Does it make sense to run a conditional model when the unconditional model is not significant but the variance is significant? I would think that the conditional model can help explain the variance.
For example, the best model fit for the depression variable in my study is quadratic (slope 0.645, quadratic -0.275) but the slope and quadratic parameters are not significant. The variance, however, is significant for intercept (p=0.002), slope (p=0.027), and quadratic (p=0.005). Conditioning the model on gender resulted in significant slope (-4.880, p<0.05) and quadratic (1.756, p<0.05) parameters. When I graphed the trajectories, the unconditional model was of course slightly convex curvilinear. For females, the curve was slightly concave curvilinear. For males, the curve was highly convex curvilinear. Am I interpreting this correctly?