# How do I structure my data in a time-lagged, multilevel mediation?

I am analyzing a repeated measures experiment using a multilevel growth curve in SPSS. I want to test a time-lagged, multilevel mediation model. My X variable is linear time in weeks (week 1, week 2, week 3, etc.) My mediator (M variable) is a measure of ruminative thinking, and my dependent variable (Y variable) is depressive symptoms. I want to test if changes in ruminative thinking each week predict subsequent changes in depressive symptoms. In other words, changes in the mediator at time t are predicting changes in Y at time t + 1. So the conceptual model would be: X (linear time) --> M (changes in rumination) --> Y (changes in depression)

My questions are:

1. How do I properly capture change in M & Y when including them in a mediation model with a linear effect of time? Will raw scores capture change from week to week, or do I need to compute difference/change scores and use those in my mediation model?

2. Do I need to person-mean center (within-person center) M & Y?

Advice on either of these questions would be greatly appreciated.

Edit (1/18/21): My X variable is a linear effect of time, measured in weekly intervals (0, 1, 2, 3, etc.) Both my M variable (ruminative thinking) and my Y variable (depressive symptoms) are continuous variables measured each week for 5 weeks. I am wanting to capture whether there is 1) A linear decrease in ruminative thinking over the course of the 5 weeks (path a), and 2) Whether these weekly decreases in ruminative thinking predict subsequent weekly decreases in depressive symptoms (path b), and 3) whether the indirect effect (path_a*path_b) is significant. Path b will be time-lagged. So, for example, the change in ruminative thinking from week 1 to week 2 would predict change in depressive symptoms from week 2 to week 3, and so on. So I'm only interested in the within-person relationship between M & Y. I believe what I'm stuck on is how to accurately estimate path b of my mediation model (i.e., the M --> Y effect). I know that for path a (X --> M), I can just use raw scores for M, and the linear coding of Time will give me the average weekly decrease in ruminative thinking. But for path b, would raw scores for M & Y capture how weekly change in M is associated with weekly change in Y, or do I need to enter difference/change scores into the model instead? If I do need to use change scores, then do I also need to enter a change score for M in path a of my model (rather than raw scores)?

Please let me know if I can clarify further, as I'm new to multilevel modeling and could be leaving important info out.

• Please clarify your model. It seems to me that you would need both a variable for the amount of change AND the current level of rumination. For example, if someone is experience very high levels of ruminating thoughts, a small change in either direction could be more impacting than for someone with low levels of rumination. Jan 16, 2021 at 16:05
• Hi Gregg, I've just added some more info. Please let me know if I can clarify further. Jan 18, 2021 at 15:23