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Did you accidentally paste the same link twice? Looking at the "Medical Specialty and the Incidence of Divorce" article, my first thought on reading "Scores on

by xapata 3y ago

Did you accidentally paste the same link twice? Looking at the "Medical Specialty and the Incidence of Divorce" article, my first thought on reading "Scores on these [psychological characteristic] scales were grouped according to quartiles for analysis" is that the researchers aren't experts in statistical methods, or else are hiding something by bucketing arbitrarily. It's a widespread problem in life sciences. They don't explain (not by this point in the article, at least) why they chose quartiles. I'm skeptical of any analysis that reduces the fidelity of the data without explanation. It smells like p-hacking. My second thought regarding their model is wondering why they didn't first build a model predicting divorce for the general population. I've heard (received wisdom) that financial stress is the primary cause. It seems unlikely that medical specialty is unrelated to household finances. Ignoring a likely confounding variable is sloppy. It makes the effort seem like a novelty article intended for amusement rather than serious research. It's in the "occasional notes" section of the periodical, which suggests less rigor. They do little to address the question of causality. They muse that, "One explanation is the longer work hours required in some specialties," but never bothered to analyze the hours worked. My conclusion: That article is literally a joke, published to give the community something to laugh about over beers after the conference. ---- If I were to perform the analysis, I'd first build a model of causes of divorce for the general population. Some candidate variables: household income, household income as a ratio to each person's childhood household income, cost of living where the household lives, frequency of attending religious services, number of children, number of hours worked per week, age when married, etc. I'm skeptical of psychological characteristic surveys, so I'd spend some time considering alternatives, but I'd want to include some measure of characteristics that might drive the choice of a medical specialty which might also be correlated with divorce. We want to isolate the choice of specialty as the cause as separate from confounders that cause both the choice and divorce. If medical specialty holds up as a factor after controlling for everything else, then I'd investigate further.

ctrlp 3y ago
https://sci-hub.st/https://doi.org/10.1136/bmj.h706 https://sci-hub.st/https://doi.org/10.1136/bmj.h706 for the other study
- xapata 3y ago
  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.

by xapata 3y ago

ctrlp 3y ago

https://sci-hub.st/https://doi.org/10.1136/bmj.h706 https://sci-hub.st/https://doi.org/10.1136/bmj.h706 for the other study

xapata 3y ago
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.