David_Moss comments on EA Mental Health Survey: Results and Analysis.

[David_Moss]

It seems like it might be worthwhile to compare these results to data from the Slate Star Codex survey (n=8171), which had a lot of data about mental health, as well as the 2016 LessWrong Survey (n=3083) which also had questions about this.

The reason why I think that this would be important, is because you note that reported rates of mental illness are substantially higher in your survey of EAs than in the general population, but we would also expect (and as it happens, the surveys confirm) that there are much higher rates of reporting formally diagnosed or self-diagnosed mental illness in those other survey samples as well. Notably EAs, LessWrongers and SSC readers differ from the general population in a host of potentially important ways (for example, many more elite university students or graduates, a population where there has been much concern about higher reported rates of mental illness). Given that, we might think that this is a problem that is not so distinctive of the effective altruism movement specifically.

One concern people might have about this approach is possible overlap between the survey samples and population, but at least the LessWrong and EA Surveys asked about membership of each community, (and in the case of the EA Survey at least, found that only around 20% of respondents were LWers), so one could examine the effect of this empirically and I would guess that it is not the case that, for example, higher rates of reported mental illness in the LessWrong Forum is largely driven by EAs in the LessWrong sample.

[Peter Wildeford]

Also of note is that the SlateStarCodex 2017 survey offered an EA ID question in addition to a mental health inventory (David links to the 2019 SSC Survey which sadly does not have the EA ID question). We benchmarked the 2017 EA Survey to the SSC 2017 survey and found the EA populations to be mostly similar.

I’d strongly urge the OP or anyone interested in this topic to dig into the SSC 2017 survey’s EA population and investigate mental health statistics… I imagine the likelihood of oversampling people with mental health concerns would be lower (though not nonexistent) there.

[NunoSempere]

Using the 2017 SSC, I’ve looked at:

Has been diagnosed with a mental illness ~ Is EA

lm(formula = Has_mental_condition_diagnosis ~ Is_EA) 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.244434   0.006544  37.352   <2e-16 ***
Is_EA       0.027635   0.017688   1.562    0.118    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak relationship, is not significant.

Has been diagnosed with a mental illness or intuits having one ~ Is EA

> summary(lm(Has_mental_condition_diagnosis_or_intuited ~ Is_EA))

Call:
lm(formula = Has_mental_condition_diagnosis_or_intuited ~ Is_EA)

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.52513    0.00756  69.463   <2e-16 ***
Is_EA        0.03782    0.02043   1.851   0.0642 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak relationship, 0.05 < p < 0.10

Has taken SSRIs ~ Is EA.

lm(formula = Has_taken_SSRIs ~ Is_EA)

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.276337   0.006728  41.070   <2e-16 ***
Is_EA       -0.041894   0.018186  -2.304   0.0213 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak negative relationship (??).

Code used to produce that (in R)

## Download the file from https://​​slatestarcodex.com/​​2017/​​03/​​17/​​ssc-survey-2017-results/​​ , then move into the directory the file is in with:
## setwd(“directory”)

C ← read.csv(file=”Survey_CSV.csv”, header=TRUE, sep=”,”, stringsAsFactors=FALSE)

c(69:77) → mental_conditions #  “Schizophrenia”, Autism”, “Depression”, “Anxiety”, “OCD”, “Eatingdisorder” “Bipolar”, “Alcoholism”, “Drugaddiction”.

Has_mental_condition_diagnosis=rep(0,dim(C)[1])
for(mental_condition in mental_conditions){
  ifelse(C[,mental_condition]==”I have a formal diagnosis of this condition”, 1, Has_mental_condition_diagnosis) → Has_mental_condition_diagnosis 
}

ifelse(C$EAID==”Yes”, 1,0) → Is_EA
summary(lm(Has_mental_condition_diagnosis ~ Is_EA))

Has_mental_condition_diagnosis_or_intuited = rep(0,dim(C)[1])
Diagnosis_or_intuited = c(“I have a formal diagnosis of this condition”,”I think I might have this condition, although I have never been formally diagnosed” )
for(mental_condition in mental_conditions){
    ifelse(C[,mental_condition]%in%Diagnosis_or_intuited, 1, Has_mental_condition_diagnosis_or_intuited) → Has_mental_condition_diagnosis_or_intuited 
  }

summary(lm(Has_mental_condition_diagnosis_or_intuited ~ Is_EA))

ifelse(C$SSRIs == “I have never taken these drugs”, 0, 1) → Has_taken_SSRIs
summary(lm(Has_taken_SSRIs ~ Is_EA))

[NunoSempere]

[this comment is also archived in my blog, here without indentation and thus easier to read]

Re: @Peter_Hurford. The 2019 SSC Survey does have an EA_ID question. Using that:

If you run some regressions, you get a significant correlation between EA affiliation and mental conditions; respondents who identified as EA differed from non-EAs by ~2-4% (see below). Note that the SSC Survey is subject to fewer biases than the EA Mental Health survey, and also note that it’s still difficult to extract causal conclusions. Data available here

Plots:

Diagnosed + Intuited

                                                                            x   y         %
1                                                                      EA Yes 959 100.00000
2         Has been diagnosed with a mental condition, or thinks they have one 580  60.47967
3 Has not been diagnosed with a mental condition, and does not think they any 347  36.18352
4                                                          NA / Didn't answer 125  13.03441

                                                                            x    y          %
1                                                                    EA Sorta 2223 100.000000
2         Has been diagnosed with a mental condition, or thinks they have one 1354  60.908682
3 Has not been diagnosed with a mental condition, and does not think they any  795  35.762483
4                                                          NA / Didn't answer  167   7.512371

                                                                            x    y          %
1                                                                       EA No 4158 100.000000
2         Has been diagnosed with a mental condition, or thinks they have one 2416  58.104858
3 Has not been diagnosed with a mental condition, and does not think they any 1587  38.167388
4                                                          NA / Didn't answer  248   5.964406

Diagnosed

                                               x   y         %
1                                         EA Yes 959 100.00000
2     Has been diagnosed with a mental condition 314  32.74244
3 Has not been diagnosed with a mental condition 613  63.92075
4                             NA / Didn't answer 125  13.03441

                                               x    y          %
1                                       EA Sorta 2223 100.000000
2     Has been diagnosed with a mental condition  718  32.298695
3 Has not been diagnosed with a mental condition 1431  64.372470
4                             NA / Didn't answer  167   7.512371

                                               x    y          %
1                                          EA No 4158 100.000000
2     Has been diagnosed with a mental condition 1183  28.451178
3 Has not been diagnosed with a mental condition 2820  67.821068
4                             NA / Didn't answer  248   5.964406

Regressions

Linear

> # D$mentally_ill = Number of diagnosed mental ilnesses
> # D$mentally_ill2= Number of mental ilnesses, diagnosed + intuited

> summary(lm(D$mentally_ill ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.5717 -0.5514 -0.4689  0.4486 10.4283 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.46890    0.01424  32.935  < 2e-16 ***
D$`EA ID`Sorta  0.08252    0.02409   3.426 0.000617 ***
D$`EA ID`Yes    0.10284    0.03283   3.132 0.001742 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9008 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.002421,	Adjusted R-squared:  0.002139 
F-statistic: 8.587 on 2 and 7076 DF,  p-value: 0.0001884

> summary(lm(D$mentally_ill2 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill2 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3711 -1.2638 -0.2638  0.7362  9.6289 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1.26380    0.02243  56.343   <2e-16 ***
D$`EA ID`Sorta  0.09637    0.03795   2.539   0.0111 *  
D$`EA ID`Yes    0.10729    0.05173   2.074   0.0381 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.419 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.001216,	Adjusted R-squared:  0.0009338 
F-statistic: 4.308 on 2 and 7076 DF,  p-value: 0.0135

> summary(lm(D$mentally_ill>0 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill > 0 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3387 -0.3341 -0.2955  0.6659  0.7045 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.295528   0.007323  40.354  < 2e-16 ***
D$`EA ID`Sorta 0.038581   0.012391   3.114  0.00186 ** 
D$`EA ID`Yes   0.043199   0.016889   2.558  0.01055 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4633 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.001835,	Adjusted R-squared:  0.001553 
F-statistic: 6.505 on 2 and 7076 DF,  p-value: 0.001505

> summary(lm(D$mentally_ill2>0 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6301 -0.6036  0.3699  0.3965  0.3965 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.603547   0.007692  78.466   <2e-16 ***
D$`EA ID`Sorta 0.026513   0.013014   2.037   0.0417 *  
D$`EA ID`Yes   0.022127   0.017738   1.247   0.2123    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4867 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.0006657,	Adjusted R-squared:  0.0003832 
F-statistic: 2.357 on 2 and 7076 DF,  p-value: 0.09481

Logistic

> summary(glm(D$mentally_ill>0 ~ D$`EA ID`, family=binomial(link='logit')))

Call:
glm(formula = D$mentally_ill > 0 ~ D$`EA ID`, family = binomial(link = "logit"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9095  -0.9018  -0.8370   1.4807   1.5614  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -0.86868    0.03464 -25.078  < 2e-16 ***
D$`EA ID`Sorta  0.17902    0.05737   3.120  0.00181 ** 
D$`EA ID`Yes    0.19971    0.07756   2.575  0.01003 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 8797.8  on 7078  degrees of freedom
Residual deviance: 8784.8  on 7076  degrees of freedom
  (354 observations deleted due to missingness)
AIC: 8790.8

Number of Fisher Scoring iterations: 4

> summary(glm(D$mentally_ill2>0 ~ D$`EA ID`, family=binomial(link='logit')))

Call:
glm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`, family = binomial(link = "logit"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4103  -1.3603   0.9612   1.0049   1.0049  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)     0.42027    0.03231  13.007   <2e-16 ***
D$`EA ID`Sorta  0.11221    0.05514   2.035   0.0419 *  
D$`EA ID`Yes    0.09344    0.07517   1.243   0.2139    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9439.1  on 7078  degrees of freedom
Residual deviance: 9434.4  on 7076  degrees of freedom
  (354 observations deleted due to missingness)
AIC: 9440.4

Number of Fisher Scoring iterations: 4

[NunoSempere]

I linked to a similar comparison: Efffective Altruists, not as mentally ill as you think, with the results you hypothesized:

This points out a limitation of my statistics above. All it shows is that effective altruists don’t differ from other rationalists in levels of mental illness. It’s possible and indeed likely that both effective altruists and rationalists differ from the general population in all kinds of ways. It’s even possible that self-hate and scrupulosity drive people into the rationality movement in general, although I can’t imagine why that would be. It’s just that they don’t seem to have any extra power to make people effective altruists once they’re there.

It would surprise me if the results don’t hold any more, but it might be worth checking