Dear Members,
I have run a market experiment which involves both a within and between subject design. I have 6 separated sessions. In each experimental session, each subject goes through 9 tasks (treatments), playing over 7 periods within each task (treatment). So I have 63 periods in each session. Having 6 sessions poses a problem of serial correlation, since observation within each session might exhibit more correlation than observations between sessions (indeed the same group of subject plays within a session). I want to run a panel model (e.g. Pooled OLS, or fixed effects or random effects), where period is the time unit. Is it correct to set the subject as cross sectional unit and then clustering the standard errors at session level (to account for serial correlation)? My concern comes from the fact that I have a within design in each session but, since I have 6 separated sessions (involving six different groups of subjects), it also seems to me that I have a between subject design in place.
Many Thanks!!!
I have run a market experiment which involves both a within and between subject design. I have 6 separated sessions. In each experimental session, each subject goes through 9 tasks (treatments), playing over 7 periods within each task (treatment). So I have 63 periods in each session. Having 6 sessions poses a problem of serial correlation, since observation within each session might exhibit more correlation than observations between sessions (indeed the same group of subject plays within a session). I want to run a panel model (e.g. Pooled OLS, or fixed effects or random effects), where period is the time unit. Is it correct to set the subject as cross sectional unit and then clustering the standard errors at session level (to account for serial correlation)? My concern comes from the fact that I have a within design in each session but, since I have 6 separated sessions (involving six different groups of subjects), it also seems to me that I have a between subject design in place.
Many Thanks!!!
Comment