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Hi all,
I'm hoping some of you have some advice regarding a (admittedly somewhat convoluted) stats question.
I have a dataset that consists of daily observations (21 days) of people's daily general mood and daily life satisfaction . I am interested in understanding the extent to which these two variables fluctuate with one another, so if one day my mood is low, am I also more dissatisfied with my life, and vice versa.
Specifically, I am interested whether the association holds using lagged variables, e.g., does mood on day t-1 predict life satisfaction on day t and vice versa. It seems to be common practice to add the DV at day t-1 as a covariate. Although this makes sense to me to some extent, I also have some concerns about this practice, but I'm unable to find any literature that supports my concern.
Obviously, the DV at time t and t-1 are going to be strongly correlated, especially when we talk about daily data. Clearly, it should be this way, otherwise the measure used to assess the construct (e.g., mood) would not be reliable. It's an established fact that mood and life satisfaction are very strongly correlated, and I want to understand what gives rise to the strong association of these phenomena, particularly, I want to understand: does being in a bad mood make me dissatisfied with my life, or does being dissatisfied make me be in a bad mood? Again, if I controlled for the DV at t-1, I would test extent to which the IV predicts the DV at time t above and beyond the correlation of the IV and DV at time t-1. However, my goal is to identify the processes that give rise to the co-occurance of these two phenomena.
To give another descriptive example to explain what I mea: The weather: Obviously, we all know that the weather fluctuates throughout the day, the week, month, the year, etc. However, over very short periods of time, it's very reliable. If I collect daily weather data over 365 days, and try to predict the weather on day t, but control for the weather on day t-1, I will end up with a horizontal line that'll say that there are no fluctuations in weather over 365 days. Clearly, we know that is not correct.
I'm not trying to convince others of this and I'm open to feedback, but I do think that this perspective has merit, but I can't locate any scholarly writings on this topic that either confirm or disprove my logic. Alternatively, I'm looking for something that provides information on the extent to which one should control for DV on the previous time-point given the frequency of assessments (e.g., should I control for DV at the previous time point when I collect daily data, but not weekly, monthly, yearly?)
I am familiar with the Bolger and Lauenceau book, but I don't seem to find an answer in there, either.
Thanks!
I'm hoping some of you have some advice regarding a (admittedly somewhat convoluted) stats question.
I have a dataset that consists of daily observations (21 days) of people's daily general mood and daily life satisfaction . I am interested in understanding the extent to which these two variables fluctuate with one another, so if one day my mood is low, am I also more dissatisfied with my life, and vice versa.
Specifically, I am interested whether the association holds using lagged variables, e.g., does mood on day t-1 predict life satisfaction on day t and vice versa. It seems to be common practice to add the DV at day t-1 as a covariate. Although this makes sense to me to some extent, I also have some concerns about this practice, but I'm unable to find any literature that supports my concern.
Obviously, the DV at time t and t-1 are going to be strongly correlated, especially when we talk about daily data. Clearly, it should be this way, otherwise the measure used to assess the construct (e.g., mood) would not be reliable. It's an established fact that mood and life satisfaction are very strongly correlated, and I want to understand what gives rise to the strong association of these phenomena, particularly, I want to understand: does being in a bad mood make me dissatisfied with my life, or does being dissatisfied make me be in a bad mood? Again, if I controlled for the DV at t-1, I would test extent to which the IV predicts the DV at time t above and beyond the correlation of the IV and DV at time t-1. However, my goal is to identify the processes that give rise to the co-occurance of these two phenomena.
To give another descriptive example to explain what I mea: The weather: Obviously, we all know that the weather fluctuates throughout the day, the week, month, the year, etc. However, over very short periods of time, it's very reliable. If I collect daily weather data over 365 days, and try to predict the weather on day t, but control for the weather on day t-1, I will end up with a horizontal line that'll say that there are no fluctuations in weather over 365 days. Clearly, we know that is not correct.
I'm not trying to convince others of this and I'm open to feedback, but I do think that this perspective has merit, but I can't locate any scholarly writings on this topic that either confirm or disprove my logic. Alternatively, I'm looking for something that provides information on the extent to which one should control for DV on the previous time-point given the frequency of assessments (e.g., should I control for DV at the previous time point when I collect daily data, but not weekly, monthly, yearly?)
I am familiar with the Bolger and Lauenceau book, but I don't seem to find an answer in there, either.
Thanks!
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