Yes, very interesting, thank you for putting in the time. I appreciate the detailed analysis. Were you able to make any inference at all about psychiatry and divorce from these data or no? It's interesting that you are using some background knowledge to evaluate the findings (e.g., hispanic divorce). I'm curious where that comes from and how it fits into our discussion. Is it that you have data about hispanic background divorce rates or is it because you know hispanics are largely Catholic and making a logical inference? As far as dentistry is concerned, it may be a hidden variable, such as makeup of those practicing dentistry. Maybe a change in composition of male/female overall. Or from different cultural or ethnic backgrounds with different background rates of divorce. My overall impression of your analytical tools is that you are willing to hypothesize causes with the caveat that they be subject to further investigation. If you reach such places in analysis and stop, do you just reserve judgment from that point? I would think not. Rather, your priors change and your probabilities change so you can go about life without perfectly constructed and complete statistical evidence, as all of us must.
To return to the topic under discussion, it sounds like you are saying, "there may or may not be a correlation between psychiatry and divorce, but these particular studies can't provide the answer." I assure you I am not basing my opinions about psychiatrists on these studies. Rather, I expect that properly constructed studies that meet your standards would bear out what I know from my own experience and encounters with people in the profession. Others in the thread provided anecdotal data that supports my own. And, I'm not making my judgment based solely on experience. My experience confirms an intellectual analysis based on the history of the profession. Those are my priors and probabilities. I would not be surprised if the data backed me up but I would be surprised if the data refuted my suppositions (and would question the study). It seems that my sort of reasoning doesn't have much place in your toolbox. Is that not the case?
I appreciate your reply and explanation. However, I think we'll probably be speaking at cross-purposes because your description of the kind of variables you would choose for your own model strike me as (necessarily) limited and reflective of the data requirements of your preferred methods (McNamara fallacy?). (I don't actually believe you live your life via models generally, but maybe you do.)
My opinion is there is no room in such models for all the many things that are part of the rich fabric of psychological experience, without which all you get is a kind of significant/not-significant binary according to available imperfect data. I mean, yes, it preserves the null hypothesis, but it feels sterile to me to attribute so much to randomness when the model itself is so obviously curated to work with available data. Or even to hidden variables that will require further study, at some point, in the future, maybe... Not to mention the cases where the null hypothesis was subsequently rejected (smoking, ulcers,...). Yes, Maybe you believe these variables are the principal components of the theoretical complete data? The don't appear orthogonal to me.
Meanwhile, life has to be lived and if you've encountered "types" of people in the world but won't allow yourself to acknowledge their existence unless one can build a rigorous predictive model to verify the existence of those types and to be sure they are not noise or sample bias or whatever, then you are enjoying a life that I would find almost barren. There are so many locked doors in this way of seeing the world. You have problems with datasets, which you have to decide whether to trust. Will you apply statistics or heuristics to those problems? Absent trustworthy data will you just decide to defer judgment? It's a form of not trusting oneself as well, which I reject on principle, and of making oneself unbiased to the point of being inconsequential. In other words, a form of nihilism (perhaps nullism is the appropriate term).
No, I don't think I can make any inferences about psychiatry from those articles. My Bayesian update is the opposite of yours: because the evidence presented was weak, I'm more skeptical of any relationship between psychiatry and divorce. I was quite ready to believe it, and was disappointed to see such weak studies. It suggests to me that it's difficult to find positive results with stronger analysis.
You seem to be suggesting I'm philosophically a Frequentist. Somewhat true, but I am, like basically everyone, a Bayesian when it comes to practical decisions. Also, I have no fear of logical deduction when statistical inference is infeasible.
Nullism is a good term. I'm of the opinion that most things are random and that humans' imaginations frequently mislead us. Absent better evidence, I'm unlikely to believe any link between psychiatry and divorce.
To return to the topic under discussion, it sounds like you are saying, "there may or may not be a correlation between psychiatry and divorce, but these particular studies can't provide the answer." I assure you I am not basing my opinions about psychiatrists on these studies. Rather, I expect that properly constructed studies that meet your standards would bear out what I know from my own experience and encounters with people in the profession. Others in the thread provided anecdotal data that supports my own. And, I'm not making my judgment based solely on experience. My experience confirms an intellectual analysis based on the history of the profession. Those are my priors and probabilities. I would not be surprised if the data backed me up but I would be surprised if the data refuted my suppositions (and would question the study). It seems that my sort of reasoning doesn't have much place in your toolbox. Is that not the case?
I appreciate your reply and explanation. However, I think we'll probably be speaking at cross-purposes because your description of the kind of variables you would choose for your own model strike me as (necessarily) limited and reflective of the data requirements of your preferred methods (McNamara fallacy?). (I don't actually believe you live your life via models generally, but maybe you do.)
My opinion is there is no room in such models for all the many things that are part of the rich fabric of psychological experience, without which all you get is a kind of significant/not-significant binary according to available imperfect data. I mean, yes, it preserves the null hypothesis, but it feels sterile to me to attribute so much to randomness when the model itself is so obviously curated to work with available data. Or even to hidden variables that will require further study, at some point, in the future, maybe... Not to mention the cases where the null hypothesis was subsequently rejected (smoking, ulcers,...). Yes, Maybe you believe these variables are the principal components of the theoretical complete data? The don't appear orthogonal to me.
Meanwhile, life has to be lived and if you've encountered "types" of people in the world but won't allow yourself to acknowledge their existence unless one can build a rigorous predictive model to verify the existence of those types and to be sure they are not noise or sample bias or whatever, then you are enjoying a life that I would find almost barren. There are so many locked doors in this way of seeing the world. You have problems with datasets, which you have to decide whether to trust. Will you apply statistics or heuristics to those problems? Absent trustworthy data will you just decide to defer judgment? It's a form of not trusting oneself as well, which I reject on principle, and of making oneself unbiased to the point of being inconsequential. In other words, a form of nihilism (perhaps nullism is the appropriate term).