Using GEE for analysis of ranking task
Posted by Kirill Orlov on Aug 04, 2016; 9:15am
URL: http://spssx-discussion.165.s1.nabble.com/Using-GEE-for-analysis-of-ranking-task-tp5732854.html
Generalized Estimating Equations (GEE) can be used to perform repeated measures ordinal regression.
Can it be used in situation when the ordinal data are ranking list given by each subject? That is, the RM (within-subject) factor is some p items ranked. And so, for each subject the sum of ranks across the p items is the same constant value, and no two items can have the same rank value. (I.e. we have ordinal "compositional" data.)
A between-subject predictors is, say, a grouping factor.
My propose is that it would be correct to use GEE - after removal of any one item, to untie summing to the constant. Then the only "unusual" thing remaining about the data is the restriction mentioned above that items are not allowed to have same values (within same subject). I guess it is not a counter-indication for GEE. Is it?
And I would probably specify the correlation structure for the GEE as "Exchangeable" - for under the null the correlation between the item variables, which are ranking values, is one value equal to 1/(p-1). GEE will estimate then the effects of GROUP, ITEM, and their interaction.
What is your opinion? Is the approach right for you? What might be alternative approaches to analyze such data - to test, at minimum, the group differences?
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URL: http://spssx-discussion.165.s1.nabble.com/Using-GEE-for-analysis-of-ranking-task-tp5732854.html
Generalized Estimating Equations (GEE) can be used to perform repeated measures ordinal regression.
Can it be used in situation when the ordinal data are ranking list given by each subject? That is, the RM (within-subject) factor is some p items ranked. And so, for each subject the sum of ranks across the p items is the same constant value, and no two items can have the same rank value. (I.e. we have ordinal "compositional" data.)
A between-subject predictors is, say, a grouping factor.
My propose is that it would be correct to use GEE - after removal of any one item, to untie summing to the constant. Then the only "unusual" thing remaining about the data is the restriction mentioned above that items are not allowed to have same values (within same subject). I guess it is not a counter-indication for GEE. Is it?
And I would probably specify the correlation structure for the GEE as "Exchangeable" - for under the null the correlation between the item variables, which are ranking values, is one value equal to 1/(p-1). GEE will estimate then the effects of GROUP, ITEM, and their interaction.
What is your opinion? Is the approach right for you? What might be alternative approaches to analyze such data - to test, at minimum, the group differences?
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