CMIST afternoon seminar: When “contexts” are geographical areas, is multilevel model still a good choice to model hierarchical data, or a new approach is needed?
|Starts:||16:00 29 Sep 2015|
|Ends:||17:30 29 Sep 2015|
|What is it:||Seminar|
|Organiser:||School of Social Sciences|
|Who is it for:||University staff, Alumni, Current University students, General public|
|Speaker:||Dr Guanpeng Dong|
It is very common that our research uses hierarchical data where the higher level units or “contexts” are defined as geographical areas—for example, individuals nest into census units or houses into districts. In such situations, we need to think about three questions:
(1) are lower-level units correlated with each other if they are in the same “context” or group?
(2) are the interactions or correlations among lower-level units strictly bounded within “contexts” or groups?
(3) are contexts themselves independent of each other? The first effect is referred to as a vertical group dependence effect. The latter two can be considered as horizontal dependence effects at each level of the data hierarchy.
If the last two dependence effects were suspected, standard multilevel models would not be a good modelling choice. Instead, an integrated spatial and multilevel model could be used to deal with the vertical and horizontal dependence simultaneously.
Dr Guanpeng Dong
Role: Research Associate
Organisation: Sheffield Methods Institute, the University of Sheffield
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Humanities Bridgeford Street