I appreciate that you have now described the variables. Otherwise, it would have been very difficult if not impossible to really detect where are the strengths and weaknesses.
After reading this new version carefully, here are what i think need to be explained.
1. If possible, for replication purposes, you should probably display your code in a supplementary file, eventually (if that is possible) the data you have used (without ID and personal information of the participants of course). In this case, say in the main text the materials are provided in the supplementals. Finally, tell us which software you are using (it's apparently not R).
2. In the text below figure 1 you didn't explain the purpose of the factor analysis and you still did not explain whether it's a factor analysis or principal component analysis. And since these three variables in first round are dichotomous you should have used a tetrachoric approach for factor analysis, e.g., fa(r, cor="tet") if using R. Also, if, as your reply suggest, you wanted to measure leftism factor, the loadings do not tell you the whole story. You need to report the scale reliability, which basically tells you how well you have measured leftism factor. In this case, use Omega (not the Hierarchical, and not Alpha, just Omega). Flora (2020) explains the strength of Omega compared to Alpha, and also provides easy guidance as to how to apply it in R (see section categorical Omega since your variables are binary).
Flora, D. B. (2020). Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates. Advances in Methods and Practices in Psychological Science, 3(4), 484-501.
However, what concerns me most is the fact that Figure 1 seems to imply your leftism factor is a binary variable (0-1). A classical factor analysis should produce continuous factor score distribution with mean zero, so I would like to know how you got a purely binary distribution of such factor.
3. About Figures 2-5 I highly recommend you display also the plots using the original metric of father age. As explained earlier, unless you have a strong theoretical reason to dichotomize the variable at exactly 35, don't do it. Dichotomizing a variable is known to cause bias, as explained earlier. It's not a recommended approach. I highly recommend you again to read MacCallum's article.
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological methods, 7(1), 19.
4. "Probability" in Figures 2-5 has been computed by taking the mean, so make sure you explain it in the beginning of Result section. Because otherwise it's not clear. Often, when I see researchers displaying their probability plot, it's actually the predicted probabilities from, say, a logistic regression.
5. Since you're keeping your graphs as well now, make sure you write in the text "Figure 2 shows..." under the respective graph so it's easier to follow. And make sure you correct this mistake: "Next, we show the leftism probabilities from Figure 3 alongside the leftism probabilities of the fathers. " because it's actually Figure 4.
6. Figure 4 apparently was produced by using both the first "baseline" data and its follow up. But since the follow up sample was much, much smaller, there is a strong attrition, which is another limitation. Make sure you discuss that in the appropriate section.
7. You need to present each of your regression tables appropriately by "calling" them, such as "in Table 1 we present results from...".
8. You did not seem to have used the variable party ID in your analyses. Or perhaps it is not made clear again. Sure, you have described each variable, but you did not explain how you computed "leftism" for all of your analyses, which is crucial for understanding what is happening. One problem is that party ID and politics are redundant and it's unclear how you obtained the 0-1 dichotomized response for the result reported in your figures and tables. A second issue is that party ID has 6 categories and only the first two ones relate two left/right, and politics variable would not include "centrist" responses which means here that you must have less than N=2,380 and N=264 in your final sample. In this case, you would need to display the sample sizes for each analyses (e.g., factor analysis, each figures and each tables). Knowing how leftism is computed would also tell me which variables were used. Because while you collected in both rounds party ID, politics, LGBT, LBM, feminism, I feel like you didn't use all variables because, to my surprise, your leftism is a binary variable.
9. In your limitation section, you indeed recognized that religiosity, birth order effects are potential confounders (see e.g., Woodley et al. I mentioned before), but you didn't explain why, so this is confusing for the reader. I suggest you explain and add eventually some references to back up. You should also explain a major limitation of leftism variable is that typically the political views, measured as a 7-Likert scale is a widely used metric and its predictive power and consistency is well replicated in the psychological field. But we don't know what are the properties of this variable. Make sure the readers understand this point well because it's actually explained loosely.
You also did not explain why it is wise to dichotomize paternal age, because as I mentioned earlier, it is known to cause serious bias in estimation, but since you also reported the paternal age effect in its original metric in a second table, I'm fine. Just don't forget to report the plots from Figures 2-5 using the original metric of paternal age as well.
10. Figures 6-7 are actually tables, not figures. Also in "Figure" 7 you did not capitalize the words but you did so for Figure 6 and previous Figures as well. Also, about those tables, I realize now that I have missed an important element in my earlier response. Your table actually shows the Dep. Variable, and it's called "factor_1bin". You never explained what this is about. Factor binary? How was it created? If it's a factor score, how can it be binary? Yet your reply indicate your leftism variable is binary. It's far from clear. But my guess is that you did not use the observed variable of either party ID or politics, because your sample is exactly totalling 2,380, i.e., you did not lose a single case. Did you really not use either of these variables?
The odds ratio in your Table 1 for parent age and respondent age should have been 1.27 and 0.98 respectively. With respect to Table 2, you should also report the odds ratio for both variables. The effect of paternal age (continuous) is not very strong, especially compared to age effect. The lower CI of paternal age is also very close to zero. Because of this, you want to mention in the discussion that the result is not robust because when using the original metric of the parent variable, the effect is not strong anymore, as opposed to its binary "transformation".
11. In your reply you said the extreme values in the right tail did not produce results contradicting your hypothesis, this is fine but you should report this robustness analysis in your main text.
12. My recommendation that you change the citation format was based on practicality or what I believe to be easier to follow for any reader, as well as easiness for editing a chunk of the discussion if references must be added in the middle without completely messing with the order. However, the journal does not seem to oblige at following a particular format. Furthermore, if you like this format better, you obviously don't have to follow my suggestion.