Dear All,
I am have decided to use a three level multilevel model and I feel somewhat unsure if the model I specified will answer my questions of interest. I am also unsure of how to assess the effect of time. I have described my data and my proposed statistical model, I would be really grateful if someone can take a look and give me some feedback.
I have searched through the earlier posts but have not found any earlier posts which could help me.
Research Aim: This study will investigate the interplay between indicators of NSC and NSD, and their shared impact on a given outcome YY.
Data Design: My data consists of young people aged 10-15, with measures collected at 5 separate time points nested with neighbourhoods. The data is organized so that there is a one year interval between each time point. In short, it is a longitudinal data (panel). However, the outcomes I am measuring are only at time point 1, 3 and 5.
Proposed Statistical Method: In order to account for the hierarchical nature of the data, and to avoid an underestimation of the standard errors, I would like to fit a three-level multilevel linear regression models. My idea is that these models will account for the fact that the data consisted of repeated measures across three waves of data- 1, 3 and 5 (at level 1) nested in individuals (at level 2) clustered within neighbourhoods (at level 3).
Questions:
Model 1:
mixed yy age10 || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Model 2:
mixed yy c.age10##c.wave || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Model 3:
mixed yy c.age10##c.wave x1 x2 x3 x4 || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Thank you kindly for your time beforehand.
Samantha
I am have decided to use a three level multilevel model and I feel somewhat unsure if the model I specified will answer my questions of interest. I am also unsure of how to assess the effect of time. I have described my data and my proposed statistical model, I would be really grateful if someone can take a look and give me some feedback.
I have searched through the earlier posts but have not found any earlier posts which could help me.
Research Aim: This study will investigate the interplay between indicators of NSC and NSD, and their shared impact on a given outcome YY.
Data Design: My data consists of young people aged 10-15, with measures collected at 5 separate time points nested with neighbourhoods. The data is organized so that there is a one year interval between each time point. In short, it is a longitudinal data (panel). However, the outcomes I am measuring are only at time point 1, 3 and 5.
Proposed Statistical Method: In order to account for the hierarchical nature of the data, and to avoid an underestimation of the standard errors, I would like to fit a three-level multilevel linear regression models. My idea is that these models will account for the fact that the data consisted of repeated measures across three waves of data- 1, 3 and 5 (at level 1) nested in individuals (at level 2) clustered within neighbourhoods (at level 3).
Questions:
- What I now feel unsure about is the fact that variable NSC was measured at one time point (wave 3). What I would like to know is the model that I have proposed reasonable given that NSC variables are only at one time point? If no, what are my alternatives?
- Will the syntax in model 1 below provide me with information about the proportion of variation to be found at each level (i.e. level 1, level 2 and level 3)? Is it possible to graph this variation? If yes, could you please provide me with a code for this?
- I also wanted to assess if time (i.e. time of data collection) has a significant influence on young people’s outcome. Would the specification in model 2 provide me with this information?
- Is the specification of model 3, the inclusion of other explanatory variables/confounders in the fixed part of the model as it should be?
Model 1:
mixed yy age10 || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Model 2:
mixed yy c.age10##c.wave || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Model 3:
mixed yy c.age10##c.wave x1 x2 x3 x4 || area:wave, covariance(unstructured) || id :wave, covariance(unstructured) mle nolog
Thank you kindly for your time beforehand.
Samantha
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