2016-May-02, 02:54:57

I'd like to request that you add the main numerical datapoints of interest to the abstract: effect sizes and sample size. If too many effect sizes, I usually give ranges or the mean/median.

I'd like to request that you provide exact p values instead of inequalities. This allows for better estimation of the strength of the evidence from reading and also allows for automatic checking with data mining tools. See e.g. https://peerj.com/preprints/1642/ When the numbers are very small it is better to use scientific notation, e.g. 1.4 * 10^-5.

Come to think of it. It is better to use a proper method than assuming that a clearly non-normal, non-continuous variable is both. At least as a robustness check. I used ordered logistic regression (http://www.ats.ucla.edu/stat/r/dae/ologit.htm) and tried the 3 main models with the western subsample. Results were similar to your OLS regressions.

It is a dangerous argument to make that because a practice is common, it is okay. I'd like that you add the full results to the supplementary materials (either in the appendix or output files in the OSF repository).

I checked, the correlations are not that strong: r's .33, .50, .69.

In two places you use the fact that adding a second order term did not result in a p < alpha to argue that there is no non-linearity. This conclusion is too strong. There are many kinds of non-linearity many of which are not detected by this crude method. In my experience, detecting non-linearity requires a somewhat large sample size (or very strong associations), larger than this study has. So, I think that if you tone down the language, it is alright what you did.

I'd like to request that you provide exact p values instead of inequalities. This allows for better estimation of the strength of the evidence from reading and also allows for automatic checking with data mining tools. See e.g. https://peerj.com/preprints/1642/ When the numbers are very small it is better to use scientific notation, e.g. 1.4 * 10^-5.

Noah Carl Wrote:I have changed the relevant sentence.

Come to think of it. It is better to use a proper method than assuming that a clearly non-normal, non-continuous variable is both. At least as a robustness check. I used ordered logistic regression (http://www.ats.ucla.edu/stat/r/dae/ologit.htm) and tried the 3 main models with the western subsample. Results were similar to your OLS regressions.

Noah Carl Wrote:Given that the focus of the paper is the effects of percentage Muslim and military intervention, I would prefer not to unnecessarily clutter the regression tables with more coefficients. This practice is quite common in economics and sociology.

It is a dangerous argument to make that because a practice is common, it is okay. I'd like that you add the full results to the supplementary materials (either in the appendix or output files in the OSF repository).

Noah Carl Wrote:Yes––unless I'm mistaken––the collinearity is close to perfect.

I checked, the correlations are not that strong: r's .33, .50, .69.

In two places you use the fact that adding a second order term did not result in a p < alpha to argue that there is no non-linearity. This conclusion is too strong. There are many kinds of non-linearity many of which are not detected by this crude method. In my experience, detecting non-linearity requires a somewhat large sample size (or very strong associations), larger than this study has. So, I think that if you tone down the language, it is alright what you did.