Thanks prof. for your kind reply. Please tell should i go for measurement invariance testing(MICOM) of my groups, if i have created them from the same collected data using cluster analysis on 4 of my observed items (assessing attitude). Although all my respondents are tourists, but they differ in their attitude, so later on after clustering, i want to know differences in them on "experience and satisfaction relationship". Shall i do measurement invariance, or how shall i proceed...?
If your survey items are the exact same and you do not believe that your groups are fundamentally different, then you can proceed without doing an invariance test. Saying that, I almost always see reviewers asking for an invariance test in multi-group analysis even if there is no fundamental difference between the groups. I would say go ahead and perform it because it makes your study look all the more rigorous.
Sir, please tell how to do Invariance of Formative Measures (Structure invariance, Slope invariance, and Residual invariance)? Please share a link of the video if u have one...
If the measures are formative to an unobservable construct, then you do not have to do an invariance test. Invariance testing is for unobserved constructs with reflective measures
If all the groups to be compared are different, then their responses to the questions would also vary, as per their attitude & perception. Then why do we want to establish " Measurement Invariance' before conducting Multigroup analysis?. Although the questions asked are same, but the responses are different for the different groups, so how can they possess measurement Invariance.... Please explain.
With a two group analysis you are looking for differences in the structural relationships across constructs. You are not looking for differences in the measurement properties of the construct across different groups. If you think managers and salespeople are going to interpret a question differently (even if it is worded similarly) then your differences have more to do with how the construct is measured as opposed to group differences. Thus, you would need to perform a measurement model invariance test.
@@joelcollier9387 Thanks prof. for your kind reply. Please tell should i go for measurement invariance testing(MICOM) of my groups, if i have created them from the same collected data using cluster analysis on 4 of my observed items (assessing attitude). Although all my respondents are tourists, but they differ in their attitude, so later on after clustering, i want to know differences in them on "experience and satisfaction relationship". Shall i do measurement invariance, or how shall i proceed...?
When you have a non-significant result from a statistic it doesn't mean that there is no difference. You can't test the invariance with a chi-square. When you have a non-significant result it means that you are not able to reject the null hypothesis, not that you are accepting the null. I am referring to "you want a non-significant difference test to establish metric invariance".
Thank you so much!
Great video! Very helpful! Keep doing them you will go viral one day!
What does it mean to be (un) constrained model, construct constrained? Constrained to be equal?
Thanks prof. for your kind reply. Please tell should i go for measurement invariance testing(MICOM) of my groups, if i have created them from the same collected data using cluster analysis on 4 of my observed items (assessing attitude). Although all my respondents are tourists, but they differ in their attitude, so later on after clustering, i want to know differences in them on "experience and satisfaction relationship". Shall i do measurement invariance, or how shall i proceed...?
If your survey items are the exact same and you do not believe that your groups are fundamentally different, then you can proceed without doing an invariance test. Saying that, I almost always see reviewers asking for an invariance test in multi-group analysis even if there is no fundamental difference between the groups. I would say go ahead and perform it because it makes your study look all the more rigorous.
@@joelcollier9387 Thanks Prof.
Sir, please tell how to do Invariance of Formative Measures (Structure invariance, Slope invariance, and Residual invariance)? Please share a link of the video if u have one...
If the measures are formative to an unobservable construct, then you do not have to do an invariance test. Invariance testing is for unobserved constructs with reflective measures
If all the groups to be compared are different, then their responses to the questions would also vary, as per their attitude & perception. Then why do we want to establish " Measurement Invariance' before conducting Multigroup analysis?. Although the questions asked are same, but the responses are different for the different groups, so how can they possess measurement Invariance.... Please explain.
With a two group analysis you are looking for differences in the structural relationships across constructs. You are not looking for differences in the measurement properties of the construct across different groups. If you think managers and salespeople are going to interpret a question differently (even if it is worded similarly) then your differences have more to do with how the construct is measured as opposed to group differences. Thus, you would need to perform a measurement model invariance test.
@@joelcollier9387 Thanks prof. for your kind reply. Please tell should i go for measurement invariance testing(MICOM) of my groups, if i have created them from the same collected data using cluster analysis on 4 of my observed items (assessing attitude). Although all my respondents are tourists, but they differ in their attitude, so later on after clustering, i want to know differences in them on "experience and satisfaction relationship". Shall i do measurement invariance, or how shall i proceed...?
When you have a non-significant result from a statistic it doesn't mean that there is no difference. You can't test the invariance with a chi-square. When you have a non-significant result it means that you are not able to reject the null hypothesis, not that you are accepting the null. I am referring to "you want a non-significant difference test to establish metric invariance".
Would you please kindly elaborate on what do we have to do to test the invariance then?