Hello, I am brand new to PsPM and its SCR capabilities. I have been using Ledalab, but running into issues so exploring alternatives. I read the GLM vs Ledalab article, and the PsPM user manual, and I am trying to determine (A) whether GLM or DCM modelling is more appropriate for my data (almost 1,000 cases), and (B) if DCM is more accurate than Ledalab's CDA (CDA.SCR) analysis.
My Data Setup: I have collected the skin conductance (MuS) responses of 975 virtual reality video experiences (13 one-minute scenes (within-subjects) for each of 75 subjects (between-subjects)). I treat each 60-second experience as 1 event. At 128 Hz, I have 7,680 data points per each one-minute video (128 Hz since I also collect other physiological data). I downsample each GSR data segment to 16 Hz (960 samples for a 1-minute VR experience). It seems to me that DCM may be a more appropriate model than GLM in my case. Based on this explanation would you agree?
If DCM is more appropriate, how would it compare to Ledalab's CDA.SCR for a within-subject then between-subject model? Would DCM in PsPM be able to provide a more accurate comparison between these 1,000 cases, and do it faster than Ledalab's GUI non-batch-mode (batch-mode does not work for me).
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Kindly post your follow-up questions there and please make sure you use the latest version of the software.
Regarding your question, I was not sure what you wanted to infer: tonic arousal during the 1-minute VR experience (which would favour the SF-DCM module in PsPM), or event-related arousal?
Dominik
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hello, I am brand new to PsPM and its SCR capabilities. I have been using Ledalab, but running into issues so exploring alternatives. I read the GLM vs Ledalab article, and the PsPM user manual, and I am trying to determine (A) whether GLM or DCM modelling is more appropriate for my data (almost 1,000 cases), and (B) if DCM is more accurate than Ledalab's CDA (CDA.SCR) analysis.
My Data Setup: I have collected the skin conductance (MuS) responses of 975 virtual reality video experiences (13 one-minute scenes (within-subjects) for each of 75 subjects (between-subjects)). I treat each 60-second experience as 1 event. At 128 Hz, I have 7,680 data points per each one-minute video (128 Hz since I also collect other physiological data). I downsample each GSR data segment to 16 Hz (960 samples for a 1-minute VR experience). It seems to me that DCM may be a more appropriate model than GLM in my case. Based on this explanation would you agree?
If DCM is more appropriate, how would it compare to Ledalab's CDA.SCR for a within-subject then between-subject model? Would DCM in PsPM be able to provide a more accurate comparison between these 1,000 cases, and do it faster than Ledalab's GUI non-batch-mode (batch-mode does not work for me).
Hi Mark.
PsPM has moved to github: https://github.com/bachlab/PsPM
Kindly post your follow-up questions there and please make sure you use the latest version of the software.
Regarding your question, I was not sure what you wanted to infer: tonic arousal during the 1-minute VR experience (which would favour the SF-DCM module in PsPM), or event-related arousal?
Dominik