm
Hi, I have a question concerning my research. I use PLS-SEM MGA to compare three groups. Each group got a different treatment: eco-friendly fashion, recycled fashion, and second-hand fashion.
Now I wanted to test for measurement invariance. However, I think in this case of research checking for measurement invariance makes no sense, its not necessary. Measurement Invariance testing is done to rule out that possible path differences between the groups stem from different interpretations or meanings of the latent variables by the different respondents of the groups, right? For instance, path differences in a structural model that differentiates three groups divided by country of residence could be the result of translation problems or different (cultural) associations instead of true differences between the groups such as perceived value or attitudes.
However, my research compares a research model across three groups which are divided by three distinct treatments. This means that the path differences between the groups are expected to be the reason of the different treatments, not the reason of the different characteristics of the participants in each group. Eg. To check for perceived monetary value I say “The second hand fashion offers value for money” and “the recycled fashion offers value for money” etc. So the items are obviously differing. Usually, scholars conducting MGA divide their groups by gender or country e.g. while the items of the entire questionnaire are identical. This means that the difference then lies within the specific group characteristics (e.g. gender), not in the questionnaire context. With my reseach, the difference does directly lie in the questionnaire context, not within the characteristics of the respondents; respondents for each group are expected to be very similar to each other. Therefore, testing for measurement invariance is not appropriate. Is that correct? Or is it only about checking the meaning of the latent variables?
Any help would be greatly appreciated!! Thank you!
Now I wanted to test for measurement invariance. However, I think in this case of research checking for measurement invariance makes no sense, its not necessary. Measurement Invariance testing is done to rule out that possible path differences between the groups stem from different interpretations or meanings of the latent variables by the different respondents of the groups, right? For instance, path differences in a structural model that differentiates three groups divided by country of residence could be the result of translation problems or different (cultural) associations instead of true differences between the groups such as perceived value or attitudes.
However, my research compares a research model across three groups which are divided by three distinct treatments. This means that the path differences between the groups are expected to be the reason of the different treatments, not the reason of the different characteristics of the participants in each group. Eg. To check for perceived monetary value I say “The second hand fashion offers value for money” and “the recycled fashion offers value for money” etc. So the items are obviously differing. Usually, scholars conducting MGA divide their groups by gender or country e.g. while the items of the entire questionnaire are identical. This means that the difference then lies within the specific group characteristics (e.g. gender), not in the questionnaire context. With my reseach, the difference does directly lie in the questionnaire context, not within the characteristics of the respondents; respondents for each group are expected to be very similar to each other. Therefore, testing for measurement invariance is not appropriate. Is that correct? Or is it only about checking the meaning of the latent variables?
Any help would be greatly appreciated!! Thank you!