Thanks. My first reaction is that this one is much better. Larger sample, better methods.

Again, I hate the arbitrary bucketing. There's no benefit to creating age buckets, as if there's some magical change that happens at 40, 50, and 60. I did have an atypically large birthday party, compared to other years, but I don't think that had a dramatic effect on my likelihood to divorce. Maybe some people have such spectacularly intense decade-related birthday parties that it increases divorce incidence for those years? Income doesn't benefit from bucketing, either. Having $199k annual income isn't much different from $201k annual income.

They do a decent job of controlling for confounders. Considering, among other things, that "if the annual rate of divorce was identical across occupations but physicians marry later in life, then at any given time physicians would be less likely to report ever having divorced compared with people in other occupations, simply because they were at risk for less time."

However, their inclusions of state and year fixed effects could have been more considered. These are proxies for other things, like cultural characteristics and neighborhood income levels. I'd like to see some discussion of why they chose to use state and year proxies instead of searching for more specific explanatory variables. Especially state, because some kind of urbanization measure might be more helpful. However, because state medical licensing regulation might affect the choice of occupation, the use of that variable is easily defensible.

Ugh! Bucketing again! Hours worked should be a continuous variable. Inexcusable, unless they feared misreporting. Perhaps they saw some banding at 40, 45, and 50 hours, so they figured it's not really a continuous variable anyway. Again, that needs explanation. Any rationale for bucketing should be thoroughly discussed.

Finally, the effects. First, with this population size, I'd be surprised if these weren't "statistically significant". I'm looking more for practical significance. Check out the estimates for dentists. Dentists appear to have lower incidence of divorce, based on the last year, yet higher prevalence of divorce. Strange. That means that dentists in past years had a higher annual incidence, but the rate has been declining, or declining relative to physicians. Has the practice of dentistry, relative to general medicine, changed that much over those years? This suggests spurious results, at least for the physician vs dentist comparison.

Hispanics were more likely to divorce? Bogus. I'll chalk it up to sampling weirdness. The CI includes 1 anyway, so they're saying it doesn't matter. They should put some asterisks in to highlight the variables we should pay attention to. It looks like they're including Black and Hispanic just to explain what Other means, because Other is the only significant one.

Wow! Income is irrelevant. Weird again. Maybe bucketing at work, turning 1 continuous variable into 4 binary variables, diluting the impact. It should have been log(dollars).

If you work more than 60 hours, you're more likely to get divorced. Makes sense. Again, log(hours) would have been better, though maybe a threshold at 40 hours would have been useful.

I enjoyed the article, but if I were the journal editor, I'd have returned it with some suggestions for improvement rather than publishing it.

Anyway, I hope my commentary was interesting/useful. This article doesn't say anything about psychology, so we've gone off on a bit of a tangent.